Irina Rish is a Full Professor at the Université de Montréal (UdeM), where she leads the Autonomous AI Lab, and is a core faculty member of MILA - Quebec AI Institute. She holds a Canada Excellence Research Chair (CERC) and a CIFAR Chair.  Irina completed her MSc and PhD in AI at the University of California, Irvine, and holds an MSc in Applied Mathematics from Moscow Gubkin Institute.  

Irina’s extensive research career spans multiple AI domains, including automated reasoning, probabilistic inference in graphical models, machine learning, sparse modelling, and neuroscience-inspired AI. Her recent work focuses on continual learning, robustness, model compression, and scaling behaviors in foundation models across various data modalities, such as language, vision, and time series. 

Irina received several large-scale compute awards from the U.S. Department of Energy, including 2023 INCITE award and several ALCC awards, and led multiple open-source AI foundation models projects on Summit and Frontier at the Oak Ridge Leadership Computing Facility, including the 2023 INCITE project on Scalable Foundation Models. She also advises AI startups including 42.com (multimodal foundation models for finance), Nolano.ai (efficient time-series foundation models), and MUUTAA (AI for healthcare supply-chain optimization).

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 the IBM Eminence & Excellence Award and IBM Outstanding Innovation Award (2018), the IBM Outstanding Technical Achievement Award (2017), and the IBM Research Accomplishment Award (2009). Irina holds 64 patents, has authored over 170 research papers, contributed to several book chapters, edited three books, and published a monograph on Sparse Modeling.