Teaching & Learning

Dr. Tian Wu (Talia) holds a PhD with a research focus in statistical genetics and is currently a Lecturer in the Department of Mathematics. She taught GFQR1045, “Making a Smart Decision”, for three years and supervised a GE capstone project with an undergraduate mathematics major that led to a poster presentation at an international conference on Bioinformatics. She co‑developed the GE capstone course GCAP3226, “Empowering Citizens through Data: Participatory Policy Analysis for Hong Kong”, with Dr. Simon Wang of the Language Centre, where she taught mathematical modelling for public management and policy and guided students in prompt‑based, AI‑assisted coding. In addition to her GE contributions, Dr. Wu teaches a faculty-wide course in the Faculty of Science and two master’s-level core courses, one of which is cross-listed as an undergraduate major elective. She also supervises master’s dissertations.

Dr. Wu teaches several GE courses, including “GFQR1045 Making a Smart Decision”, “GCAP3017 Interdisciplinary Independent Study”, and the newly launched “GCAP3226 Empowering Citizens Through Data: Participatory Policy Analysis for Hong Kong”.

The Panel appreciates Dr. Wu for her innovative pedagogical approach, which empowers students to apply complex quantitative skills to solve diverse, real-world problems. She effectively applies her expertise to make complex mathematical concepts accessible and relevant to students from diverse academic backgrounds. The Panel is particularly impressed by the dynamic and engaging classroom environment she cultivated, which is guided by her teaching philosophy of building students’ self-efficacy through successful experiences.

Furthermore, the Panel commends Dr. Wu’s commitment to continuous pedagogical improvement. She provides clear evidence of how she iteratively refines teaching materials, such as scaffolding complex theorems with relatable examples and redesigning slides to stimulate students’ thinking. Her high level of participation in GE-related activities, including the GE Buddy Programme, has allowed her to gain valuable insights from peers and to acquire transdisciplinary perspectives through collaboration. The Panel is pleased to observe the positive impact on her students, as shown through their project work and conference presentations, which highlights their intellectual and personal growth.

In recognition of her innovative pedagogies, her data-driven commitment to course refinement, and the demonstrable positive impact on her students, the Panel recommends Dr. Wu for the GE Teaching Award (Early Career) to honour her excellent performance.