About me
Currently, I am a Process Engineer at Lam Research working on Plasma-enhanced deposition techniques. Before this, I completed my Ph.D. in Chemical Engineering at UC Berkeley advised by Prof. Rui Wang. My dissertation was focused on statistical physics of concentrated electrolyte solutions near interfaces. I did my B.S. and M.S. in Chemical Engineering from IIT Delhi, India. In my master’s thesis we developed Minkowski tensor based methods to characterize structure of catalyst packings in packed bed reactors. Besides, over the years, I have also undertaken research projects on molecular simulations, chemical kinetics, applied mathematics, uncertainty quantification, machine learning, and catalyst synthesis and characterization.
In my Ph.D., we have developed a new electrolyte solution theory rooted in statistical mechanics to model electrostatic correlations and dielectric inhomogeneities that mean-field Poisson-Boltzmann cannot capture. Notably, our novel method efficiently decouples and computes short and long-range spatial correlations between ions. This new approach quantitatively captures the vapor-liquid interface in ionic fluids and the reversal in ionic conductance of multivalent ion-filled nanopores. Additionally, we have explained the multivalent salt induced short-range attraction between like-charged surfaces, long-range repulsion between oppositely charged surfaes, and conformational changes in polyelectrolytes. Our theory is in agreement with experiments and simulations and offers both enhanced accuracy and computational efficiency compared to classical density functional theories. A spectral methods-based Python code for our theory is available on my Git Hub.
In the summer of 2023, I was also a research intern at the Center of Computational Biology, Flatiron Institute, NYC. There, I performed hybrid Kinetic Monte Carlo and MD simulations to explore the roles played by motor proteins such as Kinesins and static crosslinkers like PRC1 in creating a robust mitotic spindle assembly. From March ‘24 to June ‘24, I was an intern at the Physics and Materials Science division of Lawrence Livermore National Lab. My goal was to use Deep Gaussian process regression to develop methods for modeling uncertainties associated with machine-learned free energies. These two internships have also taught me the fundamentals of high-performance computing, large-scale data handling, and, above all, the importance of interdisciplinary science in solving complex problems. In this regard, I have done multiple courses on machine learning and statistics at UC Berkeley as well as NVIDIA Deep Learning Institute.