2026 Irina Rish: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	39	9533	9257	376	359	209	10782

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Computer Science

9533

9257

376

359

209

10782

Overview

Irina Rish is affiliated with the University of Montreal in Canada and has a substantial academic footprint mainly in the field of Computer Science.

Their research predominantly focuses on several subfields including:

Artificial Intelligence
Computer Vision and Pattern Recognition
Cognitive Neuroscience
Epidemiology
Information Systems

Within these subfields, Irina has contributed notably to topics such as:

Domain Adaptation and Few-Shot Learning
Adversarial Robustness in Machine Learning
Neural Networks and Applications
Topic Modeling
Multimodal Machine Learning Applications
Privacy-Preserving Technologies in Data
EEG and Brain-Computer Interfaces

Irina Rish's scholarly output includes papers published in diverse venues, with the most frequent being:

arXiv (Cornell University)
Scientific Reports
Journal of Vision
Journal of Artificial Intelligence Research
Radiology

Among their recent publications are:

"Towards Continual Reinforcement Learning: A Review and Perspectives" (2022), Journal of Artificial Intelligence Research
"Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization" (2021), arXiv (Cornell University)
"Generative Models of Brain Dynamics" (2022), Frontiers in Artificial Intelligence
"Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting" (2023), arXiv (Cornell University)

Irina has collaborated frequently with several researchers in their field, including:

Eugene Belilovsky
Timothée Lesort
Jean-Christophe Gagnon-Audet
Yoshua Bengio
Guillaume Dumas

Best Publications

An empirical study of the naive Bayes classifier

I. Rish

4199
Citations
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

Pouya Bashivan;Irina Rish;Mohammed Yeasin;Noel Codella

605
Citations
Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference

Matthew Riemer;Ignacio Cases;Robert Ajemian;Miao Liu

454
Citations
Critical event prediction for proactive management in large-scale computer clusters

R. K. Sahoo;A. J. Oliner;I. Rish;M. Gupta

382
Citations
Mini-buckets: A general scheme for bounded inference

Rina Dechter;Irina Rish

284
Citations
Improving network robustness by edge modification

Alina Beygelzimer;Geoffrey Grinstein;Ralph Linsker;Irina Rish

269
Citations
Adaptive diagnosis in distributed systems

I. Rish;M. Brodie;Sheng Ma;N. Odintsova

239
Citations
Sparse Modeling: Theory, Algorithms, and Applications

Irina Rish;Genady Grabarnik

234
Citations
Directional resolution : the Davis-Putnam procedure, revisited

Rina Dechter;Irina Rish

227
Citations
Prediction and interpretation of distributed neural activity with sparse models

Melissa K. Carroll;Guillermo A. Cecchi;Irina Rish;Rahul Garg

222
Citations
Survey on Applications of Multi-Armed and Contextual Bandits

Djallel Bouneffouf;Irina Rish;Charu Aggarwal

192
Citations
Towards Continual Reinforcement Learning: A Review and Perspectives.

Khimya Khetarpal;Matthew Riemer;Irina Rish;Doina Precup

168
Citations
Resolution versus Search: Two Strategies for SAT

Irina Rish;Rina Dechter

165
Citations
Real-time problem determination in distributed systems using active probing

I. Rish;M. Brodie;N. Odintsova;Sheng Ma

142
Citations
A Survey on Practical Applications of Multi-Armed and Contextual Bandits.

Djallel Bouneffouf;Irina Rish

131
Citations
Optimizing Probe Selection for Fault Localization

Mark Brodie;Irina Rish;Sheng Ma

110
Citations
Intelligent probing: a cost-effective approach to fault diagnosis in computer networks

M. Brodie;I. Rish;S. Ma

106
Citations
Summarizing CSP hardness with continuous probability distributions

Daniel Frost;Irina Rish;Lluís Vila

95
Citations
Closed-form supervised dimensionality reduction with generalized linear models

Irina Rish;Genady Grabarnik;Guillermo Cecchi;Francisco Pereira

85
Citations
A scheme for approximating probabilistic inference

Rind Dechter;Irina Rish

84
Citations

Frequent Co-Authors

Guillermo A. Cecchi IBM (United States)

Dimitri Kanevsky Google (United States)

Clifford A. Pickover IBM (United States)

Rina Dechter University of California, Irvine

Aram Galstyan University of Southern California

Sahil Garg Canadian University Dubai

Sara H. Basson Google (United States)

Katya Scheinberg Cornell University

Yoshua Bengio University of Montreal

Edward E. Kelley IBM (United States)

External Links

Personal website of Irina Rish Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Choosing to study Computer Science in the USA opens doors to a wide range of related fields and career options. Many students also explore specialized online degrees, which offer affordable and flexible study options while providing skills in high-demand sectors.

For those interested in information security, earning an accredited online cyber security degree provides essential knowledge to protect digital assets and combat cyber threats. Those looking to oversee large projects can consider the cheapest online construction management degree programs, which prepare graduates for leadership roles in construction and infrastructure development.

If your interests lie in law and order, pursuing the cheapest online criminal justice degree can set you on a path toward careers in public safety, law enforcement, or corrections. For a technology-driven approach to business, a cheap online accounting degree offers training in analytics, finance, and compliance.

Exploring related online degrees allows you to diversify your skills, pursue unique career pathways, and stay competitive in a rapidly changing job market.