Welcome ! I am currently doing research in the intersection of machine learning, bayesian optimization and molecular modelling. I am broadly motivated by the question : How can we improve the accuracy and decrease the cost of running atomistic bio-molecular simulations, in a way that is theoretically principled and practically impactful.

My doctoral research in Dr. Rigoberto Hernandez's lab focused on developing models and algorithms that construct interpretable and sparse embeddings for optimization of metal halide perovskite (MHP) properties in large-dimensional and low-data settings. Some of my notable research publications in this area are constructing material embeddings using Nested Autoencoders and sparse feature selection using multi-agent reinforcement learning . I completed my Master's thesis work in the same lab, where I developed an algorithm for efficient simulations of protein unfolding and computation of the potential of mean force , using telescoping water boxes .

After 4 years of doing research in material informatics, I am switching back to doing research in computational biophysics. Specifically, investigating bio-molecular mechanisms facilitating ion mediated cellular processes through the use of all-atom and coarse-grained molecular dynamics. I am currently working in Dr. Pavel Jungwirth's group at IOCB, Prague, parametrizing physics-based force fields using Bayesian Force Fields.

I am currently seeking industry opportunities in AI + Biophysics. Whether you are in academia, tech, want to nerd out about AI or just say Hi! - let's connect !

Profile Picture

News Flash !

  1. [May 22] Almost advanced to the final round Merck Innovation Cup 2025 for team Smart Mnaufacturing.

  2. [Apr 28] Won the Graduate TA award for teaching EN 545.635 Software Carpentry to graduate students in the Chemical and Biomolecular Engineering department at Johns Hopkins University.

  3. [Sep 11] Judge for JHU AICheE UG and MSE Poster Symposium.

  4. [July 9] Mentoring Bryan Zhan, a highschooler interning at PNNL for the summer. Going through the basics of Python and Machine Learning with him.

  5. [July 24 - July 26] Reviewer for ML4LMS Workshop. Part of ICML 2024.
    Program Committee

  6. [July 2] Started my internship at Pacific Northwest National Lab ! Working with Dr. Jinhui Tao to develop machine learning models for designing crystal growth modifiers to yield calcite crystals with desired morphologies.

  7. [March 27 - March 29] Team lead in Bayesian Optimization Hackathon for Chemistry and Materials. Worked with Maitreyee Sharma Priyadarshini, Gigi (Yiren) Wang and Jarett Ren to use BO with local GP to find Covalent Organic Frameworks (COF) with the best methane storage capacity.
    Project Description Code link

  8. [March 1] Selected for round 2 in Merck Innovation Cup.

  9. [Feb 9 - Feb 10] Participated in Greenhacks to come up with solutions to reduce the climate and ecological impact of commercial farming.

  10. [Sep 27] Presented in AI-X Foundry Symposium.
    Twitter Link

  11. [July 18] Taught in a Summer workshop on Python and Machine Learning Fundamentals organized by Dr. Pratyush Tiwary from UMD. Contributed to tutorials explaining how to use PyTorch for building Machine Learning models.
    Workshop Link

  12. [Aug 21 - Aug 25] Presented at American Chemical Society Conference, Chicago, IL, USA, 2022. Talk title : "Mutational Assay of an Actophorin Protein using Adaptive Steered Molecular Dynamics"

2025 2024 2023 2022