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 is the recipient of the INCITE 2023 and several ALCC compute grants by the US Department of Energy and lead several large-scale open-source AI foundation models projects, including the 2023 INCITE project on Scalable Foundation Models, on Summit & Frontier supercomputers at the Oak Ridge Leadership Computing Facility. She is currently a CSO at 42.com (multimodal foundation models for finance) and an advisor at Nolano.ai (memory- and inference-efficient time-series foundation models).

 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.