Schedule

24 October

25 October

24 October

25 October

Speakers

Sponsors

Job Board

Exhibitors

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Yoshua Bengio

Université de Montréal
Full Professor
Yoshua Bengio is recognized as one of the world’s leading experts in artificial intelligence (AI) and a pioneer in deep learning. Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. Holder of the Canada Research Chair in Statistical Learning Algorithms, he is also the founder and scientific director of Mila, the Quebec Institute of Artificial Intelligence, which is the world’s largest university-based research group in deep learning. His research contributions have been undeniable. In 2018, Yoshua Bengio collected the largest number of new citations in the world for a computer scientist thanks to his many publications. The following year, he earned the prestigious Killam Prize in computer science from the Canada Council for the Arts and was co-winner of the A.M. Turing Prize, which he received jointly with Geoffrey Hinton and Yann LeCun. Concerned about the social impact of AI, he actively contributed to the development of the Montreal Declaration for the Responsible Development of Artificial Intelligence.

24 October

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Hugo Larochelle

Montreal
Google Brain
Hugo Larochelle is Research Scientist at Google and Assistant Professor at the Université de Sherbrooke (UdeS). Before, he was working with Twitter and he also spent two years in the machine learning group at University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at Université de Montréal, under the supervision of Yoshua Bengio. He is the recipient of two Google Faculty Awards. His professional involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015 and 2016.

24 October

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Simon Lucey

Associate Research Professor/Principal Research Scientist
Carnegie Mellon University/Argo AI
Simon Lucey (Ph.D.) is an associate research professor within the Robotics Institute at Carnegie Mellon University, where he is part of the Computer Vision Group, and leader of the CI2CV Laboratory. Before returning to CMU he was a Principle Research Scientist at the CSIRO (Australia's premiere government science organization) for 5 years. He wants to draw inspiration from vision researchers of the past to attempt to unlock computational and mathematic models that underly the processes of visual perception.

25 October

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Simon Lacoste-Julien

VP Lab Director/Associate Professor
SAIT AI Lab/Université de Montréal
Simon Lacoste-Julien is a an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal. His research interests are machine learning and applied math, with applications to computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.

24 October

24 October

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Ari Morcos

Research Scientist
Facebook AI Research
Ari Morcos is a Research Scientist at Facebook AI Research working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, Ari has worked on understanding the properties predictive of generalization, methods to compare representations across networks, the role of single units in computation, and on strategies to measure abstraction in neural network representations. Previously, he worked at DeepMind in London, and earned his PhD in Neurobiology at Harvard University, using machine learning to study the cortical dynamics underlying evidence accumulation for decision-making.

24 October

24 October

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Doina Precup

DeepMind
Doina Precup holds a Canada Research Chair, Tier I in Machine Learning at McGill University, Montreal, Canada, and she currently co-directs the Reasoning and Learning Lab in the School of Computer Science. Prof. Precup also serves as Associate Dean, Research, for the Faculty of Science and Associate Scientific Director of the Healthy Brains for Healthy Lives CFREF-funded research program at McGill. Prof. Precup’s research interests are in the area of artificial intelligence and machine learning, with emphasis on reinforcement learning, deep learning, time series analysis, and various applications of these methods. She is a Senior Member of the American Association for Artificial Intelligence

24 October

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Nicholas Léonard

Senior Data Scientist
Expedia Group
He was a core contributor to Lua Torch and worked to migrate ML infra to TensorFlow. He is currently in the Cortex Applied Machine Learning team, were he works with customer teams like Ads Prediction and Health ML to improve their existing models. He graduated from the Royal Military College of Canada and holds an MS in computer science from the University of Montreal.

24 October

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Layla El Asri

Research Team Lead
Borealis AI
Layla El Asri is a team lead at Borealis AI. She completed a Ph.D. in computer science at Université de Lorraine in France in 2016. Her Ph.D. was a joint project between Université de Lorraine and Orange Labs, the research and development branch of Orange, a telecommunication company in France. Her research focused on improving dialogue systems with machine learning. She developed methods to train dialogue systems faster while respecting strong industrial constraints. After her Ph.D, she joined Maluuba, a Canadian startup, in 2016 as a research scientist where she worked on user simulation, reinforcement learning, and datasets for dialogue systems. Layla then joined Microsoft, through the acquisition of Maluuba, in 2017 as a research manager leading a team focused on conversational AI and natural language processing. She is continuing her work on natural language processing at Borealis AI.

25 October

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Pierre-Marc Jodoin

Full Professor
Université de Sherbrooke
Pierre-Marc Jodoin is from the University of Sherbrooke, Canada where he works as a full professor since 2007. He specializes in the development of novel techniques for machine learning and deep learning applied to computer vision and medical imaging. He mostly works in video surveillance and brain and cardiac image analytics. He is the co-director of the Sherbrooke AI plateform and co-founder of the medical imaging company called "Imeka.ca" which specializes in MRI brain image analytics. His personal web site is http://info.usherbrooke.ca/pmjodoin/

25 October

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Inmar Givoni

Senior Autonomy Engineering Manager
Uber ATG
Inmar Givoni is a Senior Autonomy Engineering Manager at Uber Advanced Technology Group, Toronto, where she leads a team whose mission is to bring from research and into production cutting-edge deep-learning models for self-driving vehicles. She received her PhD (Computer Science) in 2011 from the University of Toronto, specializing in machine learning, and was a visiting scholar at the University of Cambridge. She worked at Microsoft Research, Altera (now Intel), Kobo, and Kindred at roles ranging from research scientist to VP, applying machine learning techniques to various problem domains and taking concepts from research to production systems. She is an inventor of several patents and has authored numerous top-tier academic publications in the areas of machine learning, computer vision, and computational biology. She is a regular speaker at AI events, and is particularly interested in outreach activities for young women, encouraging them to choose technical career paths. For her volunteering efforts she has received the 2017 Arbor Award from UofT. In 2018 she was recognized as one of Canada’s 50 inspiring women in STEM.

24 October

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Jian Tang

Assistant Professor
HEC Montreal
Dr. Jian Tang is an assistant professor at Mila (Quebec AI institute) and HEC Montreal since December, 2017. He is named to the first cohort of Canada CIFAR Artificial Intelligence Chairs (CIFAR AI Research Chair). His research interests focus on deep graph representation learning with a variety of applications such as knowledge graphs, drug discovery and recommender systems. He was a research fellow at the University of Michigan and Carnegie Mellon University. He received his Ph.D degree from Peking University and was a visiting student at the University of Michigan for two years. He was a researcher in Microsoft Research Asia for two years. His work on graph representation learning (e.g., LINE, LargeVis, and RotatE) are widely recognized. He received the best paper award of ICML’14 and was nominated for the best paper of WWW’16.

24 October

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Jasper Snoek

Research Scientist
Google Brain
Jasper Snoek completed his PhD in machine learning at the University of Toronto in 2013. He subsequently held postdoctoral fellowships at the University of Toronto, under Geoffrey Hinton and Ruslan Salakhutdinov, and at the Harvard Center for Research on Computation and Society, under Ryan Adams. Jasper co-founded the machine learning startup Whetlab, which was acquired by Twitter in 2015. Currently, he is a research scientist at Google Brain in Cambridge, MA.

25 October

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Nicholas Frosst

Research Scientist
Google Brain Toronto
Nicholas Frosst is a research engineer working at Google brain in Geoff Hinton's Toronto brain team. He received his undergraduate from the University of Toronto in computer and cognitive science. He focuses on capsules networks, adversarial examples and understanding representation space.

24 October

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Jonathan Frankle

Ph.D Candidate
MIT
Jonathan Frankle is a fourth-year PhD student at MIT (with Prof. Michael Carbin), where he studies empirical deep learning with the hope of improving our understanding of neural networks and making training more efficient. His dissertation explores the "Lottery Ticket Hypothesis" (for which he received a "Best Paper" award at ICLR 2019) and its implications. He has spent summers at Google Brain and FAIR, and he expects to be seeking full-time employment in the near future. Jonathan is also deeply involved in technology and AI policy: he advises policymakers, lawyers, journalists, and advocates on topics of contemporary relevance and has created a "Programming for Lawyers" course that he teaches at Georgetown Law.

24 October

24 October

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Alexia Jolicoeur-Martineau

PhD Student
MILA
Alexia is a research scientist in statistics and artificial intelligence (AI). Her main research interests are Generative Adversarial Networks (GANs), deep learning, and large-scale gene-by-environment models. Her academic and professional background is in statistics. She started pursuing the study of AI in 2017 on her own. In 2017, she released the Meow Generator, a model that generates pictures of cats 🐈. In 2018, she wrote two sole-author papers on GANs, one of which has become highly influential (See “The relativistic discriminator: a key element missing from standard GAN”). In 2019, she wrote one sole-author papers on GANs, entered the highly competitive PhD program at MILA, and received the Borealis AI Fellowship. Her ultimate goal is to push GANs beyond their current capabilities so that one day we can generate media content (such as movies, music, video games, and comics) through an artificial intelligence.

24 October

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Nimit Jain

Director, Data Science
Novartis
Nimit Jain, heads Applied AI in Novartis, IT function. He brings in 15+ years of experience with CPG, Retail, Banking and Pharma segments leveraging data for insights working cross geography. Specifically, he has been associated with McDonald’s, Target, P&G, DBS Bank in past, now is leading this initiative for Novartis. He builds data products to be consumed enterprise wise across the value chain of drug manufacturing leveraging AI/Machine learning.

24 October

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Arash Kalatian

PhD Candidate
Ryerson University
Arash Kalatian is a Ph.D. Candidate in the Transportation Engineering program at Ryerson University, Toronto. He received his B.Sc. in Civil Engineering and M.Sc in Transportation Planning, both from Sharif University of Technology, Iran. Arash’s research mainly focuses on deep learning in Cyber-Physical Transportation Systems, i.e. Virtual Reality and Ubiquitous Networks--more specifically, their applications in studying Pedestrian Behaviours and Movement Dynamics.

