Rooted in the School's interdisciplinary ecosystem
A Fertile Groundfor Theory
SDS connects computer science, statistics, operations research, AI, machine learning, big data, and decision systems, giving theory a broad data-science setting.
SDS Theory Group
Rigorous algorithmic and mathematical foundations fordata, learning, optimization, and decision systems.
We are a focused theory community at the School of Data Science, CUHK-Shenzhen. Our research connects algorithms, optimization, learning, economics, and discrete mathematics with the foundational questions behind modern data-driven systems.
Why SDS Theory Group?
Solid backgrounds and international perspective
A Well-GroundedResearch Team
Members bring rigorous theory training and international perspective, with diverse but complementary research focuses that connect mathematical and algorithmic thinking to foundational questions in data science.
From strong problem solving to research maturity
Human-CenteredResearch Training
Close advising, sustained discussion, and real research problems help students develop rigorous habits and independent taste, growing into young scholars who identify problems, distill questions, and advance research.
Open exchange and sustained encounters
A Lively Theory Community
Theory grows through discussion. Seminars, reading groups, student meetings, visitor talks, informal problem sessions, and cross-area collaborations build a steady academic rhythm.
Research Areas
01
Algorithms for Large-Scale Data and Complex Networks
Theoretical foundations for graph algorithms, dynamic and streaming models, online algorithms, data structures, and database and data mining theory.
Graph algorithmsDynamic algorithmsStreaming algorithmsOnline algorithmsData structuresDatabase theoryData mining theory
02
Optimization, Learning, and Statistical Foundations
Mathematical foundations for optimization, machine learning theory, statistical foundations, and theoretical data science.
OptimizationMachine learning theoryStatisticsMathematical foundationsTheoretical data science
03
Economics, Computation, and Decision Making
Theoretical foundations for mechanism design, information design, algorithmic game theory, learning decision-makers, and decision systems.
Mechanism designInformation designAlgorithmic game theoryLearning decision-makersDecision systems
News
2026-09-10
One paper by Prof. Tao Lin accepted to WINE 2026
Prof. Tao Lin’s paper "Does AI Help or Harm? Endogenous Information Acquisition with AI Advice" has been accepted to the Conference on Web and Internet Economics (WINE 2026).
2026-09-10
One paper by Prof. Jingbang Chen accepted to ICDE 2027
Prof. Jingbang Chen’s paper "Scalable Algorithm for Dynamic Quasi-clique Detection" has been accepted to the IEEE International Conference on Data Engineering (ICDE 2027).
2026-09-07
One paper by Prof. Jingbang Chen accepted to ISAAC 2026
Prof. Jingbang Chen’s paper "Faster Distance Oracles for Dynamic Interval Graphs" has been accepted to the International Symposium on Algorithms and Computation (ISAAC 2026).
2026-06-26
One paper by Prof. Jingbang Chen accepted to ESA 2026
Prof. Jingbang Chen’s paper "Nearly Optimal Internal Dictionary Matching" has been accepted to the European Symposium on Algorithms (ESA 2026).
2026-06-11
Website officially launched
The SDS Theory Group website is now live as a public home for members, students, events, news, and contact information.
2026-05-19
Three papers by Prof. Tao Lin accepted to EC 2026
Prof. Tao Lin has three works accepted to ACM Conference on Economics and Computation (EC 2026): "Information Design with Large Language Models", "The Price and Complexity of Explainable Information Design", and "Gradient Dynamics in First-Price Auctions: Iterative Strategy Elimination via Cubic Potentials".
2026-05-01
One paper by Prof. Aditi Dudeja accepted to CCC 2026
Prof. Aditi Dudeja’s paper "Frontier Space-time Algorithms Using Only Full Memory" has been accepted to Computational Complexity Conference (CCC 2026).
2026-04-29
One paper by Prof. Aditi Dudeja accepted to PODC 2026
Prof. Aditi Dudeja’s paper "Distributed Stochastic Graph Algorithms" has been accepted to ACM Symposium on Principles of Distributed Computing (PODC 2026).
Academic Activities
SDS Topical SeminarCompleted2 photos
Local Search for Clustering in Almost-Linear Time
Fri, Jul 31, 2026, 11:00 AM-12:00 PMProf. Yaonan Jin, Hong Kong University of Science and TechnologyRoom 401, Dao Yuan BuildingHost: Prof. Tao Lin
SDS Theory Seminar
The SDS Theory Seminar hosts research talks and discussions in algorithms and the theoretical foundations of data science.
Theory Reading Group
Reading groups will support shared learning across algorithms, optimization, economics, and data science foundations.
Student Research Meeting
Student meetings will provide a regular space for research updates, feedback, and early-stage ideas.
Visitor Talks
The group welcomes short visits and informal research discussions with external scholars.
Informal Problem Sessions
Informal sessions will help students and faculty explore early-stage questions before they become polished projects.
Cross-Area Collaborations
Cross-area collaborations connect theory with data science, operations research, statistics, learning, and decision systems.