The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Atri Rudra sits on this spectrum.
This scientist: 144 publications — 24th percentile
24% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Atri Rudra sits on this spectrum.
This scientist: 30 D-Index — 3rd percentile
3% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Atri Rudra is affiliated with the University at Buffalo, State University of New York in the United States. Their research contributions are primarily in the field of Computer Science, with notable work in several specialized subfields. These subfields include Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computer Networks and Communications, and Electrical and Electronic Engineering.
The scientist focuses on a range of research topics, such as Neural Networks and Applications, Topic Modeling, Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, Time Series Analysis and Forecasting, Machine Learning in Healthcare, and Advanced Database Systems and Queries.
Atri Rudra has published extensively, with a strong presence in the arXiv preprint repository managed by Cornell University. Their frequent publication venues include:
Frequent coauthors of Rudra include Christopher Ré, Tri Dao, Albert Gu, Isys Johnson, and Daniel Y. Fu. The collaborations with these researchers have resulted in a series of papers contributing to various aspects of computer science and artificial intelligence.
Recent notable papers authored or coauthored by Atri Rudra include:
The research covers a spectrum of topics, ranging from efficient attention mechanisms in neural networks to recurrent memory models and language modeling techniques. Their work addresses both theoretical and applied aspects of machine learning and data representation.
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A. Rudra;P.K. Dubey;C.S. Jutla;V. Kumar
Nikhil Bansal;Anupam Gupta;Jian Li;Julián Mestre
Avrim Blum;Vijay Kumar;Atri Rudra;Felix Wu
Hung Q. Ngo;Ely Porat;Christopher Ré;Atri Rudra
Hung Q Ngo;Christopher Ré;Atri Rudra
Mahmoud Abo Khamis;Hung Q. Ngo;Atri Rudra
Hung Q. Ngo;Ely Porat;Christopher Ré;Atri Rudra
V. Guruswami;A. Rudra
Albert Gu;Isys Johnson;Karan Goel;Khaled Saab
Albert Gu;Tri Dao;Stefano Ermon;Atri Rudra
Don Coppersmith;Lisa Fleischer;Atri Rudra
Ning Chen;Nicole Immorlica;Anna R. Karlin;Mohammad Mahdian
Piotr Indyk;Hung Q. Ngo;Atri Rudra
Unknown
Venkatesan Guruswami;Atri Rudra
Mahmoud Abo Khamis;Hung Q. Ngo;Christopher Ré;Atri Rudra
Charanjit S. Jutla;Anindya C. Patthak;Atri Rudra;David Zuckerman
Swapnoneel Roy;Atri Rudra;Akshat Verma
Mark L. Glasgow;Carole B. Rudra;Eun Hye Yoo;Murat Demirbas
Tri Dao;Albert Gu;Matthew Eichhorn;Atri Rudra
Charanjit S. Jutla;Anindya C. Patthak;Atri Rudra;David Zuckerman
Hung Q. Ngo;Ely Porat;Atri Rudra
Venkatesan Guruswami;Atri Rudra
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