Keynote Speakers

Snigdha Panigrahi

Snigdha Panigrahi is an Associate Professor of Statistics at the University of Michigan, where she also holds a courtesy appointment in the Department of Biostatistics. She received her PhD in Statistics from Stanford University in 2018 and has been a faculty member at Michigan since then.

Her research focuses on converting purely predictive machine learning algorithms into principled inferential methods. By integrating tools from convex analysis, nonparametric theory and generative modeling, she develops novel inferential methods that advance explainable machine learning and trustworthy decision-making. She is an elected member of the International Statistical Institute, and her work has been recognized with an NSF CAREER Award and the Bernoulli New Researcher’s Award. Her editorial service, past and present, includes Journal of Computational and Graphical Statistics, Bernoulli, and Journal of the Royal Statistical Society: Series B.

Gergely Neu

Gergely Neu is an ICREA research professor at Universitat Pompeu Fabra, Barcelona, Spain. He has previously worked with the SequeL team of INRIA Lille, France and the RLAI group at the University of Alberta, Edmonton, Canada. He obtained his PhD degree in 2013 from the Budapest University of Technology and Economics, where his advisors were András György, Csaba Szepesvári and László Györfi. His main research interests are in machine learning theory, with a strong focus on sequential decision making problems. Dr. Neu was the recipient of a Google Faculty Research award in 2018, the Bosch Young AI Researcher Award in 2019, an ERC Starting Grant in 2020, and an ERC Consolidator Grant in 2025.