Irina Rish

Biography

Irina Rish is a full professor at the Université de Montréal (UdeM), where she leads the Autonomous AI Lab, and a core academic member of Mila – Quebec Artificial Intelligence Institute.

In addition to holding a Canada Excellence Research Chair (CERC) and a CIFAR Chair, she leads the U.S. Department of Energy’s INCITE project on Scalable Foundation Models on Summit & Frontier supercomputers at the Oak Ridge Leadership Computing Facility. She co-founded and serves as CSO of Nolano.ai.

Rish’s current research interests include neural scaling laws and emergent behaviors (capabilities and alignment) in foundation models, as well as continual learning, out-of-distribution generalization and robustness.

Before joining UdeM in 2019, she 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 was awarded the IBM Eminence & Excellence Award and IBM Outstanding Innovation Award (2018), IBM Outstanding Technical Achievement Award (2017) and IBM Research Accomplishment Award (2009).

She holds 64 patents and has published 120 research papers, several book chapters, three edited books and a monograph on sparse modeling.

Current Students

Germán Abrevaya

Independent visiting researcher - Université de Montréal

Co-supervisor :

Research Intern

PhD - Université de Montréal

Co-supervisor :

Samira Ebrahimi Kahou

Rifat Arefin

PhD - Université de Montréal

Arjun Ashok

PhD - Université de Montréal

Co-supervisor :

Reza Bayat

Master's Research - Université de Montréal

Co-supervisor :

Pouya Bashivan

Rishika Bhagwatkar

Master's Research - Université de Montréal

Collaborating researcher - Université de Montréal

Ethan Caballero

PhD - McGill University

Principal supervisor :

Blake Richards

Mohammad-javad Darvishi-bayazi Darvishi Bayazi

Amin Darabi

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Karim Jerbi

Mojtaba Faramarzi

PhD - Université de Montréal

Kshitij Gupta

Collaborating Alumni - Université de Montréal

Co-supervisor :

Sarath Chandar

Nadhir Hassen

Research Intern - Université de Montréal

Master's Research

PhD - Université de Montréal

Principal supervisor :

Ioannis Mitliagkas

Nizar Islah

PhD - Université de Montréal

Principal supervisor :

Eilif Benjamin Muller

PhD - Université de Montréal

Collaborating researcher

Zafir Khalid

Master's Research - Concordia University

Principal supervisor :

Master's Research - Université de Montréal

Neeraj Kumar

Professional Master's - Université de Montréal

Gwen Legate

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

David Lemay

Master's Research - Université de Montréal

amin.mansouri@mila.quebec

Ekaterina Lobacheva

Postdoctorate - Université de Montréal

Principal supervisor :

Master's Research - Université de Montréal

Collaborating Alumni

PhD - Université de Montréal

Master's Research - Université de Montréal

Diganta Misra

Master's Research - Université de Montréal

Remus Mocanu

Master's Research - Université de Montréal

Timothy Nest

PhD - Université de Montréal

Co-supervisor :

Eilif Benjamin Muller

Mohammad Pezeshki

Collaborating researcher

Motahareh Pourrahimi

PhD - McGill University

Principal supervisor :

Pouya Bashivan

Mahta Ramezanian

Master's Research - Université de Montréal

Co-supervisor :

Guillaume Dumas

Roland Riachi

Collaborating researcher - Université de Montréal

Matthew Riemer

PhD - Université de Montréal

Alexis Roger

PhD - McGill University

Principal supervisor :

Blake Richards

Zahra Sheikhbahaee

Postdoctorate - Université de Montréal

Principal supervisor :

Guillaume Dumas

Vaibhav Singh

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

Gopeshh Subbaraj

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

Dave Whipps

Master's Research - Université de Montréal

Andrew Williams

PhD - Université de Montréal

Daria Yasafova

Master's Research - Université de Montréal

Omar Younis

Collaborating researcher

Principal supervisor :

Yoshua Bengio

Publications

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework fo… (see more)r reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and SARSA methods, as well as recently proposed Double Q-Learning approaches, on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities.

2020-01-01

AAMAS (published)

arxiv.org

Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement … (see more)learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For the AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.

