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Natural Sciences and Engineering; Health Sciences; Information and Communication Technologies

Irina Rish

Machine learning, deep learning, neuroscience

Professeure agrégée

Faculté des arts et des sciences - Département d'informatique et de recherche opérationnelle

Secondary emails: (Travail) (Personnel)


Neuro for AI, and AI for Neuro—Irina Rish, PhD—University of Montreal

In an effort to both improve current artificial intelligence (AI) models and our understanding of the brain, Associate Professor in the Computer Science and Operations Research Department at the University of Montreal, Irina Rish, PhD is studying a number of topics...

Irina Rish, Research Staff, IBM T.J. Watson Research Center @ MLconf NYC

Learning About Brain: Sparse Modeling and Beyond: Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of finding a relatively small subset of ”important” variables in high-dimensional datasets.

Episode 35 - Irina Rish

COVID-19 has swept across the world was startling speed, but with equally startling speed, the machine learning community has responded. This week I speak with Irina Rish, a professor at the University of Montreal and a Mila academic member, who is helping head a task force to understand the virus.


Research expertise

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.


Irina Rish is an Associate Professor in the Computer Science and Operations Research department at the Université de Montréal (UdeM) and a core member of MILA – Quebec AI Institute. She holds MSc and PhD in AI from University of California, Irvine and MSc in Applied Mathematics from Moscow Gubkin Institute. Dr. Rish’s research focus is on machine learning, neural data analysis and neuroscience-inspired AI.

Teaching and supervision



Publications and presentations


  • Computer Science
  • Neurosciences
  • Bioinformatics

Areas of expertise

  • Deep learning
  • Data science
  • Brain–computer interface
  • Neural Networks
  • Model Building
  • Probabilistic models
  • COVID-19
  • COVID19