Find Academic Supervisors

• Empirical Legal Studies
• Law and Economics
• Law and Technology
• +6 more

Jerrold SOH is an Assistant Professor of Law at the Yong Pung How School of Law, Singapore Management University (SMU), serving concurrently as Deputy Director of the Research Programme in Computational Law at the Centre for Digital Law. He holds an LL.M. from Harvard University and dual first-class honors degrees in Law (LL.B.) and Economics (B.Soc.Sci.) from the National University of Singapore (NUS). His academic journey includes roles as Deputy Director at the Centre for Computational Law (2021–2024) and Lecturer of Law at SMU (2018–2020). SOH’s research focuses on empirical legal studies, law and technology intersections, and computational methods in jurisprudence. He investigates topics such as judicial citation patterns, algorithmic accountability, AI-driven legal systems, and tort liability for automated vehicles. His work often bridges legal theory with data science, exemplified by projects like legal text formalization, network analysis of case law, and blockchain applications in property law. SOH has published widely in journals like the Asian Journal of Comparative Law , Frontiers in Physics , and American Business Law Journal , with recent work addressing AI legal implications, blockchain land transfers, and antitrust regulation. He is a Chief Data Scientist at Lex Quanta Pte Ltd and has received honors such as the SMU START Overseas Postgraduate Scholarship and multiple Dean’s Lists at NUS. His professional contributions span computational law infrastructure development, legal innovation advocacy, and educational initiatives in technology law. Current research priorities include AI ethics in legal contexts, control-centric liability frameworks, and empirical analyses of judicial decision-making processes.

• Auction Theory
• Mechanism Design
• Game Theory
• +3 more

Dr. Yingkai Li is an Assistant Professor in the Department of Economics at the National University of Singapore (NUS). His research focuses on the intersection of economic theory and computer science, particularly in mechanism design, auction theory, and information economics. He has co-authored over 50 publications in top conferences and journals such as the ACM Conference on Economics and Computation (EC), IEEE Transactions on Information Theory, and the Journal of Economic Theory. Key research areas include algorithmic information disclosure in auctions, dynamic information acquisition incentives, robust persuasion mechanisms, and fair resource allocation with externalities. His work often combines theoretical analysis with computational methods, addressing challenges in optimal revenue design, strategic behavior modeling, and algorithmic fairness. Selected recent contributions include developing polynomial-time approximation schemes for optimal auction design with information structures (EC 2024), analyzing contracts for agents with misspecified beliefs (2024), and designing mechanisms robust to aftermarket effects (2023). His research has been supported by collaborations with leading institutions and frequent publications in interdisciplinary venues.

Qin HanzhangView profile

Assistant Professor

• Operations Research
• Supply Chain Analytics
• Inventory Control
• +3 more

Qin Hanzhang is an Assistant Professor in the Department of Industrial Systems Engineering and Management at the National University of Singapore (NUS). He is affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His research focuses on advancing optimization and decision-making methodologies, particularly in supply chain analytics, inventory control, and machine learning applications. His work bridges theoretical foundations with practical challenges, addressing topics such as fairness in algorithms, adaptive resource allocation, and privacy-enhanced predictive frameworks. He has contributed to solving large-scale logistical problems, including vehicle routing and stochastic demand modeling. His recent publications emphasize data-driven approaches for inventory positioning, pricing strategies under shape-restricted demands, and sample-efficient reinforcement learning in inventory systems. Hanzhang’s research trends highlight a strong emphasis on operational efficiency, real-world applicability, and interdisciplinary solutions combining operations research with modern machine learning techniques.

Zhang JunyuView profile

Assistant Professor

• Optimization Algorithms
• Reinforcement Learning
• Machine Learning
• +4 more

Zhang Junyu is an Assistant Professor in the Department of Industrial Systems Engineering and Management at the National University of Singapore (NUS). He is affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His research focuses on optimization algorithms, reinforcement learning, and machine learning, with particular expertise in stochastic optimization, decentralized systems, and convex/non-convex analysis. Key research areas include first-order methods for saddle point problems, variance reduction techniques in stochastic optimization, and policy search in continuous control. His work bridges theoretical foundations (e.g., complexity bounds, convergence guarantees) with practical applications in reinforcement learning and distributed systems. Zhang has contributed extensively to publications on primal-dual algorithms, temporal difference learning with deep neural networks, and multi-agent reinforcement learning frameworks. His research emphasizes algorithmic efficiency and robustness, with applications in areas like decentralized actor-critic methods and off-policy learning in constrained Markov decision processes. No scientific awards or grants are explicitly mentioned in the provided text. His academic contributions are centered around advancing optimization theory and its intersections with machine learning, particularly in high-dimensional and non-convex problem domains.

