جستجوی استاد راهنمای دانشگاهی

Seth Lewis Gilbert is a Professor and Head of the Department of Computer Science at the National University of Singapore (NUS), within the School of Computing . He holds the Dean's Chair Associate Professor title and focuses on algorithms for large-scale distributed systems , emphasizing scalability and fault-tolerance . His work spans wireless networks , contention resolution , dynamic networks , and blockchain protocols . Ph.D. in Computer Science, MIT (2007) M.S. in Computer Science, MIT (2003) B.S. in Electrical Engineering & Mathematics, Yale University (1999) His research explores trusted coordination in systems with untrusted and unreliable participants, addressing challenges like Byzantine agreement , load balancing , and contention resolution . He has pioneered formal frameworks for accountability in distributed protocols and developed novel algorithms for asynchronous task allocation . Recent publications include work on consensus protocols , leader election , and contention resolution , reflecting his focus on dynamic network environments . His research has earned recognition at venues like DISC, ICDCS, and CCS. Scientific Awards & Honors : Young Researcher Award (NUS, 2014) Faculty Teaching Excellence Award (2013/14, 2014/15, 2015/16) School of Computing Teaching Excellence Honour Roll (2016-2021) Best Paper Awards at DISC (2023, 2022), ICDCS (2022), IPDPS (2022) Best Student Paper Award at DISC (2022) He serves on the Steering Committee for DISC as Treasurer and has chaired program committees for DISC, OPODIS, and SPAA. His work bridges foundational algorithmic theory with practical applications in blockchains and dynamic networked systems .

• Distributed Systems
• Algorithms & Theory
• Systems & Networking
• +۶ مورد دیگر

Sanjay Jain is a Provost's Chair Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). His research focuses on theoretical computer science with particular emphasis on inductive inference, recursion theory, complexity theory, and computational learning theory. Education: B.Tech. in Computer Science from Indian Institute of Technology Kharagpur, India (1986) M.S. in Computer Science from University of Rochester, USA (1988) Ph.D. in Computer Science from University of Rochester, USA (1990) Professor Jain's research spans multiple areas of theoretical computer science. His primary contributions are in computational learning theory, where he has made significant advances in understanding the intrinsic complexity of language identification and the limits of inductive inference. His work on recursion theory explores fundamental questions about computability and complexity, while his research in complexity theory addresses structural aspects of computational problems. A notable achievement was his work on "Deciding Parity Games in Quasipolynomial Time," which won the prestigious STOC 2017 best paper award and later the EATCS-IPEC Nerode Prize. Professor Jain's publication record shows a consistent focus on theoretical foundations of computer science, particularly in learning theory and computational complexity. His recent work has expanded into automatic structures, semiautomatic models, and connections between computational learning and algebraic structures. There is a clear progression from foundational work on language identification to more complex models involving automatic functions, transducers, and connections to mathematical logic. Scientific Awards: STOC 2017 Best Paper Award for "Deciding Parity Games in Quasipolynomial Time" EATCS-IPEC Nerode Prize (2021) Professor Jain has served on the editorial board of Information and Computation and has been actively involved in the academic community through program committee memberships for major conferences including COLT, ALT, LATA, TAMC, and PRICAI. He has held leadership roles as program co-chair for ALT 2000 and ALT 2013, and conference chair for ALT 2005. His work has been supported by various research grants, though specific details are not provided in the available materials. Professor Jain leads research in theoretical computer science at NUS, where he has built a strong research group focused on computational learning theory and related areas. His work often involves collaborations with researchers from around the world, particularly with Frank Stephan, with whom he has co-authored numerous papers. His research group has made significant contributions to understanding the fundamental limits and possibilities of computational learning models.

