جست‌وجوی استاد و پژوهشگر

• Computer Architecture
• Systems & Networking
• Approximate Computing
• +۶ مورد دیگر

Weng Fai WONG is an Associate Professor and Deputy Head of the Department of Computer Science at the School of Computing, National University of Singapore (NUS). With over three decades of academic experience at NUS, he has established himself as a leading researcher in computer systems, with particular expertise in the interface between hardware and software stacks. Dr. Wong received his B.Sc. (First Class Honors) and M.Sc. from the National University of Singapore in 1989 and 1991 respectively, followed by a Dr.Eng.Sc. from the University of Tsukuba in 1993. His academic journey began at NUS (then DISCS) in 1985, where he progressed from student to Senior Tutor in 1989, and later returned from Japan as a Lecturer in 1993. Dr. Wong's research focuses on systems and networking, with special emphasis on hardware-software co-optimization. His current research interests include approximate computing , neuromorphic computing , and hardware acceleration for deep learning. His work spans computer architecture , embedded systems , compilers and runtime systems , and programming languages . He has made significant contributions to optimizing software for novel hardware including FPGAs, GPUs, and non-volatile memory technologies. His recent publications (2023-2025) demonstrate a strong focus on energy-efficient AI computing, with particular emphasis on spiking neural networks, large language model acceleration, and FPGA-based solutions for graph processing and machine learning workloads. His research shows a clear trajectory toward green AI through hardware-software co-design that minimizes energy consumption while maintaining computational effectiveness. Dr. Wong is a Member of ACM and a Senior Member of IEEE. His paper "Exploiting half precision arithmetic in Nvidia GPUs" was a Best Paper Finalist at the IEEE High Performance Extreme Computing Conference (HPEC 2017). As Deputy Head of the Department of Computer Science at NUS, Dr. Wong plays a key leadership role in academic administration while maintaining an active research program. His work has been supported by numerous research grants, though specific details are not provided in the available information. Dr. Wong leads research in the Systems & Networking area at NUS, with particular focus on the Hardware-Software Interface Laboratory. His team explores innovative approaches to bridge the gap between theoretical computer science and practical hardware implementation, with applications spanning from edge computing to large-scale data centers.

Ashutosh Dinesh Thakurمشاهده پروفایل

استادیار

• Market Design
• Political Economy
• Organizational Economics
• +۵ مورد دیگر

Ashutosh Dinesh Thakur is an Assistant Professor at the Lee Kuan Yew School of Public Policy (LKYSPP), National University of Singapore (NUS). He holds a Ph.D. in Business Administration (Political Economics) from Stanford University (2021) and an A.B. in Economics from Princeton University (2014). Prior to NUS, he was a postdoctoral fellow at the University of Cologne’s ECONTribute Cluster. His research applies market design tools to political economy, focusing on institutional stability and organizational productivity, particularly in civil service allocation mechanisms and legislative committee assignments. He is a Faculty Associate of the Center on Asia and Globalization. Education: Ph.D. in Business Administration (Political Economics), Stanford GSB, 2021 A.B. in Economics with Highest Honors, Princeton University, 2014 Research Interests: Thakur explores mechanisms for allocating civil servants, designing committee assignments in legislatures, and institutional stability. Using matching theory, he analyzes how allocation systems impact productivity and meritocratic outcomes. His work addresses civil service cadre allocation in India and US Senate committee assignments, focusing on fairness, efficiency, and institutional resilience. Awards: 2022 Teaching Excellence Award, LKYSPP, NUS Teaching & Advising: Teaches courses on game theory, market design, strategic management, and economic foundations for public policy. Advises Ph.D. and Master’s students on topics like women’s political representation and regulatory ecosystems. His teaching emphasizes practical policy applications and quantitative methods. Labs/Teams: Associated with LKYSPP’s research centers, including the Center on Asia and Globalization.

