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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
• +7 more

HUYNH Bao Tan is a full-time Senior Lecturer of Economics at the School of Economics, Singapore Management University (SMU). He also serves as the Co-Coordinator of the PGPP program, contributing to academic leadership and program development within the school. Education: PhD in Economics, Singapore Management University, 2015 BEng (Hons), National University of Singapore, 2006 His research focuses on core areas of macroeconomic theory, including Real Business Cycles, Monetary and Fiscal Policy, and Macroeconomic Dynamics. His work aligns with broader fields such as International Macroeconomics, Monetary Economics, and Business Cycle Analysis. He applies theoretical and quantitative methods to understand economic fluctuations and policy impacts. The available data does not include recent publications, so no article trends can be analyzed at this time. Scientific Awards: No awards listed in the provided text. HUYNH Bao Tan is involved in academic program coordination and teaching at SMU. There is no public mention of research grants or graduate student supervision in the provided information. There is no mention of specific research labs or collaborative teams in the provided profile.

• Macroeconomic Theory
• Real Business Cycles
• Monetary and Fiscal Policy
• +6 more

Bhattacharya Prasanta is an Adjunct Assistant Professor at the Department of Analytics and Operations (DAO) within the National University of Singapore (NUS) Business School. He has designed graduate and executive-level courses on AI, business analytics, and network science. Additionally, he serves as Innovation Lead & Senior Research Scientist at the Social and Cognitive Computing Department of A*STAR's Institute of High Performance Computing (IHPC), focusing on interdisciplinary research. His research bridges network science, behavioral analytics, and psychometric modeling with AI applications in emerging markets. Key areas include: Analysis of social networks and causal inference Development of AI-driven tools for qualitative data collection Stance detection using large language models Behavioral drivers of online misinformation Teaching includes the course DBA5104 - Introduction to Network Science & Analytics . His work integrates computational methods with real-world challenges in finance, public health, and education. At IHPC, he contributes to projects like Project Communicate, which empowers children with autism in India through technology. Recent research trends show focus on: Multi-modal data analysis for pandemic forecasting Policy impact modeling in social networks Ethical AI implementation in financial literacy No scientific awards are explicitly listed in the provided materials. His roles combine academic teaching with applied research in Singapore's tech ecosystem, emphasizing practical AI solutions for socio-economic challenges.

• Social networks
• Causal inference modeling
• Behavioural Analytics
• +3 more

Kyriakos Mouratidis is a Professor of Computer Science at Singapore Management University's School of Computing and Information Systems. His research focuses on spatial and spatiotemporal databases, particularly continuous query processing, preference-based queries, and optimization in road networks. He develops algorithms for nearest neighbor monitoring, spatial cloaking, and top-k query processing under capacity constraints. Recent work integrates skyline queries with top-k methods to handle preference-based queries with controllable output size. Dr. Mouratidis has received the Distinguished Associate Editor Award (SIGMOD 2024), Lee Kong Chian Research Fellowship, and serves as Associate Editor for GeoInformatica and SIGMOD Record. He directs the MAIS program and teaches data management courses.

• Spatial Databases
• Continuous Query Processing
• Preference-based Queries
• +2 more

Dirk HARTUNG is a Full-time Assistant Professor of Law at the Yong Pung How School of Law (YPHSL), Singapore Management University (SMU). He is a Ph.D. candidate in Law (Dr.iur.) at Leibniz University Hannover and holds a German State Exam (LL.M. equivalent) from Hanseatisches Oberlandesgericht, Hamburg (2014) and an LL.B. from Bucerius Law School (2013). His academic career includes founding the Center for Legal Technology and Data Science at Bucerius Law School and leading the Summer Program Legal Technology and Operations. His research focuses on Computational Legal Studies, Legal Data Science, and the intersection of Artificial Intelligence with legal practices. Key projects include applying complexity science to legal systems and studying the future of justice in Southeast Asia. He co-founded dskrpt GmbH and serves as an Affiliate at Stanford's CodeX Center for Legal Informatics. His work spans legal drafting analysis, regulatory complexity, and digital transformation in legal education. Notable contributions include the 'LexGLUE' benchmark dataset for legal language understanding and studies on detecting problematic legal drafting patterns ('Law Smells'). He actively bridges legal theory with technological applications, emphasizing interdisciplinary approaches in legal training. His professional roles include Executive Director of Legal Technology at Bucerius Law School and Chief of Staff roles. Grants and collaborations include work with the German Research Foundation and interdisciplinary projects with computer science departments. HARTUNG leads the 'Future of Justice' initiative and explores legal education adaptations in the AI era. He is affiliated with multiple legal technology initiatives and regularly publishes in top journals like Artificial Intelligence & Law and Philosophical Transactions of the Royal Society A .