25 October

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Bindu Reddy

CEO & Co-Founder
RealityEngines.AI
Bindu Reddy is the CEO and Co-Founder of RealityEngines.AI. RealityEngines.AI is a foundational AI research company that solves the hard problems that enterprises face in the AI/ML space, and packages that research into easy to use, pay as you cloud services. Before starting RealityEngines.AI, she was the General Manager for AI Verticals at AWS, AI. She started the AI verticals organization at AWS, which created and launched Amazon Personalize and Amazon Forecast, the first of their kind AI services that enable organizations to create custom deep-learning models easily. Prior to that, she was the CEO and co-founder of Post Intelligence, a deep-learning company that created services for social media influencers that was acquired by Uber. Bindu is proud to be a Xoogler (ex-Googler) where she was the Head of Product for Google Apps, including Docs, Spreadsheets, Slides, Sites and Blogger. Bindu has a Masters Degree from Dartmouth and B.Tech degree from Indian Institute of Technology, Mumbai.
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Lucie Luneau

AI Project Manager
Kids Code Jeunesse
Lucie Luneau is the AI project manager at Kids Code Jeunesse (KCJ), a Canadian founded not-for-profit organization teaching coding skills to teachers and children. Lucie is also an instrumental part of KCJ's France-Canada team.
Lucie holds a Master degree in Neuroscience from the University of Montreal. She nurtures a keen interest in designing exciting and accessible resources to give teachers and kids the skills to thrive in a technology-driven society. Her last project involves making Artificial Intelligence (AI) accessible to kids and teachers by creating education materials that address opportunities and issues around AI ethics and digital citizenship

25 October

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Seyone Chithrananda

The Knowledge Society
Innovator
Seyone Chithrananda is a 15-year-old machine learning and genomics researcher. He began his journey working in genetics research with the help of SickKids at 13. He’s built projects to predict transcription-factor binding, RNA splicing, and generate molecules using deep learning. Now, looking to combine his interests in machine learning and biology, he’s working at the intersection of deep learning and genomics, working on tackling issues in drug discovery and early diagnosis of disease. This summer, Seyone is working at Integrate.ai, a leading artificial intelligence company building a customer intelligence platform powered by AI that helps consumer businesses make precise predictions about customer needs so that they can deliver more meaningful and relevant digital interactions. Seyone is an Innovator at The Knowledge Society.

25 October

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Myriam Côté

Director, AI for Humanity
MILA
With Mila since 2009, Executive Director from 2015 to June 2018 and Director of the Mila R&D and Tech Transfer team from January 2017 to September 2018, she is now Director of AI for Humanity. Myriam has over 15 years of professional experience in artificial intelligence, project management and software development, in the industrial as well as in the academic research environments. She holds a PhD in Artificial Intelligence and a Bachelor degree in Physics Engineering.
As Director of AI for Humanity, her goal is to put into actions, Mila’s humanitarian mission in collaboration with both our partners of the local ecosystem and our international allies, by promoting an ethical and socially responsible usage of AI.

24 October

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Jun Luo

Director of Decision Making and Reasoning
Huawei, Noah's Ark Lab
Jun Luo studied computer science at Peking University and has a PhD in Computer Science and Cognitive Science from Indiana University Bloomington. He previously taught Cognitive Science at the University of Toronto and worked for several small and large companies. He joined Huawei Technologies Canada in 2016, where he currently serves as the Director of Decision Making and Reasoning as part of Huawei Noah’s Ark Lab for Artificial Intelligence.

24 October

25 October

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Richard Mallah

Director of AI Projects
Future of Life Institure
Richard Mallah is Director of AI Projects at Future of Life Institute, an existential risk mitigation and technology beneficence NGO, where he works on the robust, safe, beneficent development of advanced AI. He helps move the world toward existential hope and away from outsized risks via meta-research, analysis, research organization, community building, and advocacy, with respect to technical progress, strategy, and policy coordination. Richard is also part of IEEE’s initiative on autonomous systems ethics, a committee member at Partnership on AI, a senior advisor to the The Future Society, and head of AI R&D at startup Avrio AI.

24 October

25 October

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Ashley Casovan

Executive Director
AI Global

25 October

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Abishek Gupta

AI Ethics Researcher/ Founder
McGill University/Montreal AI Ethics Institute
Abhishek Gupta is the founder of Montreal AI Ethics Institute. His research focuses on applied technical and policy methods to address ethical, safety and inclusivity concerns in using AI in different domains. Abhishek comes from a strong technical background, working as a Software Engineer, Machine Learning at Microsoft in Montreal.
He is also the founder of the AI Ethics community in Montreal that has more than 1350 members from diverse backgrounds who do a deep dive into AI ethics and offer public consultations to initiatives like the Montreal Declaration for Responsible AI. His work has been featured by the United Nations, Oxford, Stanford Social Innovation Review, World Economic Forum and he travels frequently across North America and Europe to help governments, industry and academia understand AI and how they can incorporate ethical, safe and inclusive development processes within their work. More information can be found on https://atg-abhishek.github.io
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Dexter Fichuk

Data Scientist (NLP)
Shopify
Dexter Fichuk as a data scientist at Shopify focused on NLP data. He is also pursuing a MSc in Machine Learning focused on unsupervised representation learning for transfer learning in the medical domain.

24 October

24 October

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Manuel Morales

Chief AI Scientist
National Bank of Canada

25 October

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Graeme Hirst

Ethical Issues in NLP
University of Toronto
Graeme Hirst is a computer scientist at the University of Toronto. His research covers a broad range of applied computational linguistics and natural language processing. His recent topics include detecting markers of Alzheimer’s disease in language; determining ideology in political texts; and the identification of the native language of a second-language writer of English. He is the editor of the series Synthesis Lectures on Human Language Technologies, the leading venue for monograph publication in natural language processing. In 2017, he received the Lifetime Achievement Award from the Canadian Artificial Intelligence Association.

24 October

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Jocelyn Maclure

Professor
Université Laval
Jocelyn Maclure is Full Professor of Philosophy at Laval University and the current President of the Ethics in Science and Technology Commission of the Quebec Government. Known for his work in political philosophy and ethics, his recent work focusses on artificial intelligence and end of life issues. He coauthored, with Charles Taylor, Secularism and Freedom of Conscience (Harvard University Press, 2011). His recent work appeared in Dialogue: Canadian Philosophical Review, McGill Law Journal and AI & Society.

24 October

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Oana Magdalena Frunza

NLP & Machine Learning Scientist
Morgan Stanley
Ph.D. Oana Frunza is a natural language processing and machine learning researcher at Morgan Stanley. Her research interests are in the field of artificial intelligence, particularly in natural language understanding, information extraction, automatic text classification, medical and financial informatics. Oana obtained her doctorate degree from University of Ottawa, Canada, in 2012 with the thesis Personalized Medicine through Automatic Extraction of Information from Medical Texts. She is the recipient of several awards and scholarships, author of more than 20 works that have been published in prestigious journals, conferences, book chapters and one book. Prior to joining Morgan Stanley she worked as a senior researcher at Nuance Communications.

25 October

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Jason Cornell

Manager, Machine Learning, Recommendation Systems
CBC
Jason Cornell is the Product Manager for the CBC Machine Intelligence team. His focus is the expansion of the CBC's machine intelligence practice through an altruistic approach, ensuring the CBC's mandate is being met. He's spent the last 15 years in the start up world, most recently as VP of Engineering at a start up that built an advertising platform for the entertainment industry.

24 October

25 October

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Nathalie de Marcellis

Full Professor
Polytechnique Montréal
Holder of a Ph.D. in Management Science (in risks and insurance management) from École Normale Supérieure de Cachan (France), Nathalie de Marcellis-Warin is Full Professor at Polytechnique Montreal, Department of Mathematics and Industrial Engineering. She is President and Chief Executive Officer at CIRANO, an interuniversity centre of research, liaison and transfer of knowledge on Public Policy and Risk Management.
Since 2015, she is Visiting Scientist at Harvard T. Chan School of Public Health. Nathalie is a member of the Montreal Declaration Responsible AI Development Committee, co--founder of the International Observatory on Socio-economic Impacts of Digital Transformation and AI, and co-PI of The International Observatory on the Societal Impacts of AI and Digital Technology.
Her research interests focus on risk management and decision-making in different risk and uncertainty contexts and more specifically emerging risks as well as public policies. Since 2011, she has been leading the CIRANO Barometer project on risk perception in Quebec, which annually collects data on Quebecers' concerns on 47 societal issues. She has published numerous scientific articles, several books and more than 30 reports for government and other organizations. She has given more than a hundred conferences and is regularly solicited to speak in the media.

24 October

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Alain Tapp

Full Professor
Université de Montréal

24 October

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Dominique Payette

Lawyer, Legal Affairs/ Collaborator
National Bank of Canada/ FinML
Dominique is a lawyer, with a law Master’s degree of UofMontreal on Regulating Robo-Advisers, published in the Banking and Finance Law Review. She is currently practising law as in-house counsel of National Bank of Canada, where she leads Legal AI initiatives and is currently working on implementing an AI governance framework. Outside her work for the Bank, she is also a collaborator of Fin-ML, where she gives workshops on legal and ethical accountability of AI in finance to graduate students and professionals. She is also one of the co-authors of the Discussion paper Responsible AI : A Global Policy Framework by ITechLaw.

25 October

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David Danks

Professor of Philosophy & Psychology
Carnegie Mellon University
David Danks is L.L. Thurstone Professor of Philosophy & Psychology, and Head of the Department of Philosophy, at Carnegie Mellon University. He is also an adjunct member of the Heinz College of Information Systems and Public Policy, and the Carnegie Mellon Neuroscience Institute. His research interests use philosophy, cognitive science, and machine learning to advance our understanding of complex, interdisciplinary problems. Most recently, Danks has examined the ethical, psychological, and policy issues around AI and robotics in transportation, healthcare, privacy, and security. He is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship.
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Mirka Snyder Caron

Associate
Montreal AI Ethics Institute

24 October

25 October

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Hilary Nicole Ervin

Sr Strategist, Trust & Safety
Google
Hilary conducts adversarial testing of artificial intelligence and machine learning models to detect and identify algorithmic bias in Google's products, tools and services.
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Chelsey Colbert

Legal Associate, Privacy and Data Governance
Sidewalk Labs/Fasken
Chelsey's practice focuses on tech law and policy, especially privacy and emerging tech, such as AI and robotics. She is particularly interested in the legal, ethical, and policy challenges arising from AI, AVs, and 'smart cities'. She is currently on an off-site work assignment at a tech company where her focus is privacy, data governance, and Responsible AI.

