جست‌وجوی استاد و پژوهشگر

Gail E. Kaiserمشاهده پروفایل

استاد

• Software Engineering
• Static and Dynamic Program Analysis
• Software Testing
• +۷ مورد دیگر

Gail E. Kaiser is a Professor of Computer Science and the Director of the Programming Systems Laboratory (PSL) in the Computer Science Department at Columbia University. She has been with Columbia University since 1985, becoming a full Professor in 1998. Prof. Kaiser's research spans software engineering, program analysis, software testing, and software security, with recent focus on addressing challenges in AI/ML systems testing and security. Prof. Kaiser received her PhD in Computer Science from Carnegie Mellon University in 1985 and her ScB in Computer Science and Engineering from MIT in 1979. Her dissertation at CMU was titled "Semantics for Structure Editing Environments" under advisor Nico Habermann, and at MIT she completed "Automatic Extension of an Augmented Transition Network Grammar for Morse Code Conversations" under advisor Al Vezza. Prof. Kaiser's research interests primarily focus on software engineering following a systems building approach, with recent emphasis on static and dynamic program analysis techniques to improve software reliability and security. Since 2005, she has investigated testing "non-testable" programs, particularly in machine learning, data mining, and scientific computing applications where traditional testing oracles are insufficient. She has developed novel techniques and tools for detecting bugs and verifying repairs in complex systems. Concurrently, she has worked on collaboration environments for computational scientists, creating knowledge sharing and domain-aware environments to support scientific workflows. Prof. Kaiser's recent publications demonstrate a strong focus on the intersection of software engineering and artificial intelligence. Her work addresses critical challenges in testing AI systems, code understanding through deep learning, vulnerability detection, and educational tools for computational thinking. There's a clear evolution from traditional software engineering topics toward AI/ML applications, with particular emphasis on metamorphic testing for non-testable systems, code similarity analysis, and educational applications. Prof. Kaiser has received numerous prestigious awards throughout her career: Distinguished Journal Award (10 Years) from 18th IEEE International Conference on Software Testing, Verification and Validation (ICST), April 2025 Best Research Paper Award at 24th IEEE International Conference on Source Code Analysis & Manipulation (SCAM), October 2024 Distinguished Reviewer Awards for ASE 2024 and FSE 2024 ACM SIGSOFT Distinguished Paper Award for "CONCORD: Clone-aware Contrastive Learning for Source Code", July 2023 Best Student Paper Award at ICCE 2021 Multiple ACM SIGSOFT Distinguished Paper Awards dating back to 2014 Presidential Young Investigator in Software Engineering and Software Systems from NSF (1988-1993) Prof. Kaiser has chaired Columbia's doctoral program since 1997 and served on editorial boards including IEEE Internet Computing and as a founding associate editor of ACM Transactions on Software Engineering and Methodology. Her lab has been continuously funded by major agencies including NSF, NIH, DARPA, ONR, NASA, and numerous companies. Current grants include significant NSF funding for secure containers architecture, learning semantics of code for software assurance, and finding semantic security bugs. As Director of the Programming Systems Laboratory (PSL), Prof. Kaiser leads research in software systems, program analysis, and software testing. The lab has developed numerous tools and techniques for software reliability and security, with recent focus on challenges in AI/ML systems. Her work bridges theoretical foundations with practical applications, often resulting in deployable tools that address real-world software engineering challenges.

