Professional Summary
Download CVI'm an MS CS student at NYU (graduating May 2027) and am currently taking Yann LeCun's deep learning course. Before NYU, I spent two years as a data scientist at Jio Platforms, building models that classify animal behavior from sensor data across 150K+ cattle. This past summer, I interned in computer vision and ML at Applied Materials. I've also trained Transformer and LSTM models up to 201M parameters to study scaling laws. My interests are world models, how AI agents decide when to act versus defer to humans, and evaluation methods that don't fool us.
Education
Master of Science - Computer Science (GPA: 3.83)
New York University, New York
September 2025 – Expected May 2027
Bachelor of Technology - Computer Science
NIT Karnataka, Mangalore, India
July 2019 – May 2023
Experience
Computer Vision and Machine Learning Intern
June 2026 – August 2026
• Reduced missed defects by 67% (held-out coverage 95.1% to 98.4%) at a fixed false-alarm budget, or false alarms 5.4× at matched recall, via bilevel Bayesian optimization of wafer-inspection recipes
• Discovered an image feature separating defects from nuisance at AUC 0.956 vs. the engine’s best 0.693 (200/200 bootstrap wins); showed Bayesian search recovers the production recipe in a median of 13 evaluations vs. 103
• Caught a coverage metric reporting a false 100% (85% of on-defect detections were on the wrong die), fixed it, and shipped 1,702 automated tests and an offline React + FastAPI dashboard
Lead Developer - Data Science and AI
January 2026 – Present
Translating the Office of Faculty Affairs Workflow at New York University with automation and AI integration
Data Scientist
June 2023 – July 2025
Improved the event detection precision from 68% to 91% and recall from 76% to 95% by developing ML models from scratch.
Advanced Application Engineering Analyst Intern
June 2022 – July 2022
Conducted a comprehensive observability study to improve monitoring, performance, and reliability across enterprise applications.
Featured Projects
Investigated neural scaling laws on symbolic music by training 9 decoder-only Transformer and LSTM models (844K–201M parameters) on 100M+ tokens of ABC notation from 54K+ folk tunes
Ranked 1st in the Kaggle competition by achieving 96%+ accuracy on US state classification with a 7-model ensemble strategy
A comprehensive object detection system implementing YOLO (You Only Look Once) algorithm combined with DeepSORT tracking for real-time multi-object tracking and detection in video streams. Features include pedestrian detection, vehicle tracking, and customizable object classes with high accuracy and performance.
Interests & Focus Areas
Before NYU, I worked as a Data Scientist at Jio Platforms Limited, where I led the development and deployment of machine learning models for time-series cattle heat detection systems. My work directly contributed to a 4x increase in revenue through smarter agricultural technology.
My technical experience spans:
- Building and optimizing production ML models
- Designing and deploying REST APIs using Flask
- Working with time-series data, semi-supervised learning, and ARIMA models
- Collaborating with teams to ship analytics dashboards
Beyond tech, I am passionate about ethical AI, tech-enabled social change, and mentoring student developers. I have served as a Campus Director for the Millennium Fellowship and led inter-NIT workshops focused on student leadership and innovation.
Currently, I am looking for Data Science Internship opportunities for Summer 2026 where I can apply my skills, curiosity, and values to make meaningful contributions.
Please reach out to collaborate