🎓 B.A. Computer Science & B.S. Engineering Mathematics and Statistics @ UC Berkeley (2023-2027)
🔬 Research: Autonomous Vehicles, AI Alignment, Robotics, Control Systems
🏎️ Currently: Autonomous Engineer @ Formula Electric Berkeley | Undergraduate Researcher @ Berkeley Deep-Drive Lab
| Lab | Role | Focus |
|---|---|---|
| Formula Electric @ Berkeley | Autonomous Engineer | MPC controllers, ROS2, trajectory planning |
| Berkeley Deep-Drive Lab | Undergraduate Researcher | AV safety, ML planners, GNNs for intersection analysis |
| Harvard University | Research Assistant | LLM safety & alignment in cybersecurity |
| Google DeepMind | Math & Data Researcher | Olympiad math problems for LLM reasoning |
| Quantum Devices Lab (Sipahigil) | Math Researcher | MEMS variable capacitor specifications |
Languages: Python, Java, C, C++, JavaScript, Ruby, OCaml, RISC-V, MATLAB
Frameworks: ROS2, PyTorch, TensorFlow, OpenCV, Keras, Pandas, FastAPI, SQL
Research: Mathematical Modeling, Algorithm Design, Statistical Analysis, Scientific Computing
Python, YOLOv8, OpenCV, FastAPI, WebSockets
- Fine-tuned YOLOv8 for workplace safety detection (+10% accuracy)
- Real-time pipeline processing video at <100ms latency
- AI voice agent (Vapi + ElevenLabs) for automatic manager alerts
RISC-V Assembly, Machine Learning
- Neural network in pure RISC-V assembly (matrix ops, ReLU, argmax)
- Optimized memory management without ML frameworks
C, SIMD, OpenMP, MPI
- Achieved 2.93x speedup using AVX, OpenMP, and MPI
- Resolved concurrency bottlenecks for video workloads
OCaml, x86-64 Assembly, C Runtime
- Full compiler pipeline with parsing, AST, and x86-64 codegen
- Optimizations (constant propagation, inlining, CSE) for 2.5x speedup
CS61A CS61B CS61C CS70 CS164 CS170 CS176 CS177 EECS126 Math104 Math110 Math128A Math172 EE128
Self-Study: Andrew NG ML/DL Specialization, ROS2, Intro to RL, MPC