Vladimir Koltchinskii's picture

Vladimir Koltchinskii

Vladimir Koltchinskii is a professor in Mathematics at Georgia Tech. His current research is primarily in high-dimensional statistics and probability.

 

Potential topic

Estimation of functionals in high-dimensional parameters: Bias reduction and concentration.

Bruno Loureiro's picture

Bruno Loureiro

Bruno is a CNRS researcher based at the Centre for Data Science at the École Normale Supérieure in Paris working on the crossroads between machine learning and statistical mechanics. He also holds an Adjunct Professor (“Professeur Attaché”) position at the Université Paris Sciences et Lettres (PSL) where he teaches at the undergraduate and graduate programs of the affiliated universities.

Before moving to CNRS & ENS, he was a postdoc at the Institut de Physique Théorique (IPhT) in Paris and at the École Polytechnique Fédérale de Lausanne (EPFL) in Lausanne, where he worked with Lenka Zdeborová and Florent Krzakala.

Before his postdoc, he read the part III of the Mathematics Tripos at the University of Cambridge, and continued into a PhD at the TCM group in the same university. During his PhD he worked on the Holographic Principle, a duality stemming from string theory that relates quantum field theories to classical theories of gravity. His thesis was centered on applications of this duality to strongly coupled condensed matter systems, with a particular focus on disordered systems.

Although all of that sounds very different from his current research, it is funny to note that many of the methods he employed at the time are the same as in his current topic. In the end, Physics is all about Gaussian integrals, isn’t it?

To find out more about Bruno's research and activities, you may visit his website here.

 

Potential topic

Statistical physics tools for high-dimensional learning problems.

Cynthia Rush

Cynthia Rush

Cynthia Rush is an Associate Professor of Statistics in the Department of Statistics at Columbia University. She received her Ph.D. in Statistics from Yale University in 2016, under the supervision of Andrew Barron. She obtained her B.S. in Mathematics at the University of North Carolina at Chapel Hill.

Her research uses tools and ideas from information theory, statistical physics, and applied probability as a framework for understanding modern, high-dimensional inference and estimation problems and complex machine learning tasks that are core challenges in statistics and data science.

To find out more about Cynthia's research and activities, you may visit her website here.

 

Potential topic

High dimensional statistics and approximate message passing.

Matus Telgarsky's picture

Matus Telgarsky

Matus Telgarsky is an Assistant Professor at the Courant Institute of Mathematical Sciences at New York University, specializing in deep learning theory.  He was fortunate to receive a PhD at UCSD under Sanjoy Dasgupta. Other highlights include: co-founding, in 2017, the Midwest ML Symposium (MMLS) with Po-Ling Loh (while on faculty at the University of Illinois, Urbana-Champaign); receiving a 2018 NSF CAREER award; and organizing two Simons Institute programs, one on deep learning theory (summer 2019), and one on generalization (fall 2024).

To find out more about Matus' research and activities, you may visit his website here.

 

Potential topic

Neural networks theory.