Education
I am a second year Ph.D. Student at Civil and Systems Engineering, Johns Hopkins University. I am fortunate to be advised by Somdatta Goswami. I hold a Master's degree in Civil Engineering with a specialization in Geotechnical Engineering from Indian Institute of Technology, Madras. This educational foundation provided me with a unique blend of computational science and machine learning skills, which I now apply to real-world engineering problems.
Stack
I work across a comprehensive data science and machine learning stack that supports advanced modeling, scientific computing, and deep learning applications in engineering. My technical foundation includes Python, MATLAB, and Java for robust programming solutions, with GitHub for version control and collaborative development. For data manipulation and analysis, I leverage NumPy, pandas, and Matplotlib, while utilizing scikit-learn for traditional machine learning implementations. My big data capabilities encompass Dask and Spark for distributed computing, complemented by joblib for efficient parallel processing. In deep learning, I employ JAX and PyTorch frameworks to develop and deploy sophisticated models including CNNs, UNets, Transformers and Neural Operators enabling me to tackle complex problems in data-driven and physics informed ML as well as physics based solvers (FEM).
Experience
Graduate Research Assistant
Johns Hopkins University
2024 - Present
Contributing to the Materials Project through support from DARPA INTACT for developing manufacturing pathways for intrinsically tough covalent ceramics stable at elevated temperatures. Working on developing scalable and efficient physics-informed machine learning architectures for control systems.
M.Tech Research Scholar
Indian Institute of Technology, Madras
2023 - June 2024
Developed Physics-Informed ML architectures for interior interface identification using adaptive schemes.