Research Group

We study mathematical and statistical problems that arise in data science and focus on when and how statistical inference and machine learning can empower each other. Our research interests center around predictive uncertainty quantification, reproducibility in data-driven inference, statistical testing, interpretability, and robustness. Other interests include signal and image processing and sparse modeling of data.

Research Associates and Postdocs

• Yaniv Tenzer

PhD Students

• Shai Feldman

• Shalev Shaer

• Bat-Sheva Einbinder

• Meshi Bashari

• Roy Maor Lotan

• Liran Ringel

• Elad Tolochinsky

MSc Students

• Noa Shoham

• Hen Davidov (co-advisor: Ron Kimmel)

• Yarin Bar (2024)
  Current: Cathalert

• Liran Ringel (2024)
  Current: PhD student

• Roy Maor Lotan, MSc (2024)
  Current: PhD student

• Meshi Bashari, MSc (2024)
  Current: PhD student

• Nelson Goldenstein, MSc (2023)
  Current: Public sector

• Shai Feldman, MSc (2022)
  Current: PhD student

• Shalev Shaer, MSc (2022)
  Current: PhD student

• Bat-Sheva Einbinder, MSc (2022)
  Current: PhD student

• Asaf Gendler, MSc (2022)
  Current: Amazon

• Nitai Fingerhut, MSc (2022)
  Current: Apple

• Marguax Zaffran (August - October 2022)
  Current: PhD student at INRIA, at CMAP, Ecole Polytechnique, and at EDF R&D, Saclay.