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Research Interests
Embodied AI, large language models, vision-language-action policies, robot
learning, sample-efficient imitation and reinforcement learning, sim-to-real transfer.
Education
University of Texas at Austin, Austin2024 — present
Graduate Student (M.S. → Ph.D.), Robotics — Grounded Embodied Learning (GEL) Lab
- Research on LLM-driven planning and vision-language-action models for manipulation.
Stony Brook University2020 — 2024
B.S. in Computer Science, Honors
- Undergraduate thesis: sim-to-real transfer for tabletop manipulation.
Research in Progress
Closed-loop LLM planning for long-horizon manipulation
Ongoing — manuscript in preparation
Data-efficient vision-language-action policies
Ongoing
Calibrated uncertainty for embodied agents
Course project (exploratory)
Experience
Research InternSummer 2025
Embodied AI startup
- Scaled vision-language-action pretraining across a fleet of mobile manipulators.
Graduate Student Instructor2024 — present
University of Texas at Austin — Intro to Robot Learning
Selected Open Source
- tinyVLA — minimal PyTorch vision-language-action policy (~1.8k★).
- armbench-lite — laptop-scale manipulation benchmarks used in two grad courses.
- concho-bot — an LLM agent that reports daily weather and Concho River levels.
Skills
Python, PyTorch, JAX, ROS2, MuJoCo, Isaac Sim, C++; distributed training; real-robot data collection.
Awards
- Graduate fellowship, University of Texas at Austin (2024)
- Undergraduate research award (2023)
This is a personal portfolio CV.