• Computational Legal Studies
• Legal Data Science
• Artificial Intelligence in Law
• +5 more

Tian Hao is a Lecturer at the School of Big Data and Computer Science , Guizhou Normal University . He received his B.Eng. and Ph.D. in Engineering from the School of Software Engineering at East China Normal University in 2014 and 2020, respectively. During his doctoral studies, he was jointly trained at the University of Maryland under the China Scholarship Council program. From 2020 to 2022, he was a postdoctoral fellow in the Department of Computer Science and Technology at the University of Hong Kong . Prior to joining Guizhou Normal University in June 2024, he served as a Senior Engineer and Team Leader at Huawei HiSilicon's Turing Robotics division. Education: 2010–2014: B.Eng., School of Software Engineering, East China Normal University 2014–2020: Ph.D. in Engineering, School of Software Engineering, East China Normal University 2018–2019: Joint Doctoral Training, University of Maryland, USA 2020–2022: Postdoctoral Fellow, Department of Computer Science and Technology, University of Hong Kong Research Interests: Tian Hao's research spans embodied intelligent learning in robots , dexterous grasping simulation and planning , human-robot interaction , and virtual/augmented reality (VR/AR) . His work integrates machine learning, computer graphics, and robotics to enable robots to manipulate complex objects in cluttered or virtual environments. Publications & Trends: His publications (2016-2023) consistently focus on robotic grasping and manipulation , virtual hand interaction , and real-time simulation . Notable venues include IEEE Transactions on Robotics , IEEE VR , and ICRA , reflecting an emphasis on high-impact robotics and graphics venues. Scientific Awards: No specific awards are listed in the provided text. Grants & Projects: Research on 3D Design and Dexterous Robot Sewing Technology for Personalized Garment Manufacturing (NSFC-RGC, 2017-2020) Visual model and environment construction based on multi-source data and its dynamic simulation (NSFC, 2016-2020) Quadruped robot dog autonomous inspection solution based on Ascend AI platform (Huawei HiSilicon) Ascend-based balloon robot platform (Huawei HiSilicon) Labs & Teams: He lectures at the Robot Operating System (ROS) Summer School in China and serves as a reviewer for premier IEEE journals and conferences, indicating active engagement in the robotics and VR communities.

• Embodied intelligent learning in robots
• Simulation and planning of dexterous grasping
• Human-robot interaction
• +3 more

Lek Hsiang Hui is a Senior Lecturer (Educator Track) at the Department of Information Systems and Analytics, School of Computing, National University of Singapore (NUS). He concurrently serves as the Assistant Dean for Industry Relations and holds roles in the Faculty Teaching Excellence Committee (FTEC) and NUS Teaching Academy. His academic journey includes a PhD in Information Systems (NUS) and a First Class Honours B.Sc. in Computer Engineering (NUS). His research focuses on Intelligent Systems, Sentiment Analysis, Natural Language Processing, and Education Technology. Notable work includes developing aspect-based sentiment analysis frameworks and educational data mining tools for collaborative learning assessment. He actively contributes to executive training in Healthcare Analytics through the Centre of Health Informatics (CHI). Dr. Hui has been consistently recognized for teaching excellence, earning multiple Annual Teaching Excellence Awards (ATEA) and Faculty Teaching Excellence Awards (FTEA) between 2014 and 2019. His awards include the prestigious National Day Awards Commendation Medal 2022. He advises the NUS StartIT student society, fostering IT and technopreneurship interests. His service roles include curriculum development, faculty teaching committees, and leadership in the NUS Teaching Academy.

• Intelligent Systems
• Sentiment Analysis
• Natural Language Processing
• +3 more

Associate Professor Qiu Lin holds joint appointments in the School of Social Sciences and College of Computing & Data Science at Nanyang Technological University. His research integrates social psychology with computational methods to study personality expression, virtual reality interventions, and social media behavior. Key research areas include using big data analytics to understand personality traits through digital footprints (social media posts, favorite songs), developing VR environments for mental health interventions, and modeling social dynamics through agent-based simulations. His work has been recognized with best paper awards at international conferences. Dr. Lin serves as Associate Editor for the Journal of Computational Social Science and Asian Journal of Social Psychology. He mentors a large research team of PhD students investigating computational social science applications.