Alexandre RUBESAMمشاهده پروفایل

دانشیار

• Finance
• Asset Pricing
• Financial Econometrics
• +۵ مورد دیگر

Alexandre RUBESAM is an Associate Professor at IÉSEG School of Management (France), specializing in Finance with a focus on asset pricing, financial econometrics, and quantitative trading. He holds a Ph.D. in Finance from Cass Business School (UK), an MSc in Statistics from the State University of Campinas (Brazil), and a Bachelor in Statistics from the same university. Education: Ph.D., Finance, Cass Business School, UK (2008) MSc., Statistics, State University of Campinas, Brazil (2004) Bachelor, Statistics, State University of Campinas, Brazil (2001) His research interests span behavioral finance, risk management, machine learning applications in finance, and portfolio optimization. Notably, he explores topics like market herding during crises, volatility forecasting, and the low-beta anomaly through behavioral lenses. Prof. Rubesam has authored influential papers on information transmission in financial markets, risk parity strategies, and the efficacy of linear models in volatility prediction. His work bridges theoretical finance with practical applications, such as developing machine learning-based portfolio construction methods for emerging markets. Awards: 2007 Dimitris N. Chorafas Foundation Prize 2006 Best Paper Award, Cass Business School His professional roles include Chief Risk Officer at Itaú-Unibanco (2013–2017) and Quantitative Researcher/Trader at Principia Capital Management (2009–2011). He is a member of LEM (Laboratory of Economics and Management) and teaches courses on financial programming, risk management, and portfolio analysis.

Mark de Rondمشاهده پروفایل

استاد

• Organisational Ethnography
• Extreme Context Research
• Negotiation and Mediation
• +۴ مورد دیگر

Mark de Rond is Professor of Organisational Ethnography at Cambridge Judge Business School and Fellow of Darwin College, University of Cambridge. His work focuses on immersive ethnographic studies of human behavior in extreme contexts including war zones, high-stakes sports, and controversial social movements, revealing how individuals navigate challenging circumstances through compromise and sensemaking. His educational background includes a DPhil from the University of Oxford alongside advanced degrees in management and economics, photojournalism, documentary photography, biography, creative nonfiction, and prose fiction. This multidisciplinary training informs his unconventional methodological approach. De Rond's research centers on extreme context ethnography, examining how pressure-cooker environments expose fundamental organizational dynamics. His work spans military medicine (Camp Bastion fieldwork), elite sports (Cambridge Boat Race), Amazon river expeditions, and controversial practices like paedophile hunting. Key contributions explore negotiation, conflict resolution, serendipity, institutional persistence, and the emotional toll of fieldwork, often employing innovative methods like enactive ethnography and linguistic analysis of digital communities. His recent publications reveal a growing focus on digital vigilantism and the societal implications of extreme practices, with increasing interdisciplinary reach across organizational studies, criminology, medical anthropology, and sociology. The work demonstrates methodological creativity through linguistic analysis of Facebook groups, embodiment studies in extreme environments, and dream-based reflexive techniques. Scientific recognition includes: BEST ARTICLE AWARD FOR 2016 Favorite MBA Professor honors from Poets & Quants (2021, 2022) Guinness World Record for first unsupported Amazon row De Rond advises MBA students and delivers executive education to top law firms (Slaughter and May, Allen & Overy), professional services (McKinsey, KPMG), corporations (Sky, BT, Diageo), and NGOs (UNICEF, NHS). His negotiation training from Harvard Law School informs both academic work and university mediation practice. Current research involves collaborative teams studying paedophile hunting (featured in Sundance-nominated documentary Predators ) and high-performance sports organizations, with fieldwork requiring deep immersion in challenging environments. His research methodology involves embedded collaboration with diverse teams including medical personnel in conflict zones, elite rowers, and controversial activist groups, often pushing methodological boundaries through photojournalism and creative nonfiction approaches.

Matthew B. Blaschkoمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۵ مورد دیگر

Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.

• Cybersecurity
• Privacy-enhancing technologies
• Payment system security
• +۵ مورد دیگر

Professor Steven J. Murdoch is a leading figure in cybersecurity and privacy-enhancing technologies at the Department of Computer Science , University College London . His work bridges computer science and law, focusing on payment system security, electronic evidence reliability, and dispute resolution mechanisms. Head of Information Security Research Group at UCL (2022–present) Director at Open Rights Group (2022–present) Research spans payment security , privacy protocols , and legal implications of computer evidence . Key contributions include analyzing EMV protocol flaws, designing secure authentication mechanisms, and improving Tor's anonymity. Publications cover privacy-preserving federated learning , time-lock puzzles , and malware ecosystem analysis Awards include Fellowships from the Institution of Engineering & Technology and British Computer Society , and a Royal Society University Research Fellowship . Teaches COMP0055 Computer Security II and COMP0061 Privacy Enhancing Technologies at UCL

