Rohit Goswami
Postdoctoral Researcher · EPFL labCOSMO
I develop Gaussian process methods for atomistic simulation, contribute to cross-language scientific software (NumPy/f2py, metatensor), and build tools for reproducible computational chemistry.
17publications
8research threads
30+talks
20+workshops taught
Research
transition-state
Transition State Methods
NEB, dimer methods, and path visualization for reaction pathways
bayesian-stats
Bayesian Statistical Methods
Hierarchical models and uncertainty quantification for algorithm benchmarking
gp-acceleration
GP-Accelerated Saddle Point Searches
Surrogate energy surfaces via Gaussian process regression for 10x fewer force evaluations
fortran-python
Bridging Fortran and Python
Modernizing interoperability between scientific computing's two most important languages
ml-atomistic
Machine Learning Interoperability for Molecular Science
Foundational libraries enabling ML models and simulation engines to communicate
molecular-sim
Molecular Simulation and Structural Analysis
Ice nucleation, self-propelled particles, lava flows, and tools for trajectory analysis
reproducible-hpc
Reproducible Scientific Computing
Nix, pixi, and workflow tools for HPC environments
ultrafast
Ultrafast Spectroscopy and Pulse Shaping
Femtosecond lasers for spectroscopy, quantum gates, and machine-learned pulse optimization
Recent Work
Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point SearchesChemPhysChem (Cover feature), 2025
Flowy: High Performance Probabilistic Lava Emplacement PredictionComputer Physics Communications, 2025