Skills
Domains
Robotics & Control, Computer Vision, Reinforcement Learning, Machine Learning, 2D to 3D Image Reconstruction, Microscopic Imaging, Scientific Computing, Experiment Automation.
Technical
- Languages: C++, Python, MATLAB, Embedded
- Robotics & Control: ROS, MoveIt, real-time control, kinematics & dynamics, trajectory optimization
- Simulation: MuJoCo, PyBullet, Gazebo
- Computer Vision: OpenCV, classical feature pipelines, microscopy image analysis
- ML / DL: PyTorch, NumPy / SciPy, scikit-learn, imitation learning & behavioral cloning
- Tooling: Git, Linux, Singularity, Jupyter, VS Code
Applied Capabilities
- Design end‑to‑end manipulation pipelines (perception → planning → control) for humanoids and 6 DoF arms.
- Denoise depth images and construct height map using reduced-order representations of height maps.
- Build autofocus, raster scanning, and stitching pipelines for biological microscopy experiments.
- Automate data collection, annotation, and evaluation loops for robotics and imaging experiments.
- Build and deploy hardware stack and simulation-to-real pipelines for quadupedal robots.
Comfort Levels
Primary: C++, Python, ROS, MuJoCo, PyBullet, OpenCV, PyTorch
Working: MoveIt, Gazebo, Singularity
Familiar: Alternative simulators, additional CV/ML libraries as needed