Daphna Weinshal

About

Daphna Weinshall is a Professor of Computer Science at the Hebrew University of Jerusalem, Israel. She has served as a Visiting Professor at MIT and NYU, and as a Visiting Researcher at IBM, NECI, and Philips Research Labs. Dr. Weinshall has served as an Area Chair on the program committees of NeurIPS, CVPR, ICCV, ECCV, and IJCAI, and on the editorial boards of IEEE PAMI, CVIU, and MVA. She has also served as a Panel Chair or Panel Member on several prestigious grant evaluation committees, including the Advanced ERC Grants evaluation panel in Computer Science. She earned her undergraduate degree in Mathematics and Computer Science from Tel Aviv University, and received her M.Sc. and Ph.D. in Statistics (Population Genetics) from Tel Aviv University. Her recent work focuses on developing and expanding methodologies for deep learning in dynamic settings, including curriculum learning, continual learning, and active learning.

Research Focus

Weinshall’s research focuses on developing alternative paradigms in deep learning that enhance the flexibility, adaptability, and robustness of modern AI systems. Drawing inspiration from strategies of human intelligence, she aims to design methods that support continuous learning and effective adaptation to new environments, even when access to reliable data is limited. A central theme of her work is addressing the challenges of costly and imperfect data annotation, which remain major bottlenecks in the deployment of machine learning. To this end, she explores adaptive learners that refine their strategies as they evolve, active learning approaches tailored to strict budgetary constraints, and frameworks for leveraging heterogeneous sources of supervision, including expert knowledge, crowdsourcing, and transfer from foundation models. Collectively, these directions seek to build AI systems that are not only data-efficient but also capable of sustained learning in dynamic, real-world settings.