Interdisciplinary Graduate Program
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
Mathematical foundations of deep learning, Graph neural networks, Applied harmonic analysis, Time-frequency and wavelet analysis Continue Reading Levie, Ron
Theory of Machine Learning, Equivariant Machine Learning, and Optimization. Continue Reading Dym, Nadav
Nonnegative Matrices, Montone Systems, Low-Complexity Modelling, Low-Rank/Sparse Optimization, Online Learning, Statistics on Manifolds
Service operations management, stochastic modeling, queueing theory and their applications in healthcare operations Continue Reading Zychlinski, Noa
Development of computational methods for bridging the data-insight gap, particularly with focus immunity and their application for understanding the drivers of immune variation and how their maturation to Immune-based Precision Medicine. Continue Reading Shen-Orr, Shai
We study how information is processed in neuronal networks to drive behavior Continue Reading Levy, Sagi
How the dynamics of neural networks in the brain support the processes of learning and memory, using
theoretical tools from the worlds of mathematics and physics and analyzing data from
experiments obtained from research laboratories with which he collaborates.
Continue Reading Barak, Omri
Machine/Deep-learning applications in medical imaging, magnetic resonance imaging, medical image analysis Continue Reading Freiman, Moti
Machine Learning and Stochastic Optimization Continue Reading Levy, Kfir