• Computational Social Science
• Social Media Analytics
• Personality Psychology
• +2 more

Lin Qika is a Research Fellow at the Saw Swee Hock School of Public Health, National University of Singapore (NUS). His research focuses on advancing natural language processing (NLP) and AI applications in healthcare, particularly leveraging large language models (LLMs) for robust healthcare solutions. He holds a PhD from Xi’an Jiaotong University (2023), an M.S. from Beijing Institute of Technology (2019), and a B.S. from the same institution (2016). His expertise spans multi-modal representation learning, neuro-symbolic systems, and logical reasoning applied to healthcare challenges. Notable research includes developing frameworks like TECHS for explainable extrapolation reasoning and integrating knowledge graphs with LLMs. His work emphasizes practical healthcare applications, such as depression detection and medical diagnostics. Lin Qika’s recent publications (2022–2025) explore cutting-edge topics like contrastive graph representations, knowledge graph completion, and adversarial attacks on knowledge embeddings. He actively contributes to conferences like ACL, SIGIR, and KDD, showcasing innovations in AI-driven healthcare and multimodal reasoning. No scientific awards or funded grants are explicitly mentioned in the provided information.

• Natural Language Processing
• Artificial Intelligence in Healthcare
• Multi-modal Representation Learning
• +3 more

Panchali GUHA is an Adjunct Lecturer at the School of Social Sciences (SOSS), Singapore Management University (SMU), and concurrently serves as a Postdoctoral Fellow in Sustainability and Governance. She holds a PhD from the National University of Singapore (2022), an MS in Agricultural and Resource Economics from the University of Maryland (2001), and an MA in Economics from the Delhi School of Economics (1996). Her research focuses on policy design, education governance, climate change impacts, and sustainability frameworks. Her work spans interdisciplinary domains, including school-based management reforms in India, policy implementation dynamics, and environmental governance. Recent contributions address green skills gaps, dengue control policy evolution, and the role of social media in shaping PhD experiences. Earlier studies explored groundwater management and pesticide impacts under climate change. No scientific awards are explicitly mentioned in the provided texts. Her teaching includes 'Education Policy - A Global Perspective' at SMU. Research outputs highlight a trajectory from environmental economics to education policy, reflecting a commitment to bridging academic research with practical governance challenges.

• Sustainability and Governance
• Education Policy
• School-Based Management
• +3 more

Rudy Setiono is an Associate Professor and Assistant Dean of Graduate Studies at the School of Computing, National University of Singapore (NUS). He has been with NUS since August 1990, following the completion of his Ph.D. in Computer Science from the University of Wisconsin-Madison. Previously, he served as Vice Dean (Undergraduate Affairs) from November 2001 to July 2005 at the School of Computing. B.Sc. in Computer Science from Eastern Michigan University (1984) M.Sc. in Computer Science from University of Wisconsin-Madison (1986) Ph.D. in Computer Science from University of Wisconsin-Madison (1990) Professor Setiono's research focuses on neural networks, particularly in rule extraction, neural network construction and pruning, and applications in optimization. His work spans theoretical foundations to practical implementations in credit scoring, poverty analysis, and business intelligence. He has made significant contributions to making neural networks more interpretable through rule extraction techniques, bridging the gap between black-box models and transparent decision systems. His recent publications show a strong trend toward practical applications of neural network rule extraction in credit scoring, poverty analysis, and document processing. The research demonstrates consistent evolution from theoretical neural network construction to real-world applications across finance, social sciences, and business analytics, with an emphasis on model interpretability and practical implementation. Senior Member of IEEE Associate Editor of IEEE Transactions on Neural Networks (2000-2005) Professor Setiono has supervised numerous research projects and taught courses including BT4103 Business Analytics Capstone Project, BT4240 Machine Learning for Predictive Data Analytics, IS5152 Data-Driven Decision Making, and IS4240 Business Intelligence Systems. His work has been published in reputable journals including IEEE Transactions on Neural Networks, IEEE Transactions on Data and Knowledge Engineering, and Neurocomputing.

• Data Science
• Business Analytics
• Intelligent Systems
• +4 more

TAN Swee Liang is a full-time Associate Professor of Economics (Practice) at the School of Economics, Singapore Management University (SMU). He also serves as the Co-Coordinator of the Postgraduate Program in Economics (PGPP), contributing to academic leadership and program development. Educational Background: Ph.D. in Econometrics, Monash University, Australia, 1994 Bachelor of Economics (First-Class Honors), Monash University, Australia, 1990 His research spans both theoretical and applied domains in economics, with a strong emphasis on quantitative methods and policy relevance. Key interests include financial sector development , country risk assessment , and pedagogy research and development . His methodological expertise lies in econometrics and data science , particularly in macro-econometrics, panel data, and spatial econometrics. He also investigates applied microeconomic issues , especially in the economics of education, and contributes to strategic themes such as digital transformation and economic growth in Asia. The available publications suggest a consistent focus on empirical economic modeling, policy evaluation, and educational innovation, though specific article details are not provided in the source text. Scientific Awards: No awards listed in the provided text. Advising and Grants: TAN Swee Liang contributes to academic mentorship through his role as PGPP Co-Coordinator, though no formal list of advisees or funded research grants is mentioned in the profile. Labs and Research Teams: No specific research labs, centers, or collaborative teams are referenced in the provided information.

