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Hilke Bahmann (Chemistry, Wuppertal) 
Ha Quang Minh (AI, RIKENAIP) 
Our Team
Adolfo VargasJiménez
Postdoctoral Researcher
Mathematics
Dmitry Evdokimov
PhD student
AI for Science
Nataliia Monina
PhD student
Math & Quantum Chemistry
Pavlo Pelikh
PhD student
OT & Machine Learning
Fanch Coudreuse
Visiting PhD student
Mathematics, ENSLyon
Vitalii Bielievtsov
MSc student
DTI & AI
Valeria Kolesnik
MSc student (with Prof. S. Schillo)
DTI & Data Sciences
Nikita Davydov
BSc student, MITACS
Computer Science, Kharkiv (Ukraine)
(with Prof. F. Gentile)
Past members
Daniel Calero
Undergraduate, MITACS (2022)
Physics, U. del Valle (Colombia)
Ben Langton
Undergraduate, MITACS (2022)
Mathematics, Durham (UK)
Akshay Raman
Undergraduate, MITACS (2022)
Computer Sciences, VIT (India)
Liam Meades
Volunteer student (2022)
Quantum Chemistry
Research interests
Calculus of Variations,
Optimal Transport,
Gradient Flows in the space of probability measures,
Numerical methods and approximation,
Theoretical and Computational Chemistry,
 Density Functional Theory
 Onebody Reduced Density Matrix Theory
Mathematical Aspects of Machine learning theory
 Likelihoodfree Variational Inference and Generative Modelling
 Normalizing flows
 Generative Adversarial Networks
 Statistical Learning Theory
Brief Research Description

Calculus of Variations
We are interested in fundamental theory and computational algorithms for multimarginal optimal transport. Examples where our methodology is applied include Wasserstein Barycenters, MeanField games and Trajectory Inference in Biology. We also develop tools to improve the understanding of density estimation and generation in GANs, VAEs, Flow and Diffusionbased Generative Models.

Quantum Chemistry
The focus of our current research is to extend the accuracy of electronic Density Functional Theory (DFT) to systems in which electronic correlation plays a prominent role. In particular using the Stricly Correlated Electron (SCE) formalism in the study of ground state properties of manyelectrons system (existence and nextorder corrections of SCE DFT) and timedependent DFT (1d). Another research line focus in extending the accuracy of electronic Density Functional Theory (DFT) to systems in which electronic correlation plays a prominent role. In particular using machine learning methods and the SCE formalism to help in the construction of improved approximate functionals.

Mathematics of Machine Learning and AI for Chemistry
We are developing tools to improve the understanding of density estimation and generation in GANs, VAES and Normalizing Flows; and developing novel deep learning methods for Computational Chemistry.