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
- Teaching Assistant: Mathematical Foundation for Machine Learning II: Linear Algebra
- 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, machine learning, and computational modeling
Work Experience
Pickle — AI Engineering & Data Science Intern
Jun 2026 – Present
New York, USA
- Building recommendation, retrieval, and ranking systems across 600K+ marketplace listings using Python, SQL, multimodal embeddings, and behavioral signals
- Deploying ML inference on AWS SageMaker and production workflows with AWS Lambda and OpenSearch for embedding updates, vector indexing, and scalable retrieval
- Designing evaluation, experimentation, monitoring, and production debugging workflows to identify model failure modes and support reliable ML rollouts
Research Assistant – Agentic LLM Text Scoring
Jan 2026 – Present
World Well Being Project, UPenn (Prof. Lyle Ungar) — Philadelphia, USA
- Designing a scalable agentic AI pipeline with LangGraph and Python, including automated QA validation, logging, and evaluation across 150K+ messages
- Applied BERTopic clustering and embedding-based modeling to detect user patterns across cohorts and improve scoring results by 11% overall
- Building reproducible ML workflows with experiment tracking, structured evaluation, and modular validation frameworks for rapid iteration and audits
Deloitte USI — Analyst, Machine Learning & Data Science
Aug 2024 – Aug 2025
Hyderabad, India
- Built large-scale ML and data pipelines using Python, SQL, PySpark, Hadoop, and AWS across 200K+ SKUs for demand forecasting and predictive optimization
- Engineered scalable ETL, feature engineering, and predictive modeling workflows across 200M+ records from 20+ enterprise data sources
- Developed machine learning models for classification and predictive analytics, including a model achieving 92% minority-class recall through tuning, validation, and error analysis
- Delivered $100M+ in operational impact for U.S. Fortune 500 clients through large-scale analytics, ML modeling, and data-driven decision-making
Aalto University, Finland — Machine Learning Research Assistant
Summer 2024
Espoo, Finland
- Developed a hybrid data-physics and machine learning framework for hydrogen-tolerant materials, reducing computational runtime by 15%
- Scaled microstructural simulations across 200+ material structures on CSC high-performance computing clusters using Slurm for parallel execution
- Built and trained temporal Graph Neural Networks with PyTorch to learn structured representations and predict dynamic stress-strain behavior
INRS, Canada — Mitacs Globalink Research Intern
Summer 2023
Montreal, Canada
- Generated scientific datasets through DFT and computational modeling for sustainable energy and materials discovery applications
- Modeled material systems using Graph Neural Networks and PyTorch Geometric for representation learning and scientific property prediction
- Developed Python and VASP-based graph data augmentation workflows using intermediate structures, improving predictive performance by 5X in limited-data settings
Projects
AI Agent for Sprint Intelligence — YHack'26
Built a multi-step AI agent using RAG, reasoning, tool use, and APIs to convert Slack conversations into structured Jira actions, rank sprint assignees from GitHub expertise signals, and coordinate engineering workflows.
Safety-Aware Multi-Agent RL for Coordination in MiniGrid
Built a safety-aware multi-agent reinforcement learning system using MAPPO and Lagrangian constraints, improving coordination efficiency and task success while reducing safety violations.
Smart Vision Based Bin Monitoring System
Built a real-time computer vision pipeline for trash detection, tracking, and classification using ROI filtering, ByteTrack, and YOLO pose-based hand removal.
CloudPhysician — Vital Extraction using Computer Vision
Developed a fast computer vision and OCR system for automated vital extraction from ECG images, delivering 96% accuracy and sub-second CPU inference for scalable structured extraction.
RADAR — Personal Research Recommendation Agent
Built an agentic research assistant that reads recent notes, infers active topics, retrieves relevant papers from arXiv and Semantic Scholar, and ranks personalized recommendations with memory.
Graph Neural Networks for Accelerated Materials Discovery
Accelerated crystal structure relaxation using graph neural networks, delivering DFT-comparable energies with 2.51% error for 300+ materials in seconds across 70+ alloy systems.
About
I am an MSE student in Computational Science at the University of Pennsylvania with a strong foundation in machine learning, data science, scientific computing, and optimization. My work spans recommendation systems, retrieval and ranking, graph neural networks, agentic AI, computer vision, and large-scale production ML systems. I enjoy taking ideas from experimentation and modeling through scalable deployment, monitoring, and measurable product impact.
- Skills: Python, PyTorch, SQL, Spark, AWS, SageMaker, Lambda, OpenSearch, Docker, Machine Learning, Deep Learning, GNNs, Recommendation Systems, Retrieval, Ranking, Production ML
- AI & ML: Recommendation Systems, Personalization, Multimodal ML, Graph Machine Learning, LLMs, Agentic AI, Computer Vision, Reinforcement Learning, Model Evaluation
- Production: ML Pipelines, Real-Time Inference, Vector Search, Embeddings, Model Deployment, Experimentation, Evaluation, Monitoring, Data Processing Pipelines
- Currently: AI Engineering & Data Science Intern at Pickle and MSE student in Computational Science at the University of Pennsylvania
Contact
Email me at [email protected] or DM me on LinkedIn .