Find Academic Supervisors

• Mechanism Design
• Auction Theory
• Behavioral Operations Management
• +3 more

Xiaoshuai Fan is an Associate Professor at the College of Business, Southern University of Science and Technology, where she has been affiliated since 2020. Her academic background includes a PhD in Industrial Engineering and Logistics Management from HKUST (2020) and a Bachelor's in Management from Huazhong University of Science and Technology (2015). Education: PhD (2020), Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology Bachelor (2015), Management, Huazhong University of Science and Technology (Management Innovation Experimental Class) Her research focuses on Mechanism Design and Auction Theory under asymmetric information, with applications to Behavioral Operations Management , Sustainable Operations (particularly poverty, income inequality, and carbon emissions), and Supply Chain Management . Recent work explores pay transparency in behavioral economics, carbon emission control through price commitments, and anti-corruption strategies in procurement systems. The 15 most recent publications highlight trends in asymmetric information theory , collusion-resistant contract design , and equity-focused operations research . Key journals include Management Science , Production and Operations Management , and Manufacturing & Service Operations Management . She serves as a doctoral supervisor and has secured research funding from the NSFC Outstanding Young Scholars Program , NSFC Youth Program , and the Shenzhen Science and Technology Innovation Commission . Her lab actively recruits research assistants and postdocs, emphasizing interdisciplinary collaboration in economic, mathematical, and computational fields.

Jianke ZhuView profile

Professor

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +3 more

Jianke Zhu is a Professor at the College of Computer Science and Technology of Zhejiang University . He obtained his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and conducted postdoctoral research at the BIWI Computer Vision Lab, ETH Zurich . His research focuses on Computer Vision and Machine Learning , with a particular emphasis on 3D scene understanding, LiDAR-based mapping, and neural rendering. Dr. Zhu’s research spans several subfields, including 3D Reconstruction , Semantic Segmentation , Multimodal Learning , and Autonomous Driving . His work integrates Neural Networks , LiDAR Processing , and Uncertainty Quantification to address challenges in real-time and adverse conditions. Selected Recent Trends: 2025 publications highlight his work in Hexagonal Mesh-based Neural Rendering , Instance-aware 3D Scene Understanding , and Efficient Visual Projectors for Multimodal LLMs . Earlier works include Box2Mask for Instance Segmentation (2024) and Token Selection for Point Cloud Learning (2025). Scientific Awards : Senior member of the IEEE Advising and Grants : As a Doctoral Supervisor , he mentors students in advanced topics like LiDAR Odometry and Multi-view Stereo Recovery . His projects have attracted funding for autonomous driving , 3D scene modeling , and neural rendering .

Xuehai QianView profile

Professor

• Computer Architecture
• Computer Systems
• Hardware Security
• +3 more

Xuehai Qian is a Tenured Full Professor in the Department of Computer Science at Tsinghua University since July 2024. Prior to this, he served as an Associate Professor at Purdue University (2022-2024), Assistant Professor at the University of Southern California (2015-2022), and Postdoctoral Researcher at the University of California Berkeley (2013-2015). Ph.D. in Computer Science, University of Illinois at Urbana-Champaign, USA (2013) ME in Computer Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, China (2007) BE in Computer Science and Technology, Beihang University, China (2004) His research focuses on parallel computer architecture , hardware/software co-designed domain-specific architectures for graph analytics and machine learning , hardware security , and quantum computer architecture . He has pioneered scalable cache coherence protocols for atomic block execution, hardware sequential consistency violation detection, and distributed frameworks for graph processing leveraging emerging memory technologies. Xuehai Qian’s publications span computer architecture , graph analytics , machine learning systems , and quantum computing , with a strong emphasis on distributed systems , accelerators , and memory optimization . His work includes novel architectures for graph processing, decentralized training protocols, and ReRAM-based accelerators for deep learning. NSF CAREER Award (2018) ACSIC (American Chinese Scholar In Computing) Rising Star Award (2019) IEEE Senior Member (2019) Hall of Fame inductions: ASPLOS (2018), HPCA (2019), ISCA (2021), MICRO (2021) W.J. Poppelbaum Memorial Award (2013) He has advised students who have received Microsoft Research Lovelace Fellowships , Ph.D. Fellowships , and Facebook Fellowships . His research has been supported by grants such as the NSF SPX project on FPGA-based machine learning platforms and smaller NSF grants. Notably, he has served as an Associate Editor for Science China (Information Sciences) since 2023 and as a Guest Editor for IEEE Transactions on Parallel and Distributed Systems (2019).

