👋🏽 I'm a postdoc in Ferguson lab, PME @UChicago.
🔭 Active works
- development of ultra coarse-grained models and enhanced sampling methods using machine learning for studying phase transitions of chiral and large multi-molecular systems.
✔️ Completed works
- development and application of a novel machine learning (ML) based solution for tackling a grand challenge in water. Featured on TACC magazine -https://texascale.org/2022/powering-discoveries/ai-and-real-solutions-frontera/
- implementation of metadynamics in SSAGES and PySAGES. Link to paper: https://doi.org/10.48550/arXiv.2301.04835
- implementation of artificial neural network based CV in PLUMED for direct enhanced sampling along system and environmental variables (PINES). Link to paper: https://doi.org/10.48550/arXiv.2308.08680
- active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices. Link to published paper — featured in MSDE recent HOT 🔥 articles collection.
- permutationally invariant enhanced sampling method (PINES) [https://doi.org/10.1021/acs.jctc.3c00923].
- implementation of collective variables in PySAGES. Link to paper: https://doi.org/10.1038/s41524-023-01189-z
- Data-driven prediction of aIIbb3 integrin activation paths using manifold learning and deep generative modeling. [https://doi.org/10.1016/j.bpj.2023.12.009]
🕰️ Previously
- Graduate research at Sarupria group, ChBE @Clemson.
- Dissertation (PhD, 2019): Towards computer aided engineering of proteins and protein-surface complexes.
- Thesis (MS, 2015): Understanding molecular interactions between proteins and carbon nanomaterials.
- Software developer in mscripts team at Exeter India @Banglore.
♾️ Interests
- Data-driven methods & applications.
- Statistical mechanics, enhanced sampling methods, probability, nonlinear dynamics.
- Modeling, numerical methods, computational design.
- Deep learning, coding, algorithms, network theory.
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