جست‌وجوی استاد و پژوهشگر

Adela Isvoranuمشاهده پروفایل

استادیار

• Network Analysis
• Psychopathology
• Mental Health Modeling
• +۳ مورد دیگر

Adela Isvoranu serves as an Assistant Professor in the Department of Psychology at the National University of Singapore (NUS), where she investigates developmental pathways from mental health to mental illness using complexity-based frameworks. Her work challenges traditional syndrome-focused approaches by emphasizing symptom interactions within psychopathological networks. Academic Background: Ph.D. (cum laude) from University of Amsterdam M.Sc. from University of Amsterdam B.Sc. (Hons.) from University of Leeds Her research program integrates network analysis with clinical psychology to model mental health conditions as dynamic systems. This approach examines fuzzy boundaries between diagnostic categories and aims to develop novel intervention strategies through collaborations with clinical psychologists, practitioners, and methodologists. Her methodological innovations focus on translating complex network theory into practical clinical applications. Recent publications reveal a strong trajectory in advancing network psychometrics methodology while applying these frameworks to psychosis and trauma research. The 2022 book establishes foundational R-based tools for behavioral scientists, the 2021 methodological paper provides crucial estimation guidelines, and the 2017 psychosis study demonstrates clinical utility in mapping trauma-symptom pathways. Scientific Awards: No awards explicitly mentioned in source material While specific student mentorship details are unavailable, her collaborative research environment with clinical teams and methodologists indicates active supervision of research projects. Her work is supported through university affiliations with infrastructure including dedicated research space (AS4-02-32) and computational resources for network analysis. Research Environment: Operates within NUS Psychology Department's clinical research ecosystem Collaborates with GROUP Investigators consortium Integrates statistical methodology with clinical practice Maintains research website (www.adelaisvoranu.com) for dissemination

Camellia Zakariaمشاهده پروفایل

استادیار

• Mobile Health
• Applied Machine Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Camellia Zakaria is an Assistant Professor at the University of Toronto, holding appointments in the Biostatistics Division and Institute of Health Policy, Management, and Evaluation (IHPME) at the Dalla Lana School of Public Health (DLSPH), and the Faculty of Information (FoI). She was recently named Canada Research Chair (Tier 2) for mobile health solutions. Her research focuses on integrating mobile technologies and machine learning to develop equitable health monitoring systems. Education: University of Toronto (Present) University of Massachusetts Amherst (2020–2023) Singapore Management University (2014–2019) Research interests span mobile health, applied machine learning, and human-computer interaction, with a focus on creating inclusive health applications. Her work addresses challenges in environmental sensing, sleep monitoring, and mental health tracking through innovative sensor technologies and data analytics. Key articles (2024–2016) explore medical decision-making, aerosol emissions, and group behavior modeling. Her work has been recognized in venues like BMJ , IMWUT , and CHI . Awards : Canada Research Chair (Tier 2, 2024) Projects like StressMon and WATCHME highlight her commitment to scalable health monitoring systems. She collaborates on lab initiatives such as sensAILabs, advancing interdisciplinary health technology solutions.

• Theoretical Computer Science
• Distributed Algorithms
• Parallel Algorithms
• +۳ مورد دیگر

Yi-Jun Chang is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS). He previously held a Junior Fellow position at the Institute for Theoretical Studies, ETH Zurich (2019–2021), and earned his Ph.D. in Computer Science and Engineering from the University of Michigan (2019). His research focuses on theoretical computer science, particularly distributed, parallel, and sublinear graph algorithms. Ph.D., University of Michigan (2019) M.S., National Taiwan University (2015) B.S., National Taiwan University (2013) Chang’s research explores the complexity and optimization of algorithms in distributed systems, including leader election, graph shattering, expander decomposition, and subgraph detection. His work addresses fundamental challenges in time-energy trade-offs, communication efficiency, and deterministic vs. randomized approaches in models like LOCAL and CONGEST. Recent publications highlight advancements in distributed triangle enumeration, optimal coloring, shortest path computation, and certification in bounded pathwidth graphs. Awards include the PODC 2019 Best Paper and Best Student Paper Awards, followed by the 2020 PODC Doctoral Dissertation Award. PODC 2019 Best Paper Award PODC 2019 Best Student Paper Award 2020 PODC Doctoral Dissertation Award Chang teaches courses such as CS3230 (Design and Analysis of Algorithms) and CS5275 (The Algorithm Designer's Toolkit). He advises Ph.D. students Hung Thuan Nguyen and Haoran Zhou, and has collaborated with postdoctoral researchers including Gopinath Mishra and Dean Leitersdorf.

