About me

Currently, I am a Process Engineer at Lam Research designing Plasma Enhanced Atomic Layer Deposition processes for semiconductor manufacturing. My work here is primarily experimental and focuses on identifying and screening new processes for the scalable production of thin films with targeted properties. Previously, as a Ph.D. candidate at UC Berkeley, my dissertation focused on the thermodynamic and transport modeling of electrolyte solutions. I earned my B.S. and M.S. in Chemical Engineering from IIT Delhi, India, where my master’s thesis involved developing new mathematical tools to improve the efficiency of CFD models for packed bed reactors. These diverse experiences, ranging from process modeling to experimental design, have given me a deep understanding and appreciation of engineering fundamentals, as well as the ability to collaborate effectively across teams.

At Lam Research, my main focus is identifying and screening new processes for the commercially feasible manufacturing of thin films with desired properties. This involves the evaluation of chemical reagents, optimization of process conditions, and reaction design to achieve desired outcomes. A key aspect of this work is ensuring seamless integration of new processes with existing manufacturing workflows. This postion offers me a unique opportunity to work at combination of process development, integration and hands-on experience with complex mechanical systems. As a member of the Product Group, I also regularly interact with Lam’s customers to understand their needs and how these needs align with the broader business strategy of Lam Research.

During my Ph.D., I developed a statistical thermodynamics-based theory to model electrical double layers in concentrated electrolytes. We derived an expression for the electrochemical potential and solvation-free energy of ions, both in the bulk and at the interface. Using this new theory, we quantitatively captured interfacial tension between coexisting vapor and liquid phases in ionic fluids, as well as the reversal of ionic current in charged nanochannels. For my master’s thesis, I worked in a chemical reaction engineering group focused on modeling flows through packed bed reactors. I used a combination of Voronoi tessellation and Minkowski tensors to mathematically characterize pore-scale features of packed beds. These features were then incorporated into fluid flow equations to improve the predictive power of reactor models. Along similar lines, I spent a summer at IMFT in Toulouse, France, working on image processing and pore network modeling to understand the mechanisms of water boiling in nuclear reactors. Additionally, from my undergraduate days, I have a year-long experience in the design, development, and characterization of catalysts.

I am also trained in modern statistical techniques crucial for efficient experimental design and complex process optimization. As an intern at the Flatiron Institute in New York City, I performed Kinetic Monte Carlo simulations to model protein assembly. In the summer of 2024, I worked on probabilistic machine learning methods at Lawrence Livermore National Laboratory, where my goal was to develop a Gaussian process regression model for thermodynamic free energies. Such advanced regression models can represent a process with a minimal number of data points, accelerating process optimization compared to traditional JMP models. To further strengthen my expertise in this area, I have completed multiple courses on machine learning and statistics at UC Berkeley, as well as training at the NVIDIA Deep Learning Institute.