Personal profile
Research interests
Gal Chechik is a Professor of Computer Science at Bar-Ilan University and a director of AI at NVIDIA, Leading NVIDIA research in Israel. His research spans learning in brains and machines, focusing mainly on deep machine learning for perception and reasoning.
In 2018, Gal founded the NVIDIA research group in Israel, and has been leading it since. Prior to that, he was a staff research scientist at Google working on machine perception and search. Gal earned his PhD in 2004 from the Hebrew University developing machine learning methods to study neural coding. In his Post-doctoral work at Stanford, he studied computational principles of molecular biology pathways. In 2007, he joined Google research, where he worked on various problems including large scale machine learning for perception and search. In 2009, he founded the learning systems lab at the Gonda brain research center of Bar-Ilan university, where he was appointed an full professor in 2019. Gal is the author of ~120 refereed publications, and ~50 patents, including publications in Nature Biotechnology, Cell and PNAS. His work won best-paper awards at NeurIPS and ICML, the world leading conferences in machine learning.
Education/Academic qualification
PhD, Hebrew University of Jerusalem
Oct 1998 → Sep 2003
Award Date: 30 Sep 2003
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Collaborations and top research areas from the last five years
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Classifier-Guided Captioning Across Modalities
Shaulov, A., Shaharabany, T., Shaar, E., Chechik, G. & Wolf, L., 2025, 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings. Rao, B. D., Trancoso, I., Sharma, G. & Mehta, N. B. (eds.). Institute of Electrical and Electronics Engineers Inc., (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).Bar-Ilan University, Tel Aviv University
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open Access -
LCM-Lookahead for Encoder-Based Text-to-Image Personalization
Gal, R., Lichter, O., Richardson, E., Patashnik, O., Bermano, A. H., Chechik, G. & Cohen-Or, D., 2025, Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T. & Varol, G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 322-340 19 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15072 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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PlaMo: Plan and Move in Rich 3D Physical Environments
Hallak, A., Dalal, G., Tessler, C., Guo, K., Mannor, S. & Chechik, G., 2025, Computer Vision – ECCV 2024 Workshops, Proceedings. Del Bue, A., Canton, C., Pont-Tuset, J. & Tommasi, T. (eds.). Springer Science and Business Media Deutschland GmbH, p. 442-463 22 p. (Lecture Notes in Computer Science; vol. 15624 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
Achituve, I., Diamant, I., Netzer, A., Chechik, G. & Fetaya, E., 1 Jan 2024, In: Proceedings of Machine Learning Research. 235, p. 117-134 18 p.Research output: Contribution to journal › Conference article › peer-review
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Breathing Life into Sketches Using Text-to-Video Priors
Gal, R., Vinker, Y., Alaluf, Y., Bermano, A., Cohen-Or, D., Shamir, A. & Chechik, G., 2024, Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024. IEEE Computer Society, p. 4325-4336 12 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open Access