Fang KongView profile

Assistant Professor

• Online Learning
• Reinforcement Learning
• Machine Learning
• +4 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.

• Digital Economy
• Social Welfare
• Consumer Behavior
• +6 more

Dr. Shaobo (Kevin) Li is a Tenured Associate Professor at the School of Business, Southern University of Science and Technology (SUSTech), with secondary appointment in the Department of Information Systems & Management Engineering. He holds a PhD in Business Administration from Nanyang Technological University (2019), Master of Business Administration (University of Virginia, 2014), Master of Finance (West Virginia University, 2012), and Bachelor of Business Administration (Lanzhou University, 2011). National High-Level Youth Talent Program awardee Overseas High-Caliber Personnel in Shenzhen City Recipient of 2018 China Scholarship Council’s Excellent Self-Funded Student Scholarship His research focuses on the intersection of digital economy, social welfare, and technology-driven marketing, employing experimental methods, empirical models, and neuroscience. He serves as Associate Editor for the Journal of Business Research and has published in top UTD-24 journals including Journal of Consumer Research, Information Systems Research, and Production and Operations Management. His work examines platform economics, brand management during crises, and consumer behavior related to AI, sustainability, and health decisions. He has led 10+ research projects (National Natural Science Foundation, Ministry of Science and Technology 2030 Program) and received multiple research awards, including Academy of Marketing Science Best Doctoral Dissertation Proposal Award (2019) and Best Paper Awards at China Marketing Science (2020,2024), China Marketing International Conference (2023), and CMAU Annual Conference (2023,2024). Teaching accolades include SUSTech’s Premier Teaching Award, Top 10 Most Popular Professor among undergraduates, and Nanyang Business School’s Best Graduate Teaching Award (2017). Teaches Financial Marketing (undergraduate) Teaches Frontiers of Management & Research Methods (PhD) Actively recruiting research assistants, postdocs, and joint PhD candidates with Hong Kong Polytechnic University

• Quantum Technologies
• Rare Earth Materials
• Solid-State Spectroscopy
• +4 more

Dr. Shuping Liu is a Research Assistant Professor at the Institute for Quantum Science and Engineering , Southern University of Science and Technology (SUSTech), China, since August 2019. Her research focuses on solid-state quantum memory, rare earth crystals, and their applications in quantum storage, communication, and computation. Ph.D. in Materials Sciences (2016), Shanghai Institute of Ceramics, Chinese Academy of Sciences Joint Ph.D. (2015), Institute of Physics, Czech Academy of Sciences Master's (2012), University of Science and Technology of China Bachelor's (2011), Wuhan University of Technology Dr. Liu's expertise spans design, synthesis, and high-resolution spectroscopy of rare earth ion-doped materials, with applications in optical quantum technologies and scintillation ceramics. Her work addresses defect engineering, size reduction of nanoparticles, and optimization of luminescence properties for quantum memory systems. Her publications emphasize quantum-grade nanomaterials (2020–2021), coherent spectroscopy (2019), and scintillator optimization (2014–2017). Key methodologies include defect control, codoping strategies, and nanoscale fabrication, aligning with quantum storage and radiation detection technologies. Dr. Liu has no explicitly listed scientific awards or advisees in the provided text. Her collaborative work spans institutions including CNRS (France), Academy of Sciences of Czech Republic, and Shanghai Institute of Ceramics.