2019-10-02

NeurIPS.cc/2019/Workshop/Neuro_AI (poster)

openreview.net

Continual Learning with Self-Organizing Maps

Pouya Bashivan

Martin Schrimpf

Robert Ajemian

Matthew D Riemer

Yuhai Tu

Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-speci… (see more)fic stationary environments where they are trained only once on large-scale controlled data repositories. When exposed to non-stationary learning environments, current neural networks tend to forget what they had previously learned, a phenomena known as catastrophic forgetting. Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones. This approach suffers from the important disadvantage of not scaling well to real-life problems in which the memory requirements become enormous. We propose a memoryless method that combines standard supervised neural networks with self-organizing maps to solve the continual learning problem. The role of the self-organizing map is to adaptively cluster the inputs into appropriate task contexts - without explicit labels - and allocate network resources accordingly. Thus, it selectively routes the inputs in accord with previous experience, ensuring that past learning is maintained and does not interfere with current learning. Out method is intuitive, memoryless, and performs on par with current state-of-the-art approaches on standard benchmarks.

2019-04-19

ArXiv (preprint)

arxiv.org

A Survey on Practical Applications of Multi-Armed and Contextual Bandits

Djallel Bouneffouf

In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and i… (see more)nformation retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.

2019-04-02

ArXiv (preprint)

arxiv.org

Predicting conversion to psychosis in clinical high risk patients using resting-state functional MRI features

Jolie Mcdonnell

W. Hord

Jenna Reinen

Pablo Polosecki

Guillermo Cecchi

Recent progress in artificial intelligence provides researchers with a powerful set of machine learning tools for analyzing brain imaging da… (see more)ta. In this work, we explore a variety of classification algorithms and functional network features derived from resting-state fMRI data collected from clinical high-risk (prodromal schizophrenia) patients and controls, trying to identify features predictive of conversion to psychosis among a subset of CHR patients. While there are many existing studies suggesting that functional network features can be highly discriminative of schizophrenia when analyzing fMRI of patients suffering from the disease vs controls, few studies attempt to explore a similar approach to actual prediction of future psychosis development ahead of time, in the prodromal stage. Our preliminary results demonstrate the potential of fMRI functional network features to predict the conversion to psychosis in CHR patients. However, given the high variance of our results across different classifiers and subsets of data, a more extensive empirical investigation is required to reach more robust conclusions.

2019-02-16

Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging (published)

doi.org

Learning Brain Dynamics from Calcium Imaging with Coupled van der Pol and LSTM

Germán Abrevaya

Aleksandr Y. Aravkin

Guillermo Cecchi

James Kozloski

Pablo Polosecki

Peng Zheng

Silvina Ponce Dawson

Juliana Y. Rhee

David Daniel Cox

Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calc… (see more)ium imaging (CaI) of different organisms (zebrafish and rat), aiming to build a model of joint activation dynamics in large neuronal populations, including the whole brain of zebrafish. We propose a new approach for capturing dynamics of temporal SVD components that uses the coupled (multivariate) van der Pol (VDP) oscillator, a nonlinear ordinary differential equation (ODE) model describing neural activity, with a new parameter estimation technique that combines variable projection optimization and stochastic search. We show that the approach successfully handles nonlinearities and hidden state variables in the coupled VDP. The approach is accurate, achieving 0.82 to 0.94 correlation between the actual and model-generated components, and interpretable, as VDP’s coupling matrix reveals anatomically meaningful positive (excitatory) and negative (inhibitory) interactions across different brain subsystems corresponding to spatial SVD components. Moreover, VDP is comparable to (or sometimes better than) recurrent neural networks (LSTM) for (short-term) prediction of future brain activity; VDP needs less parameters to train, which was a plus on our small training data. Finally, the overall best predictive method, greatly outperforming both VDP and LSTM in shortand long-term predicitve settings on both datasets, was the new hybrid VDP-LSTM approach that used VDP to simulate large domain-specific dataset for LSTM pretraining; note that simple LSTM data-augmentation via noisy versions of training data was much less effective.

2019-01-01

(published)

www.semanticscholar.org

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference

Matthew D Riemer

Ignacio Cases

Robert Ajemian

Miao Liu

Yuhai Tu

Gerald Tesauro

Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neura… (see more)l network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller.

2019-01-01

ICLR.cc/2019/Conference (poster)

openreview.net

Machine Learning and Interpretation in Neuroimaging

Georg Langs

Leila Wehbe

Guillermo Cecchi

Kai-min Kevin Chang

Brian G Murphy

2014-01-01

Lecture Notes in Computer Science (published)

doi.org

Cognitive Models as Simulators: Using Cognitive Models to Tap into Implicit Human Feedback

Ardavan S Nobandegani

Thomas R. Shultz