• Inductive Inference
• Recursion Theory
• Complexity Theory
• +۳ مورد دیگر

Bryan Kian Hsiang Low serves as Associate Professor in the Department of Computer Science at the National University of Singapore's School of Computing, while simultaneously holding leadership positions as Director of AI Research at AI Singapore and Deputy Director of the NUS AI Institute. His academic journey includes a B.Sc. (2001) and M.Sc. (2002) in Computer Science from NUS, followed by a Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University (2009). His research spans probabilistic machine learning, multi-agent systems, and trustworthy AI, with particular focus on Bayesian optimization , federated learning , and data-efficient methodologies . The Low Lab develops frameworks for collaborative AI, automated machine learning, and AI applications in scientific domains through the Group of Learning and Optimization Working in AI (GLOW.AI), which maintains a multi-disciplinary approach bridging computer science, mathematics, and engineering disciplines. Analysis of his recent publications reveals a consistent emphasis on data valuation , privacy-preserving collaborative learning , and robust optimization techniques , with increasing integration of large language models into his research framework. His work demonstrates strong theoretical foundations coupled with practical applications in computational sustainability and robotics. Andrew P. Sage Best Transactions Paper Award (2006) NUS Overseas Graduate Scholarship (2004-2009) Faculty Teaching Excellence Award (2017-2018) IEEE RAS Distinguished Lecturer (2019) World Economic Forum Global Future Councils Fellow (2016-2018) Dr. Low actively mentors PhD students including Rachael Sim, Quoc Phong Nguyen, and Zhongxiang Dai, while leading major initiatives like the AI Phenome Platform for plant breeding optimization. His research group GLOW.AI operates at the intersection of theory and practice, with strong industry engagement through AI Singapore. Current projects focus on scalable AI systems for scientific discovery and developing frameworks for equitable collaborative machine learning with robust privacy guarantees.

• Probabilistic Machine Learning
• Bayesian Optimization
• Multi-Agent Systems
• +۴ مورد دیگر

Jonathan Scarlett is an Associate Professor jointly appointed in the Department of Computer Science, Department of Mathematics, and Institute of Data Science at the National University of Singapore (NUS). He also serves as Assistant Dean (Graduate Studies) in the School of Computing. His research focuses on information theory, machine learning, and high-dimensional statistics, with applications to optimization, group testing, and statistical inference. Education: Ph.D. (Information Engineering), University of Cambridge (2014) B.Eng. (Electrical Engineering) and B.Sci. (Computer Science), University of Melbourne (2010) Research Interests: Algorithmic foundations of machine learning and statistical estimation Information-theoretic limits and adaptive algorithms Applications in group testing, compressed sensing, and DNA storage Robust optimization under uncertainty and adversarial settings His work bridges theoretical guarantees with practical algorithm design, emphasizing scalable solutions for high-dimensional problems. Key Achievements: Recipient of Singapore NRF Fellowship (2018) and NUS Presidential Young Professorship Listed in MIT Technology Review's 'Innovators Under 35' Asia Pacific (2021) Over 100 publications in top venues like ICML, NeurIPS, and IEEE Transactions Advising & Grants: Supervised multiple PhD students in areas like Bayesian optimization, group testing, and compressed sensing Lead researcher on projects supported by NRF and NUS grants Developed novel frameworks for safe Bayesian optimization and robust bandit algorithms Labs & Collaborations: Active in the Information Theory and Statistical Learning Group at NUS, collaborating with global institutions like EPFL and MIT on data science challenges.

• Machine Learning
• Information Theory
• High-Dimensional Statistics
• +۳ مورد دیگر

