Danny Klein

About

Robotics engineering intern and mechanical engineering master’s student who bridges hardware testing and ML/ROS2 software: built a PyTorch model to map motor test data across motor types using electrical specs (Kv/Kt/R/L) and validated it against new motors to meet strict torque/thermal accuracy targets. Also integrated CNN-based perception into ROS2 for real-time navigation and implemented MPC with time-synchronized multi-topic messaging to avoid stale-data control issues.

Experience

Education

Key Strengths

• Built motor performance models combining test data, motor specs, and ML to predict torque/thermal behavior for untested motors
• Delivered measurable ML accuracy targets (≤15% temperature, ≤10% torque) and validated generalization with separate new motor tests
• Real-time ROS2 ML integration: deployed vision model as a ROS node feeding motion planning via pub/sub under 100ms latency
• Latency and jitter reduction in real-time pipelines (batch/preprocessing optimization, shared memory, timestamping)
• Robust multi-sensor synchronization for control (time-synchronized filtering to prevent MPC acting on stale data)
• Cross-disciplinary robotics execution (CAD fixture design, electronics wiring, test planning, data collection, and software modeling)

Reference Highlights

• Designed the test setup without problems
• Delivered results exactly as needed
• Proactive problem identification and resolution
• Strong judgment on model complexity (not over/under-complicating)
• High-quality code for experimental work
• Works independently end-to-end
• Reliable and follows through on commitments
• Punctual (showed up on time every day)
• Communicative