Research
I have conducted research in machine learning optimization, feature selection, and image segmentation, resulting in 9 peer-reviewed publications across IEEE, Springer, and Elsevier journals, with more than 400 citations. My current work at Columbia focuses on explainable AI and privacy, while a second project applies machine learning tooling to climate simulation.
Current Research
I am currently a Research Assistant at the ARiSE (Advanced Research in Software Engineering) Lab at Columbia University. I am building a compliance benchmark for open-weight and commercial language models using constrained decoding and the PyArg solver. The benchmark uses more than 10,000 scenarios to evaluate whether models follow privacy and AI laws and to support explainable analysis.
- • Collaborating with 2 other students and 3 faculties from Columbia, Wesleyan and University of South Carolina.
- • Evaluating models from the Claude, Gemini, and OpenAI families alongside open-weight systems.
- • Constructing more than 10,000 minimal-cause scenarios for privacy and AI-law compliance testing.
- • Using XGrammar-based constrained decoding and PyArg to trace the factors behind model behavior.
Tech: Python, XGrammar, PyArg, Generative AI
Climate Modeling Research
Gentine Lab, Columbia University · Feb 2026 - Jun 2026
Developed an NSF-funded open-source PyPI module that modernizes climate-model workflows by translating Fortran implementations into Python and JAX for differentiable, GPU-accelerated simulation.
Tech: Python, JAX, Fortran, PyPI, Pytest
Previous Research
I have previously worked as a Research Assistant at the Centre for Microprocessor Applications Training and Research (CMATER) lab at Jadavpur University, India during my undergraduate studies. My work there covered machine learning optimization, stock-market prediction, feature selection, and image segmentation, alongside mentoring students and presenting research at conferences.
- • Built an open-source scikit-learn package that has earned 66+ GitHub stars and 39+ forks.
- • Published nine peer-reviewed papers with more than 400 citations and guided four students.
- • Presented findings at three or more conferences and investigated stock prediction, image segmentation, and feature selection.
Tech: Python, NumPy, SciPy, scikit-learn, PyPI, MATLAB, Statistics