جست‌وجوی استاد و پژوهشگر

LING Chun Kaiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Decision Making & Planning
• Machine Learning
• +۱۳ مورد دیگر

LING Chun Kai is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on multiagent systems, computational game theory, and machine learning applications in adversarial real-world domains like cybersecurity and logistics. Educational background includes a PhD in Computer Science (2017-2023) from Carnegie Mellon University and a First Class BEng in Computer Engineering (2015) from NUS. Previously, he was a Postdoctoral Research Scientist at Columbia University. Current research interests span computational game theory, machine learning for multi-agent systems, equilibrium characterization in imperfect information settings, and applications in network security, logistics, and recreational games. Key methodological contributions include scalable algorithms for game solving, differentiable game solvers, and copula-based statistical modeling. Recent publications focus on attacker-defender graph games, language negotiation agents, and modeling games with incomplete information. Collaborations include researchers from Columbia University, Carnegie Mellon, and institutions working on GameSec, AAAI, Neurips, and ICML venues. Scientific Awards: IJCAI 2018 Distinguished Paper Award GameSec 2023 Best Paper Award GameSec 2024 Best Paper Award Singapore Teaching and Academic Research Talent Scheme (2024) Teaching includes courses on AI Planning and Decision Making (CS4246, CS5446) and Advanced Topics in Artificial Intelligence (CS6208).

• Homogenization and Multiscale problems
• Numerical analysis
• Inverse Problems
• +۲ مورد دیگر

Associate Professor Hoang Viet Ha is a faculty member at the School of Physical & Mathematical Sciences, Nanyang Technological University (NTU), Singapore. His research focuses on advanced mathematical methods for solving complex partial differential equations with applications in multiscale modeling and uncertainty quantification. His educational background includes: Undergraduate studies at the University of Wollongong, Australia (1993-1996) Ph.D. at the University of Cambridge, UK (1996-1999) Junior Research Fellow at Gonville and Caius College, Cambridge (1999-2004) Fellow at Emmanuel College, Cambridge (2004-2008) Professor Hoang's research spans Homogenization and Multiscale problems , Numerical analysis , Inverse Problems , Stochastic partial differential equations , and Free boundary value problems . His work develops computational frameworks for high-dimensional approximation, particularly through finite element methods and polynomial chaos expansions. This enables efficient solutions for stochastic PDEs in elasticity and fluid dynamics where traditional methods face the curse of dimensionality. Analysis of his 2009-2015 publications reveals a cohesive trajectory: evolving from foundational homogenization theory to cutting-edge stochastic multiscale algorithms. Key themes include sparse tensor discretizations for high-dimensional problems, Bayesian inverse frameworks for data assimilation, and rigorous analysis of convergence rates for uncertainty quantification methods. His collaborations with leading researchers (Schwab, Stuart, Xia) demonstrate strong interdisciplinary reach across applied mathematics and computational science. Information regarding scientific awards, student advising, research grants, and laboratory affiliations is not provided in the available text.

Li Yunyueمشاهده پروفایل

استادیار

• Geophysics
• Seismic Imaging
• Mathematical Inverse Problems
• +۳ مورد دیگر

Li Yunyue is an Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS), serving since August 2016. Previously, she held a joint postdoctoral position at MIT's Earth Resources Laboratory and Department of Mathematics (2014–2016). She earned her Ph.D. and M.S. in Geophysics from Stanford University (2014 and 2010) and a B.S. in Information and Computational Science from China University of Petroleum, Beijing (2008, Highest Honors). Her research focuses on interdisciplinary geophysical challenges, including seismic imaging, subsurface characterization, and mathematical inverse problems. Key areas include seismic wave simulation, data integration across disciplines, and reservoir monitoring. Her work combines theoretical, numerical, and field-based approaches to advance underground characterization techniques. Her recent publications emphasize seismic inversion methodologies and integrated modeling frameworks, with applications in reservoir and regional-scale analyses. Notable contributions include closed-loop seismic imaging systems and low-frequency waveform inversion techniques. Awards: Centennial Teaching Assistant Award (2012), Robert G. Kirby Fellowship (2008), and multiple academic prizes. Professional Roles: Reviewer for Geophysics and Geophysical Journal International , Chair of SEG Translation Committee since 2014.

• Uncertainty Quantification
• Ensemble Kalman Methods
• Bayesian Inverse Problems
• +۳ مورد دیگر

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.

