Experience

Research Scientist · Generative Modeling & ML Systems

• Generative models for visual media: multimodal conditioning, training, evaluation, and inference.
• Experimentation and evaluation infrastructure for large-scale generative systems.

Research Engineer · Data for Embodied AI

• Shipped Embody3D: QA tooling and annotation coordination across 1.6M videos, curating 500 hours of high-quality motion data adopted by internal training teams.
• Built automated quality-filtering algorithms that removed annotation bottlenecks from the pipeline.
• Cut keypoint tracking error by 75% with a self-supervised data-generation pipeline.

Research Engineer Intern · 3D Foundation Model Training

• Built a pipeline converting synthetic mesh assets into relightable Gaussian splats for 3D foundation model training.
• Wrote custom CUDA kernels replacing spherical harmonics with neural features; scaled to 32 nodes, taking per-asset processing from 6 minutes to 30 seconds.

Research Scientist Intern · Explainable Recommendation

• Counterfactual explanations for visual recommenders: minimal image-embedding perturbations, grounded in language with CLIP.
• Published at WWW 2024 (paper); USPTO patent filed.

Machine Learning Intern · Document Understanding

• End-to-end multilingual invoice understanding: table extraction with object detection, field extraction with LayoutLMv2.

MS in Robotics · GPA 4.11/4.0 · Advisor: Prof. Ioannis Gkioulekas

• Coursework: Deep Learning Systems, Learning for 3D Vision, Robot Learning, Physics-based Rendering.

BTech in Electrical Engineering · Minor in Machine Learning · CGPA 9.24/10

• Coursework: Modern Computer Vision, Machine Learning, Computational Photography.