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Shi Li is a Professor in the Theory Group at the Department of Computer Science and Technology, School of Computer Science, Nanjing University. He previously held faculty positions at the University at Buffalo (2015–2023) as Assistant and Associate Professor, and was a Research Assistant Professor at Toyota Technological Institute at Chicago (2013–2015). He earned his Ph.D. from Princeton University in 2014 under Moses Charikar and completed his B.S. in Computer Science and Technology at Tsinghua University, where he was part of Andrew Chi-Chih Yao’s Special Pilot Class. His research lies at the intersection of theoretical computer science and combinatorial optimization, with a focus on the design and analysis of algorithms for problems in clustering, scheduling, network design, facility location, and online algorithms. He also explores learning-augmented algorithms and differential privacy in algorithmic contexts. His work combines deep theoretical insights with practical algorithmic frameworks, often leveraging linear programming relaxations, iterative rounding, and randomized techniques. The recent publications highlight a consistent trend in approximation algorithms, particularly in clustering (e.g., correlation clustering, fair k-set selection), scheduling (e.g., unrelated machine scheduling, load balancing), and robust optimization. His work frequently appears in top-tier theoretical venues such as STOC, FOCS, SODA, and ICALP, with increasing emphasis on fairness, privacy, and efficiency in algorithm design. Best Paper Award of Track A, ICALP 2011 Co-winner of Best Paper Award, FOCS 2012 Invited to Special Issue of SICOMP (FOCS 2017 paper) Best Paper Award, COCOON 2018 Invited to Special Issue of SICOMP (STOC 2019 paper) Best Paper Award of Track A, ICALP 2024 Outstanding Paper Award, SPAA 2024 Shi Li has advised several PhD and master’s students, including Yuda Feng, Han Dai, Zihao Liang, and Jia Ye, and has mentored postdoctoral researcher Ruilong Zhang. He has served on numerous program committees (e.g., STOC, SODA, ICALP) and is an Editorial Board Member of ACM Transactions on Algorithms . He teaches core algorithm courses such as Design and Analysis of Algorithms and Advanced Algorithms , and actively collaborates with researchers worldwide. His lab focuses on theoretical foundations of efficient and fair algorithm design, with applications in large-scale data analysis and distributed systems.

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Combinatorial Optimization
• +11 more

Fang Kong is an Assistant Professor in the Department of Statistics and Data Science at the Southern University of Science and Technology (SUSTech). He earned his PhD in Computer Science from Shanghai Jiao Tong University under the supervision of Prof. Shuai Li and received his Bachelor's degree in Software Engineering from Shandong University. Education: PhD in Computer Science, Shanghai Jiao Tong University (2020-2024) Bachelor's Degree in Software Engineering, Shandong University (2016-2020) Dr. Kong is broadly interested in developing theoretically guaranteed algorithms for sequential decision-making problems, with particular focus on multi-armed bandits and reinforcement learning, as well as their applications in online experimentation and recommendation systems. His research spans theoretical foundations of bandit algorithms, matching markets, influence maximization, and online learning under various feedback structures. He has made significant contributions to the understanding of best-of-both-worlds algorithms that perform well in both stochastic and adversarial environments. His publication record shows a strong trajectory of high-impact work in top-tier conferences including NeurIPS, ICML, ICLR, AAAI, WWW, and AAMAS. His research demonstrates expertise in theoretical machine learning with a focus on bandit algorithms, particularly in matching markets and sequential decision-making problems. His work often bridges theoretical guarantees with practical applications in recommendation systems and online experimentation. Scientific Awards: CCF Doctoral Dissertation Award in Agent and Multi-Agent Systems (2025) Baidu Scholarship (2024) National Scholarship for PhD students (2023, 2022) AAMAS Student Scholarship (2023) Microsoft Research Asia Excellence Award (2022) Dr. Kong actively mentors students at various levels, including PhD and Master's students at SUSTech, visiting students from other institutions, and undergraduate researchers. He serves as a reviewer for top machine learning conferences (ICLR, NeurIPS, ICML, WWW) and journals (IEEE PAMI, TMLR). His teaching includes graduate Machine Learning and undergraduate Artificial Intelligence courses at SUSTech.

• Online Learning
• Reinforcement Learning
• Machine Learning
• +4 more

Shengcai Liu is an Assistant Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology (SUSTech). Holding a PhD in Engineering, he has established himself as a researcher with over 10 publications as first or corresponding author in top artificial intelligence conferences and journals. His academic journey includes research positions at Singapore's Agency for Science, Technology and Research (A*STAR) before joining SUSTech's faculty. Education: PhD in Computer Science and Engineering, University of Science and Technology of China (2014-2020) Bachelor's in Computer Science and Engineering, University of Science and Technology of China (2010-2014) Dr. Liu's research centers on automatic algorithm design and evolutionary approaches to machine learning. He investigates how AI systems can autonomously construct efficient algorithms for complex optimization problems while maintaining robustness and reliability. His work bridges theoretical foundations with practical applications in combinatorial optimization, adversarial machine learning, and large language model development, with particular focus on making algorithm design processes more systematic and effective. His publication record demonstrates a clear trajectory from foundational work on algorithm portfolios to sophisticated integration of evolutionary methods with large language models. Recent work shows increasing focus on textual adversarial attacks, combinatorial optimization, and leveraging large language models as optimization tools themselves. This progression reflects both theoretical depth and practical relevance to current AI challenges. Professional Activities: Project Leader for Huawei-funded research on combinatorial optimization solvers (CNY 400,000) Member of IEEE, IEEE CIS, and AAAI Reviewer for top journals including TPAMI, TEVC, TCYB Conference reviewer for NeurIPS, ICML, AAAI, IJCAI Dr. Liu maintains active research collaborations through the Artificial Intelligence RAMS Technology Innovation Laboratory at SUSTech, where his team focuses on developing next-generation automatic machine learning systems with applications in optimization and security domains.

