Greater Bay Area Topology Conference 2026: Topology of Manifolds & Applied Topology

11月21–23日, 2026 | 大湾区大学（松山湖校区） | 东莞松山湖

GBATC2026

The theme of GBATC 2026 is Topology of Manifolds & Applied Topology. It aims to promote exchanges among experts and scholars in topology and related fields within the Guangdong–Hong Kong–Macao Greater Bay Area and across regions, provide a platform for communications and collaborations.

The organizers gratefully acknowledge the support from Great Bay University

Basics

Invited Speakers

Schedule

Nov. 21

时间 报告题目 报告人
08:40–09:25TDTD
09:25–09:55合影与茶歇
09:55–10:40TDTD
10:40–11:00茶歇
11:00–11:45TDTD
11:45–14:00午餐
14:00–14:45TDTD
14:45–15:05茶歇
15:05–15:50TDTD
15:50–16:10茶歇
16:10–16:55TDTD
17：30—晚宴

Nov. 22

时间 报告题目 报告人
09:00–09:45TDTD
09:45–10:05茶歇
10:05–10:50TDTD
10:50–11:10茶歇
11:10–11:55TDTD
11:55–14:30午餐
14:30–15:15TDTD
15:15–15:35茶歇
15:35–16:20TDTD
17:00– 晚餐

Nov.23

时间 报告题目 报告人
09:00–09:45TDTD
09:45–10:05茶歇
10:05–10:50TDTD
10:50–11:10茶歇
11:10–11:55TDTD
11:55–12:00会议闭幕

Talk Information

(hint: click bolded talk titles to show abstracts)

刘健（重庆理工大学）

Persistent Local Laplacian Prediction of Protein–Ligand Binding Affinities Accurate prediction of protein–ligand binding affinity remains a central challenge in structure-based drug discovery. The effectiveness of machine learning models critically depends on the quality of molecular representations, for which advanced mathematical frameworks provide powerful tools. In this work, we employ a novel mathematical theory, termed the persistent local Laplacian (PLL), to construct molecular descriptors that capture localized geometric and topological features of biomolecular structures. The PLL framework addresses key limitations of traditional topological data analysis methods, such as persistent homology and the persistent Laplacian, which are often insensitive to local structural variations, while maintaining high computational efficiency. The resulting molecular descriptors are integrated with advanced machine learning algorithms to develop accurate predictive models for protein–ligand binding affinity. The proposed models are systematically evaluated on three well-established benchmark datasets, including PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016, demonstrating consistently strong and competitive predictive performance. Computational results show that the PLL-based models outperform existing approaches, highlighting their potential as a powerful tool for drug discovery, protein engineering, and broader applications in science and engineering.

李平（复旦大学）

Topological obstructions to geometric positivity on Calabi-Yau manifolds 一般认为卡-丘流形的拓扑型具有较强的刚性，即不能承载某些正性或负性几何结构。在这个报告中我将回顾一些相关背景并在某些情形给出一些不存在结果来支撑这个刚性猜测。

朱一飞（南方科技大学）

Towards a topological trinity for speech intelligence Over more than a decade of interdisciplinary research in topological analysis and deep learning with image and video data, Carlsson and colleagues have established a paradigm for detecting and utilizing their topological distributions and dynamics that incorporate the three interacting areas of data science (of signals), artificial intelligence (of machines), and neuroscience (of humans). In this talk, I’ll report on progress towards topologically understanding speech data both by analogy and in contrast. Specifically, we integrate topological time series analysis with machine learning to capture structural features in consonant classification, while enhancing interpretability and robustness against noise compared to traditional techniques. This is joint work with Zeyang Ding, Pingyao Feng, Qingrui Qu, Siheng Yi, Zhiwang Yu, and Haiyu Zhang.

Organizers

Participants

Others

If you have any questions, comments or suggestions, please contact us at gba.topology@outlook.com.

Registrations will still be accepted after October 20, and assistance with hotel reservations will be provided; however, on‑campus hotel accommodation cannot be guaranteed.