Robotics Engineer focused on Perception & Autonomy
I work across both hands-on robotic systems and simulated environments, with a focus on perception, autonomy, and end-to-end decision-making models.
My experience spans sensor fusion, 3D reconstruction, navigation, and vision-language-action (VLA) models. I have deployed perception and planning stacks on real robots and built scalable simulation pipelines for real-to-sim transfer and policy training.
I enjoy working across the full robotics stack—from data and models to deployment and evaluation— particularly in research-driven and applied autonomy settings.
Experience
Research Assistant
PSI Lab, USC
Oct 2025 – Present
- Built simulated data pipelines in IsaacSim and MuJoCo for real-to-sim VLA training.
- Implemented domain randomization and force-feedback for diverse data generation.
- Integrated heuristic and VLA policies for trajectory collection.
- Open-source real-to-sim data collection framework: OpenReal2Sim
Research Assistant
CPS Vida Lab, USC
Nov 2024 – Nov 2025
- Worked on interpretability and safety analysis for VLA models.
- Improved action success rate by 20% using human-in-the-loop policies.
- Developed imitation learning strategies for multi-agent path finding.
Robotics Engineering Intern
ERIC Robotics
May 2025 – Aug 2025
- Designed real-time railtrack and 3D safety algorithms for AGVs.
- Reduced AMR localization time by 40% using optimized LiDAR pose estimation.
- Deployed CUDA-accelerated perception pipelines on Jetson AGX Orin.
Education
M.S. in Computer Science
University of Southern California
Aug 2024 – May 2026
GPA: 3.9 / 4.0
Coursework: DL for Robotic Manipulation, Robotic Perception, Autonomous Systems, Robotic Learning
B.Tech in Computer Science
MIT Pune
Dec 2020 – Aug 2024
GPA: 3.8 / 4.0
Projects
Digital Twin via 3D Reconstruction
Photogrammetry-based 3D reconstruction pipeline for high-fidelity digital twins, including partial-view scans using Poisson surface reconstruction.
RL-Based Stealth Navigation
Multi-agent reinforcement learning environment with partial observability, achieving 85% task success under adversarial constraints.