جستجوی استاد راهنمای دانشگاهی

LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.

• Responsible AI
• Dynamic Decision-Making
• Neurocognitive Modeling
• +۳ مورد دیگر

Professor Guan Cuntai serves as Deputy Dean of the College of Computing & Data Science and holds the President's Chair in Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He also maintains a courtesy appointment as Professor at the Lee Kong Chian School of Medicine. His leadership extends to multiple research institutes including serving as Director of the Artificial Intelligence Research Institute (AI.R) and Co-Director of both S-Lab for Advanced Intelligence (2020-2025) and the Rehabilitation Research Institute of Singapore (2015-2018 & 2024-2025). Professor Guan's research focuses on Brain-Computer Interfaces (BCI), Machine Learning, Neural Signal & Image Processing, and Artificial Intelligence. His work at the Centre for Brain-Computing Research (CBCR) investigates deep learning algorithms for motor activity decoding, cognitive mechanism understanding, and explainable AI for brain decoding modeling. His research spans applications in motor decoding, attention and emotion detection, silent speech decoding, and olfactory response analysis from brain signals. His significant contributions to the field have been recognized through numerous awards: Annual BCI Research Award (First Prize) King Salman Award for Disability Research Nanyang Research Award IES Prestigious Engineering Achievement Award Achiever of the Year (Research) Award Finalist of the President Technology Award Professor Guan has delivered over 100 keynote speeches and invited talks globally, including at the opening ceremony of the 7th International BCI Meeting and IEEE International Conference on Rehabilitation Robotics. His extensive publication record includes 420 refereed journal and conference papers, and he has secured significant research funding resulting in 26 granted patents and patent applications, with 17 technologies licensed to six companies across Singapore and USA. He leads the Centre for Brain-Computing Research (CBCR) which focuses on fundamental and applied BCI research, and previously established the Brain-Computer Interface research program and founded the Department of Neural & Biomedical Technology at the Institute for Infocomm Research, A*STAR before joining NTU in 2016.

• Brain-Computer Interfaces
• Machine Learning
• Neural Signal Processing
• +۵ مورد دیگر

Suranga Chandima Nanayakkara is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), leading the Centre for Holistic Inquiry into Lifelong Learning (CHILL) and the Smart Systems Institute (SSI). He holds roles such as AI+HCI Theme Lead at NUS+CNRS IPAL Lab and Residential Fellow at NUS College. His research focuses on assistive human-computer interfaces, emphasizing technologies that enhance perceptual and cognitive capabilities for individuals with sensory deficits. He earned his PhD and BEng from NUS, followed by postdoctoral work at MIT Media Lab. Notable projects include the Augmented Human Lab, iTILES for rehabilitation, and the FingerReader device for visually impaired shoppers. His work has garnered awards like the MIT TR35, TOYP, and INK Fellowship. Research interests include Intelligent Systems, Human Augmentation, and Design Science. He explores applications in assistive technologies, healthcare informatics, and educational tools. Over 15 publications highlight innovations in wearables, stress management, and multimodal interaction. Awards: 40+ accolades, including MIT TR35, TOYP, and multiple design awards for projects like Kiwrious and FingerReader. Grants: Led projects funded by institutions like NUS and Singapore’s innovation initiatives. Labs: Augmented Human Lab (founded 2011), focusing on humanizing technology through natural interfaces.

• Intelligent Systems
• Human Augmentation
• Human-Computer Interaction
• +۳ مورد دیگر

Aniello Murano is a Professor of Computer Science at the Department of Electrical Engineering and Information Technologies , University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic Reasoning) Laboratory and leads cutting-edge research in Artificial Intelligence, Strategic Reasoning, Multi-Agent Systems , and Formal Verification . Research Interests : Strategic reasoning under perfect/imperfect information, specification/verification/synthesis of reactive systems, temporal/modal logics, automata theory, parity games, game theory, mechanism design, and formal languages. Notable Projects : PNRR Research Unit Coordinator (2023-2025) on Resilient AI, PRIN 2020 Unit Coordinator (RIPER: Resilient AI-Based Self-Programming and Strategic Reasoning), H2020-MSCA SEAL (Principal Coordinator). Awards & Honors : JPMorgan Faculty Research Award (2022), Royal Society Award (2016), Best Paper PRIMA (2015), INDAM Project Leader (2023), Italian Scientific Habilitation (2017-2018). Students & Postdocs : Supervised 6 PhD students (e.g., Silvia Stranieri, Vadim Malvone) and mentored postdocs such as Munyque Mittelmann and Bastien Maubert. Laboratory : Leads ASTREA Lab, focusing on automated strategic reasoning and resilient AI systems.