Trevor E. Carlsonمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware/Software Co-Design
• Performance Modeling
• +۴ مورد دیگر

Trevor E. Carlson is an Assistant Professor at the School of Computing, National University of Singapore (NUS), focusing on high-efficiency microarchitectures, hardware/software co-design, and secure chip design for IoT and server applications. He earned his Ph.D. in Computer Science from Ghent University (2014) and B.Sc./M.Sc. in Electrical & Computer Engineering from Carnegie Mellon University (2002/2003). Research Interests include energy-efficient processors, secure computing platforms, neuromorphic accelerators, and fast simulation methodologies. He co-developed the Sniper Multi-Core Simulator used globally for performance/power evaluation. Scientific Awards : Best Paper Award, International Conference on Embedded Computer Systems (2016) Best Paper Award, International Symposium on Performance Analysis of Systems and Software (2013) Heidelberg Laureate Forum participation (2015) HiPEAC Technology Transfer Award for Sniper Simulator (2013) Current Research involves secure Systems-on-Chip (SOCure project), hardware security for IoT, and simulation methodologies. He leads a lab with researchers working on topics like Capstone for trustless secure memory access and LABS for laser fault injection benchmarks.

CHAN Chee Yongمشاهده پروفایل

دانشیار

• Database Systems
• Query Processing
• Query Optimization
• +۳ مورد دیگر

CHAN Chee Yong is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS) . He earned his Ph.D. in Computer Science from the University of Wisconsin-Madison and holds B.Sc. and M.Sc. degrees in Computer Science from NUS. Education : Ph.D., Computer Science, University of Wisconsin-Madison M.Sc., Computer Science, NUS B.Sc., Computer Science (1st Class Honours), NUS His research focuses on database systems , emphasizing query processing and optimization , transaction management , and database usability . He has contributed extensively to XML data dissemination, skyline computation, and multicore database performance optimization, with publications in venues like ACM SIGMOD, VLDB, and IEEE ICDE. Recent publications show increasing emphasis on join optimization , transaction healing , and spatial-keyword queries , reflecting trends in multicore systems, complex query processing, and XML data management. His work combines theoretical rigor with practical applications in distributed databases and data engineering. Notable professional roles include Associate Editor for the VLDB Journal , ACM SIGMOD Record , and IEEE Transactions on Knowledge and Data Engineering . He has served on program committees for major conferences like SIGMOD, ICDE, and VLDB across 2003-2026. Dr. Chan has supervised 9 PhD students and 10 M.Sc./M.Comp. students , including WANG TaiNing (2021), LI Meiying (2020), and TRAN Quoc Trung (2011). His advisees have been placed in institutions like the Institute for Infocomm Research and Huawei Shannon Lab.

Michael Qizhe Shiehمشاهده پروفایل

استادیار

• Large Language Models
• Deep Learning
• Natural Language Processing
• +۴ مورد دیگر

