Annual Summer School on Mathematical Aspects of Data Science

Format of Summer School:

• First 5 days (Week 1): Speakers delivering multi-session lectures/tutorial.
• Final 3 days (Week 2): A larger number of speakers giving single-session talks with an ‘introduction/mini-tutorial’ component and a ‘recent research’ component.
• Both weeks: Discussion sessions.

Speakers of Mini-Courses in Week 1:

• Nicolò Cesa-Bianchi (University of Milan, Italy) [Topic: Sequential decision-making]
• Shuangping Li (Yale University, USA) [Topic: Statistical physics of random computational problems]
• Stanislav Minsker (University of Southern California, USA) [Topic: Moment and deviation inequalities for random matrices]
• Ankur Moitra (Massachusetts Institute of Technology, USA) [Topic: Dynamical systems, graphical models, and language models]
• Shay Moran (Technion, Israel) [Topic: PAC learning and beyond]
• Roberto Imbuzeiro Oliveira (IMPA, Brazil) [Topic: Optimal mean estimation and robust statistics]
• Alexandre Tsybakov (CREST-ENSAE Paris, France) [Topic: Online learning]

Speakers on Week 2: 

• Arnab Bhattacharrya (University of Warwick, UK) [Topic: Causal discovery]
• Sinho Chewi (Yale University, USA) [Topic: Sampling]
• Kevin Jamieson (University of Washington, USA) [Topic: Bandits and reinforcement learning]
• Holden Lee (Johns Hopkins University, USA) [Topic: Sampling algorithms and diffusion models]
• Jasper Lee (University of California Davis, USA) [Topic: All-purpose mean estimation]
• Yuanyuan Lin (Chinese University of Hong Kong, Hong Kong) [Topic: Generative learning-based nonparametric statistics]
• James Saunderson (Monash University, Australia) [Topic: Sparsification]
• Claire Vernade (University of Technology Nuremberg, Germany) [Topic: Reinforcement learning and control]
• Van Vu (University of Hongkong, HK) [Topic: Matrix perturbation bounds and their applications]
• Pengkun Yang (Tsinghua University, China) [Topic: Functional estimation]
• Pierre Youssef (New York University, Abu Dhabi, United Arab Emirates) [Topic: Random matrices and deep neural networks]