• Artificial Intelligence
• Strategic Reasoning in Multi-Agent Systems
• Formal Verification
• +۶ مورد دیگر

Rajesh Krishna BALAN is a Full-Time Professor at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU) . His research focuses on Human-Machine Collaborative Systems , Pervasive Sensing , and Health & Wellbeing technologies. Based in Singapore, he leverages mobile computing to address urban sustainability and quality-of-life challenges. PhD from Carnegie Mellon University (2006) Specializes in WiFi sensing , VR/AR , and health monitoring Advises PhD students in areas like urban mobility , empathetic design , and cyber-physical systems Beyond academia, BALAN's work bridges ubiquitous computing and public health , with applications in ageing populations , mental health analytics , and smart city optimization . His recent publications highlight cross-disciplinary approaches to sleep analysis , group behavior modeling , and contactless physiological sensing . BALAN actively contributes to educational technology through projects like Technology-Enhanced Learning frameworks. He is also a mentor in collaborative research areas including biomedical informatics and lifestyle monitoring , with a focus on mobile GPU optimization and low-power systems .

• Human-Machine Collaborative Systems
• Pervasive Sensing & Systems
• Learning & Work
• +۹ مورد دیگر

FANG Yuan is a tenured Associate Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He holds the prestigious Lee Kong Chian Fellowship and leads research in artificial intelligence and data science. His institutional affiliation includes: School of Computing and Information Systems, Singapore Management University Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2014) Bachelor of Computing (First Class Honors), National University of Singapore (2009) - Top student in Computer Science Research Focus: Dr. FANG specializes in data mining, machine learning, and AI with emphasis on graph learning, information networks, recommendation systems, and knowledge graph applications. His work bridges theoretical foundations with practical applications in social analytics, biomedical informatics, and digital transformation, often employing advanced neural network architectures. Publication Trends: Recent works (2024-2025) demonstrate strong focus on graph machine learning innovations, including graph foundation models, prompt-based learning for dynamic graphs, and LLM-graph integrations. Key themes include few-shot/zero-shot learning, non-homophilic graph processing, and applications in recommendation systems, bioinformatics, and NLP. Methodological advancements frequently involve contrastive learning, transformer architectures, and explainable AI techniques. Awards & Honors: Lee Kong Chian Fellow World's Top 2% Scientist (2024) by Stanford/Elsevier #1 Most Influential Paper at WWW'23 (GraphPrompt) - Paper Digest (2024-09) Top 5 Most Influential Papers at WWW'23 (GraphPrompt) - Paper Digest (2024-05) Top Computer Science Graduate, NUS (2009) Student Advising: Currently advises doctoral candidates including DONG Viet Hoang, LIU Ran, and NIU Yudong. Recently supervised Dr. Zhongzhou Liu's successful PhD defense (2024) on trustworthy recommendation systems. Professional Engagement: Regularly organizes tutorials at premier venues (WWW, KDD) and delivers invited talks internationally on graph learning advancements. Leads multiple research projects in collaboration with industry partners.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