Marcelo H Ang JrView profile

Professor

• Robotics
• Mechatronics
• Intelligent Systems
• +4 more

Marcelo H. Ang Jr. is a Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), Faculty of Engineering. He serves as Director of the Advanced Robotics Centre at NUS and has been actively contributing to robotics research since joining NUS in 1989. Education: Ph.D. in Electrical Engineering, University of Rochester, NY (1988) M.Sc. in Electrical Engineering, University of Rochester, NY (1986) M.Sc. in Mechanical Engineering, University of Hawaii (1985) B.Sc. in Mechanical Engineering and Industrial Management Engineering, De La Salle University, Philippines (1981) Professor Ang's research spans robotics, mechatronics, and intelligent systems with particular expertise in mobile robotics, robot control systems, and intelligent control methodologies. His work consistently bridges theoretical foundations with practical industrial applications, especially in manufacturing automation. His research interests evolved from fundamental control theory to complex integrated systems involving multiple robots and sensor networks. His publication record shows a strong emphasis on solving practical robotics challenges, with increasing focus on vision-based systems, mobile manipulation, and multi-robot coordination in recent years. The research demonstrates progression from single robot systems to complex networked robotic systems. Scientific Awards: Awards for Excellence 2000, for Most Outstanding Paper in 1999 Volume (for "A Walk-Through Programmed Robot for Welding in Shipyards") Professor Ang has supervised numerous graduate students including PhD candidates like Zheng Liu who worked on multi-robot surveillance systems. His research has been supported by significant grants including "Integration of Solid Modeling Systems and Robot Controller Architectures Towards Intelligent Robotic Systems" ($267,150), "Management of Manufacturing Technologies" ($33,000), and "Research and Development of a Ship-Welding Robot" ($1,651,200 plus $60,000). He leads the robotics group at NUS involved in the Robust Mobile Manipulation project with SIMTech, focusing on developing advanced robotic systems for unstructured environments. Professor Ang is also the founding chairman of the Singapore Robotic Games, demonstrating his commitment to robotics education and outreach.

• Uncertainty Quantification
• Ensemble Kalman Methods
• Bayesian Inverse Problems
• +3 more

Xin T. Tong is Associate Professor in the Department of Mathematics at the National University of Singapore, specializing in uncertainty quantification, machine learning, and operations research. His research develops theoretical foundations and methodologies for structured problem-solving in high-dimensional settings. Current investigations focus on ensemble Kalman methods, Bayesian inverse problems, sampling algorithms, and stochastic optimization with non-i.i.d. data. Research emphasizes mathematical analysis of algorithm efficiency and structure-aware computational methods. Professional background includes postdoctoral work at NYU's Courant Institute and Ph.D. from Princeton University.

Hu ZhenyuView profile

Associate Professor

• Revenue management
• Dynamic pricing
• Supply chain management
• +3 more

Zhenyu Hu is an Associate Professor in the Department of Analytics & Operations (DAO) at NUS Business School. He is affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS) at NUS. His research focuses on revenue management, dynamic pricing, supply chain management, dynamic programming, and game theory. He currently serves as an Associate Editor for Manufacturing & Service Operations Management . Education: Ph.D., Industrial Engineering, University of Illinois at Urbana-Champaign (2015) B.Sc., Mathematics, Sun Yat-sen University (2011) His research explores optimization under uncertainty, strategic interactions in operations, and robust decision-making. Key themes include dynamic pricing strategies with customer reference effects, supply chain coordination mechanisms, and game-theoretic frameworks for resource allocation. Recent work addresses challenges in renewable resource management and vehicle-sharing systems under uncertainty. His articles examine topics such as offer optimization under delayed feedback, robust capacity planning, and multi-location inventory pooling. Research emphasizes both theoretical contributions (e.g., duality approaches in screening problems) and practical applications (e.g., vehicle pre-allocation algorithms). Affiliated labs/teams: IORA focuses on operations research applications for Singapore's Smart Nation initiatives. IDS leverages data science for interdisciplinary solutions.

Archan MISRAView profile

Professor

• Human-Machine Collaborative Systems
• Pervasive Sensing & Systems
• Human-Computer Interaction
• +5 more

Archan Misra is a Lee Kong Chian Professor of Computer Science and Vice Provost (Research) at Singapore Management University (SMU). He leads the School of Computing and Information Systems and oversees research strategy across SMU. His work focuses on Human-Machine Collaboration, Pervasive Sensing, and AI-driven systems. Misra holds a PhD from the University of Maryland (2000) and has extensive industry experience at IBM Research and Bellcore. He is a Distinguished Member of ACM and Senior Member of IEEE. Research interests include Embodied AI, LiDAR-based localization, energy-efficient AIoT, and smart city analytics. Notable projects include the MIT/SMART M3S program on human-machine collaboration and leadership roles in SMU's CASA and LiveLabs initiatives. His work spans 300+ publications and multiple industry-funded grants, including NRF Singapore and US Army projects. Advises PhD students in areas like edge AI, wearable computing, and computer vision. Awards include ACM Distinguished Membership and IEEE Senior Membership. Current roles include Director of SMU's Resilient Workforces Institute and co-PI for the M3S program. Active in teaching, including courses on mobile systems and software foundations.