Chua Tat Sengمشاهده پروفایل

استاد

• Multimedia Information Retrieval
• Social Media Analytics
• Unstructured Multimodal Data Analytics
• +۴ مورد دیگر

Professor Chua Tat Seng is a distinguished academic at the National University of Singapore's School of Computing, serving as KITHCT Chair Professor and Director of the NUS-Tsinghua Extreme Search Center (NExT). He also holds Distinguished Visiting Professorships at Tsinghua and Zhejiang Universities in China. PhD in Computer Science (University of Leeds, 1983) Founding Dean of School of Computing (1998-2000) Co-founded ViSenze and 6Estates technology startups His research focuses on unstructured multimodal data analytics, with particular emphasis on multimedia information retrieval, social media analytics, recommendation systems, and trustworthy AI. He has pioneered work in computational wellness and fintech applications, establishing the Lab for Media Search and leading NExT++ research initiatives. Over 300 publications in leading venues (CVPR, SIGIR, WWW, AAAI) Recipient of ACM SIGMM Technical Achievement Award (2015) Supervised 37 PhD students since 2004 Editorial leadership in ACM Transactions and IEEE Multimedia Recent work explores multimodal LLMs, knowledge editing techniques (AlphaEdit), and 3D generation frameworks, reflecting his commitment to advancing web intelligence and user empowerment.

• Statistical Learning Theory
• Recommender Systems
• Matrix Completion
• +۵ مورد دیگر

Antoine LEDENT is a tenure-track Assistant Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), holding a PhD in Mathematics from the University of Luxembourg (2017) and undergraduate/graduate degrees from Clare College, University of Cambridge. His academic trajectory includes a postdoctoral position at TU Kaiserslautern under Prof. Marius Kloft, establishing his foundation in theoretical computer science. His educational background spans rigorous mathematical training at Cambridge followed by doctoral research on stochastic differential equations at Luxembourg, directly informing his current theoretical work. This path transitioned into computer science through postdoctoral research in algorithmic learning theory. Professor LEDENT specializes in Statistical Learning Theory and Recommender Systems, with groundbreaking contributions to generalization bounds for deep neural networks, contrastive learning frameworks, and matrix completion theory. His research uniquely bridges abstract mathematical principles with practical AI applications, particularly in high-dimensional data analysis and robust recommendation engines, emphasizing provable guarantees over empirical results. Analysis of his 15 most recent publications reveals a dominant focus on theoretical machine learning, with 70% concerning generalization analysis in deep learning and matrix completion. His work consistently targets top-tier venues (ICML, NeurIPS, AAAI), demonstrating expertise in transforming mathematical insights into scalable AI solutions, especially for non-IID data and uncertainty-aware systems. Key recognitions include: Outstanding Meta Reviewer (ICML 2025, top 2%) Area Chair appointments for NeurIPS/ICML 2025 Action Editor for TMLR (2024) Certificate of Excellence in Reviewing (KDD 2023) Top Reviewer distinctions at NeurIPS/ICML/ICLR (2022-2024) He actively mentors PhD candidates including AISG Scholarship recipient NONG Minh Hieu and SENARATH ARACHCHIGE Dilan Dinushka, offering fully funded 4-year positions with pathways to Presidential Scholarships. His recruitment targets mathematically strong candidates for projects in Statistical Learning Theory, Matrix Completion, or Recommender Systems, with stipends exceeding 6000 SGD/month for exceptional performers. Based in Singapore's AI hub at SMU—ranked #41 globally in AI (CSRankings)—he operates within SCIS's dynamic ecosystem that connects academia with regional AI startups and industry partners, leveraging Singapore's strategic position at the Asia-Pacific research nexus.

Toh Kim Chuanمشاهده پروفایل

استاد

• Optimization Algorithms
• Semidefinite Programming
• Machine Learning
• +۴ 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.

• Information Systems & Technology
• Software Engineering
• Digital Transformation
• +۴ مورد دیگر

Alan Megargel is a Full-time Associate Professor of Information Systems (Practice) at the School of Computing and Information Systems, Singapore Management University. He holds a Doctor of Innovation from SMU (2018). His research focuses on Information Systems & Technology, Software Engineering, and Digital Transformation, with a strong emphasis on banking technology and ESG risk assessment. Megargel teaches enterprise integration, ePayment processes, and corporate banking technology. He advises three graduate students and has published extensively on topics like blockchain in finance, NewSQL databases, and AI-driven banking solutions. Education: Doctor of Innovation, Singapore Management University, 2018 Research Interests: Banking Technology Modernization (microservices, cloud migration) AI/ML Applications in Finance (ESG risk modeling, customer-centric AI) ESG Integration in Corporate Systems Low/no-code development for digital banking His work bridges technical innovation with strategic priorities like digital transformation and sustainable finance. Article Trends: Recent work emphasizes blockchain applications, machine learning for ESG risk assessment, and architectural frameworks for modern banking systems. Early career publications focused on legacy system modernization and educational case studies like the SMU Teaching Bank project. Awards: No scientific awards explicitly listed in the profile. Advising & Projects: Research advisor to 3 graduate students Developed the SMU Teaching Bank simulation tool Contributed to LGB Bank case studies on microservices architecture Labs/Teams: Works within SMU's Information Systems research group, collaborating on FinTech, digital banking, and enterprise architecture projects.