Supriyo Ghosh is a Senior Researcher at Microsoft Research, India. Prior to this role, he held positions at IBM Research AI Lab (2019–2021) and the Institute of Infocomm Research (I2R), A*STAR. He completed his PhD in Information Systems at Singapore Management University (2017) under Prof. Pradeep Varakantham and conducted postdoctoral research at MIT's SMART and LIDS centers (2016–2017). His research focuses on data-driven decision analytics, including algorithmic optimization, reinforcement learning, urban logistics, and network resilience in cyber-physical systems. His work has addressed cloud incident management, proactive decision-making under uncertainty, and applications of large language models (LLMs) in system reliability. Notable contributions include developing automated root-cause analysis frameworks and improving incident response strategies in large-scale cloud environments. He has also explored reinforcement learning applications in healthcare treatment optimization and air traffic control systems. Award-winning research includes the Best Paper Award at ACM SoCC'22 for an empirical study on high-severity cloud service incidents. He actively serves as a PC member for top conferences like AAAI, NeurIPS, and ICML, demonstrating his leadership in advancing AI and optimization fields. His academic background includes a graduate exchange at Carnegie Mellon University (CMU) and collaborations with MIT faculty like Prof. Patrick Jaillet. His work bridges theoretical foundations with real-world applications in transportation, cybersecurity, and enterprise systems.

• Algorithmic Foundations of Mathematical Optimization
• Reinforcement Learning
• Data-centric Learning
• +۵ مورد دیگر

Assoc Prof Xiaohui Bei is an Associate Professor at Nanyang Technological University (NTU), holding dual appointments in the School of Physical & Mathematical Sciences (Division of Mathematical Sciences) and the College of Computing & Data Science. His research focuses on algorithmic game theory, fair division, auction design, and computational economics. He earned his Ph.D. from Tsinghua University in 2012 under Prof. Andrew Yao and held postdoctoral positions at NTU and the Max Planck Institute for Informatics. His work bridges theoretical computer science and economics, addressing challenges in fair resource allocation, truthful mechanisms, and market design. Key interests include cake-cutting protocols, auction optimization, and voting systems with mixed goods. He has contributed to foundational studies on Nash welfare, submodular valuations, and connectivity constraints in fair division. Prof. Bei’s research is supported by grants exploring topics such as optimal auction mechanisms and dynamic resource allocation. His publications often emphasize practical algorithmic solutions to complex economic challenges, ensuring both efficiency and fairness.

• Computer Science
• Economics
• Mathematics
• +۳ مورد دیگر

Assoc Prof Frederique Elise Oggier is an Associate Professor in the School of Physical and Mathematical Sciences, Division of Mathematical Sciences at Nanyang Technological University (NTU). She holds a PhD from the Swiss Federal Institute of Technology (EPFL) and has held visiting positions at Caltech and the Research Center for Information Security (Tokyo). Her research focuses on algebraic coding theory, lattice-based cryptography, and applications of number theory to secure and reliable communication systems. Education: Bachelor’s and Master’s in Mathematics from the University of Geneva PhD in Mathematics from EPFL Research Interests: Her work bridges abstract algebra with practical coding challenges, emphasizing lattice codes for wiretap channels, distributed storage systems, and security protocols. Specialized in algebraic structures like cyclic division algebras and modular lattices, her contributions advance both theoretical foundations and real-world implementations of secure communication systems. Publications: Her recent work addresses cutting-edge topics such as MDS codes, non-GRS code constructions, and lattice-based security in noisy channels. These contributions highlight her expertise in coding theory and its interdisciplinary applications. Grants/Advising: While specific grants are not detailed, her prolific publication record reflects sustained research activity. She advises on projects related to distributed storage and secure coding, though student names are not listed in the provided texts. Labs/Teams: Affiliated with NTU’s mathematical sciences division, she collaborates with global researchers on projects such as lattice coding for 5G/6G systems and cryptographic protocols leveraging algebraic number theory.