• Artificial Intelligence
• Evolutionary Algorithms
• Automatic Machine Learning
• +4 more

Peng Zhan is an Associate Professor in the Department of Medicinal Chemistry at the School of Pharmaceutical Sciences, Shandong University, where he has been employed since November 2014. Prior to this, he served as a JSPS Postdoctoral Fellow at Kyoto Prefectural University of Medicine in Japan (2012-2014) and as a Researcher (Lecturer) at Shandong University (2010-2012). His extensive academic background and research contributions have established him as a prominent figure in medicinal chemistry and drug discovery. Dr. Zhan received his educational training entirely at Shandong University: BS in Pharmaceutical Engineering (2001-2005), MS in Medicinal Chemistry (2005-2008), and PhD in Medicinal Chemistry (2008-2010). His academic journey demonstrates a consistent focus on pharmaceutical sciences with progressive specialization in medicinal chemistry. Dr. Zhan's research primarily focuses on the discovery of novel antiviral, anticancer, and neurodegenerative disease-related agents based on rational drug design and combinatorial chemistry approaches. His expertise spans medicinal and pharmaceutical chemistry, synthesis, molecular docking, organic synthesis, nuclear medicine, HIV pharmacology, and molecular modeling. His work demonstrates a strong emphasis on structure-based drug design, particularly targeting HIV reverse transcriptase and capsid proteins, with significant contributions to overcoming drug resistance in antiretroviral therapy. Analysis of Dr. Zhan's recent publications reveals a consistent focus on antiviral drug discovery, particularly for HIV treatment, with increasing exploration of novel mechanisms including protein degradation-based approaches and molecular proximity techniques. His work shows progression from traditional inhibitor development to more innovative therapeutic modalities, with strong emphasis on addressing drug resistance challenges and improving pharmacokinetic profiles. The research spans structural biology, computational chemistry, and medicinal chemistry with practical applications in antiviral therapy. CPA-Servier Young Investigator Awards in Medicinal Chemistry (2016) National Excellent 100 Doctoral Dissertation nominee (2012) Superiority doctor graduation thesis of Shandong Province (2011) Multiple first prizes in Shandong Province for scientific research achievement (2010) President Scholarship of Shandong University, the supreme award for students (2009) Multiple postgraduate scholarships and superiority awards throughout his academic career Dr. Zhan has successfully secured substantial research funding from multiple prestigious sources including the National Natural Science Foundation of China (multiple grants), Research Fund for the Doctoral Program of Higher Education, Science and Technology Development Project of Shandong Province, Postdoctoral Science Foundation of China (both Special and General Programs), JSPS Research Project in Japan, and Independent Innovation Foundation of Shandong University. His research group has produced an impressive volume of scholarly work, with over 500 publications and more than 120,000 reads on ResearchGate, demonstrating significant impact in the field of medicinal chemistry. As a principal investigator, Dr. Zhan leads a productive research laboratory focused on drug discovery, with particular emphasis on antiviral agents. His lab employs advanced techniques including molecular docking, structure-based drug design, combinatorial chemistry, and rational drug design approaches. The team has developed numerous patented compounds targeting HIV, influenza, and other viral infections, with significant contributions to understanding drug resistance mechanisms and developing next-generation therapeutics with improved profiles.

• Medicinal Chemistry
• Drug Discovery
• Antiviral Research
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Hao Wang is an Assistant Researcher in the Department of Computer Science and Technology at Nanjing University, China. He is affiliated with the Reasoning & Learning Group and conducts research at the intersection of data management and machine learning. Bachelor of Mathematics, Nanjing University (2005) Master of Computer Science, Nanjing University (2008) Ph.D. in Computer Science, The University of Hong Kong (2014) His research interests span Data Management and Machine Learning , with recent focus on rank-aware query processing, recommender systems (especially location-based), reinforcement learning, and transfer learning. His work often integrates user behavior modeling, efficient indexing, and scalable algorithms for large-scale data. Hao Wang's publication record shows a consistent trend in solving practical problems in data-intensive AI systems. His recent articles focus on personalized location recommendation , crowdsourced data labeling , distributed learning for imbalanced data , and reinforcement learning transfer . These works appear in top venues such as VLDB, SIGMOD, ICDM, AAAI, and journals like TKDE and GeoInformatica, indicating strong technical depth and interdisciplinary impact. Hao Wang has not been explicitly mentioned as receiving scientific awards in the provided text. He has collaborated extensively with researchers such as Yang Gao, Nikos Mamoulis, and David Cheung. While no formal advisees are listed, his involvement in supervising PhD work (as co-supervisor) and publishing with students suggests an active mentoring role. His research has been supported through academic collaborations and institutional affiliations, though specific grant details are not provided. Hao Wang is a member of the Reasoning & Learning Group at Nanjing University, where he contributes to advancing intelligent systems that combine logical reasoning with statistical learning. The group focuses on foundational and applied aspects of AI, particularly in data mining, knowledge discovery, and autonomous decision-making.