25 October

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Shalev Lifshitz

AI Researcher
University of Waterloo & SickKids Hospital
16 years old, Canadian, and striving to develop the future of technology: Shalev Lifshitz is one of the world’s youngest AI researchers and entrepreneurs. He has spoken at various conferences, inspiring many to think about the future of AI and explore which questions need to be asked to prepare for human-level artificial intelligence.
Shalev is conducting research at the University of Waterloo to further the AI community’s efforts in reaching human-level AI by developing new Artificial Neural Networks that aim to behave more like the human brain. He is also developing a new image search technique that increases cancer diagnosis accuracy while working on solutions for some of Canada’s top hospitals: creating a computer vision system to expedite diagnostic and drug discovery processes at SickKids Hospital in Toronto and designing a remote system that will assist stay-at-home patients and caregivers at St. Joseph's Hospital in Hamilton.
His goal is to spark the next wave of human innovation and help humanity reach a new evolutionary step.

25 October

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Bharatendra Rai

Professor
University of Massachusetts
Dr. Rai is Professor of Business Analytics in the Charlton College of Business at UMass Dartmouth. He teaches courses on topics such as Applied Deep Learning, Analyzing Big Data, Business Analytics & Data Mining, and Applied Decision Techniques. His current research interests include machine learning & deep learning applications. He is currently writing a book titled "Advanced Deep Learning with R". His YouTube channel that includes lecture videos on deep learning are watched in over 200 countries. For deep learning videos using Keras & TensorFlow (see this link: https://goo.gl/PsScA1 ).

24 October

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Toni Perämäki

COO
Valohai
Former promising software engineer lured into the dark arts of business development and sales. Toni has decade long experience ranging from different technology companies working with both tech and business. Currently shielding data scientists from the mundane devops & infrastructure tasks in model development as the Chief Operating Officer of Valohai.

25 October

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Yanqi Xu

VP of Data Science and AI
AlpsAnalytics.com
Yanqi Xu is co-founder and VP of Data Science & AI of Alps Analytics Group, where he is in charge of researching and developing machine learning and predictive models to create intellectual property and to help companies better leverage their assets to grow revenue, market share and improve profitability. His analytics experience spans several industries, and as part of the data science leadership team, Yanqi has helped companies such as United Airlines, Avis, Princess Cruises and Raytheon (Flight Options) make strides in improving revenue and profits by developing award-winning models in price optimization, machine learning, combinatorial optimization, and customer analytics.

24 October

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Tina Dacin

Full Professor
Queen's University
Tina Dacin is the Stephen J.R. Smith Chaired Professor of Strategy and Organizational Behavior in the Smith School of Business. Tina’s teaching interests are in the areas of AI and ethics, social innovation, social finance as well as strategy, organizational change and leadership. She is the Director of the Community Impact Research Program. Tina’s research interests include traditions and place-making, social entrepreneurship, and strategic collaboration.Tina received her doctorate from the University of Toronto and prior to joining Queen's University, she spent nine years at Texas A & M University. She has most recently been a Visiting Fellow for several years at the Judge Business School and Sidney Sussex College at University of Cambridge.

25 October

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Julian Villella

SW/ML Manager, Robotic Grasping
Kindred.ai
Julian Villella is SW/ML Manager at Kindred AI building solutions that combine artificial intelligence and robotics to unlock new levels of autonomy in the real-world. He leads the team working on autonomous grasping where reinforcement learning is used in production at retail fulfillment centers across the continent. Prior, he led the machine learning team at 500px working on computer vision systems for image search across hundreds of millions of photos in real-time, auto keywording, and user and photo recommendations.

25 October

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Christopher Pal

Full Professor
Polytechnique Montréal, Mila & Element AI
Christopher Pal is a Full Professor in the department of information and software engineering at Polytechnique Montreal, one of the founding faculty members of Mila, the Quebec AI institute, and a Principal Research Scientist at Element AI. He has served as an Area Chair for conferences such as CVPR, ICCV, NeurIPS, ICML and ICLR and he will be one of the program chairs for ICCV 2021 in Montreal. He is also one of the general chairs of the conference MIDL 2020. He also holds a Canada CIFAR AI chair.

24 October

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Frankie Cancino

Senior Engineer & Data Scientist
Target
Frankie Cancino is a Senior Engineer and Data Scientist for Target, a Fortune 50 company, in Minneapolis. While working at Target, he is also a graduate student at the University of Minnesota earning a Master of Science degree in Business Analytics. Frankie is also known as the organizer and founder of the Data Science Minneapolis group. Data Science Minneapolis is a community that brings together professionals, researchers, data scientists, and AI enthusiasts. This community is dedicated to learning, teaching, and building technologies related to data science topics.

25 October

25 October

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Amy Zhang

PhD Student
McGill University
I am a PhD student at McGill University with affiliations at Mila and Facebook AI Research, advised by Joelle Pineau. My research interests are causal inference and representation learning for sequential decision making problems for improved transfer and generalization. I have an M.Eng. in EECS and dual B.S. degrees in Mathematics and EECS from MIT.
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Jean-Simon Venne

CTO
BrainBox AI
Jean-Simon Venne is a co-founder and CTO of BrainBox AI. As a technology expert specializing in the fast and efficient migration of technological innovations to commercial applications, Jean-Simon has over 25 years of experience developing and implementing new technology to solve long-standing commercial issues in the fields of telecommunications, biotechnology, and energy-efficiency.
Prior to joining BrainBox AI, he was responsible for the successful integration of M2M technology in over 200 Smart Buildings across North America, Europe, and the Middle East. Jean-Simon holds a B.Eng. in Industrial Engineering from École Polytechnique de Montréal and a Certificate in Logistics from the University of Georgia Tech.

25 October

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Ulrich Aivodji

Postdoctoral Researcher
Université du Québec à Montréal
I am a postdoctoral researcher at UQAM, working with Sébastien Gambs. My research interests are data privacy, optimization, and machine learning. I earned my Ph.D. in Computer Science at Université Toulouse III, under the supervision of Marie-José Huguet and Marc-Olivier Killijian. During my Ph.D. I was affiliated to LAAS-CNRS, a member of both ROC and TSF research groups, and have worked on privacy-enhancing technologies for ridesharing. Before that, I received my Engineer’s degree in Software Engineering from ENSA Khouribga.

24 October

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Sergio Escobar

Partner
BCF Ventures
Sergio A. Escobar is the CEO & Partner at BCF Ventures, a Canadian Corporate Venture Capital Fund investing across the United States and Canada in B2B Cloud & SaaS Enterprise startups leveraging Artificial Intelligence, Big Data, Business Intelligence, Business Analytics and Security. Sergio has +15 years of experience as a multiple times tech entrepreneur with startups in Aerospace, eCommerce, Manufacturing, AgTech and Financial Technologies. Over the last 8 years, he has been deeply involved in the international entrepreneurial scene (incubators and accelerators) through his involvement as program advisor and startup mentor in Founder Institute, Techstars Startup Programs, McGill X-1, Startup Chile, Startup Peru, Startup Bootcamp FinTech London, MsB Tunisia, Flat6Labs Tunisia, Kuwait National Fund, World Bank ‘Tech Entrepreneurship Program for Innovation in the Caribbean’, Krypto Labs in the Middle East, NEXT AI and Creative Destruction Lab in Artificial Intelligence.

25 October

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Valentine Goddard

Founder
AI Impact Alliance
Lawyer, certified mediator and artist, Valentine Goddard is the founder and executive director of AI Impact Alliance, an international NGO whose mission is to facilitate an ethical and responsible implementation of artificial intelligence. She is also a member of the United Nations expert group on The Role of Public Institutions in the Transformative Impact of New Technologies. Ms. Goddard sits on several committees related to the ethical and social implications of artificial intelligence, while being regularly invited to speak at international conferences. In her multidisciplinary and applied approach to the ethics of AI, she places special emphasis on human dignity, having initiated throughout her career cultural and social mediation projects for human rights education.

24 October

25 October

25 October

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Subhodeep Moitra

Research Software Engineer
Google
The software development process can be frustrating, painful and costly; rife with bugs, project delays and unexpected outages. If machine learning were to help with software engineering it would make for the stuff of dreams. ML4SE (Machine Learning for Software Engineering) is an active research area in this space. In this talk we describe progress we've made at Google on training deep learning models to fixing build error encountered by software developers.

25 October

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Christopher Choquette Choo

Vector Institute
Christopher is a researcher in the CleverHans Lab at the Vector Institute exploring Adversarial ML, and in particular, membership inference attacks, differential privacy, and adversarial examples. He is also a researcher with the Aspuru-Guzik lab at the Vector Institute exploring the applications of Bayesian models and active learning in molecular discovery. Christopher has worked at Georgian Partners LP, where he developed open source solutions for differential privacy and AutoML. Christopher also worked at Intel where he researched and developed a deep neural network bug triager.

24 October

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Rosin Claude Ngueveu

PhD Student
Université du Québec à Montréal
I am a third year PhD student in Computer Science at Université du Québec à Montréal advised by Sébastien Gambs (UQAM) and Alain Tapp (UDEM).
My research interests lie in Machine learning, Adversarial training of models and Computer Security. Currently, I am working on Fairness, Accountability and Transparency of machine learning models, topics in which we develop new techniques to prevent the amplification of existing bias towards minorities or underrepresented population groups. In fact, as discrimination is present in our realities, we ought to formulate approach to prevent machine learning models to learn to discriminate. Similarly, we need to clearly understand the decision path of models (or their internal reasoning), to decide whether the models and their predictions are trustworthy, before taking actions based on such predictions.
I graduated from Georgia Institute of Technology (Gatech) and Université de Technologie de Troyes (UTT) with a double degree in Electrical and Computer Engineering from both schools. Prior to that, I obtained my bachelor degree in Systems Networks and Telecommunications with a speciality in Mobile Technology and Embedded systems from UTT.

24 October

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Jeff Lui

Director of Artificial Intelligence
Deloitte
Jeff Lui is currently a Director of Artificial Intelligence in Deloitte’s Applied Innovations Practice, and PhD Student in AI & Analytics at Queen's University. Jeff has a passion for people and ethics, and has a keen interest in how organizations can optimize for happiness. His current role has Jeff spearheading Deloitte’s EmotionPlus Platform, which uses machine learning to predict emotions and sentiment. Prior to Deloitte, Jeff spent 4 years working at Google on their People & AI Team, both in Toronto and at GooglePlex in Mountain View, where he helped Google transform from a traditional tech organization into one that is now fully driven by data science.

24 October

25 October

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Hessam Amini

Ph.D. Student
Concordia University
Hessam is currently a Ph.D. student of computer science at Computational Linguistics at Concordia (CLaC) Lab, Concordia University, with a research focus on artificial intelligence (AI) and natural language processing (NLP). His current line of research involves the use of NLP for mental health assessment of social media users. Hessam is also the lead organizer of Montreal Natural Language Processing (MTL-NLP), the greatest community of NLP enthusiasts in eastern Canada.