• Algebra
• Number Theory
• Coding Theory
• +۳ مورد دیگر

Michael Choi is an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA). Previously, he held a joint appointment with Yale-NUS College (2022–2025) and worked at the School of Data Science (SDS) at the Chinese University of Hong Kong, Shenzhen. He earned his PhD from Cornell University’s School of Operations Research and Information Engineering (ORIE), advised by Prof. Pierre Patie, and holds an undergraduate degree in Actuarial Science from the University of Hong Kong. His research focuses on Markov chains and processes, stochastic algorithms (e.g., MCMC, simulated annealing, Langevin dynamics), and their applications in statistical physics, optimization, Bayesian statistics, information theory, game theory, and theoretical computer science. He explores interdisciplinary connections with quantum computation, control theory, and computational chemistry. Choi actively contributes to the academic community, serving as an Associate Editor of Statistics and Computing since August 2023. He has presented at numerous international conferences and workshops, including the IMS APRM 2026 (Hong Kong), INFORMS International Meeting 2025 (Singapore), and BayesComp 2025 (Singapore). His work bridges theoretical foundations with practical computational methods in stochastic systems.

• Markov Chains
• Stochastic Algorithms
• Statistical Physics
• +۶ مورد دیگر

Tianyang Hu is a postdoctoral researcher at the National University of Singapore, set to join the Chinese University of Hong Kong, Shenzhen as an Assistant Professor in Fall 2025. His academic journey includes a PhD in Statistics from Purdue University under Prof. Guang Cheng, an M.S. in Statistics from the University of Chicago, and a B.S. in Mathematics from Tsinghua University. PhD in Statistics, Purdue University M.S. in Statistics, University of Chicago B.S. in Mathematics, Tsinghua University His research lies at the intersection of statistics and artificial intelligence, focusing on statistical machine learning, representation learning, and deep generative modeling . He is particularly interested in the theoretical foundations of AI, including diffusion models, large language models (LLMs), and overparametrized neural networks. His recent work explores the duality between prompting and fine-tuning, contrastive learning mechanisms, and efficient sampling in generative models. The trends in his recent publications show a strong emphasis on diffusion models , especially in conditional generation, sampling efficiency, and integration with language models. His work spans both theoretical analysis and practical algorithm design, with applications in image generation, 3D modeling, and multimodal reasoning. He has contributed to understanding the behavior of in-context learning in transformers and the role of regularization in deep networks. His scientific achievements include: Spotlight presentation at NeurIPS 2023 Spotlight presentation at NeurIPS 2022 Oral presentation at ICML Workshop 2025 Tianyang Hu is actively building his research group and is seeking PhD/MPhil students and research assistants with strong theoretical and practical AI skills. He has held research positions at Huawei Noah’s Ark Lab and is currently mentored by Prof. Kenji Kawaguchi at NUS. His work has been supported by collaborations across academia and industry, though specific grants are not listed in the text. While no formal lab name is mentioned, his future group at CUHK-Shenzhen is expected to focus on theoretical AI and generative models , with research directions in LLMs, diffusion models, and representation learning.

• Statistical Machine Learning
• Representation Learning
• Deep Generative Modeling
• +۷ مورد دیگر

Arnd Hartmanns is an associate professor in the Formal Methods and Tools group at the University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). Previously, he was a postdoc at both the University of Twente and Saarland University's Dependable Systems and Software group, where he also completed his Ph.D. in computer science in 2015. He has led multiple research projects including the NWO VIDI project 'Trustworthy Analysis of Stochastic Timed Systems (TruSTy)', the Interreg North Sea project 'STORM_SAFE', and coordinates the MSCA RISE project 'MISSION'. His primary research interests focus on modeling tools and formalisms for stochastic timed and hybrid systems, particularly the Modest framework. Hartmanns has been a strong advocate for reproducibility in Computer Science research through artifact evaluation initiatives, tool competitions like QComp, and standardized benchmark sets. His work bridges theoretical foundations with practical applications across various domains including network-on-chip systems, power grids, and space communication infrastructure. He has made significant contributions to probabilistic model checking, statistical verification techniques, and formal methods for system analysis. Hartmanns serves on numerous program committees, particularly focusing on artifact evaluation for conferences like TACAS, QEST, and FORMATS. His leadership in establishing reproducibility standards has influenced the formal methods community significantly. The trends in his recent publications show a strong emphasis on verified implementations of verification algorithms, statistical model checking techniques that go beyond standard approaches, and applications of formal methods to increasingly complex real-world systems. Scientific Recognition 2016 Best Dissertation Award by the GI/ITG Technical Committee for 'Measurement, Modelling and Evaluation of Computing Systems' Hartmanns has supervised numerous Master's and PhD students through his research projects, though specific names aren't listed in the provided information. His grant portfolio includes several major projects funded by NWO (VENI, VIDI, Open Competition) and European programs (MSCA RISE, Interreg North Sea). He is actively involved in developing educational materials for formal methods and has contributed to establishing benchmark sets that are now standard in the field. His work on the Modest modeling language and toolset has created an important infrastructure for quantitative verification research. The integration of formal verification with machine learning techniques, particularly in strategy learning and decision tree generation, represents one of his more recent research directions that connects formal methods with emerging AI approaches.