LEE Mong Li is a Professor of Computer Science at the National University of Singapore (NUS) and serves as Director of the NUS Centre for Trusted Internet and Community. She holds a Ph.D., M.Sc., and B.Sc. (First Class Honours) in Computer Science from NUS, where she was awarded the IEEE Singapore Information Technology Gold Medal as the top Computer Science student in 1989. Her academic career includes a visiting fellowship at the University of Wisconsin-Madison (1999) and consultancy with QUIQ USA (2000). Her research spans Data Management, Spatio-temporal Databases, Biomedical Informatics, and Retinal Image Analysis . She has pioneered work in data cleaning, data fusion, and analysis of semistructured data, with applications in social media analytics and healthcare. Her recent publications demonstrate strong interdisciplinary focus, particularly in AI-driven medical diagnostics including diabetic retinopathy screening and chronic kidney disease detection from retinal images. She co-authored foundational books on 'Designing Semi-structured Database' and 'Temporal and Spatio-Temporal Data Mining'. Her 150+ publications in major database conferences and journals reflect leadership in both theoretical and applied research. Recent work shows significant emphasis on Medical AI applications (retinal analysis, kidney disease prediction) Temporal fact verification systems Misinformation detection in multimodal environments Privacy challenges in large language models Key honors include: Singapore's President Technology Award (2014) for co-inventing an AI system screening eye conditions IEEE Singapore Information Technology Gold Medal (1989) She actively contributes to government-funded multidisciplinary projects building practical deployable systems. Her leadership extends to program committees of prestigious database conferences and directing the NUS Centre for Trusted Internet and Community. She teaches BT5110 Data Management and Warehousing and has co-developed an AI system for diabetic retinopathy screening deployed in Singapore's national teleophthalmology program.

• Data Management
• Data Cleaning
• Data Fusion
• +۵ مورد دیگر

**FENG Mengling** is an Associate Professor at the National University of Singapore (NUS) and holds primary affiliation with the Saw Swee Hock School of Public Health. She serves as the Domain Leader for the Biostatistics, Modelling, AI and Data Analytics (B.MAD) Domain and Director of the AI for Public Health (AI4PH) Program. Her academic credentials include a Senior Post-doc from Harvard-MIT Health Science Technology Division, a PhD from Nanyang Technological University (2009), and a Bachelor's degree (2003) from NTU. Research & Teaching: Her research focuses on causal inference for evidence-based medicine, generative models for medical time-series analysis, and healthcare data analytics. She teaches courses on big data technologies for healthcare problems and healthcare data analytics. Professional Roles & Awards: She has led the Biomedical and Healthcare Analytics Lab at the Institute for Infocomm Research (2014–2015) and currently serves as an Affiliate Scientist at Harvard-MIT. Notable accolades include the MIT Teaching & Learning Laboratory Kaufman Teaching Certificate and recognition as a finalist in MIT’s 2013 Innovation Showcase. Her work has been featured in prominent media outlets like The Straits Times and Channel NewsAsia, highlighting breakthroughs such as AI nurses and Singlish-speaking healthcare assistants. Publications & Impact: Over 50 peer-reviewed publications span AI-driven clinical decision support, medical imaging analysis, and predictive modeling in critical care. Key contributions include frameworks like MedDreamer (reinforcement learning for EHR analysis) and DivScore (LLM-generated text detection). Her research bridges causal inference, generative AI, and scalable healthcare solutions. Labs & Initiatives: As a leader in NUS’s Public Health AI Innovation Center (launching early 2025), she drives initiatives like FxMammo (AI for breast cancer screening) and the Biomedical and Healthcare Analytics Lab. Her work emphasizes ethical AI deployment and cross-disciplinary collaboration in healthcare.

• Biostatistics
• Healthcare Data Analytics
• Medical AI
• +۳ مورد دیگر

Seeram Ramakrishna is a Professor of Materials Engineering at the Department of Mechanical Engineering, National University of Singapore (NUS), affiliated with the College of Design and Engineering. He holds a PhD from the University of Cambridge and a TGMP from Harvard University. His research focuses on circular economy, nanotechnology, and sustainable materials engineering, with notable contributions to electrospinning, biomaterials, and energy storage systems. Key educational background includes: PhD in Materials Science (University of Cambridge) TGMP in Advanced Manufacturing (Harvard University) Research interests span cross-disciplinary areas such as: Development of eco-friendly nanocomposites Electrospun nanofiber applications in healthcare and energy Circular economy frameworks for sustainable materials Advanced manufacturing techniques for biomedical devices His publications highlight innovations in: High-efficiency solar steam generation via core-shell fibers Flexible wearable sensors for health monitoring Green synthesis of supercapacitor materials Plastic waste circularity through informatics-driven approaches Notable projects include books on circular economy fundamentals and biomaterials. His work bridges material science with environmental sustainability, addressing global challenges in energy, healthcare, and pollution control.