Lars Augestad Lochstoerمشاهده پروفایل

استاد

• Asset Pricing
• Asset Management
• Quantitative Finance
• +۵ مورد دیگر

Lars Augestad Lochstoer is a Professor of Finance at the UCLA Anderson School of Management, where he teaches Empirical Methods in Finance and Data Analytics and Machine Learning in the Master of Financial Engineering program. He previously held faculty positions at Columbia University and London Business School, and served on the Asset Allocation Advisory Committee for the Norwegian Sovereign Wealth Fund from 2016 to 2022. Dr. Lochstoer earned his Ph.D. in Finance from the University of California, Berkeley's Haas School of Business in 2005, following his Sivilingeniør Business Economics degree from the Norwegian University of Science and Technology in 1999. His research focuses on understanding the economic mechanisms that drive asset prices, including stock market return dynamics, cross-sectional stock returns, exchange rates, and commodity markets. He has made significant contributions to asset pricing literature, particularly in volatility expectations, risk-return tradeoffs, and currency risk. His publication record reveals a strong focus on behavioral aspects of asset pricing, with recurring themes of investor expectations, volatility dynamics, and market anomalies. His work often combines theoretical models with empirical evidence, frequently incorporating quantitative methods and data science approaches. Recent publications show increasing attention to currency risk and multi-horizon risk-return relationships, reflecting evolving market conditions and research interests. EFA Viz Risk Management Prize for best paper in Energy Markets, Securities and Prices (2009) Michigan Ross School of Business Mitsui Finance Symposium Best Discussant Award (2012) UCLA Anderson Excellence in Teaching Award (2017, 2020, 2021) RFS Distinguished Referee Award (2021) As an active member of the academic finance community, Lochstoer serves as an associate editor for the Review of Finance and the Critical Finance Review, having previously served in the same capacity for the Review of Financial Studies. His professional service includes committee roles in major finance associations and extensive reviewing for top finance and economics journals. He has also contributed to practical finance through his service on the Asset Allocation Advisory Committee for the Norwegian Sovereign Wealth Fund.

• Computational Linguistics
• Natural Language Processing
• Multilingual Models
• +۷ مورد دیگر

Malvina Nissim is a leading researcher in computational linguistics and NLP at the University of Groningen's Department of Artificial Intelligence, with a focus on multilingual modeling, bias mitigation, and human evaluation frameworks. Key Contributions : Developed CALAMITA (Italian LLM benchmark), IT5 models for Italian language processing, and ReproHum framework for NLP evaluation reproducibility Research Pillars : Multilingual reasoning consistency, perspective-based text analysis, and figurative language modeling Her work spans activation steering techniques, cross-lingual transfer learning, and the creation of specialized language resources like the EurekaRebus dataset and MAGPIE idiom corpus. She pioneered methods for gender bias measurement in BERT and developed the SocioFillmore tool for perspective visualization. Recent publications explore model uncertainty as MCQ difficulty proxy, Italian headline generation benchmarks, and multilingual multi-figurative language detection. She actively participates in teaching initiatives like the "NLP with Bracelets" workshop for Italian high school students. Scientific Awards : ACL Best Paper Award (2025) EMNLP Outstanding Reviewer (2023) EVALITA Leadership Recognition (2024) She advises PhD students in model bias analysis and has contributed to the development of the Dutch Abusive Language Corpus (DALC) and the ReproNLP reproducibility framework. Her collaborations span institutions in Italy, Netherlands, and international NLP communities.

