Education
University of Pennsylvania
M.S.E. in Computational Science & Engineering (Scientific Computing)
Expected Graduation: 2027
- Focus areas: Machine Learning, Scientific Computing, Optimization, Stochastic Processes
- Relevant coursework: Machine Learning, Computer Vision, Stochastic Processes, Big Data Analytics, Atomistic Modeling, Advanced Topics in ML
- Affiliation: Penn Institute for Computational Science
Indian Institute of Technology, Indore
B.Tech in Metallurgical Engineering and Materials Science
Graduated: 2024
- Thesis: Graph Neural Networks for Accelerated Materials Discovery
- Strong foundation in Mathematics, Materials Science, and Computational modeling
Work Experience
Deloitte USI — Analyst, AI & Data Analytics
Aug 2024 – Aug 2025
Hyderabad, India
- Led a real-time AI based liver disease prediction project(Python, Streamlit) enhancing early detection and outcomes.
- Built probabilistic, statistical, and optimization models(PySpark, SQL, Azure) for inventory & supply-chain optimization
- Delivered 100M USD+ in operational impact for U.S. Fortune 500 clients through data & model-driven decision making
- Automated data pipelines and storytelling dashboards(PowerBI) to deliver insights and drive strategic business decisions
Aalto University, Finland — Research Intern (Machine Learning)
Summer 2024
Espoo, Finland
- Developed a novel hybrid data-physics + AI model for hydrogen-tolerant metals, targeting runtime improvement by 15%
- Scaled microstructural simulations across 200+ metal elements on CSC HPC clusters using Slurm for parallel execution
- Built and trained temporal Graph Neural Networks to predict dynamic stress–strain behavior from microstructural graphs
INRS, Canada — Mitacs Globalink Research Intern
Summer 2023
Montreal, Canada
- Generated data via advanced computational techniques(DFT) for designing materials for sustainable energy application.
- Modeled 300+ materials using Graph Neural Networks(PyTorch Geometric) to predict Carbon Dioxide adsorption
- Developed a novel data augmentation(Python, VASP) method with intermediate graphs, improving model accuracy 5X
Projects
Smart Vision Based Bin Monitoring System
Built a real-time CV pipeline for trash detection, tracking, and classification using ROI filtering, ByteTrack, and YOLO pose-based hand removal.
Cloudphysician - Vital Extraction using CV
Developed a fast CV–OCR system for automated vital extraction from ECG images, delivering 96% accuracy and sub-second CPU inference to support clinical decision
Graph Neural Networks for Accelerated Materials Discovery
Accelerated crystal structure relaxation using GNNs, delivering DFT-comparable energies (2.51% error) for 300+ materials in seconds across 70+ alloys.
About
I am an MSE student in Computational Science at the University of Pennsylvania with a strong foundation in machine learning, scientific computing, and optimization. My work spans graph neural networks for materials discovery, computer vision systems for real-time inference, and large-scale optimization and analytics in industry. I enjoy solving complex, puzzle-like problems and translating them into efficient, high-impact models, with prior work delivering multi-million-dollar business impact. I am currently seeking internships in Machine Learning and Computational Science, and I am also open to quantitative roles where rigorous problem-solving and critical thinking are central.
- Skills: Python, PyTorch, SQL, ML, DL, CV, GNN, Optimization, …
- Focus: Machine Learning & Optimization, Computer Vision, Graph Neural Networks, Bayesian ML, Statistical Data Modeling
- Currently: Looking for internships / research opportunities
Contact
Email me at [email protected] or DM me on LinkedIn.