Michael Qizhe Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the Tree and Rock AI Lab (TRAIL). He holds a PhD and Master's from Carnegie Mellon University (Machine Learning and Language Technologies) and a Bachelor's from Shanghai Jiao Tong University's ACM Class. His research focuses on Large Language Models, Deep Learning, and Natural Language Processing, with notable contributions to semi-supervised learning techniques like Noisy Student and UDA, and the RACE benchmark for reading comprehension. Education: PhD in Machine Learning, Carnegie Mellon University (2020) Master's in Language Technologies, Carnegie Mellon University (2018) Bachelor's in Computer Science, Shanghai Jiao Tong University (2016) His research explores robustness, safety, and scalability of AI systems. He has served as Area Chair for top conferences like NeurIPS, ICML, and ICLR. Current research directions include adversarial robustness, LLM self-evaluation, and alignment mechanisms. His lab, TRAIL, emphasizes foundational AI research. Selected contributions include: Developing UDA and Noisy Student techniques for semi-supervised learning Creating the RACE benchmark for exam-based reading comprehension Advancing methods for LLM safety and adversarial defense Prospective students are encouraged to apply to NUS's PhD program for collaborative research opportunities.

Ooi Beng Chinمشاهده پروفایل

استاد

• Database Systems
• Distributed Systems
• Blockchain
• +۶ مورد دیگر

Ooi Beng Chin is a Professor at the School of Computing , National University of Singapore (NUS). He holds concurrent roles as an adjunct Chang Jiang Professor at Zhejiang University, Visiting Distinguished Professor at Tsinghua University, and Director of NUS AI Innovation and Commercialization Centre in Suzhou, China. He earned his B.Sc. (1st Class Honours, 1985) and Ph.D. (1989) from Monash University, Australia. His research spans database systems, blockchain, machine learning, and large-scale analytics , focusing on system architectures, security, and cross-domain applications. Notable contributions include initiating the Apache SINGA distributed deep learning platform and developing Blockbench, the first blockchain benchmarking system. He also co-founded MZH Technologies (2018) for healthcare analytics. Key publications highlight his work in blockchain-database integration, AI for healthcare/finance, and 5G-enabled data systems. Awards include the ACM SIGMOD EF Codd Innovation Award (2020), Singapore President's Science Award (2011), and fellowships from SNAS, IEEE, ACM , and SAEng (2023). He leads the Singapore Blockchain Innovation Programme (SBIP) and contributes to industry collaborations with healthcare institutions and financial organizations. Fellow, Singapore National Academy of Science (SNAS) Fellow, IEEE Fellow, ACM Singapore President's Science Award, 2011 IEEE Kanai Award, 2012 NUS Outstanding Researcher Award, 2013 ACM SIGMOD EF Codd Innovation Award, 2020 Foreign Member, Chinese Academy of Sciences, 2023

• Computational Social Science
• Text-as-data methods
• Natural Language Processing
• +۷ مورد دیگر

Kenneth BENOIT is the Dean and Full-time Professor of Computational Social Science at the School of Social Sciences, Singapore Management University (SMU). Previously, he served as Director of the Data Science Institute at the London School of Economics (LSE) from 2020 to 2024. He holds a PhD in Government from Harvard University, specializing in statistical methodology. His research focuses on computational methods for analyzing textual data, particularly political texts and social media. Key areas include text-as-data techniques, natural language processing, and the application of large language models in social sciences. He has pioneered methods combining machine learning with crowd-sourced coding to improve the accuracy of political text analysis. Ken’s work emphasizes the analysis of big data, electoral systems, and comparative party competition, with notable contributions to the European Parliament and policy positioning studies. His expertise extends to software development, including R packages like quanteda and spacyr , which are widely used in text analysis. His articles and publications span methodological innovations, policy analysis, and interdisciplinary applications. Notable projects include scaling political party positions and examining the role of AI in public policy. He is actively involved in academic leadership, having served on editorial boards and organized collaborative research initiatives like the CIVICA research hackathon. Beyond SMU, he maintains professional profiles on LinkedIn and GitHub , reflecting his commitment to open-source tools and scholarly collaboration.

Natalie Agusمشاهده پروفایل

مدرس ارشد

• Interactive Computing
• Room Acoustics
• Music Information Retrieval
• +۳ مورد دیگر

Natalie Agus is a Senior Lecturer at the Singapore University of Technology and Design (SUTD), specifically within the ISTD (Information Systems Technology and Design) pillar. Her office is located at 1.402.28. She specializes in teaching undergraduate computing courses, including programming languages such as Python, Java, C#, and C/C++. Agus is dedicated to innovating teaching methods to enhance student engagement with academic materials. Her research focuses on room acoustics and music information retrieval, with a particular emphasis on binaural audio processing. Her doctoral work centered on developing perceptually realistic real-time binaural room reverberation algorithms. Agus holds a B.Eng. (Robotics, 2015) and a Ph.D. (Digital Audio Signal Processing, 2018), both from SUTD. Her recent publications explore topics such as binaural room modeling, perceptual evaluation of spectral variance, and energy-based acoustic simulation techniques. She actively participates in collaborative research projects within the audio engineering domain.

Artur Konrad EKERTمشاهده پروفایل

استاد پژوهشی

• Quantum Information Processing
• Quantum Cryptography
• Quantum Computing
• +۳ مورد دیگر

Artur Konrad EKERT is a Research Professor at the National University of Singapore , a Lee Kong Chian Centennial Professor , and a Fellow of the Royal Society (FRS) . His work bridges theoretical and experimental quantum physics, computer science, and information theory, with a focus on quantum cryptography and quantum computing. Education: DPhil from the University of Oxford (1991). His research explores the foundational principles of quantum information processing, emphasizing secure communication protocols and algorithmic efficiency. Key contributions include advancing entanglement-based cryptography and analyzing quantum privacy limits. His publications span quantum key distribution, relativistic quantum effects, and computational complexity, reflecting interdisciplinary collaborations. Scientific awards include: Fellow of the Royal Society (FRS) Lee Kong Chian Centennial Professorship He has mentored researchers in quantum cryptography and contributed to experimental implementations of quantum communication technologies.

Marc HONمشاهده پروفایل

استادیار

• Asteroseismology
• Time-domain Astronomy
• Machine Learning
• +۳ مورد دیگر

Marc HON is an Assistant Professor at the National University of Singapore (NUS) under the NUS Presidential Young Professorship, specializing in time-domain astronomy and machine learning applications for NASA missions including Kepler, TESS, and the Roman Space Telescope. His work focuses on characterizing stellar populations and discovering novel astrophysical phenomena through data-driven methodologies. His research spans asteroseismology for probing stellar interiors and Galactic archaeology to map the Milky Way's evolution using variable stars, alongside exoplanetary science investigations into planetary system evolution, habitable worlds, and James Webb Space Telescope atmospheric characterization. A core methodology involves developing machine learning frameworks like deep learning classifiers and generative models for large-scale astronomical datasets. HON's publication trends (2018-2024) reveal consistent innovation at the astrophysics-ML intersection, with emphases on red giant asteroseismology, exoplanet dynamics, and scalable analysis pipelines for space telescope data. Key contributions include flow-based stellar evolution emulators, deep learning oscillation detectors, and large-scale TESS Galactic archaeology studies. Scientific recognition includes: NASA Hubble Fellowship (2020) He actively contributes to major international collaborations as a member of both the TESS and Kepler Asteroseismic Science Consortia, with direct involvement in MIT's TESS mission operations and data pipelines.

Peng Zhouمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Peng Zhou is an Assistant Professor at the School of Advanced Engineering, The Great Bay University , and the Principal Investigator of the Embodied Manipulation Intelligence (EMAIL) Robotics Lab . His research integrates robotics, machine learning, and computer vision, with a strong focus on deformable object manipulation, robot perception, and task-motion planning. Education: Ph.D. in Robotics, The Hong Kong Polytechnic University (Supervised by Dr. David Navarro-Alarcon) Postdoctoral Research Fellow, The University of Hong Kong (Advised by Dr. Pan Jia) Exchange Ph.D. Student, KTH Royal Institute of Technology (Supervised by Prof. Danica Kragic) Research Interests: Dr. Zhou's work spans robotics , machine learning , and computer vision , with specialized expertise in deformable object manipulation , robot perception and learning , and task and motion planning . His lab, EMAIL, pioneers solutions for robotic manipulation of soft and deformable materials. Scientific Awards & Honors: 2024 : Track 3 Champion, Zhuhai International Dexterous Manipulation Challenge 2023 : IEEE R10 Outstanding Volunteer Award 2022 : Outstanding Young Researcher Award, National Engineering Research Center 2022 : Best AI Implementation Award, Hong Kong AI Open Competition 2022 : IEEE MGA Young Professional Achievement Award Editorial & Leadership Roles: Dr. Zhou serves as an Associate Editor for IEEE Robotics and Automation Letters and has organized key workshops like the IROS 2025 Workshop on Contact and Impact-aware Manipulation . He is also a Guest Editor for special issues in Electronics and Frontiers in Robotics and AI .

Siew, Shu Qin Cynthiaمشاهده پروفایل

استادیار

• Psycholinguistics
• Cognitive Science
• Network Analysis
• +۳ مورد دیگر

Siew, Shu Qin Cynthia is an Assistant Professor at the National University of Singapore, specializing in psycholinguistics and cognitive science. She holds a Ph.D. and M.A. from Kansas University (KU) and a B.Soc.Sci. (Hons.) from NUS. Her research focuses on applying network analysis to study cognitive structures like the mental lexicon and semantic memory. Education: Ph.D. in Psychology, KU M.A. in Psychology, KU B.Soc.Sci. (Hons.) in Linguistics, NUS Her work integrates cognitive psychology experiments, computational modeling, and linguistic corpora to explore two core themes: (1) How lexicon structure influences processing (e.g., phonological/orthographic similarity affecting word recognition), and (2) How lexicon structure evolves over time (e.g., language acquisition across monolinguals and bilinguals). Recent publications highlight her innovative use of network science to model phonological and semantic networks and software tools like spreadr for simulating spreading activation. This work bridges computational methods with empirical studies on lexical retrieval and memory organization.

Anthony TUNG Kum Hoeمشاهده پروفایل

استاد

• Database Systems
• Indexing and Searching Complex Objects
• Time Series Analysis
• +۱۳ مورد دیگر

Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.

• Organisational Behaviour
• Human Resources Management
• Creativity
• +۵ مورد دیگر

Roy CHUA is a Professor of Organisational Behaviour & Human Resources and Associate Dean for Post-Experience Postgraduate Programmes at the Lee Kong Chian School of Business, Singapore Management University. He holds a Ph.D. in Management (Organizational Behavior) from Columbia University and has held academic positions at Harvard Business School and Singapore Management University since 2008. His research focuses on creativity, cross-cultural management, leadership, and innovation in organizational contexts, particularly in Asian business ecosystems. Education: Ph.D., Management (Organizational Behavior), Columbia University (2008) M.A., Philosophy, Columbia University (Year not specified) B.Sc., First Class Honors, Computer and Information Services, National University of Singapore (1998) Research Interests: Dr. Chua's work examines how cultural factors influence creativity and innovation, including studies on cultural tightness, intercultural collaboration, and trust dynamics in global business environments. He has conducted groundbreaking research on gender disparities in creative processes and the impact of cultural diversity on organizational outcomes. His empirical studies often combine quantitative analysis with qualitative insights from multinational corporations and institutional data. Awards: Best Reviewer Award - Academy of Management Journal (2017) Multiple teaching awards including 'Mind Opener' MBA Award (2016) Academic excellence scholarships including Asia Life Gold Medal (1997) Research grants from Columbia University CIBER and other institutions Advising & Grants: Supervises doctoral candidates in General Management and DBA programs Lead researcher on projects funded by Singapore Management University and international grants Collaborations with institutions like MIT Sloan and Harvard Business School Labs/Teams: Leads research initiatives in organizational creativity and cross-cultural management at Lee Kong Chian School of Business, with active participation in global academic networks focused on Asia-Pacific business strategies.