Meet Our Scholars
Becoming a part of
the IDS research team
Interested in becoming a part of IDS research team?
Meet Our Scholars
- Decision-making under uncertainty;
- Data-driven decision-making;
- Sequential decision-making;
- OR and ML applications in operations management, and
- smart society
The kinds of potential students I am looking for:
- Who has strong interest in doing quantitative research.
- Who has solid analytical skills.
- Who is self-motivated.
- Who can think critically.
- Who is optimistic towards obstacles in study and life.
- Who has the spirit of dedication to his/her research fields.
I am interested in the theory of complex networks and statistical physics, as well as their applications in urban and social systems. My main areas of focus are:
- Theory of complex networks
- characterization of structure in networks with metadata
- analysis and algorithms for statistical inference with network data
- Statistical physics of urban systems
- structure and dynamics of human mobility
- spatial socioeconomic inequality
My research interests cover computer vision, machine learning, and artificial intelligence, with special emphasis on multimodal AI, vision and language, open-world recognition, visual synthesis, and generative models.
I am looking for self-motivated Ph.D. students to join my group in Spring 2023 or Fall 2023.
- Strong academic background (especially math and programming)
- Passion and interest in research
My research interests are nonconvex, stochastic and robust optimization, with all types of applications including machine learning and data science.I’m hiring Research Assistants and Excellent PhD Students. Students with strong mathematical background (liner algebra, mathematical analysis and probability) are especially welcome!
Research interests: computer vision & graphics, machine learning, embodied artificial intelligence, information theory, multimodal data mining
Join us if you: have solid mathematical modeling and coding skills
Are experienced with: computer vision & graphics, machine learning & robotics
information theory, multimodal data mining, human-machine interaction
And more importantly: are strongly motivated to build fundamentals for embodied AI
I am generally interested in machine learning, stochastic optimization, and graph learning, with a special focus on the theoretical/empirical understanding of the deep learning algorithms. I am also open to any challenging research topic in artifical intelligence and data science.
Potential Students in My Group
- Self-motivated (important!)
- Know how to find answers from literature
- Know how to validate the hypothesis by running simple experiments
- Know how to approach a complicated problem by working on simple examples
- Critical-thinking (important!)
- Know how to find the drawbacks or limitations of a paper
- Think about what’re the reasons leading to the limitations of the prior works
- Know how to find out possible approaches to address the limitations
- Hard-working (important!)
- Concentrate on your project and try to complete it as soon as possible
- Actively ask for help from your lab-mates or advisor if you cannot figure it out for many days
- Do not easily give up
- Background (less important)
- Mathematical background: calculus, linear algebra, matrices and tensors, statistics, optimization, etc.
- Programming: familiar with Python is fine
I am looking for self-motivated Ph.D. students/Research (Remote) Interns to work together on various data mining/machine learning topics.
If you are interested in working with me, please drop me an email. Please include your CV (including your publications, ranking/GPA, anything important) with the email.
For current HKU students, you are also welcome to contact me if you would like to conduct research in my lab.
My research interest is in Natural Language Processing and Deep Learning, with a focus on designing and building conversational natural language interfaces that can help humans explore and reason over data in any application (e.g., relational databases and mobile apps) in a robust and trusted manner.
If you would like to conduct research with my Group, don’t hesitate to drop me an email.
- Robust optimization and distributionally robust optimization
- Algorithmic design and analysis for large-scale optimization (non-convex optimization, manifold optimization, interior-point methods, etc.)
- Applications of optimization in machine learning, operations research and signal processing
Notes for Online Application
Choose “Data Science”, as one of the “Interdisciplinary areas” in the proposed faculty of study, from the drop-down list, and you will be referred to the IDS scholars