OOI Wei Tsangمشاهده پروفایل

دانشیار

• Interactive Media Streaming
• Computer Networking and Distributed Systems
• Multimedia Middleware and Applications
• +۱۰ مورد دیگر

Associate Professor OOI Wei Tsang is affiliated with the National University of Singapore's School of Computing, Department of Computer Science. He co-directs the Image & Pervasive Access Lab (IPAL) and holds a PhD in Computer Science from Cornell University (2001) and a B.Sc. in Computer & Information Sciences from NUS (1996). Current Positions: Associate Professor at NUS School of Computing Education: PhD (Cornell), B.Sc. (NUS) Labs: Co-director, IPAL Lab His research focuses on interactive multimedia systems, particularly volumetric video streaming, cloud gaming, augmented reality, and human-AI collaboration. He has pioneered queuing theory-based approaches for DASH rate adaptation and developed assistive drone technologies for visually impaired individuals through the DroneBuddy project. Recent publications demonstrate expertise in streaming optimization, wearable health monitoring, and conversational AI. His work has been recognized with multiple teaching awards and competitive research accolades including the DASH-IF Excellence in DASH Award (Runner Up). He teaches CS2030S Programming Methodology II and has supervised students contributing to video streaming startups like Atlastream.

TAY Seng Chuanمشاهده پروفایل

دانشیار

• Remote Sensing
• Image Processing
• Parallel Simulation
• +۳ مورد دیگر

Dr. Tay Seng Chuan is an Associate Professor in the Department of Physics at the National University of Singapore (NUS). He is also the Deputy Director of the Centre for Remote Imaging, Sensing and Processing (CRISP) since 2001. His roles include coordination of the SM2/SM3 Bridging Courses under the Office of the Provost and Warden at Ridge View Residences . PhD in Computer Science from NUS (1999) Master of Science in Computer and Information Sciences from NUS (1994) His research interests span Remote Sensing , Image Processing , Parallel Simulation , Performance Modeling , High Level Architecture (HLA), and Information Technology Applications . He has contributed to Parallel Discrete-Event Simulation , Performance Analysis , and IT in Education . His publications focus on Time Warp Algorithms , Distributed Simulation , Parallel Processing , and Java-based Educational Tools . He has received multiple Meritorious Teaching Awards from NUS. Meritorious Teaching Award (2000) Meritorious Teaching Award (1999) Science Teaching Award (1997) In administration , he has served as Deputy Director at CRISP, Head of Education Division at CITA (1998–2001), and Webmaster of the Physics Department (1996–2001). He has also supervised UROPS projects on topics like Memory Management for Parallel Simulation and Remote Sensing Data Processing .

Li-Shiuan Pehمشاهده پروفایل

استاد

• Energy-Efficient Hardware Systems
• Neural Network Hardware Acceleration
• Wearable Sensing Technologies
• +۴ مورد دیگر

Li-Shiuan Peh is a Provost's Chair Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS) . She previously held faculty positions at MIT (2013-2016) and Princeton University (2002-2009), advancing from Assistant to full Professor. Education: B.S. from National University of Singapore (1995) Ph.D. from Stanford University (2001) Her research focuses on energy-efficient hardware systems , spanning neural network accelerators for edge computing , privacy-preserving embedded systems , on-chip communication networks , and hardware security . Current projects include AI-on-skin interfaces , secure Network-on-Chip (NoC) architectures , and low-power wearable sensors . Scientific Awards: IEEE Fellow (2017) National Research Foundation Returning Singaporean Scientist Award (2016) ACM Distinguished Scientist (2011) MICRO Hall of Fame Award (2011) National Science Foundation CAREER Award (2003) Her work has driven innovations in bufferless NoC architectures , collaborative traffic advisory systems , and laser attack benchmarks , with publications in top venues like DATE, NOCS, and DAC. She leads an interdisciplinary research group at NUS, advising graduate students in computer architecture and hardware security.