24 October

25 October

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Bahar Pourbabaee

Machine Learning Team Lead
Sportlogiq
Bahar Pourbabaee holds a PhD degree in Electrical and Computer Engineering with over a decade of experience in machine learning and estimation theory and also a diverse background from designing safety critical systems for aircraft control to the bimedial signal processing for healthcare application. She came to the sport technology world to build machines that understand and predict sport games. At Sportlogiq, she is the machine learning team lead for the Montreal office and contributing to the development of intelligent machines using state-of-the-art deep learning and time series analysis techniques with a focus on analyzing and understanding spatio-temporal data.

25 October

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Patrick Surry

Chief Data Scientist
Hopper
Patrick leads data science at Hopper, extracting insight and generating value from the large volumes of travel data that Hopper collects. He is a leading global analytics practitioner with a wealth of experience in real-world delivery of customer insight, predictive analytics and behavior modeling, as well as a pioneering innovator in uplift modeling research.

He was one of the founders of Quadstone Inc, a predictive customer analytics company, since acquired by Pitney Bowes Software, where he developed high-performance parallel engines for generalized additive models.
Patrick holds a PhD in mathematics and statistics from the University of Edinburgh, where he studied optimization based on evolutionary algorithms, following an HBSc in continuum mechanics from the University of Western Ontario.

24 October

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Hannah Le

Genetics Student Researcher/Innovator
TKS
I am a 17-year-old Innovator at North America human accelerator and innovation program, The Knowledge Society. My passion lies mainly in genetic engineering, and I have recently been focusing on Nanotechnology and Machine Learning. I constantly think about how these exponential technologies can merge to open new avenues for treating diseases, regenerating artificial tissues, and prolonging human life.

25 October

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Jonathan Kelly

Assistant Professor
University of Toronto
Dr. Jonathan Kelly directs the Space & Terrestrial Autonomous Robotic Systems (STARS) Laboratory at the University of Toronto Institute for Aerospace Studies, where his group carries out research at the nexus of sensing, planning, and control, with an emphasis on the study of fundamental problems related to perception, representation, and understanding of the world. Dr. Kelly holds a Dean's Catalyst Professorship (an early-career award for research excellence and potential) and a Canada Research Chair in Collaborative Robotics. Prior to joining the University of Toronto, he was a postdoctoral researcher in the Robust Robotics Group at the Massachusetts Institute of Technology. Dr. Kelly received his PhD degree in 2011 from the University of Southern California. Before starting graduate school, he was a Software Engineer in the Space Technologies division of the Canadian Space Agency.

25 October

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Prashant Raina

Ph.D Candidate
Concordia University

24 October

25 October

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Kris Lachance

Partner Associate
Real Ventures
Kris is an incurable optimist who strongly believes in the transformative potential of breakthrough technologies. He is a Partner Associate at Real Ventures, where he is involved in thesis develop and works with early-stage companies pursuing fundamental technology innovation in domains spanning AI/ML, quantum computing, robotics, advanced materials and optical hardware.
Kris was formerly a corporate attorney at Stikeman Elliott, where he focused on cross-border M&A in the technology and telecommunications sectors as a member of the private equity practice group. He holds a joint B.C.L/LL.B. in civil law and common law, as well as a B.A. in history, both from McGill University.

25 October

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Mohammad Amini

Graduate Research Assistant
MILA
Mohammad Amini is a final year master student at McGill University / Mila where he works with Prof. Doina Precup. His research interests include deep learning, imitation learning, and model based reinforcement learning.

24 October

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Saif Mohammad

Senior Research Scientist
National Research Council Canada
Dr. Saif M. Mohammad is Senior Research Scientist at the National Research Council Canada (NRC). He received his Ph.D. in Computer Science from the University of Toronto. Before joining NRC, Saif was a Research Associate at the Institute of Advanced Computer Studies at the University of Maryland, College Park. His research interests are in Emotion and Sentiment Analysis, Computational Creativity, Fairness in Language, and Information Visualization. Saif has served as General Chair for the Canada--UK Symposium on Ethics in AI, Workshops Co-chair for ACL, co-chair of SemEval (the largest platform for semantic evaluations), and co-organizer of WASSA (a sentiment analysis workshop). He has also served as the area chair for ACL, NAACL, and EMNLP in Sentiment Analysis and Fairness and Bias in NLP. His work on emotions has garnered media attention, with articles in Time, Washington Post, Slashdot, LiveScience, The Physics arXiv Blog, Popular Science, etc. Webpage:http://saifmohammad.com

24 October

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Martin Duchaîne

Director
Défi Montréal
Martin Duchaine, B.Eng. MBA - Director of Défi Montréal, the largest innovative business acceleration program in Quebec with over 500 alumni over 10 years.

Mr. Duchaîne is a recognized expert in the financing and management of high-growth innovative companies with 20 years of experience and more than 1000 companies supported. Its network includes many leaders from the main financial and economic development institutions in Quebec. A specialist in Quebec's ecosystem, he co-founded Anges Québec and supported numerous successful companies including Frank & Oak, BonLook, TickSmith and Sofdesk, advised institutional investors and government programs. He wrote the Best Practices in Raising Capital for the Quebec Ministry of Innovation and Economy.

25 October

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Brainbox.ai

BrainBox AI is a unique technology combining deep learning, cloud-based computing and algorithms to support a 24/7 self-operating building. BrainBox AI is the first autonomous artificial intelligence (AI) technology for Heating, Ventilation and Air Conditioning systems (HVAC). Its mission is to redefine building automation and to be at the forefront of the green building revolution. BrainBox AI’s solution leverages AI to predict building energy consumption at a very granular level and enable a 25-35% reduction in total energy costs, 20-40% decrease in a building’s carbon footprint, and a 60% increase in occupant comfort.
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Reality Engines

"RealityEngines.AI is an AI research company that is focused on hard problems that enterprises face. These problems include: incomplete and noisy datasets, obtaining necessary talent, avoiding bias, and the black-box nature that often comes with creating such systems. We work on a number of research areas to solve these issues. Our research will be packaged into an easy-to-use, pay-as-you-go service accessible to all enterprises. In the meantime, we’re partnering with a few select organizations who apply it to their current problems."
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Deeplite - HIRING

Deeplite are hiring for the following roles:

• AI Research Scientist
• Deep Learning Engineer
• Senior Software Engineer

Find them in the Exhibition area! 
 
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Capital One - HIRING

Capital One has an exciting opportunity for a Data Analyst.

On any given day, you could be:

Writing SQL to clean, transform, investigate data.

Working closely with business partners to harness the power of data.

Designing rich data visualizations to communicate complex ideas.

Ensure data and intent integrity by automated data quality verification pipelines.

Supporting and consulting with the business to propagate data management best practices.

Investigating the impact of new technologies on the future of digital banking and the financial world of tomorrow.

Investigating the use of technology for various automation applications.

The Ideal Candidate will be:

Curious: You ask why, you explore, and you’re not afraid to blurt out your disruptive idea. You know SQL and are constantly exploring new open source tools, and hitting up stack overflow on a regular basis.

A Wrangler: You know how to programmatically extract data from a database or an API, bring it through a transformation or two, and convert into a human-readable form (Matplotlib, d3 visualization, Tableau, etc.).

Creative: Big, undefined problems and petabytes of data don’t frighten you. You’re used to working with abstract data, and you love discovering new narratives in un-mined territories.

Proactive: You will want to share your knowledge with your peers and contribute back to inner/open source projects which you might consume.

Benefits:

Your choice of hardware - latest MacBook Pro or HP EliteBook and all the monitors you want!

Various internal training opportunities across our US and Canada locations.

$5000/yr education budget.

Flexible work hours, dress code and environment.

Basic Qualifications:

At least 3 years of experience with relational databases and programming in SQL

At least 3 years of experience with version control system like GitHub.

At least 2 years of experience with Python or R coding.

Preferred Qualifications:

1+ year experience working with AWS (EC2, S3, Lambda, RDS, etc.).

1+ year with Linux scripting experience.

1+ year experience working with advanced Git Workflows (Pull Requests, Code Reviews, Issues, and Branching).

At least 1 year of experience in open source programming languages for large scale data analysis (Python or Scala).

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Expedia Group - HIRING

Expedia is hiring for a Machine Learning Scientist: https://lifeatexpedia.com/jobs/job?jobid=R-43106
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Dialogue - HIRING

Dialogue is looking for a Principal Applied Research Scientist: https://medium.com/@alexissmirnov/dialogue-is-looking-for-a-principal-applied-research-scientist-6c8fd3981500
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Glaucus.ai - HIRING

Glaucus.ai is a young startup, democratizing the way we design, train, deploy Artificial intelligence models by decentralizing and simplifying its use and accessibility. We are building the next-gen system with cutting edge modular machine learning models, with unparalleled ease of access and use for everyone. 
AiResearchScientist_glaucusai.pdf Download Link
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Ericsson - HIRING

• Architectes des données / Data Architects - Machine Learning & AI, Montreal(248745):
https://performancemanager12.successfactors.eu/sfcareer/jobreqcareer?jobId=248745&company=Ericsson&username=

• Architectes des données juniors/ Junior Data Architects - ML et AI, Montréal(248749): 
https://performancemanager12.successfactors.eu/sfcareer/jobreqcareer?jobId=248749&company=Ericsson&username=

• Scientifiques des données / Data Scientists - ML et AI, Montréal(270689): 
https://performancemanager12.successfactors.eu/sfcareer/jobreqcareer?jobId=270689&company=Ericsson&username=

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AIR - HIRING

We believe in a better future.

A future where humans are augmented by AI agents and not replaced by them.

A future where safe artificial general intelligence is possible thanks to human and AI synergy.