• lifelong continual learning
• reinforcement learning
• game theory
• +3 more

Tanvi Verma is a Research Scientist at the Institute of High Performance Computing (IHPC), A*STAR in Singapore. She holds a PhD in Information Systems from Singapore Management University (SMU), where she was awarded the Presidential Doctoral Fellowship for outstanding research. Prior to her PhD, she earned a B.Tech. in Computer Science and Engineering from the National Institute of Technology Warangal, India. Her professional experience includes software development roles at NetApp India and Dell India R&D before transitioning to academia in 2015. Her research focuses on lifelong continual learning, reinforcement learning, game theory, and auto ML . Applications of her work include optimizing revenue maximization through deep RL methods, decision support systems for healthcare (e.g., visual perimetry, retinal pathology detection), and sustainable indoor farming via generative deep learning. She also explores multi-agent systems for large-scale coordination and privacy-preserving techniques in medical image classification. Key contributions span scalable MARL algorithms, entropy-based learning in anonymous environments, and light recipe design for vertical farming. Her articles reflect a blend of theoretical advancements and practical applications across AI, healthcare, and agriculture. Awards: Presidential Doctoral Fellowship (SMU). Labs/Teams: Affiliated with IHPC’s AI and Data Science research group.

Gauthama RAMANView profile

Researcher

• Machine Learning
• Data Analytics
• OT Cyber Security
• +3 more

Dr. Gauthama Raman is a postdoctoral researcher at the Singapore University of Technology and Design (SUTD), affiliated with the iTrust research center under Prof. Aditya P Mathur. His work focuses on bridging advanced technologies with industrial applications, particularly in machine learning, data analytics, and operational technology (OT) cybersecurity. He holds a doctoral degree from SASTRA University, India. His research emphasizes integrating design knowledge with data-driven models to enhance anomaly detection in critical infrastructure such as water treatment plants and industrial control systems. Key research themes include real-time anomaly detection frameworks (e.g., AICrit), PLC command analysis tools (PCAT), and hybrid physics-based machine learning models. His contributions address challenges in industrial cybersecurity, predictive maintenance, and energy management systems. Dr. Raman collaborates actively within iTrust’s multidisciplinary team, contributing to projects that apply iTrust’s innovations to real-world industrial environments. His work highlights the intersection of theoretical advancements and practical industrial needs.

Xu YingView profile

Senior Lecturer

• Operations Management
• Data Science & Analytics
• Supply Chain Management
• +5 more

Xu Ying is a Senior Lecturer in the Engineering Systems and Design (ESD) department at Singapore University of Technology and Design (SUTD). Her research focuses on applying stochastic modeling and game theory to address challenges in service operations management, socially responsible operations, and renewable energy demand-side management. She holds a Ph.D. in Operations Management from Carnegie Mellon University (2015), an M.Phil. in Industrial Engineering from Hong Kong University of Science and Technology (2008), and a B.E. in Control Science from Zhejiang University (2006). Key research areas include optimizing service systems through variability introduction, energy consumption coordination in consumer cooperatives, and crowdfunding models for green energy investment. Her work integrates analytical frameworks with real-world applications in transportation, healthcare, and multi-robot systems. Xu has received notable awards including Honorable Mentions in POMS Student Paper Competitions and the William Larimer Mellon Doctoral Fellowship. Her research trends emphasize interdisciplinary approaches, combining operations research with emerging technologies like multi-agent systems and renewable energy. Advising and grant details are not explicitly listed in the provided materials. Xu's contributions span both theoretical advancements and practical solutions for sustainable and efficient operations management.

U-Xuan TANView profile

Professor

• Robotics & Automation
• Mechatronics
• Localization & Mapping
• +5 more

Professor U-Xuan TAN is affiliated with the Sensing, Actuation, and Mechanism (SAM) Design Group within the Engineering Product Development (EPD) school at Singapore University of Technology and Design (SUTD). His research focuses on Robotics & Automation, Mechatronics, Localization & Mapping, and Sensing & Control systems. His work spans applications in medical robotics, 3D food printing, industrial IoT, and human-robot interaction. He leads a dynamic research group, evidenced by frequent group photos (e.g., 2017/2019) and ongoing projects involving multi-robot systems. Notable contributions include advancements in UWB-based localization, neural network-driven vibration compensation, and autonomous medical service robots like ARIS 1.0. His team has developed systems for precision tasks such as kidney stone treatment and high-rise glass cleaning. Over 40 publications since 2018 demonstrate expertise in robotics algorithms, sensor fusion, and AI-driven solutions across healthcare, manufacturing, and environmental domains. Current openings include Post-Doctorate Fellowships and Research Assistant roles in robotics. PhD scholarships are available for candidates with project/publication portfolios.