• Digital Watermarking
• Neural Representation
• Copyright Protection
• +۵ مورد دیگر

Renjie Wan is an Assistant Professor in the Department of Computer Science at Hong Kong Baptist University. He holds a BEng from the University of Electronic Science and Technology of China (2012) and a Ph.D. from Nanyang Technological University (2019). His research focuses on digital watermarking, neural representation, copyright protection, and AI security, with notable contributions in computational photography and image processing. He has received awards including the Microsoft CRSF Award and VCIP 2020 Best Paper Award. His academic journey includes a Wallenberg-NTU Presidential Postdoctoral Fellowship (2020–2022). He currently advises Ph.D. students in areas like neural radiance field watermarking and backdoor attack mitigation. Teaching responsibilities include courses on AI application development and Python for data analysis. Research Projects: Developing multispectral imaging algorithms for coastal surveillance Dynamic environment reflection separation techniques Face image reflection removal using neural radiance fields Awards: Meritorious Winner at Mathematical Contest in Modeling 2023 Silver Medal of 50th International Exhibition of Inventions Geneva His lab focuses on advancing AI security and 3D content protection through innovative techniques like neural radiance field watermarking and adversarial defense mechanisms.

• Multiscale Problems
• Probabilistic Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Assoc Prof Viet Ha Hoang is an Associate Professor and Assistant Chair (Academic) in the School of Physical and Mathematical Sciences (SPMS), Division of Mathematical Sciences at Nanyang Technological University (NTU). He holds a PhD in Mathematics from the University of Cambridge and has extensive research experience in multiscale modeling and numerical analysis. His work focuses on stochastic processes, partial differential equations, and applications in computational science. Education: Bachelor of Mathematics, University of Wollongong, 1996 PhD in Mathematics, University of Cambridge, 2000 Research interests include multiscale problems , probabilistic PDEs , and numerical methods , with contributions to homogenization theory, Bayesian inversion, and computational fluid dynamics. Current grants support his work on multiscale systems and stochastic modeling. Key achievements include pioneering finite element methods for multiscale equations and developing Bayesian frameworks for inverse problems. His lab collaborates on interdisciplinary projects in mathematical physics and biomedical engineering.

• Stochastic Models
• Applied Probability
• Algorithms in Stochastic Models
• +۳ مورد دیگر

Professor Zhang Hanqin is a faculty member at the Department of Analytics & Operations, National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His research focuses on stochastic models, applied probability, algorithms in stochastic systems, supply chain management, and queueing theory. He has contributed extensively to optimization in inventory systems, manufacturing systems, and service operations. His work spans theoretical advancements and practical applications, including optimal staffing for queues, assemble-to-order systems, and quantity flexible contracts. Recent studies address time-varying queues, minimal phase-type distributions, and impulse control strategies. His research bridges stochastic processes, operations research, and applied mathematics with real-world systems. Key achievements include foundational contributions to diffusion approximations, Coxian representations, and policy design for complex systems. His articles often explore multi-source inventory systems, tandem queues, and expediting strategies, reflecting a deep engagement with both theoretical rigor and industrial relevance.

Tong Xinمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Optimization
• Machine Learning
• +۳ مورد دیگر

Tong Xin is Associate Professor in the Department of Mathematics at National University of Singapore. His research develops theoretical frameworks for high-dimensional stochastic systems, focusing on algorithmic stability in optimization and sampling methods. Core research areas include dimension-independent convergence guarantees for stochastic gradient descent, efficiency of ensemble Kalman methods in inverse problems, and replica exchange techniques for non-convex optimization. Recent work establishes robustness bounds for Markov Chain Monte Carlo variants and analyzes generalization errors in machine learning. Applications span uncertainty quantification in physical systems, statistical inference for non-i.i.d. data streams, and computational methods for large-scale inverse problems. Editorial roles include SIAM Journal of Uncertainty Quantification and Statistics and Computing.

• Applied Mathematics
• Statistics
• Strategy
• +۷ مورد دیگر

Prof. Hanqin Zhang is a Professor in the Department of Analytics And Operations at the National University of Singapore (NUS Business School). He holds a Doctor of Science from the Chinese Academy of Sciences (1991) and a Master of Science from Xian Jiaotong University (1985). His research focuses on applied mathematics, statistics, strategy, transportation logistics, control engineering, and numerical methods. He has served as an associate editor for journals like European Journal of Operational Research and TOP , and has reviewed for top-tier journals including Operations Research and Queueing Systems . His recent work addresses optimal inventory policies, queueing systems, and stochastic control, with notable contributions to dual-sourcing strategies, staffing optimization in ticket queues, and fluid models of parallel service systems. He has supervised students in stochastic processes and inventory management, reflecting his expertise in bridging theoretical advancements with practical applications in operations research. Notable research trends include leveraging order-tracking information for inventory systems, analyzing ergodicity in Markov chains, and optimizing multi-source supply chains under uncertainty. His publications span high-impact journals such as Operations Research and Queueing Systems , showcasing a focus on stochastic systems and operations management.