I’m a researcher interested in pushing the boundaries of chemical timescale and system complexity. During my Ph.D. under the supervision of Prof. Cecilia Clementi, I investigated rare events dynamics with the help of machine learning. During this time, I led the development of the open-source package ExTASY, which automates the execution of complex rare event sampling workflows on supercomputers, scaling up to 2000+ GPUs. This enabled me to validate that adaptive sampling achieves accurate protein folding and protein dynamics even for larger systems, as well as determine both optimal adaptive sampling strategies for folding proteins and the upper limit for speed up with adaptive sampling. During my Postdoc with Prof. Fang Liu, I streamlined the high-throughput solvation and simulation of explicitly solvated systems by co-developing the open-source package AutoSolvate. This has enabled me to generate larger datasets, including calculating explicit solvation redox potentials and reorganization energies for hundreds of systems. With machine learning corrections I could significantly reduce errors in redox potential predictions compared to experiments, as well as increase robustness to functional choice.

An overview of the research areas I focus on, detailed in the Research section

Timeline of my research/education

Postdoctoral Fellow, Emory University
Investigated the molecule-solvent interface with high-throughput simulation and machine learning with Prof. Fang Liu.
Ph.D., Physics, Rice University
Thesis title: Adaptive sampling of Conformational Dynamics Advisor: Prof. Cecilia Clementi
Bachelor, Biochemistry, University of Regensburg
Bachelor, Technical Physics, University of Ilmenau
Thesis title: NMR-spectroscopic Analysis of Interaction between Polycystin-2 and mDia1, Advisor: Prof. Hans R. Kalbitzer