We are working on improving how we will interact with Artificial Intelligence, putting strong emphasis on human-in-the-loop training.
Machine Learning Developer - AI Redefined.pdf Download Link
Software developer - AI Redefined.pdf Download Link
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NOVARTIS - HIRING

Find open opportunites with Novartis here: https://jobs.brassring.com/1033/ASP/TG/cim_jobdetail.asp?partnerid=13617&siteid=5268&Areq=280079BR
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Coveo - HIRING

Coveo are looking for Machine Learning Developers! You can apply here:

https://grnh.se/9b524b7c2
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Thales - HIRING

Thales AI Research and Technology Scientist - Final.pdf Download Link
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Symend - HIRING

Symend are looking for a Python Developer
Symend_job_ad_python.pdf Download Link
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Alejandro Espinosa - CV

CV_Alex.pdf Download Link
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Ernesto Cuadra Foy - CV

Ernesto Cuadra Foy CV.pdf Download Link
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Gary Choy - CV

Gary_Choy_Resume.pdf Download Link
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Neda Ebrahimi - CV

Neda_Ebrahimi_CV_201910.docx Download Link
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Quan Xue - CV

QuanXue_Resume.pdf Download Link
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Atif Mahmud - CV

Atif Mahmud.pdf Download Link
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Deeplite

Deeplite created Neutrino™, an intelligent optimization software for deep learning deployed on cloud servers and edge devices. Neutrino™ uses AI and a novel design space exploration to automatically optimize high-performance Deep Neural Networks (DNNs) to satisfy computation constraints. Our technology leverages years of research at the Brown University SCALE Lab and new developments in the field of reinforcement learning to produce fast, efficient and scalable deep learning solutions for challenging real-world environments. At Deeplite, our vision is to make AI more accessible and affordable to benefit everyone's daily life.
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Axionable

We help our clients to get positive impact from data & AI leveraging our end-to-end professional services, our ecosystem of academic partners and our alliances with data tech leaders. Our team is unique and composed of data strategists & consultants, certified data technology experts and passionate AI Researchers.
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Valohai

Scale models to hundreds of CPUs or GPUs at the click of a button. Create an audit trail and reproduce any previous run with built-in version control for input data, hyperparameters, training algorithms and environments. Manage your entire deep learning pipeline with automatic coordination from feature extraction and training to inference.
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Umaneo

Umaneo is the company behind Aloha software. Aloha simplifies and automates your RFP responses using AI. More specifically, it finds the right RFPs for you, matches products / services and helps draft the right proposals. Thanks to AI, we make the RFP response process a better experience for everyone, helping save numerous hours and headaches, all the while maximizing revenue opportunities.
VueJSDeveloper - Umaneo.docx Download Link
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IVADO

Founded by the Université de Montréal, HEC Montréal and Polytechnique Montréal, IVADO aims to bring together industry professionals and academic researchers to develop cutting-edge expertise in data science, operational research and artificial intelligence. In essence, IVADO creates opportunities for knowledge exchange and collaborations between the specialists, partners, researchers, and students in its network.
The objective of IVADO is to be the link between academic expertise and the business needs of organizations, from multinational corporations to start-ups. With over 1000 affiliated scientists (researchers, post-docs, PhD candidates, and research associates), IVADO is an advanced multidisciplinary centre for knowledge in sectors including statistics, business intelligence, deep learning, applied mathematics, data mining, and cybersecurity.
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Unity

Unity is the world’s most popular real-time 3D platform, powering over 50% of the world’s mobile games and expanding into markets from architecture and engineering to automotive and film. Believing that the world is a better place with more creators in it, Unity provides tools that help people bring their ideas to life.We’re making massive investments to maximize the transformative impact of machine learning in our technologies. ML is already at the core of Unity’s personalized ad content delivery, allowing smart decisions to make the best possible gaming experience.
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Montreal International

Montréal International is a non-profit organization funded by the private sector, the governments of Canada and Québec, the Communauté métropolitaine de Montréal and the City of Montréal. Its mandate is to attract and retain foreign investment (companies and startups), international organizations, skilled workers and international students to Greater Montréal by providing support services tailored to their needs.
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Exhibition Area & Refreshment Breaks

REGISTRATION & LIGHT BREAKFAST

08:00 AM 09:00 AM

Registration will open from 8:00am, please have your registration details to hand on your device. A light breakfast, tea and coffee will be available for you to help yourself!
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Deep Learning Summit
Responsible AI Summit

WELCOME

09:00 AM 09:15 AM

The host for the Deep Learning Summit is Hessam Amini, PhD Student at Concordia University. The host for the Responsible AI Summit morning sessions is Saif Mohammad, Senior Research Scientist at the National Research Council Canada.

Speakers

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Deep Learning Summit

Towards Combining Statistical Relational Learning and Graph Neural Networks - HEC Montreal

09:15 AM 09:35 AM

Developing statistical machine learning methods for predictions on graphs has been a fundamental problem for many applications such as semi-supervised node classification and link prediction on knowledge graphs. Such problems have been extensively studied by traditional statistical relational learning methods and recent graph neural networks, which are attracting increasing attention. In this talk, I will introduce our recent efforts to combine the advantages of both worlds for prediction and reasoning on graphs. I will introduce our work on combining conditional random fields and graph neural networks for semi-supervised node classification (Graph Markov Neural Networks, ICML'19) and also recent work on combining Markov Logic Networks and knowledge graph embedding (Probabilistic Logic Neural Network, in submission) for reasoning on knowledge graphs. Key Takeaways: (1) Reasoning is important for decision making and missing in existing deep learning systems (2) In this presentation, I will introduce our recent works on combing traditional statistical relational learning and recent graph neural networks for relation reasoning (3) A more general direction for the future is how to combine traditional symbolic logic rule based approaches with deep neural networks for reasoning?

Speakers

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Responsible AI Summit

AI Ethics: A Deflationary Yet Cautionary Tale - UNIVERSITE LAVAL

09:15 AM 09:40 AM

The field of “AI ethics” is effervescent. While some scholars wonder whether the interests of artificial agents endowed with “general intelligence” will be aligned with human values, others want us to focus on the immediate challenges raised by the weak and narrow AI systems that are currently being developed. Civil society actors, policy makers, elected officials, international organizations and some business leaders reflect on the best ways to regulate AI. In this talk, I will first suggest that what I call “inflationary” thinking about the future development of AI and its likely impacts on human life hinders a sober reflection on the risks created by current AI technologies and the ways to mitigate them. Second, I will attempt to show how a deflationary view can help addressing the “explainability problem” plaguing decision-making or predictive machine learning algorithms.    ​

Speakers

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Deep Learning Summit

Few-Shot Learning: Thoughts On Where We Should Be Going - GOOGLE BRAIN

09:35 AM 10:05 AM

Few-shot learning is the problem of learning new tasks from little amounts of labeled data. This is achieved by performing a form of transfer learning, from the data of many other existing tasks. This topic has gained tremendous interest in the past few years, with several new methods being proposed each month. In this talk, I suggest we take a step back, look at what we have achieved and, most importantly, consider where this research should be going next. Key Takeaways: 1) We can achieve surprisingly high accuracy on current few-shot learning benchmarks, without using the labels. 2) The most popular few-shot learning methods aren't robust to training on heterogeneous sets of tasks. 3) None of these few-shot learning methods dominate in all settings (e.g. of number of shots).

Speakers

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Responsible AI Summit

Social Inclusion in the AI Pipeline - POLYTECHNIQUE MONTREAL

09:40 AM 10:05 AM

Key Takeaways: 1) risks and biases associated with the development of AI, 2) development of an ethical framework, 3) process reflecting inclusiveness and diversity.

Speakers

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Deep Learning Summit

Building Knowledge For AI Agents With Reinforcement Learning - DEEPMIND

10:05 AM 10:35 AM

Reinforcement learning allows autonomous agents to learn how to act in a stochastic, unknown environment, with which they can interact. Deep reinforcement learning, in particular, has achieved great success in well-defined application domains, such as Go or chess, in which an agent has to learn how to act and there is a clear success criterion. In this talk, I will focus on the potential role of reinforcement learning as a tool for building knowledge representations in AI agents whose goal is to perform continual learning. I will examine a key concept in reinforcement learning, the value function, and discuss its generalization to support various forms of predictive knowledge. I will also discuss the role of temporally extended actions, and their associated predictive models, in learning procedural knowledge. Finally, I will discuss the challenge of how to evaluate reinforcement learning agents whose goal is not just to control their environment, but also to build knowledge about their world.

Speakers

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Responsible AI Summit

AI for Humanity: How AI Could Benefit Us All- MILA

10:05 AM 10:35 AM

This will be a TedX format: IA could be scary, we would like to tell another story: how ai could benefit our society, exemples of real applications at Mila, call for actions

Speakers

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Exhibition Area & Refreshment Breaks

COFFEE

10:35 AM 11:20 AM

Help yourself to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors!
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Deep Dive Sessions

DEEP DIVE: Introduction to Deep Reinforcement Learning - MILA

10:40 AM 11:15 AM Tchaikovsky Room

In this talk, I will introduce the field of Reinforcement Learning (RL). Furthermore, I will discuss various approaches to solve the RL problem which includes policy gradient methods, value based learning and model based learning. I will describe how deep learning is effective in all these solutions to RL.

Speakers

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Deep Dive Sessions

DEEP DIVE: Neural Networks for Lawyers and Other Non-Technical Professionals - MIT

10:40 AM 11:15 AM Vivaldi Room

In this presentation, I will explain typical machine learning workflows and neural networks in technical detail from scratch. We will begin by defining concepts like "artificial intelligence" and "machine learning" before discussing general machine learning concepts, logistic regression, and neural networks. This presentation is suitable for audiences with no technical background.

Speakers

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Deep Learning Summit

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks - MIT

11:20 AM 11:40 AM

Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, until recently, it was believed that the sparse architectures produced by pruning were difficult to train from the start, which would similarly improve training performance. In this talk, I will discuss a series of experiments that contradicted this received wisdom, showing that sparse neural networks are indeed trainable, provided they are given the same initialization they received at or near the start of training. This observation culminates in a new conjecture about opportunities to improve neural network training: the lottery ticket hypothesis. Key Takeaways: 1. The neural networks that we train in practice are much larger than they have to be to learn. 2. There is an unexploited opportunity to substantially improve the performance of practical neural networks. 3. Neural networks are objects worthy of scientific study.

Speakers

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Responsible AI Summit

Ethical Issues in NLP - UNIVERSITY OF TORONTO

11:20 AM 11:45 AM

Ethical issues in natural language processing relate both to issues in the applications of NLP and to problems of bias and discrimination within the systems themselves. I will talk about virtuous and evil applications of NLP, and I will suggest some characteristics of the latter. I’ll then describe the problem of bias in learning from linguistic data, especially for minority groups and for languages other than English that have limited resources, and the bias in word-embeddings that results from linguistic data and that affects even second-order uses of the data. Key Takeaways: 1. Natural language processing has great potential for unethical uses. 2. Bias in NLP systems may arise from the structure of the research milieu in which they are developed. 3. NLP systems may learn both direct and latent bias from real-world data.