• Stochastic Timed Systems
• Hybrid Systems
• Probabilistic Model Checking
• +۵ مورد دیگر

Leo Krippner serves as a Research Fellow at the Sim Kee Boon Institute for Financial Economics (SKBI) within Singapore Management University and holds an Honorary Senior Research Fellow position at the University of Waikato. His academic career bridges theoretical physics and quantitative finance, with significant contributions to monetary policy analysis and term structure modeling. His educational foundation includes: PhD in Finance and Economics from the University of Waikato (2006) Master of Science in Theoretical Physics (First Class Honours) from the University of Waikato (1991) Bachelor of Science with majors in Physics, Mathematics, and Chemistry from the University of Waikato (1989) Krippner specializes in term structure modeling at the zero lower bound, monetary policy transmission mechanisms, and advanced time series econometrics. His research develops rigorous frameworks for yield curve analysis, shadow rate estimation, and macro-finance modeling. He has pioneered eigensystem representations of vector autoregressions and created practical tools for measuring unconventional monetary stimulus. His publication trajectory reveals consistent innovation in macro-finance methodologies, with recent work focusing on eigensystem VAR techniques and zero lower bound modeling. These contributions demonstrate evolving sophistication in handling unconventional policy environments while maintaining strong connections to empirical finance applications. No scientific awards were documented in the provided materials. Available information does not indicate formal student supervision or specific research grants administered by Dr. Krippner. His primary research affiliation is with SKBI at Singapore Management University, where he contributes to financial economics research initiatives.

• Term Structure Modeling
• Monetary Policy
• Macro-finance
• +۱ مورد دیگر

Kai Fong Ernest Chong is an Assistant Professor in the Information Systems Technology and Design (ISTD) pillar at Singapore University of Technology and Design (SUTD). He also served as an adjunct assistant professor at Nanyang Technological University's Division of Mathematical Sciences. His research focuses on combinatorics, commutative algebra, and artificial intelligence, particularly in algebraic machine reasoning and robust AI systems. He leads SUTD's thrust on the fundamentals of AI systems and has secured over SGD 8.3 million in research grants as principal investigator (PI) or co-investigator (Co-I). Ernest holds a PhD in Mathematics from Cornell University (2015) under Edward Swartz. He has extensive teaching experience, including courses on algorithms, discrete mathematics, and probability. His work includes pioneering algebraic machine reasoning, connecting commutative algebra to AI, and developing frameworks for federated learning and robustness against data noise. Notable achievements include a 2023 breakthrough in algebraic machine reasoning surpassing human performance in IQ test tasks and a 2003 National Science Talent Search Grand Prize for his work on twin primes. He supervises numerous PhD, master’s, and postdoctoral researchers and actively collaborates with industry partners through projects like ASTRALIS and Alpha-DRACONIS.

