Irina Rish is a Full Professor at the Université de Montréal (UdeM), where she leads the Autonomous AI Lab, and a core faculty member of MILA - Quebec AI Institute. She holds Canada Excellence Research Chair (CERC) and a CIFAR Chair. Dr. Rish completed her MSc and PhD in AI at the University of California, Irvine, and also holds an MSc in Applied Mathematics from Moscow Gubkin Institute. Irina is leading several open-source foundation models projects on Summit & Frontier supercomputers at  ORNL (DoE), and is a co-founder and CSO of Nolano.ai.

Irina’s extensive research career spans multiple AI domains, from automated reasoning and  probabilistic inference in graphical models, to machine learning, sparse modeling, and neuroscience-inspired AI.  Irina’s current research endeavors concentrate on continual learning, out-of-distribution generalization, robustness;  and  understanding neural scaling laws and emergent behaviors (w.r.t. both capabilities and alignment) in foundation models - a vital stride towards achieving maximally beneficial Artificial General Intelligence (AGI).  She teaches courses on AI scaling and alignment, and  runs  Neural Scaling & Alignment workshop series. 

Before joining UdeM 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 IBM Eminence & Excellence Award and IBM Outstanding Innovation Award (2018), IBM Outstanding Technical Achievement Award (2017), and IBM Research Accomplishment Award (2009). Irina holds 64 patents, has authored over 160 research papers, contributed to several book chapters, edited three books, and published a monograph on Sparse Modeling.