• Materials Science
• Computational Method Development
• Catalytic Mechanism Study
• +3 more

Dr. Li Lei is an Associate Professor and doctoral supervisor in the Department of Materials Science and Engineering at the Southern University of Science and Technology (SUSTech). He holds a BS from the University of Science and Technology of China and a PhD in Chemistry (2016) from the University of Nebraska-Lincoln. From 2016-2020, he conducted postdoctoral research with Prof. Graeme Henkelman at the University of Texas at Austin, where he developed the machine-learning software package PyAMFF. Education : BS (Materials Science, USTC), PhD (Chemistry, UNL) Affiliation : Southern University of Science and Technology, Shenzhen Peacock Plan Category B Talent Research Focus : His work bridges computational method development and catalytic mechanism discovery through: Machine learning algorithms for force field training Long-timescale dynamics simulations of nanomaterials Saddle-point searching methods for reaction pathways Monte Carlo-based catalyst optimization Development of catalytic reaction databases His recent publications in JACS , Nat. Commun. , and ACS Catalysis demonstrate expertise in: Machine learning-enhanced molecular dynamics Electrochemical catalyst design (ORR/OER) in situ reaction mechanism analysis Atomic-scale electrostatic modeling Scientific Contributions : - Led development of PyAMFF machine learning potential framework - Published 27+ SCI papers in top-tier journals - Collaborated with leading computational chemists (Henkelman, Zeng, Francisco) Awards : Shenzhen Peacock Plan Category B Talent Contact : Office: 5th Floor, North Building, College of Engineering, SUSTech Phone: 0755-88015040 Email: lil33@sustech.edu.cn | lei.li@sustech.edu.cn

• Machine Learning
• Quantum Chemistry
• Computational Chemistry
• +3 more

Pavlo O. Dral serves as Associate Professor at Xiamen University's Department of Chemistry, College of Chemistry and Chemical Engineering since 2019, following postdoctoral research at the Max-Planck-Institut für Kohlenforschung. His academic credentials include: BS (2004-2008), National Technical University of Ukraine M.Sc. (2008-2010), University of Erlangen-Nürnberg Mag. (2008-2010), National Technical University of Ukraine Dr. (2010-2013), University of Erlangen-Nürnberg Dr. Dral's research pioneers the integration of Machine Learning with Quantum Chemistry , focusing on developing the MLatom package for atomistic simulations, creating accurate NDDO-based semiempirical methods, and designing hybrid computational approaches. His work targets efficient solutions for complex physicochemical problems through innovative algorithm development. Analysis of his 15 publications (2014-2020) reveals consistent advancement in machine learning applications across quantum chemistry domains, including potential energy surface modeling, excited-state dynamics, and molecular property prediction. Key thematic trends involve hierarchical learning architectures, big data integration for quantum approximations, and methodological innovations in organic semiconductor characterization. Information regarding scientific awards, student advising, research grants, and laboratory teams is not provided in the source documentation.

• Optimization Theory
• Variational Analysis
• Bilevel Programming
• +6 more

Zhang Jin is a Professor at the Department of Mathematics in the College of Science at Southern University of Science and Technology (SUSTech) since December 2024. He also serves as the Associate Vice Director of the Shenzhen National Applied Mathematics Center since February 2023. Education: Ph.D. in Applied Mathematics (2014, University of Victoria); M.Sc. in Operational Research (2010, Dalian University of Technology); B.Art in Journalism (2007, Dalian University of Technology) Research Interests span optimization theory , variational analysis , bilevel programming , and their applications in machine learning , economics , and data science . His work includes convergence analysis of first-order methods , stochastic/robust optimization , and error bound conditions . Recent Publications focus on nonconvex bilevel optimization , gradient-based algorithms , and stochastic programming , with papers in IEEE TPAMI , SIAM Journal on Optimization , and conferences like ICML , NeurIPS , and ICLR . Scientific Awards include the Youth Science and Technology Innovation Award from Guangdong Province (2022) , Youth Science and Technology Award from the Operations Research Society of China (2020) , and Junior Research Award from SUSTech's Faculty of Science (2020) . Students: Supervises Ph.D. candidates like Yixia Song , Peixuan Yang , and Qichao Cao , along with Master's students Yixuan Zhang , Kaiqi Sun , and Feifan Wang . Grants: Leads projects such as the National Key R&D Program (3.2M RMB, 2024-2028) and National Science Fund for Distinguished Young Scholars (2M RMB, 2023-2025) .

