Research at Element AI

Element AI Research

Research is at the core of Element AI

We tackle difficult problems from multiple fields and industries that require us to push the boundaries of what existing science and technology can achieve. To do so we conduct fundamental research to increase human knowledge and applied research that translates it into tangible impact in our everyday lives.

To be successful in this endeavour, we leverage strengths from many areas. With our network of Fellows and our deep relationships with research labs such as the MILA and McGill’s RLL, we embody an open and collaborative research culture. We hire from the full breadth of the AI spectrum — from deep learning to operations research — and from fields like physics, applied mathematics, computational statistics, biology and neuroscience. Finally, and perhaps our greatest strength, our learning-oriented culture ensures that everyone in the company who is interested can contribute to advancing our research, no matter their title and experience.

Why You Should Join Us

  • Be part of a company that:

    • Is ambitious and fast-growing, with AI deep in its DNA.
    • Builds solutions that have real world impact on society and across many organizations.
    • Embraces risk where the reward is high, and prioritises continual learning.
  • Work with people:

    • Such as Fellows, fundamental researchers, applied research scientists, developers and designers
    • Motivated by solving hard problems
    • With a variety of backgrounds
  • Enjoy access to:

    • Unique problems and data that offer novel fundamental and applied challenges.
    • State of the art hardware and software support to enable AI research.
    • 20% personal project time to explore your own ideas.

Meet some of the team

We're working on understanding AI to build a better world.

Marie-Claude Coté, PhD (Director - AI Core and Applied Research Practices)

Marie-Claude Coté, PhD

Director - AI Core and Applied Research Practices

Marie-Claude is the Director of AI Core and Applied Research practices at Element AI. Prior to joining Element AI, Marie-Claude worked as an operational researcher at ExPretio technologies and led the Data Science team at JDA Software, a company specializing in execution and planning systems for the supply chain and retail sectors. She holds a PhD in Applied Mathematics from Ecole Polytechnique, in which she focused on solving multi-activity shift-scheduling problems using formal language-based formulations and approaches. She joined Element AI to apply the transformative powers of AI to society within a company who cares about doing it in a responsible way.

Christopher Pal, PhD (Principal Research Scientist)

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 Rostamzadeh, PhD (Fundamental Research Scientist)

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 (Montreal Institute of Learning Algorithm) 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, PhD

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 Montreal Institute of Learning Algorithms (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, PhD (Fundamental Research Scientist)

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.

We want to hear from you

Our goal is to push the limits of AI to make it much more flexible. If you're passionate about exploring these fundamental concepts with us, we’d love to hear from you.

Join our team