Andrew P. Dicksمشاهده پروفایل

استاد آموزشی

• Chemistry Education
• Green Chemistry
• Sustainable Lab Practices
• +۵ مورد دیگر

Andrew P. Dicks is a Teaching Professor in the Department of Chemistry at the University of Toronto, serving as Associate Chair for Undergraduate Studies. His work focuses on transforming chemistry education through green laboratory practices, sustainable curricula, and innovative pedagogical strategies. He has spearheaded the redesign of undergraduate organic chemistry labs to emphasize microscale experiments, green chemistry principles, and collaborative learning. His research explores methods to integrate the United Nations Sustainable Development Goals into education and has been recognized with the ACS-CEI Award for an undergraduate catalytic chemistry course. Dr. Dicks’ administrative contributions include mentoring faculty and developing programs like the Chemistry Teaching Fellowship. He advocates for teaching assistant training, writing instruction, and student learning communities. His lab initiatives emphasize real-world relevance, using waste materials (e.g., McDonald’s cooking oil) and natural media (citrus juice) to demonstrate chemical principles. Notable research themes include solvent-free reactions, energy-efficient microwave techniques, and metrics for evaluating reaction greenness. His articles highlight trends in green chemistry education, pandemic-driven pedagogical adaptations, and interdisciplinary approaches to materials science. Awards include the ACS-CEI Award, reflecting his impact on advancing sustainable chemistry education globally. Dr. Dicks also champions humor and puzzles in science communication to engage learners and foster a culture of curiosity.

Chris Peikertمشاهده پروفایل

استاد

• Cryptography
• Lattice-based Cryptography
• Coding Theory
• +۲ مورد دیگر

Chris Peikert is a Professor in the Department of Computer Science and Engineering at the University of Michigan's College of Engineering. He received his Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory in 2006 under the supervision of Silvio Micali. Peikert is a leading researcher in cryptography, particularly known for his foundational work in lattice-based cryptography. His research interests span cryptography, lattices, coding theory, algorithms, and computational complexity, with a particular focus on cryptographic schemes whose security can be based on the apparent intractability of lattice problems. Peikert has made significant contributions to the development and analysis of lattice-based cryptographic primitives, including ring-LWE, fully homomorphic encryption, and zero-knowledge proofs. Peikert's recent work demonstrates continued leadership in post-quantum cryptography, with publications in top venues like CRYPTO, EUROCRYPT, and STOC. His research spans theoretical foundations of lattice problems to practical implementations of lattice-based cryptographic systems, including hardware acceleration for fully homomorphic encryption. IACR Fellow (2024) Test-of-Time Award from Crypto 2008 (2023) TCC Test-of-Time Award (2017) Patrick C. Fischer Development Professor of Theoretical Computer Science (2017) Best Paper Award at Eurocrypt 2010 Best Paper Award at STOC 2009 Alfred P. Sloan Foundation Fellowship Google Research Award Peikert has been actively involved in the cryptographic research community, serving on program committees for major conferences including CRYPTO, EUROCRYPT, FOCS, and TCC (where he was program co-chair in 2021). He has also developed educational resources, including extensive lecture materials on lattice-based cryptography and teaching courses on cryptography and theoretical computer science at both the undergraduate and graduate levels.

Haiyang Aiمشاهده پروفایل

دانشیار

• Corpus Linguistics
• Natural Language Processing
• Second Language Acquisition
• +۳ مورد دیگر

