Yoshua Bengio

Yoshua Bengio, PhD

Co-Founder & Deep Learning Pioneer

Widely considered one of the three pioneers of deep learning, Dr. Bengio is a world-renowned researcher with more than 300 publications and over 80,000 citations to his name. In addition to being the Scientific Director of the Quebec Artificial Intelligence Institute (Mila), he holds a Canada Research Chair in Statistical Learning Algorithms and an NSERC Industrial Chair. He is a Senior Fellow of the Canadian Institute for Advanced Research (CIFAR) and co-directs its program focused on deep learning. He has been Program Chair and General Chair of NIPS, the leading academic machine learning conference, and co-created ICLR, the leading deep learning conference.

Christopher Pal V3

Christopher Pal, PhD

Principal Research Scientist

Christopher Pal is an associate professor in the department of software and information engineering at Polytechnique Montreal and an adjunct faculty member in the department of computer science and operations research at the University of Montreal. He is also one of the founding faculty members of the Montreal Institute for Learning Algorithms. He is a co-author of the newest edition of the well-known book Data Mining: Practical Machine Learning Tools and Techniques. He has a PhD from the University of Waterloo and worked with both the University of Toronto's Machine Learning group and Microsoft Research in Redmond Washington extensively during his graduate studies. He has over two decades of experience in artificial intelligence research and the application of artificial intelligence techniques to real world problems.

Negar V3

Negar Rostamzadeh, PhD

Fundamental Research Scientist

Negar Rostamzadeh is a Research Scientist at Element AI. Her areas of interests are Machine Learning (particularity deep learning approaches) applied to Computer Vision problems (mainly Video Understanding).Negar got her Ph.D. at the Mhug (Multimedia and Human understanding) group, University of Trento, Italy. There she did research under the direction of Prof. Nicu Sebe. She worked as a research intern at the MMV (Multimedia and Vision) lab at the Queen Mary University of London, where she was supervised by Prof. Yiannis Patras. Negar spent more than 2 years of her Ph.D. at the Mila (Quebec Artificial Intelligence Institute) lab under the supervision of Prof. Aaron Courville. She was a Research Intern in the Research and Machine Intelligence group at Google (Seattle) in summer 2016. She finished her Ph.D. in April 2017. She was a co-founder of Women in Deep Learning (WiDL) workshop in 2016, co-organizer of the Women in Machine Learning (WiML) workshop at NIPS 2017, Women in Computer Vision (WiCV) workshop at CVPR 2017, and Women in Deep Learning workshop at Mila deep learning summer school 2017.

David Vazquez V3

David Vázquez, PhD

Fundamental Research Scientist

David Vázquez is a Fundamental Research Scientist at Element AI, where he works on computer vision. Previously he was a postdoctoral researcher at Computer Vision Center of Barcelona (CVC) and Quebec Artificial Intelligence Institute (Mila) and Assistant Professor in the Department of Computer Science at the Autonomous University of Barcelona (UAB). He is an expert in machine perception for autonomous vehicles and on domain adaptation from simulation to real-world environments. David was attracted to Element AI by our Fellow network: ‘It gives you virtual access to the best AI researchers of the world. This makes EAI unique and that's why I came here; if you want to be the best you have to be with the best.’

Anqi Xu V3

Anqi Xu, PhD

Fundamental Research Scientist

Anqi is a Fundamental Research Scientist at Element AI. Anqi has over 10 years of research experience with diverse facets of mobile robotics, including human interaction, perception, control, localization, and planning. He holds a PhD in Computer Science from McGill University, where he studied trust in human-robot interactions. Anqi was attracted by the vast breadth of client applications and fundamental research goals at Element AI, allowing him to flexibly explore diverse problems, including human-enhanced learning (e.g. imitation learning, inverse reinforcement learning, reward shaping); reinforcement learning for robotic control, mobile perception, and embedded/wearable deployment.