skylining

Resume

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

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

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Tien-Ning (Sky) Lee

Applied ML · Full-stack AI · AI Products

Experience

Physical AI Engineer Intern

Classmethod

Jul. 2026 - Sep. 2026

Tokyo, Japan

  • TBD
Physical AIRoboticsAI Integration

BRAIN Quantitative Research Consultant

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

Project Research Assistant | Advisor: Prof. Tsung-Nan Lin (IEEE Fellow)

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

AI Research Intern

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