Haiyang Ai is an Associate Professor in the Literacy and Second Language Studies program at the University of Cincinnati's School of Education. His research applies corpus linguistics and natural language processing to investigate second language acquisition, writing complexity, and bilingual language processing. His educational background includes a Ph.D. in Applied Linguistics from The Pennsylvania State University (2015), an M.A. in Linguistics & Applied Linguistics from the University of Chinese Academy of Sciences (2006), and a B.A. in English from Shaanxi Normal University (2003). Dr. Ai's research spans corpus linguistics, natural language processing, second language acquisition, and computer-assisted language learning. He specializes in compiling and analyzing native and learner corpora to develop intelligent language learning systems and investigate lexical/syntactic complexity in L2 writing. His methodological approach integrates computational tools with theoretical linguistics to address practical language teaching challenges. His recent publications (2019-2023) demonstrate consistent focus on lexical bundles in professional communication, automating complexity measurement in Chinese, speech perception mechanisms in bilinguals, and grammatical puzzles in English learning. These works bridge corpus-based analysis with psycholinguistic experimentation across diverse subfields including morphosyntax, pragmatic competence, and cognitive processing in L2 acquisition. Dr. Ai has secured multiple University of Cincinnati grants including three CECH Faculty Development Grants ($2500 in 2017-2018 for verb-noun collocation research, $2000 in 2016-2017 for corrective feedback tools) and the NCFDD Faculty Success Program ($3250 in 2017), all supporting his development of computational language learning resources.

• Actuarial Science
• Actuarial Statistics
• Life Insurance
• +۶ مورد دیگر

Steven Haberman is a Professor of Actuarial Science at Bayes Business School, City, University of London. He has held senior leadership roles, including Deputy Dean, Director, and Dean of Bayes Business School until 2015. Prior to this, he was Dean of the School of Mathematics (1995–2002). His academic journey began at the University of Cambridge (BA in Mathematics), followed by PhD and DSc in Actuarial Science from City University. He has served as a Lecturer in Actuarial Science since 1974 and worked part-time at the Government Actuary's Department (1986–2006). He is a Fellow of the Institute of Actuaries and Royal Statistical Society. Research focuses on mortality modeling (e.g., the Renshaw-Haberman model), longevity risk, pensions, and annuities. He has authored/co-authored 5 books and over 180 papers, and edited journals like Journal of Pension Economics and Finance . Awards include an honorary doctorate from the University of Haifa (2018) and research prizes from the Institute of Actuaries. He has supervised 33 doctoral students and served on professional bodies like the Institute and Faculty of Actuaries and the Financial Reporting Council’s Board for Actuarial Standards. Current roles include Editor-in-Chief of Risks and Chair of the Board of Governors of the London Foundation for Banking and Finance.

• Heuristic Search
• Combinatorial Search
• Artificial Intelligence
• +۲ مورد دیگر

Nathan Sturtevant is a Professor at the University of Alberta's Department of Computing Science, an Amii Fellow, and Canada CIFAR Chair. His research spans heuristic and combinatorial search problems, with applications in game AI and pathfinding algorithms. He collaborates with the games industry to implement his research in commercial products. Dr. Sturtevant's research explores search algorithms for single and multiple agents, covering areas such as bidirectional search, meta-learning for game theory, procedural content generation, and multi-agent pathfinding. His work integrates machine learning techniques with classical search algorithms to solve complex problems in game environments. Recent publications demonstrate innovations in search optimization, including novel frameworks for suboptimal bidirectional search, new puzzle difficulty metrics, and applications of transformer models to card game planning. His FarmQuest player telemetry dataset provides resources for studying player behavior in farming simulations.

Alberto Monge Roffarelloمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Digital Wellbeing
• End-User Development
• +۷ مورد دیگر

