Iddo Drori

Associate Professor, Department of Computer Science
Director of MS in AI
Co-Director of MS Admissions
Boston University, Department of Computer Science (practice),
Columbia University, Department of Computer Science (adjunct)

My research focuses on artificial general intelligence, computer vision, and machine learning for education and climate science.

If you are interested in working together in my group on AGI then please send me an email about your background and research interests.

Recent Activities

NeurIPS 2024: Senior area chair of datasets and benchmarks track.
ICML 2024: Senior area chair.
ECCV 2024: Area chair.
MIT Press 2024: Textbook reviewer.

The science of deep learning
Iddo Drori
Cambridge University Press, 2022
📖 Textbook available on Amazon
🥇 #1 new release in computer vision and pattern recognition

A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level
Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, Roman Wang, Nikhil Singh, Taylor L. Patti, Jayson Lynch, Avi Shporer, Nakul Verma, Eugene Wu, Gilbert Strang
Proceedings of the National Academy of Sciences (PNAS), 119(32), 2022.
Paper / Data and code / MIT news
💡 First work to use program synthesis for solving math and first dataset of university-level math problems.

Predicting the Atlantic multidecadal variability
Glenn Liu, Peidong Wang, Matthew Beveridge, Young-Oh Kwo, Iddo Drori
🌎 Tackling Climate Change with Machine Learning workshop at NeurIPS (CCAI), 2021.
🥇 Best paper award winner

Solving machine learning problems
Sunny Tran, Pranav Krishna, Ishan Pakuwal, Prabhakar Kafle, Nikhil Singh, Jayson Lynch, Iddo Drori
Asian Conference on Machine Learning (ACML), 2021.
🥇 Best student paper award winner

Recent Research

ReviewerArena: Evaluate LLM reviewer quality based on preferences by direct and anonymous comparison of reviews.
🎓 OpenReviewer: Learn to improve your academic papers by generating real-time reviews.
📚 Papers with reviews: Read top ranked arXiv and open access Nature papers.
🏛 Machine Learning for Education: Can a machine solve, explain, and generate university-level mathematics and STEM courses? Our latest research published in PNAS and featured by MIT news demonstrates that a neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level.
🌎 Machine Learning for Climate Science: In the spirit of MIT's leading efforts to take action against climate change for a better world, we've published multidisciplinary work in Nature Scientific Reports on computer vision methods for tracking turbulent structures in the plasma of a fusion reactor, and on predicting the Atlantic Multi-decadal Variability and ocean biogeochemistry, awarded best paper: pathway to impact at NeurIPS CCAI.
🚘 Machine Learning for Autonomous Driving: Can we accurately predict trajectories and learn to drive? we've won the ICCV learning to drive challenge and continuously improve performance; society will accept autonomous vehicles once they are orders of magnitude safer than humans.

Recent Awards

🏅 NeurIPS 2022 open-ended learning competitions: MineRL BASALT and Neural MMO
🌎 CCAI NeurIPS 2021 Best Paper Award Winner
🏆 FG 2021 Competition Winner, Kinship Verification Challenge
🥇 ACML 2021 Best Student Paper Award Winner
🚘 ICCV 2019 Competition Winner, Learning to Drive Challenge

 Boston University

Department of Computer Science
Faculty member, Associate Professor (of practice)
Program Director of Masters in AI
Center for Computing and Data Sciences
Department of Computer Science
665 Commonwealth Avenue, Boston, MA 02215,

 Columbia University

Department of Computer Science
I'm Adjunct Associate Professor since 2017
School of Engineering and Applied Science
500 W 120th Street, New York, NY 10027

 Cornell University

School of Operations Research & Information Engineering
I was Visiting Associate Professor at Cornell University.

The Science of Deep Learning

Cambridge University Press, 2022.

Academic Service

Profile on Google Scholar




Selected Invited Talks and Panels