Speakers

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Deep Dive Sessions

DEEP DIVE: AI, Deep Learning, Optimization: How to Frame and Solve Real World Problems Effectively to Get the Most Business Benefits - ALPSANALYTICS.COM

11:20 AM 12:00 PM Vivaldi Room

AI, Deep Learning, and Machine Learning (ML) have been receiving much attention in recent years due to their big potentials to deliver significant business benefits. In this talk, we will discuss the interactions and relationships of AI, deep learning, ML, optimization models, the utilities of each and their and best uses in different business contexts. We will look into how to frame your business problems properly, ways to select the more effective models, and how to implement efficient solutions that highlight and optimize the most critical business tradeoffs in line with your (sometimes subtle and unspoken) business priorities and strategies. We will include examples from various industries for illustration. This presentation can benefit both data scientists and business executives who are interested in leveraging machine learning to gain business advantages for their companies. Takeaways for the audience include: - better understanding of AI, deep learning, machine learning, optimization modeling concepts, their common roots, and where they can be best applied - a framework that helps guide the definition, framing and formulation of the business problems to get the model solution that can best support your business priorities - industry examples that help demonstrate how it's done

Speakers

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Deep Learning Summit

Generalizing The Lottery Ticket Hypothesis Across Datasets and Optimizers and Beyond Supervised Image Classification - FACEBOOK AI RESEARCH

11:40 AM 12:00 PM

The success of lottery ticket initializations (Frankle and Carbin, 2019) suggests that small, sparsified networks can be trained so long as the network is initialized appropriately. This phenomenon is intriguing and suggests that initialization strategies for DNNs can be improved substantially, but a number of open questions remain. Do winning tickets contain generic inductive biases for training or are they just overfitted to a particular problem? Is the lottery ticket phenomenon simply an artifact of image classification or is it present in other domains as well? In this talk, I will discuss recent work to address both of these critical questions.

Speakers

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Responsible AI Summit

Finding Bias In Your Systems - SHOPIFY

11:45 AM 12:10 PM

In a large organization, it's easy for bias to live in the form of machine learning models, but also from hand-coded rules. This talk will explore the ways of using machine learning from an analytical perspective and detecting where bias can live in different types of systems. Key Takeaways: 1. Bias lives in everything we do. 2. It's extremely hard to determine what is fair. 3. How can you better spot potential bias and its implications.

Speakers

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Deep Learning Summit

Outlines, Explanation, and Deflecting Adversarial Examples - GOOGLE BRAIN

12:00 PM 12:20 PM

Adversarial examples have been a topic of interest since they were first discovered. They illustrate an interesting failure mode in neural networks. Many researchers have attempted to solve this problem by creating detection methods. However these mechanisms are inevitable broken shortly after release by a defense aware attack. One approach to getting ahead of this cycle is to create a model that when adversarially attacked, yields inputs that resemble the target class, thereby deflecting the attack. I will talk about recent work done under Geoff Hinton at google brain that takes such an approach. Key Takeaways: 1. Adversarial examples are hard to beat. 2. Deflecting attacks is a good idea. 3. Capsule reconstruction networks do a good job of this.

Speakers

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Deep Dive Sessions

DEEP DIVE: How The Pharmaceutical Industry Is Leveraging Deep Learning - NOVARTIS

12:00 PM 12:40 PM Vivaldi Room

A case study of NOVARTIS, from conceptual design to enterprise level implementation.Pharmaceutical industry is very rich in “unstructured”, “semi-structured” data sets, generated throughout the process of drug discovery, clinical trial, manufacturing, testing and commercial launches in the form of pdfs, word document, databases etc.. Business functions are well equipped to manually/semi-manually mine the information, extract insights and use in their business process. However, typical issues around limitation with respect to understanding causal effects, learnings from prior experiments/mistakes, siloes data connections are vast area of opportunity. The talk sites specific use cases touching 1000’s of business users using AI and ML-based model insights. This focuses on data science and software engineering learnings which has made deep learning based initiatives successful. The talk summarizes the gaps in research which can enable the pharmaceutical industry to be more embracing to advanced technologies. Key Takeaways: 1. Pharma data is rich and less is more in AI. 2. Novartis is leading data science in a true sense. 3. How to scale data science products in enterprise.

Speakers

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Responsible AI Summit

Agnostic Data Debiasing Through a Local Sanitizer Learnt from an Adversarial Network Approach - UNIVERSITE DU QUEBEC A MONTREAL

12:10 PM 12:35 PM

The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting discriminations. In this work, we propose an approach called GANsan whose objective is to prevent the possibility of any discrimination, )direct or indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our sanitization algorithm GANsan is partially inspired by the framework of generative adversarial networks (in particular the Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions.
In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible and thus preserving the interpretability of the sanitized data. As a consequence, once the sanitizer is trained, it can be applied to new data, such as for instance, locally by an individual on his profile before releasing it. We explore the utility preserved by the sanitization by conducting on a dataset various experiments, which we believe represents possible real world use case of the sanitization procedure and the sanitized data. Our observations bring forward new research questions which we will briefly introduce.

Speakers

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Deep Learning Summit

Learning to Act More Like Humans, and Learning with Less Data - POLYTECHNIQUE MONTREAL

12:20 PM 12:40 PM

Humans recognize objects, read and understand language, observe the world and control their bodies with incredible skill. Furthermore, we learn to do these things with very little training data. In this talk I survey recent work from my group and collaborators on deep learning techniques for these settings.  I'll focus on the theme of learning with less labelled data using techniques such as multi-task learning, encoder-decoder architectures and a new technique for visual imitation learning with reinforcement learning (VIRL). I'll show how this new VIRL method permits a humanoid robot living in a software simulation to learn new behaviours by simply watching video of a desired activity.

Speakers

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Exhibition Area & Refreshment Breaks

LUNCH

12:35 PM 01:40 PM

A hot, 3-course, lunch buffet will be served in the foyer area. A great time for networking and to get to know your fellow participants or you can join the Lunch & Learn Session taking place in the Deep Dive Track and take a seat at one of the tables to hear more from the speakers and exhibitors.
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Deep Dive Sessions

DEEP DIVE: Lunch and Learn

12:45 PM 01:45 PM Tchaikovsky Room & Vivaldi Room

Join speakers and exhibitors to explore various AI & DL topics over lunch! You can find Huawei, CBC, Facebook AI Research & Axionable in Tchaikovsky and Shopify, Deeplite, Deloitte, Umaneo, & Valohai in Vivaldi.

Speakers

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Responsible AI Summit

PM Host Introduction

01:35 PM 01:40 PM

The host for the Responsible AI Summit afternoon sessions is Valentine Goddard, Founder of the AI Impact Alliance.

Speakers

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Deep Learning Summit

Productionizing Deep Learning Models - EXPEDIA

01:40 PM 02:00 PM

Lessons learned from productionizing deep learning models. How to go about improving existing production models. How to organize the infra and modeling efforts. Automating continuous training. Online vs offline metrics.

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Responsible AI Summit

Fairwashing: The Risk of Rationalization - UNIVERSITÉ DE MONTRÉAL

01:40 PM 02:00 PM

Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques can be beneficial by providing interpretability, they can be used in a negative manner to perform fairwashing, which we define as promoting the false perception that a machine learning model respects some ethical values. In particular, we demonstrate that it is possible to systematically rationalize decisions taken by an unfair black-box model using the model explanation as well as the outcome explanation approaches with a given fairness metric. Our solution, LaundryML, is based on a regularized rule list enumeration algorithm whose objective is to search for fair rule lists approximating an unfair black-box model. We empirically evaluate our rationalization technique on black-box models trained on real-world datasets and show that one can obtain rule lists with high fidelity to the black-box model while being considerably less unfair at the same time.

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Deep Dive Sessions

DEEP DIVE: Algorithmic Profiling & Illegal Discrimination: A Cross-Industry Analysis - MONTREAL AI ETHICS INSTITUTE

01:45 PM 03:20 PM Vivaldi Room

Workshop and mini meet up. *Laptops are recommended (but not required)*

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Deep Learning Summit

AI for Self Driving - From Research to Production - UBER ATG

02:00 PM 02:20 PM

At Uber ATG R&D centre, we are working on advanced state-of-the-art AI models for solving a large range of problems in self driving - perception and prediction, motion planning, mapping and localization, sensor simulation, and more. All that work is publicly available through academic conferences and venues. In this talk I will cover some exciting recent advances and also discuss the path to production - how we go from research prototypes to deployed systems on vehicle. Key Takeaways: how self driving works, Uber's R&D Lab - what and why, new tech coming out of Uber ATG R&D.

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Responsible AI Summit

Learning Fair Rule Lists - UNIVERSITE DU QUEBEC A MONTREAL

02:00 PM 02:25 PM

The widespread use of ML models in high stakes decision-making systems raises many ethical issues concerning fairness and interpretability. While the research in these domains is booming, very few works have addressed these two issues simultaneously. To solve this shortcoming, we propose FairCORELS, a supervised learning algorithm whose objective is to learn at the same time fair and interpretable models. FairCORELS is a multi-objective variant of CORELS, a branch-and-bound algorithm, designed to compute accurate and interpretable rule lists. We also made additional contributions regarding search strategies for improving the performances of FairCORELS.

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Deep Learning Summit

New Optimization Perspectives on Generative Adversarial Networks - SAIT AI LAB

02:20 PM 02:40 PM

Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training. In this work, we cast GAN optimization problems in the general variational inequality framework and investigate the effect of the noise in this context. We thereby propose to use a variance reduced version of the extragradient method, which shows very promising results for stabilizing the training of GANs. Key Takeaways: 1) variance is a bigger problem in solving games (such as GANs) than standard minimization in machine learning; 2) Our method, SVRE, combines extragradient and variance reduction to solve games (like GANs) with more stability; 3) It's a new exciting direction of investigation for GANs work!

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Responsible AI Summit

The Evolving Relationship Between Ethics and Safety in AI - FUTURE OF LIFE INSTITUTE

02:25 PM 02:55 PM

As AI systems support more distant training supervision, wider action space ranges, more powerful optimization, more general multi-task algorithms, and more meta specification, the dependencies between AI ethics and AI safety become more pronounced. We discuss why architectural foresight for both safety and ethical concerns is both possible and warranted, and how technical architecture, professional ethics, safety engineering, and stakeholder interests are increasingly inextricably bound together. Through the paradigm of technical value alignment we explore ways to predict new classes of misalignment bugs and ways to prevent or mitigate them as early as possible.

