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Hilke Bahmann (Chemistry, Wuppertal) |
Ha Quang Minh (AI, RIKEN-AIP) |
Our Team
Augusto Gerolin
CRC in AI at Math & Chemistry
Assistant professor
Elitta Khoury
Lab Manager
Sumiya Baasandorj
Postdoctoral Researcher
Mathematics, with SNS Pisa
Mohammad Ahmadpoor
Postdoctoral Researcher
Mathematics, with NRC
Michelle Richer
Postdoctoral Researcher
Chemistry
Zak Brannan
PhD student
Mathematics and AI
Alex Dzhenzherov
PhD student
Quantum Information Theory
Zhiyi Lin
PhD student
Mathematics and Quantum Physics
Nataliia Monina
PhD student
Math & Quantum Chemistry
Pavlo Pelikh
PhD student
OT & Machine Learning
Mariam Elsayed
MSc student
Data Sciences and Statistics
Denys Ruban
MSc student
Mathematics and AI
Ivan Zhytkevych
MSc student
Mathematics and AI
Mairi Hallman
MSc student
Stats and Machine Learning
Hossein Hajmirbaba
Undergraduate student
CS and Quantum
Rachel Love
Visiting Undergraduate student
Math and Machine Learning
Iryna Voitsitska
UCU, Lviv
(with Rostyslav Hryniv)
Maksym Zhuk
UCU, Lviv
(with Rostyslav Hryniv)
Maksym-Vasyl Tarnavskyi
UCU, Lviv
(with Rostyslav Hryniv)
Past members
Rieli dos Santos
MSc student (MITACS)
Quantum Information Theory
Katarine Domingues
Visiting Undergraduate student
Mathematics
Beatrice Aresi
Undergraduate student (Fields)
Optimal Transport
Cédric Bierlaire
Undergraduate student
Mathematics of Machine Learning
Maria Gabriela Scapin
Undergraduate student (Fields)
OT & Machine Learning
Dohyoung Ko
Undergraduate student (Fields)
Quantum Optimal Transport
Anray Liu
Undergraduate student
Scientific Computing
Melissa Junqueira
Undergraduate student (Fields)
Scientific Computing
Vitalii Bielievtsov
MSc student
DTI & AI
Valeria Kolesnik
MSc student (with Prof. S. Schillo)
DTI & Data Sciences
Nikita Davydov
MSc student, MITACS
Computer Science, Kharkiv
(with Prof. F. Gentile)
Emanuele Caputo
Postdoctoral Researcher
Mathematics
Dmitry Evdokimov
PhD student (Withdraw)
AI for Science
Annina Lieberherr
Visiting PhD student
Chemistry, Oxford (UK)
Olivia Green
BSc student, MITACS
Mathematics, Nottingham (UK)
Rebecca Mulder
BSc student, UNB
Chemistry, New Brunswick
(with Prof. S. De Baerdemacker)
Adolfo Vargas-Jiménez
Postdoctoral Researcher (2022/23)
Mathematics
Marco Caroccia
Visiting professor (2023)
Politecnico di Milano
Oliver Tse
Visiting professor (2023)
TU/e, Netherlands
Fanch Coudreuse
Visiting PhD student (2023)
Mathematics, ENS-Lyon
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
- One-body Reduced Density Matrix Theory
Mathematical Aspects of Machine learning theory
- Likelihood-free Variational Inference and Generative Modelling
- Normalizing flows
- Generative Adversarial Networks
- Statistical Learning Theory
Brief Research Description
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Calculus of Variations
We are interested in fundamental theory and computational algorithms for multi-marginal optimal transport. Examples where our methodology is applied include Wasserstein Barycenters, Mean-Field games and Trajectory Inference in Biology. We also develop tools to improve the understanding of density estimation and generation in GANs, VAEs, Flow and Diffusion-based Generative Models.
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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 many-electrons system (existence and next-order corrections of SCE DFT) and time-dependent 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.
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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.
Collaborators and Mentors