Boston, Massachusetts, US
Summary
AI Engineer Intern track participant; contributed to AI-driven projects and gained hands-on experience in machine learning applications within an e‑learning context.
Chicago, Illinois, US
Summary
ML Engineering Intern in a high‑impact AI internship program focusing on search intelligence: built end‑to‑end ML research with real-world data for search ranking and discoverability.
Highlights
Developed a repeatable ML ranking framework analyzing search/content performance.
Performed feature engineering, baseline comparisons, leakage checks, and validation, translating model insights into recommendations for content optimization.
, ,
Summary
AI Engineer Intern engaged in AI research and development in a technology/internet startup environment.
Bhopal, Madhya Pradesh, India
Summary
Founded Scandium Labs to build physics‑informed AI infrastructure and systems for accelerating materials discovery, focusing on graph neural networks, scientific ML pipelines, open‑source research tools, datasets, and scalable screening systems for computational materials science. Leader of research and development direction, including projects like PIGNet V2 and others aiming to bridge AI with scientific principles.
Highlights
Designed and advanced PIGNet V2, a physics-informed graph neural network for materials prediction and discovery.
Established reproducible training pipelines, open‑source tools, and datasets to support AI-driven materials science research.
Bhopal, Madhya Pradesh, India
Summary
Co‑founded a financial‑tech startup focused on AI/ML engineering for quantitative financial services.
New Delhi, Delhi, India
Summary
Conducted research and analysis as a Research Associate at a SEBI‑registered financial research firm.
San Francisco, California, US
Summary
Participated in an AI Startup School fellowship via Google for Startups.
, , US
Summary
Completed the McKinsey Forward business leadership and training program.
Delhi, Delhi, India
Summary
Contributed to open‑source projects via the GirlScript Summer of Code initiative.
graph neural networks, scientific machine learning, physics‑informed ML, deep learning, prompt engineering, generative AI, large language models, natural language processing, time series analysis, quantitative analysis.
Python, PyTorch, TensorFlow, scikit‑learn, NumPy, Plotly, Seaborn, Git, GitHub, LangChain, LangGraph.
data science, data visualization, model validation, algorithms, statistical analysis, quantitative finance, risk management.
founder, co‑founder, leadership, entrepreneurship, critical thinking, emotional intelligence, teamwork.