@article{goswamiRsxHighperformanceStreaming2026,
mypdf = {https://arxiv.org/abs/2606.06434},
title = {rsx: a high-performance streaming toolkit for {RAD-seq} sex determination},
author = {Goswami, Rohit and Goswami, Ruhila},
year = {2026},
month = {sep},
journal = {BMC Bioinformatics},
publisher = {BioMed Central},
doi = {10.1186/s12859-026-06628-4},
langid = {english},
archivePrefix = {arXiv},
eprint = {2606.06434},
eprinttype = {arxiv},
keywords = {journal, selected},
}
Rohit Goswami
Postdoctoral Researcher · EPFL labCOSMO
Algorithms and software for scientific computing and applied AI: numerical methods, Leidarljos (memory, provenance, and coordination), and HPC, with applications in chemistry, biology, and earth sciences.
I develop algorithms and software systems for scientific computing and applied AI. Published work includes Gaussian-process saddle search, generated language interfaces, interoperable learned models, streaming bioinformatics, and distributed simulation. Leidarljos extends that work to persistent memory, provenance, and coordinated AI tools. Chemistry, biology, and earth sciences supply the problems and an independent check on the results.
Research Threads
leidarljos
Leidarljos
Persistent memory, provenance, and coordination for AI tools
transition-state
Transition State Methods
NEB, dimer methods, and path visualization for reaction pathways
bayesian-stats
Bayesian Statistical Methods
Hierarchical models, marker evidence, and uncertainty quantification
gp-acceleration
GP-Accelerated Saddle Point Searches
Surrogate energy surfaces via Gaussian process regression for 10x fewer force evaluations
sci-sw-interop
Scientific Software Interoperability
Cross-language bindings, build systems, and packaging for computational science codes
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, spack, 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
@article{goswamiEnhancedClimbingImage2026,
mypdf = {https://arxiv.org/abs/2601.12630},
title = {Enhanced Climbing Image Nudged Elastic Band Method with Hessian Eigenmode Alignment},
author = {Goswami, Rohit and Gunde, Miha and J{\'o}nsson, Hannes},
year = {2026},
month = {may},
journal = {Frontiers in Chemistry},
publisher = {Frontiers Media SA},
volume = {14},
doi = {10.3389/fchem.2026.1807063},
langid = {english},
archivePrefix = {arXiv},
eprint = {2601.12630},
eprinttype = {arxiv},
keywords = {journal, selected},
}@article{goswamiTwodimensionalRMSDProjections2026,
mypdf = {https://arxiv.org/abs/2512.07329},
title = {Two-Dimensional {{RMSD}} Projections for Reaction Path Visualization and Validation},
author = {Goswami, Rohit},
year = {2026},
month = {mar},
journal = {MethodsX},
pages = {103851},
issn = {2215-0161},
doi = {10.1016/j.mex.2026.103851},
eprint = {2512.07329},
eprinttype = {arxiv},
archiveprefix = {arXiv},
urldate = {2026-03-06},
langid = {english},
keywords = {journal, selected},
}@article{bigiMetatensorMetatomicFoundational2025,
mypdf = {https://arxiv.org/abs/2508.15704},
title = {Metatensor and Metatomic: Foundational Libraries for Interoperable Atomistic Machine Learning},
shorttitle = {Metatensor and Metatomic},
author = {Bigi, Filippo and Abbott, Joseph W. and Loche, Philip and Mazitov, Arslan and Tisi, Davide and Langer, Marcel F. and Goscinski, Alexander and Pegolo, Paolo and Chong, Sanggyu and Goswami, Rohit and Febrer, Pol and Chorna, Sofiia and Kellner, Matthias and Ceriotti, Michele and Fraux, Guillaume},
year = {2026},
month = {feb},
journal = {The Journal of Chemical Physics},
volume = {164},
number = {6},
doi = {10.1063/5.0304911},
issn = {1089-7690},
eprint = {2508.15704},
eprinttype = {arxiv},
archiveprefix = {arXiv},
urldate = {2026-02-01},
langid = {english},
keywords = {journal, selected},
}