My long-term goal is to understand intelligence; understanding its underlying principles would give us access to artificial intelligence (AI), and I believe that learning algorithms are essential in this quest. Learning algorithms could give computers the ability to capture operational knowledge (not necessarily in symbolic / verbal form) from examples.

Her current research interests include continual lifelong learning, optimization algorithms for deep neural networks, sparse modeling and probabilistic inference, dialog generation, biologically plausible reinforcement learning, and dynamical systems approaches to brain imaging analysis. Before joining UdeM and MILA in 2019, Irina was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She received multiple IBM awards, including IBM Eminence & Excellence Award and IBM Outstanding Innovation Award in 2018, IBM Outstanding Technical Achievement Award in 2017, and IBM Research Accomplishment Award in 2009.

Dr. Rish holds 64 patents, has published over 80 research papers, several book chapters, three edited books, and a monograph on Sparse Modeling. She is IEEE TPAMI Associate Editor (since 2019), a member of the AI Journal (AIJ) editorial board (since 2016), served as a Senior Area Chair for NIPS-2017, NIPS-2018, ICML-2018, an Area Chair for ICLR-2019, ICLR-2018, JCAI-2015, ICML-2015, ICML-2016, NIPS-2010, tutorials chair for UAI-2012 and workshop chair for UAI-2015 and ICML-2012; she gave several tutorials (AAAI-1998, AAAI-2000, ICML-2010, ECML-2006) and co-organized multiple workshops at core AI conferences, including 11 workshops at NIPS (from 2003 to 2016), ICML-2008 and ECML-2006.

Houari Sahraoui is a Professor in the GEODES Software Engineering Lab within the Department of Computer Science and Operations Research at the Université de Montréal. He also serves as Vice-Dean of the Faculty of Arts and Sciences. He received his Ph.D. in Computer Science from Pierre and Marie Curie University (LIP6) in 1995, specializing in Artificial Intelligence.

His research focuses on AI for Software Engineering, including software automation, model-driven engineering, digital twins, and the use of generative AI for code and modeling tasks. He has authored more than 200 publications in leading conferences and journals and has received numerous distinctions, including Best Paper Awards, ACM SIGSOFT Distinguished Paper Awards, and the IEEE TCSE 10-Year Most Influential Paper Award.

He has held several leadership positions within the software engineering community, including serving as General Chair of ASE, MODELS, and VISSOFT, Program Chair of MODELS and VISSOFT, and member of numerous IEEE and ACM conference program committees. He has also served as Associate Editor for several scientific journals, including Software and Systems Modeling, and is a founding member of CS-Can | Info-Can, the Canadian computer science society. He is a Fellow of Automated Software Engineering and the recipient of the CS-Can | Info-Can Lifetime Achievement Award in Computer Science.

My research interests are centered around discovering fundamental computational principles that underlie the extraordinary capabilities to learn from the environment, understand it and adapt to it that characterize intelligence. The development of novel machine learning algorithms based on such principles, and trained on very large data sets, is at the heart of the latest technological breakthroughs in artificial intelligence.