• Combinatorics
• Commutative Algebra
• Artificial Intelligence
• +۵ مورد دیگر

Professor Toh Kim Chuan is the Leo Tan Professor in Science and serves as Research Director (Technical) at the Institute of Operations Research and Analytics (IORA) within the National University of Singapore (NUS). His primary affiliation is with the Department of Mathematics in the Faculty of Science. His research focuses on matrix optimization problems, large-scale semidefinite programming, fast algorithms for statistical and machine learning tasks, and iterative methods for solving large linear systems in optimization contexts. His recent work emphasizes contributions to nonsmooth nonconvex optimization, distributionally robust optimization, and scalable algorithms for sparse and low-rank optimization problems. Notable methodologies include Bregman proximal algorithms, adaptive sieving techniques, and inexact augmented Lagrangian methods. Professor Toh is also actively involved in developing computational tools like CDOpt for Riemannian optimization and frameworks for low-bit communication in distributed model training. While no specific awards are listed, his extensive publication record (over 150 papers from 2020-2025) demonstrates significant contributions to optimization theory and applications. His research bridges theoretical foundations with practical implementations, addressing challenges in large-scale systems, machine learning, and operations research.

• Optimization Algorithms
• Semidefinite Programming
• Machine Learning
• +۴ مورد دیگر

Satoshi ITO is a Professor and Assistant Director at the Institute of Statistical Mathematics, where he has been serving since March 2024. His academic career spans several decades with a strong focus on mathematical optimization theory and applications. Previously, he held various research and academic positions at the same institution, contributing significantly to the field of optimization. Doctor of Engineering from Keio University (1991) Master's and Doctoral studies at Keio University Graduate School of Science and Technology Undergraduate degree in Instrumentation Engineering from Keio University Professor ITO's research primarily centers on mathematical optimization, with particular expertise in semi-infinite programming, convex optimization in measure spaces, and applications to sports analytics and optimal control problems. His work bridges theoretical mathematics with practical applications in various domains including sports league scheduling, robust optimization, and probability measure optimization. His publication record demonstrates a consistent focus on advancing optimization techniques, with recent work exploring chance-constrained linear programs in probability measure spaces (2023) and earlier foundational contributions to semi-infinite programming. ITO's research shows a clear trajectory from theoretical optimization methods toward increasingly complex real-world applications. Member of Mathematical Optimization Society since 1991 Member of Society for Industrial and Applied Mathematics since 1991 Member of Japan Statistical Society since 1991 Active participant in numerous international optimization conferences Professor ITO has secured substantial research funding through Japan Society for the Promotion of Science grants, including a current Grant-in-Aid for Scientific Research (A) project on universal manifold learning algorithms for super-smart society realization. He teaches courses in computational mathematics and system optimization at The Graduate University for Advanced Studies (SOKENDAI), mentoring the next generation of mathematical scientists. His committee work spans multiple prestigious organizations including the Japan Association of Statistical Societies and the Mathematical Society of Japan.

• Mathematical Optimization
• Applied Mathematics
• Statistical Mathematics
• +۳ مورد دیگر

Ilia Tsetlin is the INSEAD Chaired Professor of Decision Sciences at INSEAD, specializing in decision-making under uncertainty. His research focuses on multiattribute decision models, target-based and contest-based settings, and background risk analysis. He also explores auction theory, negotiation, and collective choice mechanisms. His work is published in top journals like Management Science and Operations Research. Education: PhD in Business Administration from Duke University MSci in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Teaching includes MBA modules on uncertainty and strategic planning, PhD courses in probability/statistics, and executive education programs on decision-making. His research interests span stochastic dominance, risk analysis, and operational frameworks for complex decisions. Recent articles explore multivariate stochastic dominance under dependence uncertainty, delegated search strategies, and epistemic rationality in noisy evidence contexts. His work bridges theoretical foundations with practical applications in business and policy. No scientific awards are explicitly listed. Advising no formally recorded students. Active in case studies on analytics, risk-weighted assets, and banking product strategies.

• Decision Making
• Auction Theory
• Bayesian Statistics
• +۵ مورد دیگر