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Deep Dive Sessions

DEEP DIVE: Consumer Airfare Prediction and other Big Data AI Challenges at Hopper- HOPPER

02:25 PM 02:55 PM Tchaikovsky Room

At Hopper, we help consumers make smarter decisions about booking their air & hotel at the right time for the right price. This session is a deep dive into airfare pricing, including data visualization, how we enable millions of personalized conversations with our users focused on travel flexibility and alternative suggestions, building trust with consumers around data, our price prediction algorithms, tackling big data AI challenges in a low-frequency industry and other problems that keep us up at night.

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Deep Learning Summit

PANEL: How Can We Overcome Challenges To Fully Leverage the Oportunities of GANs?- MILA, UNIVERSITY OF MASSACHUSETTS, SAIT AI LAB/UNIVERSITE DE MONTREAL, CONCORDIA UNIVERSITY

02:40 PM 03:20 PM

The purpose of this panel discussion is to consider what challenges and obstacles are currently in play which are halting the progression, development and more widespread application of GANs. Particular topics include the challenges/solutions relating to the type of data, computing resources, software/packages.

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Responsible AI Summit

Adversarial Machine Learning: Ensuring Security of ML Models and Sensitive Data - VECTOR INSTITUTE

02:55 PM 03:20 PM

As machine learning (ML) has seen dramatic growth in industrial applications, so have we begun to question what trust and security mean in the context of ML. I will give an overview of adversarial ML as a research area and explore some of the attack and defense strategies that have been developed in recent literature. In particular, I will showcase some of the use cases and implementations of differential privacy and how it can be used to protect sensitive data used for training ML models.

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Exhibition Area & Refreshment Breaks

COFFEE

03:20 PM 04:00 PM

Help yourself to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors!
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Deep Dive Sessions

DEEP DIVE: Talent and Talk

03:25 PM 04:00 PM Tchaikovsky Room

Find out who's hiring!
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Deep Learning Summit
Responsible AI Summit

Deep Learning and Cognition - UNIVERSITÉ DE MONTRÉAL

04:00 PM 05:00 PM

Neural networks and deep learning have been inspired by brains, neuroscience and cognition, from the very beginning, starting with distributed representations, neural computation, and the hierarchy of learned features. More recently, it has been for example with the use of rectifying non-linearities (ReLU) - which enables training deeper networks - as well as the use of soft content-based attention - which allow neural nets to go beyond vectors and to process a variety of data structures and led to a breakthrough in machine translation. Ongoing research is now suggesting that brains may use a process similar to backpropagation for estimating gradients and new inspiration from cognition suggests how to learn deep representations which disentangle the underlying factors of variation, by allowing agents to intervene in their environment and explore how to control some of its elements. Key Takeaways: Lots of interesting challenges toward human level AI.

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Exhibition Area & Refreshment Breaks

CONVERSATION & DRINKS

05:00 PM 06:00 PM

Join us in the foyer area and grab a drink to celebrate the end of Day 1 and continue to network with attendees.
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Exhibition Area & Refreshment Breaks

END OF SUMMIT

06:00 PM 06:05 PM

Thank you for attending! Doors open at 8am tomorrow morning. See you tomorrow for more discussions, presentations and networking! And please remember to bring your badge! :)
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Exhibition Area & Refreshment Breaks

REGISTRATION & LIGHT BREAKFAST

08:00 AM 09:00 AM

Registration will open from 8:00am, please have your registration details to hand on your device. A light breakfast, tea and coffee will be available for you to help yourself!
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Deep Learning Summit
Responsible AI Summit

WELCOME

09:00 AM 09:15 AM

The host for the Deep Learning Summit is Hessam Amini, PhD Student at Concordia University. The host for the Responsible AI Summit is Valentine Goddard, Founder of the AI Impact Alliance.

Speakers

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Deep Learning Summit

Visual Perception for Better Understanding of Sports Games  - SPORTLOGIQ

09:15 AM 09:35 AM

This talk will be about some of the main challenges in developing and deploying deep learning algorithms at scale, i.e. processing more than 60,000 sports videos which are coming from different sources, and touches the issues from proper problem definition to generalization and robustness in the deep learning models.

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Responsible AI Summit

AI and The Evolution of Work - KINDRED.AI

09:15 AM 09:35 AM

The replacement of human workers with automata has been a topic of debate since the time of Aristotle. Ancient Rome had been known to ban labour-saving inventions in fear it would prevent the poor from earning their bread. It has motivated radical factions of textile workers in the 19th century to destroy machinery as a form of protest. Today there is a growing belief that AI will soon make skilled workers obsolete; some even calling it the “AI catastrophe”. Are there reasons to believe this? What tendencies are baked-in to the technological systems we are building that help us understand their direction? How can we make better sense of the evolution of work?

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Deep Learning Summit

Version Control for Jupyter Notebooks - VALOHAI

09:35 AM 09:55 AM

While Jupyter Notebook is an excellent tool for experimenting with machine learning models on early stage, there is still a big leap to enable company-wide, transparent, and production-scale ML development.During Toni’s presentation, you’ll learn how to support machine learning exploration, experimentation, and deployment in Jupyter Notebooks using Jupyhai. Jupyhai is Valohai’s Jupyter Notebook extension letting data scientists run their experiments asynchronously on the cloud while having automatic versioning for code, data, hyperparameters, and more. Time to step up your Jupyter game to the next level!

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Responsible AI Summit

Artificial Intelligence & Ethical Deployment - DELOITTE

09:35 AM 09:55 AM

Artificial Intelligence (AI) is poised to permeate and potentially disrupt many, if not all, industries. With new technology comes a new set of challenges and considerations from an ethical perspective. Join Jeff Lui in this session, where he will discuss the impact of AI and the questions that must be considered when developing any responsible AI implementation and the ethical concerns through the lens of his AI Ethics Funnel. He will also discuss his career journey through the field of AI and how he continues to be involved in the wider industry. Key Takeaways: 1. Cognitive Diversity critical for AI & Ethics, 2. Multidisciplinary Team Composition recommended for AI deployments, 3. AI Ethics must be a shared conversation with all stakeholders.

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Deep Learning Summit

Pushing the Energy Efficiency Frontier with Deep Learning - BRAINBOX AI

09:55 AM 10:15 AM

Deep learning will be imperative in optimizing our use of energy and ultimately supporting our global fight against climate change. More specifically, deep learning can be used to predict the energy consumption in real time of one of the world’s biggest energy consumers, buildings. Equipped with these predictions, it becomes possible to shift energy loads by creating thermal batteries within buildings, resulting in a more efficient use of energy resources and less waste. This talk will also introduce the concept of swarm intelligence applied to buildings and the ways in which it can benefit grid operators and ultimately the planet as a whole.

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Responsible AI Summit

Fireside Chat: Responsible AI at Target - TARGET & AI IMPACT ALLIANCE

09:55 AM 10:15 AM

Discover more about how Frankie Cancino’s team at Target are designing models with Responsible AI topics in mind!

Speakers

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Exhibition Area & Refreshment Breaks

COFFEE

10:15 AM 11:00 AM

Help yourself to tea, coffee and refreshments in the foyer and make sure you check out the exhibitors!
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Deep Dive Sessions

DEEP DIVE: Investing in AI Startups: Challenges & Opportunities

10:20 AM 10:55 AM Tchaikovsky Room

Speakers

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Deep Learning Summit

Deep Learning for Conversational AI - BOREALIS.AI

11:00 AM 11:25 AM

In this talk, I will review recent progress in building conversational AI to complete tasks and hold general conversations. Today's dialogue systems are the fruit of decades of progress in linguistics, compute power, and machine learning. In particular, modern machine learning techniques like deep learning hold the promise of accelerating the development of dialogue systems and achieving more complex interactions but they also entail many challenges including controllability, evaluation, and data efficiency. I will describe these challenges as well as some of the promising solutions that will bring the next generation of dialogue systems. Key Takeaways: 1) Conversational agents can now combine casual chatting capabilities and task-achieving capabilities (e.g., playing songs, informing about the weather,...), 2) Big language models such as BERT and GPT-2 are giving a great boost in performance and the gap towards good conversational agents seems to be shrinking, 3) conversational AI is making a foray not only in customer service but also in education, retail, and finance.

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Responsible AI Summit

AI Governance in Banking: The Role and Challenges of Deploying AI in Banking - NATIONAL BANK OF CANADA

11:00 AM 11:25 AM

In the last ten years, we have witnessed the evolution of the concept of Artificial Intelligence into a notion that has captured the collective imagination with its promises of transformation. Deep learning and its flagship applications such as Alpha Go, self-driving cars, image recognition, automatique translation, etc., have created a lot of expectation in terms of its potential to bring fundamental transformation to various economic sectors. Historically, the banking sector has always been a large ship that is hard to steer into new directions. In terms on innovation and technology, they have been less agile than Fintech startups. Now that banks are beginning to catch up, alongside technological challenges, there come structural and cultural shifts that bring about a different type of challenges. In this presentation, we discuss the potential of transformation in banking made possible by AI technologies but also the cultural and structural challenges that this transformation represents. We will discuss these issues around a few exemples of applications of machine learning in banking.

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Deep Dive Sessions

DEEP DIVE: Deep Learning in Transport & Cities: The Human Urban Mobility - RYERSON UNIVERSITY

11:00 AM 11:45 AM Vivaldi Room

As autonomous vehicles are finding their way on the streets of the future, they have the potential to fundamentally alter the dynamics of urban areas. Interaction between pedestrians and autonomous vehicles is one of the lesser discussed topics in the field. In this context, a virtual reality-based framework is presented to collect and analyze a large naturalistic dataset of pedestrians’ road crossing behaviour. To analyze the data, an interpretable deep survival model, along with a deep LSTM with auxiliary information is used to capture the inherently complex behavioural characteristics of pedestrians, including their crossing intention, waiting time and trajectory. Key Takeaways: - How to utilize deep learning to manage interactions of pedestrians and autonomous vehicles. - Interpretability of Deep learning Models: a case study on pedestrian behaviour. - What cities can do to prepare a pedestrian-friendly urban area in the age of autonomous vehicles.

