I am an Assistant Professor (Website) in the Department of Computer Science at National Yang Ming Chiao Tung University (NYCU), starting in Feb. 2026.
I received my PhD degree in Computer Science and Information Engineering from National Cheng Kung University (NCKU) in 2025, advised by Prof. Kun-Ta Chuang in the NetDB Lab.

More details about me ▼

📢 [Recruiting] I am actively seeking motivated undergraduate / Master / Ph.D. students to join our lab. If you are passionate in learning the skills of Data Mining / AI / Data Science / Reinforcement Learning, or interested in AI for Healthcare, Time Series Analysis, LLM Reasoning, Social Network, Recommender Systems and Smart City, please feel free to email me your CV/resume and discuss the possibility of joining us.

NOTICE If you are applying for a Master’s degree, please apply to the Institute of Computer Science and Engineering (資訊工程所甲組) or Data Science and Engieering (數據科學與工程研究所) programs. After you receive an offer of admission, please feel free to reach out!

Research Interests

My research focuses on Data Mining, Reinforcement Learning, Knowledge Graph Embedding/Reasoning, Artificial Intelligence in Healthcare, and Time Series Analysis. I have collaborated with organizations across healthcare, smart energy, integrated circuits, education, etc. More details can be found in my CV.

Recent representative works are below:

CAND: Cross-Sign Ambiguity Inference for Early Detecting Nuanced Illness Deterioration [PDF] [ArXiv]

Lo Pang-Yun Ting, Zhen Tan, Hong-Pei Chen, Cheng-Te Li, Po-Lin Chen, Kun-Ta Chuang, Huan Liu

TS4H @ NeurIPS 2025 (🏆 Best Paper Award)

We propose a Bayes-based representation learning that models how knowledge from one vital sign influences another, enabling precise detection of fine-grained worsening signals for patients.

knowledge graph embedding time series analysis AI in healthcare

early detection nuanced illness deterioration vital sign data wearable device

Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes [PDF] [ArXiv]

Lo Pang-Yun Ting*, Chengshuai Zhao*, Yu-Hua Zeng, Yuan Jee Lim, Kun-Ta Chuang, Huan Liu

GenAI4Health @ NeurIPS 2025

Presents an RL-driven retriever that evolves with knowledge and guides LLMs on when to take smart leaps, enabling deeper information discovery for clinical note generation.

knowledge graph embedding knowledge graph reasoning reinforcement learning AI in healthcare

large language models retrieval augmented generation clinical note generation reasoning path exploration

Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System [PDF] [ArXiv]

Lo Pang-Yun Ting, Hong-Pei Chen, An-Shan Liu, Chun-Yin Yeh, Po-Lin Chen, Kun-Ta Chuang

IJCAI 2025

Transforms patients’ wearable data into a KG to model intra-vital sign changes and proposes a transition-aware embedding that reinforces relationships among vital sign subsequences and quantifies missing data impacts.

knowledge graph embedding time series analysis AI in healthcare

early detection nuanced illness deterioration vital sign data wearable device

DeCo: Defect-Aware Modeling with Contrasting Matching for Optimizing Task Assignment in Online IC Testing [PDF] [ArXiv]

Lo Pang-Yun Ting, Yu-Hao Chiang, Yi-Tung Tsai, Hsu-Chao Lai, Kun-Ta Chuang

IJCAI 2025

Proposes a defect-aware representation learning to model co-failure relationships among IC modules and enables the identification and assignment of capable engineers to handle IC failures.

knowledge graph embedding

graph structure modeling task assignment ATE logs

Towards Hierarchical Multi-Agent Decision-Making for Uncertainty-Aware EV Charging [ArXiv]

Lo Pang-Yun Ting, Ali Senol, Huan-Yang Wang, Hsu-Chao Lai, Kun-Ta Chuang, Huan Liu

IEEE BigData 2025

Designs a hierarchical multi-agent RL structure with an uncertain-aware critic mechanism to control bidirectional energy charging actions and improve the evaluation of power-level decisions under real-world dynamic factors.

reinforcement learning time series analysis

hiearachical reinforcement learning uncertain-aware control EV bidirectional charging

Online Spatial-Temporal EV Charging Scheduling with Incentive Promotion [PDF]

Lo Pang-Yun Ting, Huan-Yang Wang, Jhe-Yun Jhang, Kun-Ta Chuang

ACM TIST 2024

Proposes an online spatio-temporal charging scheduling framework that leverages preference embedding, incentive mechanisms, and explore–exploit strategies to improve user acceptance while minimizing overall charging costs.

knowledge graph embedding reinforcement learning time series analysis

spatial-temporal scheduling online knapsack problem EV charging control

📢 News

💼 Experiences

Education

• 2020.09 - 2025.06 Ph.D.
  Computer Science and Information Engineering, National Cheng Kung University
• 2017.09 - 2019.06 M.S.
  Computer Science and Information Engineering, National Cheng Kung University
• 2013.09 - 2017.06 B.S.
  Computer Science and Information Engineering, National Cheng Kung University

Work

• 2026.02 - current Assistant Professor
  Dept. of Computer Science, National Yang Ming Chiao Tung University (➤ Hsinchu, Taiwan)

• 2025.08 - 2025.12 Postdoctoral Researcher
  Computer Science and Information Engineering, National Cheng Kung University (➤ Tainan, Taiwan)

• 2023.08 - 2025.01 Visiting Scholar
  School of Computing and Augmented Intelligence, Arizona State University (➤ Tempe, Arizona, United States)
  Data Mining and Machine Learning Lab (DMML), led by Prof. Huan Liu

• 2019.10 - 2020.09 Exchange Student
  Graduate School of Information Sciences, Tohoku University (➤ Sendai, Miyagi, Japan)
  Kato Laboratory, led by Prof. Nei Kato

Service & Talks

• Program Committee (PC) Member / Reviewer
  ACM KDD (2025 Cycle 1&2, 2026 Cycle 1&2), WWW (2026), NeurIPS (2025), CIKM (2025), PAKDD (2026), SDM, AAAI, IEEE ICDM, KAIS, TKDE, TKDD, ACM TIST

• Lectures & Invited Talks

  • From Single-Agent Optimization to Multi-Agent Coordination: A Perspective on Reinforced Control for EV Charging Systems
    TSMC, AI4BI Technical Seminar (2025)
  • Workshops of Big Data Analysis
    Information Technology Curriculum Center (資訊科技學科中心) (2021, 2022)
  • Deep Learning Foundations
    NCKU, Data Science and Artificial Intelligence Course (2021, 2022)
  • Introduction to Monte Carlo Methods
    NCKU, Artificial Intelligence Camp (2018)

Honors & Programs

• Best Paper Award, TS4H workshop @ NeurIPS 2025
• Honorary Membership, The Phi Tau Phi Scholastic Honor Society, 2025
• Graduate Student Study Abroad Program, Ministry of Science and Technology (MOST), 2023
• Oustanding Award, Grand Review and Competition for PhD Student and Postdoctoral Research Fellow Research (NCKU), 2022
• Honorary Membership, The Phi Tau Phi Scholastic Honor Society, 2020
• Cooperative Laboratory Study Program (COLABS), Tohoku University, 2019