Duan LingjieView profile

Associate Professor

• Machine Learning & AI
• Network Economics
• Algorithmic Game Theory
• +5 more

Lingjie Duan is an Associate Professor (tenured) and Associate Head of Pillar (Research) in the Engineering Systems and Design School at Singapore University of Technology and Design (SUTD). He holds a PhD in Information Engineering from The Chinese University of Hong Kong (2012) and was a visiting scholar at UC Berkeley's EECS Department. His research focuses on network economics, algorithmic game theory, and AI-driven networking, with emphasis on optimizing wireless systems, UAV networks, and distributed learning mechanisms. He has received prestigious awards including the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award (2015) and SUTD Excellence in Research Award (2017). His work spans over 150 publications in top-tier journals/conferences, including 6 ESI Highly Cited Papers. He leads the Network Economics & Optimization Lab (NEOL) and serves as Editor for IEEE Transactions on Networking and Mobile Computing. Key research areas include: Machine Learning & AI in Networking Game Theory-driven Resource Allocation UAV-assisted Communication Systems Cyber-Security & Privacy Energy-Efficient IoT Systems Recent grants include projects on privacy-protected AI methods (SGD$0.5M) and decarbonizing urban transportation via crowdsourcing (SG$0.3M). He actively collaborates with institutions like MIT, UC Berkeley, and NUS.

Shihao YuView profile

Assistant Professor

• Market Microstructure
• Decentralized Finance
• Financial Innovation
• +4 more

Shihao Yu is an Assistant Professor of Finance at the Lee Kong Chian School of Business, Singapore Management University. His research focuses on market microstructure, decentralized finance, and high-frequency trading strategies. He holds a PhD from Vrije Universiteit Amsterdam and Tinbergen Institute (2023), an MPhil in Econometrics from the same institutions (2017), and a BA in Economics from Southwestern University of Finance and Economics (2015). His academic career includes a postdoctoral research position at Columbia University’s Center for Digital Finance and Technologies, supervised by Prof. Agostino Capponi. He has received notable awards such as the KRX Outstanding Paper Award (2019) and a Research Talent Grant from NWO (2017). Research interests span financial innovation, quantitative finance, and fintech applications. Recent work explores blockchain fee dynamics, decentralized exchange price discovery, and high-frequency trading in FX markets. Awards: KRX Award (2019), NWO Grant (2017) Grants/Advising: Postdoctoral research at Columbia University (2023) Labs/Teams: Active in Singapore Management University’s finance research group, collaborating on projects related to digital finance and market structure.

Natarajan PrabhuView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Natarajan Prabhu is a Senior Lecturer on the Educator Track at the Department of Computer Science, National University of Singapore (NUS), Faculty of Computing. He also serves as the Director of the Center for Computing for Social Good and Philanthropy, highlighting his leadership in technology for societal impact. Education: Ph.D. in Computer Science, National University of Singapore M.E. in Computer Science (Gold Medalist, University First Rank) B.Tech. in Information Technology His research focuses on Artificial Intelligence, Machine Learning, Computer Vision, and Multimedia Systems , particularly in multi-camera surveillance, fairness in observation, decision-theoretic coordination, and active perception . His work bridges intelligent systems with ethical considerations in automated surveillance. He teaches foundational courses such as CS2100 (Computer Organisation), CS2103/CS2103T (Software Engineering), and IT1244 (Artificial Intelligence: Technology and Impact). The analysis of his publications reveals a strong thematic focus between 2008 and 2015 on intelligent, decision-theoretic approaches to multi-camera surveillance, emphasizing fairness, scalability, and real-time coordination. His early work on whiteboard documentation also reflects interest in practical, human-centered computing applications. Scientific Awards: Annual Teaching Excellence Award (ATEA) - 2023 Annual Teaching Excellence Award (ATEA) - 2024 Faculty Teaching Excellence Award (FTEA) - 2023 Faculty Teaching Excellence Award (FTEA) - 2024 Natarajan Prabhu is a dedicated educator and researcher with significant contributions to teaching excellence and ethically informed AI systems. His leadership in the Center for Computing for Social Good indicates active engagement in grants and initiatives promoting technology for philanthropy. While specific grants are not listed, his directorship implies oversight of funded projects and research teams. He has mentored research at the doctoral level, as seen in his PhD forum publications. He leads the Center for Computing for Social Good and Philanthropy , which likely involves interdisciplinary teams working on AI for social impact, ethical computing, and community-driven technology solutions.