• Data Management
• Machine Learning
• Recommender Systems
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Xiang Ziqing is an Associate Professor at the Department of Mathematics, Southern University of Science and Technology. Their academic journey includes a Ph.D. in Mathematics from the University of Georgia (2014-2019), a postdoctoral research scholar role at Academia Sinica (2019-2022), and a B.Sc. in Computer Science from Shanghai Jiao Tong University. Current affiliation: Southern University of Science and Technology (since 2022) Education: Ph.D. in Mathematics (University of Georgia), B.Sc. in Computer Science (Shanghai Jiao Tong University) Research focuses on Algebraic Combinatorics , Representation Theory , and Number Theory . Key contributions include work on spherical designs, q-Schur algebras, and balanced combinatorial structures. Publications demonstrate expertise in theoretical proofs and geometric configurations. Recent articles analyze explicit constructions, nonexistence theorems, and inequalities in design theory, spanning applications in discrete geometry and algebraic coding. Trends show a consistent exploration of symmetry properties and combinatorial optimization problems.

• Algebraic Combinatorics
• Representation Theory
• Number Theory

Hu Ding is a pre-tenure Professor in the School of Computer Science and Engineering at the University of Science and Technology of China (USTC), where he directs the Data Intelligence, Algorithms, and Geometry (DIAG) research group. He previously held positions as a tenure-track Assistant Professor at Michigan State University (2016-2018) and a Simons-Berkeley Research Fellow jointly at Tsinghua University and UC Berkeley (2015-2016). Education: • Ph.D. in Computer Science, State University of New York at Buffalo (2015) • B.S. in Mathematics, Sun Yat-Sen University (2009) Research Interests: Hu Ding's research focuses on developing efficient algorithms for geometric optimization problems with applications in machine learning, big data, and biomedical imaging. His work bridges theoretical computer science (especially computational geometry) with practical challenges in distributed systems, outlier detection, and high-dimensional data analysis. Key areas include constrained clustering, truth discovery in crowdsourced data, and geometric methods for biomedical image analysis. Publication Trends: His recent publications demonstrate a strong focus on scalable algorithms for high-dimensional geometric optimization, particularly in distributed environments with noisy data. A consistent theme is developing theoretically-grounded solutions with practical efficiency, evidenced by work on sublinear-time algorithms, coreset constructions, and approximation frameworks for problems like k-center clustering and SVM optimization with outliers. Awards and Honors: Young Investigator Award, Ministry of Science and Technology (2021) Simons-Berkeley Research Fellowship (2015-2016) CCF Committee Member for Theoretical CS and Big Data (2021) Grants and Projects: USTC Innovation Group Grant: 'Toward Electronic Design Automation: Theories and Algorithms from AI' (2021) MOST Young Investigator Grant: 'Optimal Transportation in Medical Imaging' (3M RMB, 2021) Research Group: Leads the DIAG group with focus on geometric algorithms for data intelligence. Current team includes 6 PhD students and 15 Master's students working on problems in clustering, distributed optimization, and biomedical applications. Former students hold positions at Alibaba, ByteDance, and academic institutions.

• Algorithms
• Computational Geometry
• Machine Learning
• +4 more

Dr. Wang Zhenkun is an Assistant Professor at the School of System Design and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), with a joint appointment in the Department of Computer Science and Engineering. He earned his PhD in Circuits and Systems from Xidian University in 2016 and held postdoctoral and research fellow positions at Nanyang Technological University (NTU), City University of Hong Kong, and its Shenzhen Research Institute. Research Interests: Multiobjective optimization, artificial intelligence-assisted design automation, supply chain management, deep learning, and UAV traffic scheduling. Grants & Recognition: Principal Investigator (PI) for a National Natural Science Foundation of China (NSFC) youth project and multiple grants; selected as a high-level C-category overseas talent in Shenzhen. Leadership Roles: Associate Editor of Swarm and Evolutionary Computation (JCR Q1); Chair of IEEE CIS Shenzhen Chapter Student Activities; Organizer of EMO 2021 Competition. Teaching: Courses include Fundamentals of Systems Engineering , Machine Learning System Design , and Intelligent Optimization Algorithms . Group Leadership: Founder of the Computational Intelligence and Advanced Manufacturing (CIAM) group, focusing on developing AI-driven optimization algorithms for logistics, manufacturing, and aerospace applications.

• Multiobjective Optimization
• Supply Chain Management
• Artificial Intelligence Assisted Optimal Design
• +13 more