I am an Associate Professor at the Courant Institute of Mathematical Sciences, NYU, and in the Department of Radiology, NYU Langone Health, where I'm also the Director of Machine Learning Research. My interests lie in advancing AI research, focusing on deep learning models and AI applications in Healthcare.
Before NYU, I was a Co-founder and VP of AI at
Imagen Technologies: a well funded startup transforming Healthcare using AI.
Before co-founding Imagen, I was a research scientist at
Facebook AI Research (FAIR), working on understanding natural language. I graduated with a Ph.D., in computer science from New York University working with Prof. Yann LeCun. My thesis proposed a first-of-its-kind neural network model for relational regression, and was a conceptual foundation for a startup for modeling residential real estate prices. Following my Ph.D. I joined AT&T Labs–Research as a scientist in the machine learning department. I focused on building novel deep learning models for speech recognition, natural language processing, and computer vision. I also worked on other machine learning areas, such as recommender systems, computational advertisement, and ranking.
Google Scholar
LinkedIn
Assessment of a Deep-Learning System for Fracture Detection in Musculoskeletal Radiographs
Deep Neural Network Improves Fracture Detection by Clinicians
Abstractive sentence summarization with attentive recurrent neural networks
Towards ai-complete question answering: A set of prerequisite toy tasks
Memory Networks
Discovering the Hidden Structure of House Prices with a Non-Parametric Latent Manifold Model
A Tutorial on Energy-Based Learning
Efficient Learning of Sparse Overcomplete Representations with Energy-Based Model
Learning a Similarity Measure Discriminatively with Applications to Face Verification
Rebecca M. Jones, Anuj Sharma, Robert Hotchkiss, John W. Sperling, Jackson Hamburger, Christian Ledig, Robert O’Toole, Michael Gardner, Srivas Venkatesh, Matthew M. Roberts, Romain Sauvestre, Max Shatkhin, Anant Gupta, Sumit Chopra, Manickam Kumaravel, Aaron Daluiski, Will Plogger, Jason Nascone, Hollis G. Potter, and Robert V. Lindsey.
Assessment of a Deep-Learning System for Fracture Detection in Musculoskeletal Radiographs.
Nature Digital Medicine, October 2020.
Anant Gupta, Srivas Venkatesh, Sumit Chopra, and Christian Ledig.
Generative Image Translation for Data Augmentation of Bone Lesion Pathology.
Medical Imaging with Deep Learning (MIDL), London U.K., July 2019.
Robert Lindsey, Aaron Daluiski, Sumit Chopra, Alexander Lachapelle,
Michael Mozer, Serge Sicular, Douglas Hanel, Michael Gardner, Anurag Gupta, Robert Hotchkiss, Hollis Potter.
Deep Neural Network Improves Fracture Detection by Clinicians.
Proceedings of the National Academy of Sciences (PNAS), November 2018.
Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine. Bordes, Jason Weston.
StarSpace: Embed All The Things!
Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans LA, February 2018.
Sam Wiseman, Sumit Chopra, Marc'Aurelio Ranzato, Arthur Szlam, Ruoyu Sun, Soumith Chintala, and Nicolas Vasilache.
Training Language Models Using Target-Propagation.
arXiv:1702.04770.
Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, and Jason Weston.
Learning Through Dialogue Interactions.
International Conference on Learning Regresentations (ICLR), Toulon France, April 2017.
[Code+Data]
Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, and Jason Weston.
Dialogue Learning With Human-In-The-Loop.
International Conference on Learning Regresentations (ICLR), Toulon France, April 2017.
[Code+Data]
Sumit Chopra, Michael Auli, and Alexander M. Rush.
Abstractive sentence summarization with attentive recurrent neural networks.
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
(NAACL-HLT), San Diego CA, June 2016.
Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, and Jason Weston.
Evaluating prerequisite qualities for learning end-to-end dialog systems.
International Conference on Learning Representations (ICLR), San Juan Puerto Rico, May 2016. [Data]
Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba.
Sequence level training with recurrent neural networks.
International Conference on Learning Representations (ICLR), San Juan Puerto Rico, May 2016.
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston.
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations.
International Conference on Learning Representations (ICLR), San Juan Puerto Rico, May 2016. [Data]
Alexander M. Rush, Sumit Chopra, and Jason Weston.
A neural attention model for abstractive sentence summarization.
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Lisbon Portugal, September 2015.
[Code]
Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston.
Large-scale simple question answering with memory networks. arXiv:1506.02075.
[Data]
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, and Tomas Mikolov.
Towards ai-complete question answering: A set of prerequisite toy tasks.
International Conference on Learning Representations (ICLR), San Diego CA, May 2015.
[Code+Data]
Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, and Marc'Aurelio Ranzato.
Learning longer memory in recurrent neural networks.
International Conference on Learning Representations (ICLR), San Diego CA, May 2015.
Marc'Aurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra.
Video (language) modeling: a baseline for generative models of natural videos.
International Conference on Learning Representations (ICLR), San Diego CA, May 2015.
Jason Weston, Sumit Chopra, and Antoine Bordes.
Memory Networks.
International Conference on Learning Representations (ICLR), San Diego CA, May 2015.
Antoine Bordes, Sumit Chopra, and Jason Weston.
Question answering with subgraph embeddings.
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha Qatar, October 2014.
Jason Weston, Sumit Chopra, and Keith Adams.
# TagSpace: Semantic embeddings from hashtags.
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha Qatar, October 2014.
Sumit Chopra, Suhrid Balakrishnan, and Raghuraman Gopalan.