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Deep Learning Summit

Understanding the Behavior of Time Series Data Using the Matrix Profile and Deep Learning - TARGET

11:25 AM 11:45 AM

Target is a large retail company with over 1,800 stores in the U.S. Because of this scale, it can be difficult to find anomalous behavior in data or pinpoint what metrics could potentially be correlated. In order to understand the behavior of this data at scale, Target open-sourced the Python library matrixprofile-ts. Using this library, we can layer models on top of the Matrix Profile to find when anomalous behavior occurs or when different metrics in different areas of the company affect each other. This talk will briefly go over the matrixprofile-ts library and examples of where deep learning models can be applied to complement it. Key Takeaways: Target implements research at scale, the difficulty of having hundreds of thousands of data streams, how deep learning can be applied at a large company.

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Responsible AI Summit

Informing, Delighting and Entertaining ALL Canadians through Machine Learning - CBC

11:25 AM 11:45 AM

The CBC's mandate is to Inform, Delight and Entertain all Canadians, which the CBC does not take lightly. As part of the fulfilment of this mandate, the CBC has embarked on the creation of a personalization team to ensure we're reaching all Canadians. At the core of this objective we're ensuring that we uphold the privacy of our audience, that we focus on Fairness, Accountability and Trust in machine learning, and that we ensure that our personalization approaches are accessible to everyone. Key Takeaways: An understanding Privacy by design, FATRec and key approaches to make it all happen.

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Deep Learning Summit

Recent Advances in Real-Time Road Traffic Analytics - UNIVERSITE DE SHERBROOKE

11:45 AM 12:10 PM

In this presentation, I will present our latest advances on traffic surveillance. In the wake of the CVPR 2017 MIO-TCD challenge, we developed various models to analyze traffic based on ultra-low frame rate videos. This includes applications such as vehicle recognition, orientation estimation, and motion detection. We also explored solutions to compress deep conv nets on cameras whose hardware does not accommodate with large state-of-the-art convolutional neural network. The proposed compression algorithm is a budgeted dropout sparsity learning approach. We also studied ways of measuring the complexity of certain datasets following a novel "complexity measure" which allows to assess the fundamental complexity of a give image classification problem. Key Takeaways: Real-life concerns in video traffic analysis, network compression, vehicle localization and recognition.

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Responsible AI Summit

Collaboratively Building Trust in AI - AI GLOBAL

11:45 AM 12:10 PM

There are several good reasons why there's increasing wariness and a limited degree of trust in the use and adoption of AI, especially in the public sector. We've all read the news stories about predictive policing, the use of affective computing in the education systems, and inaccurate health diagnosis made by automated systems. While these technologies have yet to be perfected, they are not going away. This is why it's imperative to work together not to protest these technologies, but influence how they are being designed, developed, and deployed. This presentation will highlight work underway to build the practical tools needed to support the responsible implementation of AI in the public sector and beyond.

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Deep Dive Sessions

DEEP DIVE: Deep Learning in the 3D World - CONCORDIA UNIVERSITY

11:45 AM 12:30 PM Vivaldi Room

Applying deep learning to 3D geometry is a challenging area of research because we typically represent 3D shapes as non-Euclidean data. This makes it difficult to apply convolutional neural networks (CNNs), because CNNs are designed to operate on Euclidean domains. Nevertheless, the deep learning community has devised many ingenious techniques to use deep learning for processing and generating 3D shapes. This deep dive session will cover the most exciting developments in the field of deep learning for 3D geometry. The following broad topics will be covered:1) Using grid structures to create Euclidean domains for convolution. 2) Applying deep learning directly to unstructured 3D point clouds. 3) Generating 3D shapes using deep neural networks. Along the way, we will encounter popular 3D deep learning architectures such as PointNet and AtlasNet. Key Takeaways: 1) 3D geometry can be made compatible with CNNs using grid structures. 2) Point clouds can be used directly with deep neural networks. 3) Deep neural networks can be used to generate 3D geometry.

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Deep Learning Summit

Deep Learning for Program Repair - GOOGLE

12:10 PM 12:30 PM

The software development process can be frustrating, painful and costly; rife with bugs, project delays and unexpected outages. If machine learning were to help with software engineering it would make for the stuff of dreams. ML4SE (Machine Learning for Software Engineering) is an active research area in this space. In this talk we describe progress we've made at Google on training deep learning models to fixing build errors encountered by software developers.

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Responsible AI Summit

Trustworthy Decision Making: The Case of Autonomous Driving - HUAWEI

12:10 PM 12:30 PM

How to make AI-based decision making trustworthy is a core research question at Noah’s Ark Lab at Huawei. We take the position that trustworthy decision making must be responsive, reliable, reasonable, and resilient. We review some of the specific challenges from and efforts in areas such as communication network, data centre management, supply chain optimization, and autonomous driving.

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Exhibition Area & Refreshment Breaks

LUNCH

12:30 PM 01:30 PM

A hot, 3-course, lunch buffet will be served in the foyer area. A great time for networking and to get to know your fellow participants or you can join the Lunch & Learn Session taking place in the Deep Dive Track and take a seat at one of the tables to hear more from the speakers and exhibitors.
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Deep Dive Sessions

DEEP DIVE: Rising Stars

12:35 PM 01:25 PM Tchaikovsky Room

Grab lunch and hear quick-fire presentations from the next generation of AI pioneers.

12:40 - 12:55: Shalev Lifshitz - "Humanity & AI: Preparing for an Intelligent Future"
12:55 - 13:10: Hannah Le - "Evolving a Soft Robot to Explore Space"
13:10 - 13:25: Seyone Chithrananda - "How Deep Learning Will Help Us Eradicate Disease and Disrupt Personalized Medicine"

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Deep Learning Summit

Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift - GOOGLE BRAIN

01:30 PM 01:50 PM

Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive uncertainty. Quantifying uncertainty is especially critical in real-world settings, which often involve input distributions that are shifted from the training distribution due to a variety of factors including sample bias and non-stationarity. In such settings, well calibrated uncertainty estimates convey information about when a model's output should (or should not) be trusted. Many probabilistic deep learning methods, including Bayesian-and non-Bayesian methods, have been proposed in the literature for quantifying predictive uncertainty, but to our knowledge there has not previously been a rigorous large-scale empirical comparison of these methods under dataset shift. We present a large-scale benchmark of existing state-of-the-art methods on classification problems and investigate the effect of dataset shift on accuracy and calibration. We find that traditional post-hoc calibration does indeed fall short, as do several other previous methods. However, some methods that marginalize over models give surprisingly strong results across a broad spectrum of tasks.

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Responsible AI Summit

Why Public Competence in AI Ethics is Essential to the Future of AI? - MONTREAL AI ETHICS INSTITUTE

01:30 PM 01:50 PM

Given all the work that has been coming out of the field of responsible and ethical AI, there has been a push for finding universal solutions, often mediated and prepared by a group of experts. But, relying on a small group of experts and hunting for universal solutions is an exercise in futility. What we really need is cultural and contextual sensitivity. This can only be achieved by engaging at the grassroots with the public and tapping into their implicit knowledge of the local culture and context. But this needs to be nurtured and built over time through a public engagement process which is what this session will dive into. Key Takeaways: 1) How to do effective public consultations in AI ethics? 2) Learn from the case study in Montreal 3) Key pitfalls that any public effort such as this can suffer from.

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Deep Learning Summit

How Do You Know What A Deep Network Has Learned? - CARNEGIE MELLON UNIVERSITY/ARGO AI

01:50 PM 02:10 PM

Modern deep learning algorithms are able to learn on training sets such that they achieve almost zero train error. What is all the more amazing, is that this performance tends to generalize well to unseen data - especially for visual detection and classification tasks. Increasingly, deep methods are being utilized in vision tasks such as object tracking and visual SLAM. These tasks differ fundamentally to traditional vision tasks where deep learning has been effective (e.g. object detection and classification) tasks as they are attempting to model the relative relationship between image frames. Although receiving state of the art performance on many benchmarks, it is easy to demonstrate empirically that deep methods are not always learning what we want them to learn for a given visual task - limiting their practical usage in real-world applications. In this talk we shall discuss recent advances my group has made towards making better guarantees over the generalization of deep learning methods for visual tasks where the relative relationship between images is important - most notably object tracking and VSLAM. In particular we shall discuss a new paradigm for efficient and generalizable object tracking which we refer to as Deep-LK and its extension to 3D PointNet-LK. We shall also, discuss how these insights can be utilized in recent applications of deep learning to VSLAM. Finally, we will show some initial results on how geometric constraints can be elegantly combined with deep learning to further improve generalization performance. Key Takeaways: (i) Integrating classical methods with deep learning is a good way to ensure generalization. (ii) The application of deep learning to problems in 3D geometry is still in its infancy (but moving fast). (iii) Unsupervised 3D learning has a lot of untapped potential.

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Responsible AI Summit

PANEL: Discussing Ethical Approaches to AI: Reviewing Regulation, Risk Management & Policy- SIDEWALK LABS, FUTURE OF LIFE INSTITUTE, MORGAN STANLEY, QUEEN'S UNIVERSITY

01:50 PM 02:30 PM

The focus of this panel discussion is assessing organization wide ethical approaches to AI and what must be considered in this process with regards to regulation, policy as well as managing and mitigating risk. Questions include: How can organizations/enterprises best harness the capabilities of AI whilst mitigating the risks from the unethical use of data? Are there differences in approaches across different industries and/or enterprises of varying sizes? What are the key aspects of managing data and digital rights in an ethical manner for businesses? How do you expect this to change in the future? What process/people/teams need to be involved in these processes internally and externally to ensure compliance & proper governance of data?

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Deep Learning Summit

The Difference is Night and Day: Appearance Modelling for Robust Robot Visual Navigation - UNIVERSITY OF TORONTO

02:10 PM 02:30 PM

Long-term localization and mapping are essential capabilities for autonomous mobile robots that must operate independently in dynamic environments. In this talk, I will describe two deep neural network systems that have been developed in my laboratory group to improve vision-based navigation. The first system, Sun-BCNN, uses the sun as an absolute orientation reference to reduce drift in visual motion estimates. Crucially, Sun-BCNN learns to recognize how solar illumination affects images, and does not require the sun to be visible in the image stream to operate. The second system relies on image-to-image translation and learned colour constancy models to transform incoming images in appearance space. This remapping enables a robot to localize against a visual map created under substantially different lighting conditions (for example, between dawn and dusk).

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Exhibition Area & Refreshment Breaks

NETWORKING MIXER

02:30 PM 03:30 PM

Join us in the foyer area and grab a drink to celebrate the end of the summit and continue to network with attendees.
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Exhibition Area & Refreshment Breaks

END OF SUMMIT

03:30 PM 03:35 PM

Thank you for attending! Feel free to continue discussions via the app with your new connections. We look forward to seeing you again soon!