Quek Ser Tongمشاهده پروفایل

استاد

• Structural Engineering
• Structural Reliability
• Structural Health Monitoring
• +۷ مورد دیگر

Quek Ser Tong is a Professor of Civil and Environmental Engineering at the National University of Singapore (NUS), holding the Keppel Chair Professorship from 2018–2020. He has served as Head of the Department of Civil and Environmental Engineering (2016–2020), Vice Dean of the Faculty of Engineering (2010–2015), and Inaugural Director of the Global Engineering Programme (2009–2012). His academic career began at NUS in 1982, following early roles as a systems engineer and civil engineer in Singapore’s Ministry of Defence. Quek holds a BEng (1st Class) from Monash University (1979), MEng from NUS (1984), and MSc/PhD from the University of Illinois at Urbana-Champaign (1986). He is a registered Professional Engineer (PEng, 1994) and has extensive consultancy experience in structural engineering, including in-situ concrete analysis, finite element simulations, and risk assessment. His research focuses on structural reliability, stochastic simulation, smart materials, structural health monitoring, and digital twin technology. Notable contributions include work on fatigue crack growth, seismic-tsunami risk modeling, and viscoelastic material behavior under aging. He has published over 250 papers and holds patents for optical fiber sensors and smart material systems. Quek has received awards such as the Public Administration Medal (Bronze, 2018), IES Prestigious Engineering Achievement Award (2024), and the IASSAR Distinguished Research Award (2025). He serves on editorial boards of journals like Structural Safety and International Journal of Structural Stability and Dynamics .

Law Wing Keung, Adrianمشاهده پروفایل

استاد

• Coastal Engineering
• Hydraulic Engineering
• Environmental Hydraulics
• +۵ مورد دیگر

Adrian Law Wing Keung is a Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS), serving since 2024. He holds a PhD and MSc from the University of California, Berkeley, specializing in coastal and hydraulic engineering, and a BEng (Civil) from the University of Hong Kong. His academic roles include Editor-in-Chief of the Journal of Hydro-environment Research and leadership in international committees like the IAHR/IWA Joint Committee on Marine Outfall Systems and ASEAN Hydroinformatics Data Centre. He has extensive consulting experience for water infrastructure projects and focuses on applying research to real-world solutions through his RDTA (Research-Development-Translation-Application) philosophy. Education: PhD in Civil and Environmental Engineering, University of California, Berkeley, USA (1991) MSc in Civil and Environmental Engineering, University of California, Berkeley, USA (1985) BEng (Civil Engineering), University of Hong Kong (1984) Research Interests: Coastal and hydraulic engineering, environmental hydraulics, remote sensing, data-driven process control, machine learning optimization in water systems, and coastal renewable energy. His work emphasizes translating fundamental research into practical applications, such as monitoring coastal environments under climate change and optimizing solar/wind energy systems. Awards: Top-2% Global Scientist by Stanford University (2023) Karl Emil Hilgard Hydraulic Prize (ASCE, 2014) UPS Foundation Visiting Professorship at Stanford (2001-2002) Wesley Horner Award (ASCE, 2000) Professional Activities: Council member of the International Association of Hydro-environment Engineering and Research, editorial board roles in technical journals, and advisory roles for major water infrastructure projects globally. His research outputs span 100+ peer-reviewed articles, with a focus on numerical modeling, remote sensing, and sustainable engineering solutions.

Long Zhaoمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Decision Sciences
• +۵ مورد دیگر

Long Zhao is an Assistant Professor in the Department of Analytics and Operations at the National University of Singapore Business School. He holds a Ph.D. in Decision Sciences from the University of Texas at Austin (2019). His research focuses on data-driven decision making, emphasizing the development of intuitive yet high-performing decision tools. Key research areas include synthetic data generation, federated learning, neural networks, and control systems. Notable contributions span privacy-preserving techniques, generative adversarial networks (GANs), and optimization algorithms for complex systems. His work addresses challenges in vertical federated learning, time-series data synthesis, and underwater robotics applications. Zhao’s publications reflect interdisciplinary strengths, blending machine learning with control theory, privacy engineering, and natural language processing. He has explored topics such as robust neural control for nonlinear systems and adaptive algorithms for multi-agent game scenarios. Research trends show a strong emphasis on synthetic data’s ethical and practical applications, with over 20 publications since 2019. His projects often bridge theoretical advancements and real-world implementations, such as improving autonomous underwater vehicle intent recognition and enhancing medical data privacy through federated learning frameworks. Grants and advising activities are not explicitly detailed in the provided text.

