Ziyue LI

Professor in Data Mining and Machine Learing

University of Cologne (Germany)

EWI gGmbH (Germany)

Biography

Dr. Ziyue LI (Bonald) is an assistant professor in the Information System Department, WiSo Faculty, University of Cologne. He is also the Chief Machine Learning Scientist at EWI. His research focuses on resource-efficient machine learning models for spatiotemporal decision intelligence. High-dimensional tensor (data efficiency) and self-supervised learning (model efficiency) are the two main streams. His work has been published in top-tier venues (AAAI, SIGKDD, NeurIPS, TMM, etc.), awarded with 8+ best paper awards and also deployed in real industries as well-proven products, especially in smart mobility.

He actively collaborates with academics, i.e., Prof. Fugee Tsung from Hong Kong University of Science and Technology, Prof. Hao Yan from Arizona State University and Prof. Chen Zhang from Tsinghua University, as well as industries, Dr. Rui Zhao, Nokia Bell Labs (Germany), HKSTP, and so on.

Open Positions: I am always looking for students who are interested in spatiotemporal and smart mobility problems with strong coding abilities. Feel free to shoot me an email. The funding sources are here.

Interests

Spatiotemporal Analysis
Resource-efficient ML
Smart Transportation
High-dimensional Data, Tensor
Deep Learning

Education

Ph.D. in Industrial Engineeing and Decision Analytics

Hong Kong University of Science and Technology, Hong Kong
Mechanical and Manufacturing Engineering

Univerisity of New South Wales, Australia
B.Eng in Mechanical Engineering & B.Econ in Finance

Xi'an JiaoTong University, China

Selected Awards and Honors

All Awards»

Peter Luh Young Researcher Award, IEEE Robotics and Automation Society, Runner-up Award (2023)
ACM Travel Grant Award, ACM SIGSPATIAL, Winner (2023)
Best Paper Award, IISE Quality Control and Reliability Engineering (QCRE), Runner-up (2023)
Best Student Paper Award, IISE QCRE, Finalist (2022)
Best Theoretical Paper Award, INFORMS 2021 Data Mining and Decision Analytics Workshop, Runner-up Award (2021)
Best Applied Paper Award, INFORMS 2021 Data Mining and Decision Analytics Workshop, Finalist Award (2021)
2nd Runners-up, HKUST Three Minute Thesis Competition (2021)
Best Student Paper Award, INFORMS 2020 Data Mining Section, Finalist Award (2020).
Best Student Paper Award, INFORMS 2020 Quality, Statistics, and Reliability (QSR) Section, Finalist Award (2020).
Best Conference Paper Award, IEEE International Conference on Automation Science and Engineering (CASE) 2020, Winner (2020).

News

All News»

Nov 2023: One paper accepted to AISTATS 2024
Oct 2023: Two papers were accepted to ACM SIGSPATIAL 2023
Oct 2023: I started to teach the course “Advanced Data Analytics and Deep Learning” for Master Program
Oct 2023: I started to serve as the Sub-Council Member in INFORMS QSR
Oct 2023: I served as Chair in INFORMS Annual Meeting 2023 (Phoenix, Arizona). The two sessions were “Structure Learning from Heterogenous Data” and “Spatiotemporal Decision Intelligence”.
Aug 2023: I served as Chair in IEE CASE 2023 (Phoenix, Arizona), with theme in “Spatiotemporal Decision Intelligence”.
Aug 2023: I served as Chair in SIGKDD (Long Beach, CA), with theme in “Graph Neural Network”.

Experience

Assistant Professor

University of Cologne

Mar 2022 – Present Germany

Researcher in Machine Learning Algorithms and Smart Cities.

Responsibilities include:

Conduct research in spatiotemporal and resource-efficient machine learning
Design and instruct three master courses (Advanced Seminar Machine Learning, Advanced Data Analytics and Deep Learning, and Decision Making under Uncertainty) and one bachelor course (Bachelor Seminar Machine Learning).

Senior Machine Learning Researcher in Smart Mobility

SenseTime Research

Jan 2020 – Mar 2022 Hong Kong

Lead the R&D in AI-driven Smart Mobility.

Responsibilities include:

Led a team of 10 researchers (3 full-time, 5 interns, 1 project manager), conducted front-tier research in smart mobility and spatiotemporal analysis, and delivered 5+ pipelines of AI-driven products, including:
- One machine learning-based traffic data infrastructure platform;
- Traffic Signal Control Systems based on optimization-based and Reinforcement Learning: deployed in an actual city.
- One causal inference-based Traffic congestion management and cause analysis: deployed in an actual city.
- Developed our LLMs for traffic domain knowledge and traffic data analysis.
Corresponding research work published in CIKM, KDD, AAAI, ICLR, AAMAS, TMM, and more, with topics including:
- Self-supervised learning and contrastive learning.
- Reinforcement Learning.
- Resource-efficient domain adaptation, transfer learning, and meta-learning.
Admin and Technical support for business needs: interviewed 100+ applicants, gave 5+ research seminars, and so on.

Cloud Computing Research Scientist

Bell Labs

Sep 2019 – Feb 2020 Stuttgart, Germany

Research Scientist in serverless computing, machine learning system in AWS and KNIX MicroFunctions.

Responsibilities include:

Research in serverless computing, machine learning system based on Amazon Web Service (AWS) and Bell Labs KNIX MicroFunctions.
Conducted serverless machine learning inference (regression, nature language process, image recognition) based on ResNet, MXNET, Tensorflow in AWS and Microfuntions, performance analysis, component profiling and system optimization.

Project Manager

MTR Co.-HKUST Project

Sep 2019 – Aug 2021 Hong Kong

Research Collaboratoin in Big Data Driven Smart Transporation.

Responsibilities include:

Research project collaboration and management with MTR and Computer Science & Engineering Dept, HKUST, developing an Intelligent Transport System (ITS) for smarter and individualized services based on data-driven methods.
Delivered the solution for demand prediction, station clustering, passenger individual pattern mining.