Augusto Gerolin :: Academic Webpage

Augusto Gerolin

CRC in AI at Math & Chemistry
Assistant professor

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

Rieli dos Santos

MSc student (MITACS)
Quantum Information Theory

Mariam Elsayed

MSc student
Data Sciences and Statistics

Denys Ruban

MSc student
Mathematics and AI

Ivan Zhytkevych

MSc student
Mathematics and AI

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

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

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.
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.
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

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Hilke Bahmann (Chemistry, Wuppertal)
Giuseppe Buttazzo (Pisa, Ph.D. advisor)
Simone Di Marino (Math, Genoa)
Dario Feliciangeli (Math, IST-Austria)
Chris Finlay (AI, McGill & Deep Render)
Gero Friesecke (Math/Chemistry, TU Munich)
Klaas Giesbertz (Chemistry, Amsterdam)
Juri Grossi (Chemistry, UC Merced)
Paola Gori-Giorgi (Chemistry, Amsterdam)
Timothy J. Daas (Chemistry, Amsterdam)
Anna Kausamo (Math, Firenze)
Anton Mallasto (AI, SILO.AI)

Ha Quang Minh (AI, RIKEN-AIP)
Guido Montúfar (AI, UCLA & MPI)
Luca Nenna (Math, Paris-Orsay)
Mircea Petrache (Math, PUC Chile)
Aram Pooladian (AI, New York)
Lorenzo Portinale (Math, Bonn)
Tapio Rajala (Math, Jyväskylä)
Michael Seidl (Physics, Regensburg)
Robert van Leeuwen (Physics, Jyväskylä)
Bozhidar Velichkov (Math, Pisa)
Stefan Vuckovic (Chemistry, Lecce & Amsterdam)
Johannes Zimmer (Math, Bath).