R. Ramanujamمشاهده پروفایل

استاد

• Automata theory
• Mathematical logic
• Modal and epistemic logics
• +۵ مورد دیگر

R. Ramanujam is a Professor at the Institute of Mathematical Sciences (IMSc), Chennai, where he has been a faculty member since 1987. He holds a BE (Hons) in Electrical and Electronics Engineering from BITS, Pilani, and a PhD from the Tata Institute of Fundamental Research (TIFR), Mumbai. His research interests span Automata theory, Mathematical logic (including modal and epistemic logics), finite model theory, distributed systems, game theory, security theory, and science education. He has taught advanced courses on topics such as Mathematical Logic, Model Theory, Temporal Logics, Automata Theory, and Game Theory. His work emphasizes formal methods in security, verification of distributed systems, and the application of logic to computer science and game theory. Ramanujam's publications include seminal works on cryptographic protocol analysis, temporal logics for web services, and game-theoretic models. Notable contributions include studies on definability in first-order theories and the complexity of intuitionistic logic operations. His research bridges theoretical foundations with practical applications in security, distributed computing, and educational initiatives.

Zhang Yunfengمشاهده پروفایل

دانشیار

• Computational Intelligence in Design and Manufacturing
• Reverse Engineering
• CAPP and Production Scheduling
• +۲ مورد دیگر

Zhang Yunfeng is an Associate Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), where he has been a faculty member since 1992. He teaches undergraduate courses including ME3261: Principles of CAD/CAM and ME4262: Automation in Manufacturing, contributing to advanced manufacturing education. He earned his B.Eng. from Shanghai Jiao Tong University (1985) and Ph.D. from the University of Bath (1991), establishing his expertise in computational manufacturing systems. His research spans Computational Intelligence in Design and Manufacturing—with focus areas in reverse engineering, CAPP coupled with production scheduling, and multi-axis tool-path planning—and UAV Applications, particularly mission planning for heterogeneous UAVs in complex environments. Analysis of his 2009-2015 publications reveals strong emphasis on sustainable manufacturing (energy-efficient machining processes) and intelligent UAV coordination systems. His work consistently bridges theoretical computational intelligence with industrial implementation, yielding practical algorithms for manufacturing optimization and aerial robotics. Professional recognition includes: Senior Member of the Society of Manufacturing Engineers (SME) His academic service encompasses key CAD/CAM instruction and research supervision. While specific grant details and student lists are unreported, his sustained publication output and 30+ year tenure at NUS indicate substantial research funding and mentorship activities in manufacturing automation and UAV technology.

Nghia Hoangمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Federated Learning
• +۵ مورد دیگر

Nghia Hoang is a tenure-track Assistant Professor at the School of Electrical Engineering and Computer Science, Washington State University. Prior to his current appointment, he held positions as a Senior Research Scientist at AWS AI Labs (Amazon, 2020-2022), Research Staff Member at the MIT-IBM Watson AI Lab (2018-2020), Postdoctoral Research Associate at MIT's Laboratory for Information and Decision Systems (2017-2018), and Research Fellow at the National University of Singapore (2015-2017). Dr. Hoang earned his B.Sc. from the University of Science (Vietnam) in 2009 and completed his Ph.D. in Computer Science at the National University of Singapore in 2015 under the supervision of Associate Professor Kian Hsiang Low. His research focuses on Machine Learning , particularly in Federated Learning , Bayesian Methods , Optimization , and Deep Learning . He has made significant contributions to probabilistic federated learning, offline optimization techniques, and knowledge representation for collaborative learning across heterogeneous systems. His work often bridges theoretical foundations with practical applications in diverse domains including healthcare and multi-agent systems, with recent publications addressing challenges of data heterogeneity and scarcity in distributed learning environments. Dr. Hoang's publication record reveals strong trends in making federated learning more robust to non-IID data distributions, improving offline optimization through surrogate modeling, and developing techniques for knowledge transfer across heterogeneous systems without requiring orthologue mappings. His work spans both theoretical contributions and practical implementations, with increasing emphasis on real-world applicability of machine learning techniques. Dr. Hoang actively mentors students in research, with several publications led by his advisees including PhD students Pei-Yau Weng and Long Bui, as well as undergraduate mentee Cuong Dao. He has successfully guided multiple students to publish at top-tier conferences including NeurIPS, ICML, and UAI, demonstrating his commitment to student development and collaborative research. Professionally, Dr. Hoang serves on the boards of prestigious journals including Machine Learning Journal and Neural Networks as an Action Editor. He has been a program committee member for major AI conferences including AAAI, ICLR, ICML, NeurIPS, and IJCAI across multiple years (2018-2024), and organized workshops such as the Practical Bayesian Methods for Big Data workshop at IBM Research AI Week and the NeurIPS-21 Workshop on New Frontiers in Federated Learning.