• Econometrics
• Financial Sector Development
• Country Risk Assessment
• +9 more

Bryan HOOI Kuen-Yew is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the School of Computing and the Institute of Data Science. His research focuses on enhancing the reliability and applicability of machine learning systems, particularly in trustworthiness, graph learning, and real-world applications such as cybersecurity and biomedical informatics. He holds a PhD in Machine Learning from Carnegie Mellon University (2019), and dual MSc (Computer Science) and BSc (Mathematics) degrees from Stanford University (2014). His work has been recognized with awards including the KDD 2016 Best Paper Award (Research Track) and the ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award. Research Interests: Trustworthy AI: Mitigating hallucinations, biases, and distribution shifts in large language models. Graph Algorithms: Developing robust methods for graph mining, anomaly detection, and fraud detection in dynamic networks. Multimodal Learning: Leveraging vision-language models and knowledge graphs for improved reasoning and security. Biomedical Applications: Applying AI to healthcare challenges, including drug design and medical data analysis. Selected Awards: ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award KDD 2016 Best Paper Award (Research Track) NUS Computing Teaching Excellence Award (2024) Advising and Grants: Actively supervises PhD and master’s students, with research supported by grants focusing on AI security, graph learning, and trustworthy systems. Collaborates on projects such as PhishAgent (multimodal phishing detection) and UniGraph (cross-domain graph foundation models).

• Machine Learning
• Graph Algorithms
• Anomaly Detection
• +7 more

Jerrold SOH is an Assistant Professor of Law at the Yong Pung How School of Law, Singapore Management University (SMU), serving concurrently as Deputy Director of the Research Programme in Computational Law at the Centre for Digital Law. He holds an LL.M. from Harvard University and dual first-class honors degrees in Law (LL.B.) and Economics (B.Soc.Sci.) from the National University of Singapore (NUS). His academic journey includes roles as Deputy Director at the Centre for Computational Law (2021–2024) and Lecturer of Law at SMU (2018–2020). SOH’s research focuses on empirical legal studies, law and technology intersections, and computational methods in jurisprudence. He investigates topics such as judicial citation patterns, algorithmic accountability, AI-driven legal systems, and tort liability for automated vehicles. His work often bridges legal theory with data science, exemplified by projects like legal text formalization, network analysis of case law, and blockchain applications in property law. SOH has published widely in journals like the Asian Journal of Comparative Law , Frontiers in Physics , and American Business Law Journal , with recent work addressing AI legal implications, blockchain land transfers, and antitrust regulation. He is a Chief Data Scientist at Lex Quanta Pte Ltd and has received honors such as the SMU START Overseas Postgraduate Scholarship and multiple Dean’s Lists at NUS. His professional contributions span computational law infrastructure development, legal innovation advocacy, and educational initiatives in technology law. Current research priorities include AI ethics in legal contexts, control-centric liability frameworks, and empirical analyses of judicial decision-making processes.

• Empirical Legal Studies
• Law and Economics
• Law and Technology
• +6 more

Stanley KOK is an Assistant Professor at the Department of Information Systems & Analytics, School of Computing, National University of Singapore (NUS). He also serves as the Associate Director of the NUS AI Lab. He holds a Ph.D. in Computer Science from the University of Washington and a combined A.B./S.cB. in Computer Science & Economics from Brown University. His research focuses on artificial intelligence, machine learning, and their applications in e-commerce, fintech, healthcare, and social networks. He has led projects on AI-driven product categorization and interpretable healthcare models. Education: Ph.D., Computer Science, University of Washington, 2010 Combined A.B./S.cB., Computer Science & Economics, Brown University, 1999 Research Interests: AI/ML applications in e-commerce and healthcare Machine Translation for Product Categorization Interpretable Machine Learning Models Social Network Analysis Recent work emphasizes scalable AI solutions for product taxonomy (e.g., translating product descriptions to categories) and healthcare predictive systems. His articles span topics like graph neural networks for credit scoring and accountable relational triple extraction. Awards include teaching excellence and research grants from NUS and industry partners. Advising and Grants: Guided over 20 students to academic and industry roles Recipient of National Innovation Challenge grant (2019) and Rakuten collaborations Labs/Teams: Leads the SGEMS lab, focusing on energetic and motivated student-driven AI research. Collaborates with NUS AI Lab and industry partners like Rakuten.

• Artificial Intelligence
• Machine Learning
• E-Commerce
• +6 more