DLID: Deep Learning for Domain Adaptation by Interpolating between Domains.
Proceedings of the ICML 2013, Workshop on Representation Learning, Atlanta, Georgia, USA, 2013.
Suhrid Balakrhishnan and Sumit Chopra.
Collaborative Ranking.
Proceedings of the fifth ACM international conference on Web search and data mining (WSDM) 2012.
Suhrid Balakrishnan, Sumit Chopra, David Applegate, and Simon Urbanek.
Computational television advertising.
Data Mining (ICDM), 2012 IEEE 12th International Conference on, 71-80.
Sumit Chopra and Srinivas Bangalore.
Weakly Supervised Neural Networks for Part-Of-Speech Tagging
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto Japan, May 2012.
Suhrid Balakrhishnan and Sumit Chopra.
Two of a kind or the ratings game? Adaptive pairwise preferences and latent factor models.
Frontiers of Computer Science 6 (2), 197-208.
Piotr Mirowski, Sumit Chopra, Suhrid Balakrishnan, and Srinivas Bangalore.
Feature-rich continuous language models for speech recognition.
Spoken Language Technology Workshop (SLT), 2010 IEEE, 241-246.
Diego Aragon, Andrew Caplin, Sumit Chopra, John V. Leahy, Yann LeCun, Marco Scoffier, and Joseph Tracy.
Reassessing FHA risk.
National Bureau of Economic Research, March 2010.
Suhrid Balakrishnan, Sumit Chopra, and Ian D. Melamed.
The business next door: Click-through rate modeling for local search.
NIPS Workshop on Machine Learning in Online Advertising.
Sumit Chopra, Trivikraman Thampy, John Leahy, Andrew Caplin, and Yann LeCun.
Factor Graphs for Relational Regression.
Technical Report: TR2007-906, January 2007.
Sumit Chopra, Trivikraman Thampy, John Leahy, Andrew Caplin, and Yann LeCun.
Discovering the Hidden Structure of House Prices with a Non-Parametric Latent Manifold Model.
13th International Conference on Knowledge Discovery and Data Mining (KDD), San Jose CA, August 2007.
Yann LeCun, Sumit Chopra, Marc'Aurelio Ranzato, and Jie Huangfu.
Energy-Based Models in Document Recognition and Computer Vision.
Proceedings of the International Conference on Document Analysis and Recognition (ICDAR) 2007.
Marc'Aurelio Ranzato, Y-Lan Boureau, Sumit Chopra, and Yann LeCun.
A Unified Energy Based Framework for Unsupervised Learning.
Proceedings of the 2007 Conference on Artificial Intelligence and Statistics (AISTATS) 2007.
Neelima Gupta and Sumit Chopra.
Output-Sensitive Algorithms for Optimally Constructing Upper Envelope of Straight Line Segments in Parallel.
Journal of Parallel and Distributed Computing 2007.
Yann LeCun, Sumit Chopra, Raia Hadsell, Jie Huangfu, and Marc'Aurelio Ranzato.
A Tutorial on Energy-Based Learning.
Predicting Structured Outputs, Bakir et al. (eds), MIT Press 2006.
Marc'Aurelio Ranzato, Christopher Poultney, Sumit Chopra, and Yann LeCun.
Efficient Learning of Sparse Overcomplete Representations with Energy-Based Model.
Advances in Neural Information Processing Systems 19, in Scholkopf et al. (eds), MIT Press, Cambridge, MA, 2006.
Raia Hadsell, Sumit Chopra, and Yann LeCun.
Dimensionality Reduction by Learning an Invariant Mapping.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York City, NY, June 2006.
Sumit Chopra, Raia Hadsell, and Yann LeCun.
Learning a Similarity Measure Discriminatively with Applications to Face Verification.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego CA, June 2005.
T. Agarwal, A. Agarwal, S. Chopra, A. Feldman, N. Kammenhuber, P. Krysta, and B. Voeking.
An Experiment Study of Different Strategies of DNS-Based Load Balancing.
Proceesings of Euro-Par, Klagenfurt Austria, August 2003.
Neelima Gupta, Sumit Chopra, and Sandeep Sen.
Optimal Output-Sensitive Algorithms for Constructing Upper Envelope of Line Segments in Parallel.
Proceedings of Foundations of Software Technology and Theoretical Computer Science (FSTTCS), Bangalore India, December 2001.
Training Language Models Using Target-Propagation
A neural attention model for abstractive sentence summarization
Learning Through Dialogue Interactions
Dialogue Learning With Human-In-The-Loop
Babi Tasks and Other Datasets
I'm always looking for students passionate about making a real-world impact
through their research at the intersection of machine learning and healthcare.
If you are interested in joining my lab, here's what you can do based on your
current status.
Current NYU student: Send me an email and I'll be more than happy to talk.
Do not hesitate to make your case by attaching your CV and any relevant
research you might have done in the past.
Prospective PhD Student: Applications for Ph.D., program are reviewed by a
department wide committee and are very competitive. Please apply directly to the
Computer Science Ph.D., Program
of the department. Do not forget to mention my name (and of other faculty you'd like to
work with) in the application AND in your research statement.
Courant Institute of Mathematical Sciences, NYU
Computer Science Department
60 Fifth Avenue
New York, NY 10011
Email: my_firstname [at] cs [dot] nyu [dot] edu
Grossman School of Medicine, NYU
Department of Radiology
650 First Avenue
New York, NY 10016
Email: my_firstname [at] nyu [dot] edu