Computer simulations of processes at the molecular level greatly improve our understanding of the behaviour of biological processes, chemical reactions, and material properties. I'm an applied mathematician working on new algorithms to model and simulate molecular systems, leveraging the power of both data science and physical insight. I'm currently head of the DMP Group at MPI DCTS Magdeburg.
News
Research Topics
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Koopman Methods for Molecular Simulation Molecular simulations lead to huge data sets requiring automated methods to extract equilibrium and kinetic properties. Highlights of our research in this domain include a variational approach, tensor methods, randomized kernel methods. |
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Error Theory for Koopman Modeling Determination of provable error bounds for Koopman models remains a key challenge for data-based dynamical systems research. Some of our recent results cover stochastic systems with ergodicity and kernel methods. Improved results for general ergodic systems were described here. |
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Data-driven Coarse Graining Learning reduced models that can be simulated more efficiently is an essential step towards scaling up molecular simulations. Our results include a sparse identification method, spectral learning, and error bounds. |
Recent Publications
Full list at Google Scholar.Team
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02/22-: Max Planck Group Leader, MPI DCTS Magdeburg 10/23-: Guest Lecturer, Freie Universität Berlin 09/19-01/22: Postdoc, Institute of Mathematics, Paderborn University 03/17-08/19: Rice Academy Fellow, CTBP Rice University 10/12-03/17: Graduate Student, Freie Universität Berlin |
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06/22-: Ph.D. student, MPI DCTS Magdeburg 09/21-12/21: Intern, GIPSA-Lab, Universite Grenoble Alpes 09/18-12/21: Master's student, DAER, Politecnico di Milano |
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01/23-: Ph.D. student, MPI DCTS Magdeburg 04/19-09/22: M.Sc. Mathematics, Technische Universität Kaiserslautern |
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09/23-: Ph.D. student, MPI DCTS Magdeburg 07/21-05/23: M.Sc. Mathematics, Indian Institute of Technology Bhubaneswar (IIT Bhubaneswar) 07/18-05/21: B.Sc. (Hons.) Mathematics, University of Delhi (DU) |
Main Collaborators
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Variational Methods for metastable dynamics e.g. MMS 2013 JCTC 2014 JCP 2016 |
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Data-driven Molecular Coarse Graining e.g. JCP 2018 JCP 2019 ACS Cent Sci 2023 |
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Koopman Operator Methods e.g. PhysD 2020 JCP 2023 |
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Koopman Error Theory e.g. JNLS 2023 PhysD 2024 |
Some Presentations
- Modeling Molecular Kinetics with Koopman Operators and Kernel-based Learning
- IMSI Workshop Data Sciences for Mesoscale and Macroscale Materials Models, May 2024 (Invited) Slides
- Kernel Methods for Koopman-based Modeling in Molecular Simulation
- Chalmers AI4Science Seminar, April 2024 (Invited) Slides Video
- Kernel Methods for Koopman-based Modeling
- GAMM Annual Meeting, March 2024 (MS Talk) Slides