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Hallak, Nadav

Continuous Optimization: Theory and Algorithms, First and Second Order Methods, Nonconvex Optimization, Sparse Optimization: Theory and Algorithms, Applications in Machine Learning, Engineering, Finance and Science Continue Reading Hallak, Nadav

Keren, Sarah

Building a set of theoretical and applied tools that support and encourage collaborations between autonomous AI agents and robots, as well as between AI agents and humans. This, using a variety of tools that include automated design and model-based inference, reinforcement learning in multi-agent systems and hierarchical design of robots. Continue Reading Keren, Sarah

Romano, Yaniv

machine learning, deep learning, deep generative models, scientific reproducibility, selective inference, false discovery rate, knockoffs, uncertainty estimation, fairness, sparse representations, convolutional sparse coding, dictionary learning, image processing, inverse problems. Continue Reading Romano, Yaniv

Weinberger, Nir

I focus on core problems in data-science, statistical inference and information theory. Topics include:
Algorithms and theory for non-parametric regression; Statistical inference in high dimensional models; Exploration-exploitation problems with information-based rewards; Information-theoretic limits in prediction; Theoretical limits of DNA information processing.
Continue Reading Weinberger, Nir