• Film and Television Animation
• Creative Photography
• Art History
• +2 more

Xue Hang is a Lecturer in the Animation Department at Tongji University's School of Art and Media, where he has been teaching since 2002. He holds a Master of Science degree in Optics from Fudan University (1993) and has expanded his expertise through international exchanges at Hungary's Academy of Art and Design (1998-1999) and Germany's Bauhaus University (2010). His multidisciplinary career spans physics, visual design, and new media arts. Research interests include: Film and television animation Creative photography Art history Visual aesthetics in digital media Key research projects involve: Teaching reform for high-definition film/TV development Multimedia documentation of historical narratives (e.g., 'Jinggangshan' music-dance epic) Cultural value preservation through modern creative industries Scientific awards highlight: 2021 Special Prize in Chinese Poetry Competition Multiple teaching excellence awards (2008-2014) 1996 Li Guohao Young Faculty Award 2009 Shanghai Teaching Achievement Award (Team member) His 15 most recent publications demonstrate expertise in multimedia production, cultural adaptation, and digital art education, with projects spanning both theoretical and practical domains of new media arts. Notable collaborations include international academic exchanges with Germany and Hungary.

• Electronic Design Automation (EDA)
• Analog/RF Circuit Simulation
• Multi-physics Analysis
• +2 more

Dr. QUAN Chen is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), holding this position since May 2025 after serving as Assistant Professor (2019-2025) and Research Assistant Professor at the University of Hong Kong (2012-2018). A Shenzhen high-level overseas talent, he earned his PhD from the University of Hong Kong and conducts cutting-edge research in electronic design automation. His academic credentials include: Ph.D. from The University of Hong Kong (2010) Master's degree from The University of Hong Kong (2007) Bachelor's degree from Sun Yat-Sen University (2005) Dr. Chen's research pioneers advanced EDA algorithms for large-scale analog/RF circuit simulation, post-Moore multi-physics analysis, and AI-assisted design technologies. His work addresses critical challenges in nanodevice modeling and quantum computing circuits, resulting in over 50 publications in top venues like IEEE TCAD and DAC, plus four Chinese patents. Analysis of his recent publications reveals dominant trends in exponential integrator methods for transient simulation, model order reduction techniques, and physics-informed machine learning for reliability analysis. His work bridges numerical mathematics with practical EDA applications across analog circuits, quantum hardware, and emerging memory technologies. Key recognitions include: Wu Wenjun Artificial Intelligence Science and Technology Award, Second Prize (2020) ICCAD Best Paper Award Nomination (2012) Dr. Chen actively recruits Postdoctoral Fellows, Research Assistants, and Graduate Students while leading major funded projects including NSFC key/general programs and Guangdong Provincial R&D initiatives. His industry partnerships with Huawei, Empyrean, and Guowei Group translate theoretical advances into real-world EDA solutions. He directs a specialized research group at SUSTech focused on computational methods for next-generation circuit design, fostering innovation in simulation algorithms and multi-physics analysis through academic-industry collaboration.

• Solid Mechanics
• Fracture Mechanics
• Soft Matter Mechanics
• +9 more

Hong Wei is a Professor at the Department of Mechanics and Aerospace Engineering , Southern University of Science and Technology (SUSTech) . He also serves as Director of SUSTech Global and Associate Dean of Academic Affairs at Shuli College . Previously, he held tenured positions at Iowa State University and dual appointments at Hokkaido University . Hong Wei earned his B.S. and M.S. in Solid Mechanics from Tsinghua University , followed by a Ph.D. in Engineering Science from Harvard University (2006) . His research spans solid mechanics , soft matter mechanics , dielectric breakdown , smart materials , electrochemical reactions , and surface instability . Current work focuses on phase-field modeling and coupled deformation-transport phenomena in soft materials. His representative publications cover cutting-edge topics in Science Advances , J. Mech. Phys. Solids , and Soft Matter , with trends in hydrogel mechanics , electromechanical coupling , and material failure analysis . Scientific recognition includes being named a Fellow of the American Society of Mechanical Engineers (ASME) , alongside editorial roles in journals like Meccanica and International Journal of Applied Mechanics . Hong Wei has mentored students such as Sun Xingjian (doctoral student), Wang Sicong , Pan Lei , Feng Xianke , and Shen Zhiyuan , who received the Lixue Scholarship . He has led international projects and secured grants from the National Science Foundation (USA) and National Natural Science Foundation of China .

