Resume

Experience across applied ML, full-stack AI, and AI research.

A web version of my resume, with a downloadable PDF available for sharing.

Tien-Ning (Sky) Lee

Applied ML · Full-stack AI · AI Products

Classmethod

Jul. 2026 - Sep. 2026

Tokyo, Japan

Developed Physical AI applications for Unitree G1 and Go2 robots, including a visual inspection agent that converts live camera input into object detections, risk assessments, and operator alerts.
Integrated Unitree SDK2, CycloneDDS, and persistent GPU inference services for continuous camera ingestion, structured outputs, deterministic geometry policies, and automated inspection reports.
Profiled the end-to-end vision pipeline and optimized image processing and camera capture, reducing overlay latency by 92% and improving Go2 image updates from 3-4 seconds to approximately 1 second.
Evaluated vision-language models and built a Grounding DINO monitoring pipeline with ROI-based risk analysis, real-time overlays, inference telemetry, process controls, and audio warnings.

Physical AIRoboticsUnitreeComputer Vision

WorldQuant

Apr. 2024 - Jul. 2025

Remote

Found and submitted trading alphas, mathematical models that seek to predict future price movements of financial instruments, with performance evaluated in real-world stock markets.
Developed optimization algorithms using the WorldQuant API and stock market data to automate the mining of trading alphas.

Quant ResearchOptimizationFinancial Data

Artificial Intelligence Lab, National Taiwan University

Jun. 2024 - Dec. 2024

Taipei, Taiwan

Co-developed trustworthy AI recommendation systems with PhD students for BankTaiwan Life Insurance, focusing on minimizing default risk through anti-recommendation algorithms.
Processed large-scale real-world insurance data and engineered robust features to enhance model generalization.
Implemented and evaluated collaborative filtering and anomaly detection models using PyTorch, optimizing for both ranking accuracy and risk mitigation.
Collaborated with stakeholders to officially deploy the model after successful enterprise testing.

Recommender SystemsPyTorchRisk Control

National Institute of Information and Communications Technology (NICT)

Jul. 2023 - Sep. 2023

Tokyo, Japan

Conducted research to enhance the resilience of model-based network intrusion detection systems for IoT devices by investigating advanced adversarial attacks and robust defense strategies.
Engineered and trained diverse NIDS models, including Logistic Regression, kNN, Random Forest, Autoencoder, 1D-CNN, and 2D-CNN, using scikit-learn and TensorFlow on real-world TON_IoT datasets.
Developed a SHAP-guided feature selection algorithm that reduced input features by 65% while maintaining model performance and interpretability.
Investigated adversarial training using SHAP-informed attacks, providing insights for NICT researchers' ongoing studies on adversarial training and defense strategies.

Adversarial MLNIDSSHAP