Alberto Monge Roffarello is an Assistant Professor (RTDb) at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, where he is a member of the e-Lite Research Group. He teaches courses including Human-AI Interaction, Digital Wellbeing, and Web Applications across multiple degree programs such as Computer Science and Systems Engineering, Computer Engineering, and Film and Media Engineering. His research centers on Human-Computer Interaction, with a strong focus on Digital Wellbeing and End-User Development in the Internet of Things . He investigates how users can personalize smart environments through trigger-action programming and how design patterns in digital interfaces—such as Infinite Scroll and Never-Ending Autoplay—exploit psychological vulnerabilities to capture attention. His work aims to empower users with tools for self-control, meaningful interaction, and habit mitigation. The recent publications highlight a consistent trajectory in digital self-control tools , attention-aware design , and semantic recommendation systems for IoT. His research bridges theoretical analysis with practical tools, including conversational agents (HeyTAP), debugging platforms (EUDebug, My IoT Puzzle), and ontologies (EUPont) to lower the barrier for non-technical users. Scientific contributions include: Systematic review of digital self-control tools (TOCHI 2022) Typology of Attention-Capture Damaging Patterns (CHI 2023) Design frameworks for meaningful interactions (AVI 2022) Behavioral interventions for smartphone habits (TiiS 2021) He actively supervises PhD students—Francesca Russo, Robert Everett Schwartz, and Luca Scibetta—on topics ranging from AI for education to digital wellbeing in high-schools. His work is supported by competitive research projects such as EMPATHY - Empathetic Mobility Platform . He has contributed to multiple national and international collaborations, notably with Santa Clara University (USA). Alberto leads and participates in research initiatives within the e-Lite - Intelligent and Interactive Systems group, focusing on intelligent interfaces, user empowerment, and ethical design. His lab develops tools that translate high-level user intentions into executable IoT rules, promote digital literacy, and support conscious technology use.

Jay Ryuمشاهده پروفایل

استاد

• Public Policy and Administration
• Public Budgeting and Finance
• Program Evaluation
• +۷ مورد دیگر

Jay Ryu is a Professor in the Department of Political Science at Ohio University's College of Arts and Sciences, where he also serves as Graduate Director. He is based in Bentley Annex 237 and can be contacted via email at ryu@ohio.edu or by phone at 740-593-1993. Education: Ph.D., Public Administration, University of Georgia, 2003 MPA, George Washington University, 1998 MPA, Seoul National University, 1988 B.A., Political Science, Seoul National University, 1986 Dr. Ryu specializes in public policy and administration, with a strong focus on public budgeting and finance, program evaluation, and fiscal equity. His research increasingly integrates advanced data science techniques such as R, Python, and transformer-based text analysis to study education finance, school aid policies, and monetary policy. He teaches courses in quantitative research methods, policy implementation, taxation, public sector accounting, and financial management in the public sector. His recent scholarly output centers on the impacts of school aid design on efficiency, equity, and student outcomes, with several articles published in journals like Public Budgeting and Finance , Journal of Education Finance , and Public Finance and Management . He has also explored the diminishing effectiveness of U.S. monetary policy and the price puzzle. His working papers reflect a deep engagement with neural network analysis of court cases and longitudinal studies on school funding reforms. Scientific Awards: No awards listed in the provided text. Dr. Ryu is actively involved in research and mentorship, supervising graduate students and contributing to academic discourse through publications and conference presentations. He leads research initiatives analyzing big data in public finance and education policy. While no formal lab or research center is mentioned, his work suggests leadership in data-driven policy analysis within the department.

Martin Henzمشاهده پروفایل

دانشیار

• Programming Languages
• Software Engineering
• Artificial Intelligence
• +۸ مورد دیگر

Martin Henz is an Associate Professor at the National University of Singapore , affiliated with the School of Computing and its Department of Computer Science . His academic journey includes an M.Sc. in Computer Science from Stony Brook University (1993) and a Dr.rer.nat. in Computer Science from Saarland University (1997). He has also worked as a Research Scientist at the German Research Centre for Artificial Intelligence. Research Focus : Scalable Experiential Learning, Systems for Teaching/Learning, AI in Education, Programming Languages, Algorithms, and Constraint Programming. Key Projects : Source Academy (immersive programming environment), Deep Teaching (LMS enhancements), and NUS Seafarers (maritime experiential learning). Publications span education technology, programming languages, and sustainable engineering, with recent works focusing on JavaScript-based pedagogy, automated question generation, and electric vehicle conversions. He supervised Rahul Singhal 's PhD, leading to the educational startup Cerebry, and co-founded Workforce Optimizer Pte Ltd with Alan Sevugan. Awards : NUS Annual Digital Education Award (2021) NUS Annual Teaching Excellence Award (2016/17) Fulbright Scholarship (1990) Startup @ Singapore Champion (2001)