• Ultra-Low Power Analog/Mixed-Signal Circuits
• Biomedical Wearable/Implantable Devices
• Artificial Intelligence of Things (AIoT)
• +5 more

Dr. Huan Hu is an Assistant Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), Shenzhen, China. He holds a Ph.D. from Washington State University (2021) and has worked in industry at Skyworks Solutions and Biotronik/MSEI on low-power circuits for IoT and biomedical applications. Education: Ph.D., Washington State University, 2021 M.S., Oregon State University, 2015 B.S., University of Electronic Science and Technology of China, 2013 His research focuses on ultra-low power analog/mixed-signal integrated circuits for biomedical wearable/implantable devices and artificial intelligence of things, including applications in sensor interfaces, energy-efficient radios, and in-memory computing architectures. Recent publications highlight advancements in time-domain processing , biofuel cell integration , and injection-locked transmitters for IoT and biomedical applications, with awards including Shenzhen Overseas High-level Talent (2022) and IEEE RFIC Best Student Paper Award Nomination (2020). Scientific Awards: Shenzhen Overseas High-level Talent (Class C), 2022 RFIC Best Student Paper Award Nomination, 2020 Alaska Airline Travel Award, 2018 WSU Grand Challenges Fellowship, 2016-2017 OSU Laurels Scholarship, 2014-2015 He seeks motivated researchers for Research Assistant Professor , Postdoctoral Fellow , and Graduate Student positions in his group. Visit his lab page for details.

Qihang LiuView profile

Professor

• Theoretical Condensed Matter Physics
• Computational Materials Science
• Spin-Orbit Coupling
• +5 more

Qihang Liu serves as Professor in the Department of Physics at Southern University of Science and Technology (SUSTech), where he leads a research group focused on theoretical condensed matter physics and computational materials science. Previously Associate Professor at SUSTech (2018-2023), he maintains active collaborations with Peking University and international institutions including University of Colorado Boulder and Northwestern University. Education: Ph.D. in Condensed Matter Physics, Peking University (2007-2012) B.S. in Physics, Peking University (2003-2007) Research Focus: His work centers on symmetry-driven phenomena in quantum materials, particularly spin-group theory for magnetic systems and topological effects induced by spin-orbit coupling. Key contributions include: Development of FINDSPINGROUP program for spin symmetry identification Prediction of unconventional magnetism in antiferromagnets Design principles for quantum anomalous Hall effect in superlattice structures Computational exploration of spin-orbitronics materials Publication Trends: Recent publications (2019-2022) demonstrate consistent innovation in symmetry applications to magnetic topological materials, with emphasis on antiferromagnetic spintronics and computational design of novel quantum states. His work bridges theoretical predictions with experimental realizability in systems like Mn-Bi-Te compounds. Scientific Recognition: SUSTech Young Researcher Award (2021) SUSTech Young Professor Award (2022) Shenzhen Jieqing Talent Project (2022) Stanford Top 2% Scientists (2021, 2022) Grants & Leadership: Principal Investigator for China's National Key R&D Program "Quantum Control and Quantum Information" (2020), securing major funding for quantum materials research. His group actively mentors graduate students while developing next-generation spintronic materials and symmetry-based computational tools. Research Infrastructure: The Liu Research Group maintains specialized computational facilities for first-principles calculations and collaborates with experimental teams to validate theoretical predictions on spin-orbit coupling effects and topological phenomena in low-dimensional systems.

Shan ChenView profile

Assistant Professor

• Cryptography
• Information Security
• Computer Science
• +2 more

Shan Chen is an Assistant Professor at the Southern University of Science and Technology (SUSTech) , Shenzhen, China, with research expertise in Cryptography and Information Security , particularly focused on real-world applications and secure protocols. Ph.D. in Computer Science , Georgia Institute of Technology (2020) MEng in Computer Science and Technology , Tsinghua University (2014) BSc in Mathematics and Applied Mathematics , Beijing Normal University (2011) His research addresses practical challenges in cryptographic protocol design, including security analysis of TLS 1.3 and QUIC, causality preservation in secure messaging, and committing authenticated encryption. Key trends include improving protocol efficiency while maintaining provable security against real-world adversaries. He leads the Applied Cryptography Group and teaches courses like Algorithm Design and Analysis and Discrete Mathematics . Contact: chens3@sustech.edu.cn