• Circular Economy
• Nanotechnology
• Materials Engineering
• +۴ مورد دیگر

Baosheng Yu serves as an Assistant Professor of Digital Health at the Lee Kong Chian School of Medicine, Nanyang Technological University (NTU), Singapore, with prior experience as a Research Fellow at the University of Sydney, Australia. His academic credentials include: Bachelor of Engineering (B.E.) from University of Science and Technology of China (USTC), 2014 Ph.D. from University of Sydney (USYD), 2019 Dr. Yu's research integrates cutting-edge artificial intelligence with multimodal medical data—spanning imaging, clinical text, and physiological signals—to revolutionize diagnostic precision and therapeutic efficacy. His work bridges Artificial and Augmented Intelligence , Biomedical Informatics , and Data Science , with specialized focus on medical image segmentation, clinical NLP for electronic health records, and real-time signal analysis for patient monitoring systems. He actively recruits PhD candidates and Research Associates/Fellows for digital health initiatives, indicating robust research momentum. While specific grant portfolios remain unspecified, his methodology suggests strong alignment with Singapore's national priorities in AI-driven healthcare transformation and precision medicine.

• Artificial and Augmented Intelligence
• Biomedical Informatics and Data Science
• Medical Image Analysis
• +۳ مورد دیگر

Min Lee is a Full-time Assistant Professor of Computer Science at Singapore Management University's School of Computing and Information Systems (SCIS). Holding a PhD from Carnegie Mellon University (2021), Lee specializes in Artificial Intelligence with a focus on Human-AI Collaborative Systems and their applications in healthcare. Their research bridges technical innovation with human-centric design, emphasizing trustworthiness, explainability, and accessibility in AI systems. Research interests include decision-making optimization, human-machine collaboration, and AI-driven solutions for healthcare challenges such as stroke rehabilitation, elderly care monitoring, and clinical decision support. Lee has pioneered low-cost AI/robotic solutions for post-stroke rehabilitation exercises, integrating socially assistive robotics with real-time feedback mechanisms. Recent work highlights AI's role in enabling trustworthy clinical decision-making through explainable AI (XAI) techniques like counterfactual explanations and gradient-based methods. Their systems address ethical concerns in predictive healthcare and advocate for stakeholder-inclusive design processes. Awards and grants: No specific awards mentioned in the profile, though research has been supported through collaborative projects with clinical partners and iterative user evaluations involving therapists and patients. Advising focuses on multidisciplinary projects spanning AI, healthcare, and human-computer interaction. Current advisees include BHOSALE Rimmon Saloman, TRAN Truong Thuy, and YANG Xinlin. Lee collaborates closely with clinical stakeholders to translate technical advancements into practical healthcare solutions, emphasizing iterative design and real-world applicability. Labs/teams: Active in SCIS's AI and Data Science initiatives, leading projects on intelligent decision support systems, socially assistive robotics for rehabilitation, and human-centered AI ethics frameworks.

• Artificial Intelligence and Data Science
• Decision Making & Optimization
• Machine Learning & Intelligence
• +۸ مورد دیگر

MA Dong is an Assistant Professor at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU). He holds a PhD from the University of New South Wales (2020), and was a postdoctoral researcher at the University of Cambridge. His research focuses on mobile computing, wearable-based human sensing systems, and health monitoring using embedded machine learning. He is also a visiting researcher at the University of Cambridge since April 2025 and will join as an Associate Professor there in 2026. Education: PhD (UNSW), MEng and BEng (Central South University). Research Interests: Wearable systems for health monitoring (e.g., respiratory rate tracking, gait analysis), robust physiological sensing, tiny machine learning on embedded devices, and human-computer interaction through earables. His work emphasizes practical implementations of wearable technology for real-world scenarios, such as smart earbuds for authentication, health tracking, and activity sensing. Publications: Over 50+ peer-reviewed articles in top venues like MobiCom, PerCom, CHI, and Nature journals. Key topics include earable technology, ECG-text multimodal learning, and energy-efficient sensing systems. Awards: Google South Asia Research Award (2024), Mark Weiser Best Paper Awards (2024, 2025), and EPFL Engineering Ph.D. Summit recognition. Students: Advising PhD candidates Xiao Ma, PHAM Hung Manh, and Changshuo Hu. Also hosts visiting scholars from Shandong University and Beijing Institute of Technology.

• Mobile Computing
• Wearable-Based Human Sensing Systems
• Health Monitoring
• +۳ مورد دیگر

Professor TAN Ah Hwee is a Full-time Faculty member and Lee Kong Chian Professor of Computer Science at the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU). He serves as the Associate Dean (Research) in SCIS and leads research in Artificial Intelligence, Machine Learning, and Health Informatics. His work spans neural networks, multi-agent systems, and healthcare applications such as Mild Cognitive Impairment prediction. He holds a PhD from Boston University (1994). Research Focus: His research integrates adaptive resonance theory, federated learning, and spatial-temporal modeling. Key areas include knowledge graph refinement, episodic memory systems for Activity of Daily Living (ADL) prediction, and explainable AI in multi-agent reinforcement learning. He also develops technologies for aging-in-place support and social media analytics. Recent Contributions: Recent work emphasizes hierarchical multi-agent models (HiSOMA), federated learning frameworks (FedART), and AI-driven health monitoring systems. His publications address challenges in self-organizing neural networks, context-aware reinforcement learning, and medical diagnostics through ambient sensing. Advising & Impact: Advises students like TEH Seng Khoon and Cassandra TAN Hui Ming. His projects include the eHealthPortal for elderly support and Silver Assistants for aging-in-place solutions. Research outputs bridge theoretical advancements in AI with real-world applications in healthcare and smart environments.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۷ مورد دیگر

Professor Li Leida is a National Young Talent and doctoral supervisor at Xidian University's School of Artificial Intelligence. His research focuses on computer vision, image/video quality assessment, computational aesthetics, and visual sentiment analysis. He leads the Brain-like Visual Perception and Evaluation Laboratory and collaborates with industry partners like OPPO, Huawei, and Tencent. He has published over 90 papers in top venues like TPAMI and CVPR, with 6 highly cited papers and 8,400+ citations. He serves as deputy editor-in-chief of IEEE Transactions on Image Processing and is a senior member of IEEE/CCF/CSIG. Education & Academic Roles: PhD Supervisor/Master Supervisor Member of OPPO Computational Imaging Expert Committee Area Chair for ACM Multimedia 2025 Research Interests: Dr. Li's work bridges theoretical computer vision with practical applications in imaging quality, aesthetic evaluation, and sentiment analysis. His team develops benchmarks like AesBench for large model evaluation. Recent advancements include scanned image enhancement algorithms and personalized aesthetic datasets like PARA. Industry Impact: OPPO ColorOS integration Live broadcast camera technologies DXOMARK Portrait Quality Assessment Challenge winner (2024) Awards & Recognition: First Prize in Shaanxi Natural Science (2021) 2022 OPPO Industry Collaboration Award Stanford's Top 2% Global Scientists (2023-2024) Grants & Labs: Secured 5 National Natural Science Foundation projects. Active in lab collaborations with companies like DJI and Huawei's imaging teams.

• Computer Vision
• Image and Video Quality Assessment
• Computational Aesthetics
• +۳ مورد دیگر

Asst Prof LIU Boxiang holds the position of Assistant Professor and NUS Presidential Young Professorship at the Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore (NUS), within the Faculty of Science. His research focuses on integrating multi-omics approaches with computational methods to study complex diseases such as coronary artery disease and age-related macular degeneration. He specializes in developing statistical and machine learning tools for genomic analysis, including eQTL mapping and deep learning architectures for gene expression regulation. Education: BA in Biophysics (Illinois Wesleyan University), MS and PhD in Bioinformatics (Stanford University). He contributed to the GTEx consortium and is part of the Asian Immune Diversity Atlas (AIDA) initiative. His lab develops methods like ANTseq for ancestry determination and scPrediXcan for cell-type-specific transcriptome studies. Research Interests: Functional genomics, eQTL analysis, deep learning in biomedicine, and computational tools for omics data integration. His work bridges disciplines such as natural language processing and computer vision with biological questions. Scientific Awards: NUS Presidential Young Professorship (2021). His lab's innovations include ParaMed, a biomedical translation dataset, and LinearDesign for optimized mRNA stability. Advising and Grants: Leads the Liu Lab (boxiangliulab.com), focusing on single-cell genomics, mitochondrial dynamics, and computational biomedicine. Collaborates on projects like the RESET cohort study for cardiovascular disease prevention.

• Genomics
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر