جستجوی استاد راهنمای دانشگاهی

David Hsuمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Machine Learning
• +۲ مورد دیگر

David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.

• Machine Learning
• Artificial Intelligence
• Combinatorial Optimization
• +۱۳ مورد دیگر

Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .

Andrew Lanمشاهده پروفایل

دانشیار

• Artificial Intelligence in Education
• Educational Data Mining
• Personalized Learning
• +۵ مورد دیگر

Andrew Lan is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he also serves as the CS Undergraduate Program Director. He was granted tenure by the UMass Board of Trustees in June 2025 and is currently on leave through Spring 2026. His research focuses on developing human-in-the-loop machine learning methods to enable scalable, effective, and personalized learning experiences in education. Dr. Lan received his BS in Physics and Mathematics from the Hong Kong University of Science and Technology, followed by his MS (2014) and PhD (2016) in Electrical and Computer Engineering from Rice University. He completed postdoctoral research at Rice University (2016) and Princeton University's EDGE Lab (2017-2018). His research spans artificial intelligence for education, with particular expertise in educational data mining, knowledge tracing, personalized learning systems, and human-AI collaboration in educational contexts. Dr. Lan's work leverages massive and multimodal learner and content data collected from both traditional classrooms and online learning platforms to develop systems that deliver high-quality, affordable, and personalized learning experiences. He has made significant contributions to areas including computerized adaptive testing, math word problem generation, student affect detection, and automated grading systems. His recent work increasingly focuses on leveraging large language models for educational applications while maintaining rigorous scientific validation of these approaches. Best Student Paper Award at the 2024 AIED Conference (with Alexander Scarlatos) Best Paper Nominee at LAK 2021 Best Student Paper Award at IEEE Big Data 2020 NAEP Math Automated Scoring Challenge Grand Prize Winner Dr. Lan actively mentors graduate students and postdoctoral researchers, with several of his advisees receiving recognition for their work. He has secured substantial funding from the National Science Foundation, including a $90M grant for the SafeInsights project, a secure cyberinfrastructure for educational research. His research group collaborates with institutions including Worcester Polytechnic Institute, University of Pennsylvania, and Rice University. He teaches undergraduate and graduate courses including COMPSCI 240 (Reasoning under Uncertainty) and COMPSCI 590OP (Applied Numerical Optimization), with a focus on the practical application of theoretical concepts in machine learning and artificial intelligence. His educational philosophy emphasizes bridging the gap between theoretical foundations and real-world implementation in educational technology.

Niki Kitturمشاهده پروفایل

استاد

• AI-Augmented Cognition
• Human-Machine Collaboration
• Sensemaking Systems
• +۴ مورد دیگر

Aniket 'Niki' Kittur is a Professor in the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science. His research focuses on AI-augmented cognition, exploring how human and machine intelligence can collaborate to enhance creativity, decision-making, and innovation. He leads projects like the Semantic Reader and Skeema browser extension, aiming to reduce cognitive overload through intelligent systems. Education: BA in Psychology & Computer Science from Princeton University; PhD in Cognitive Psychology from UCLA. His work bridges HCI, crowdsourcing, and cognitive science, with 100+ publications and 17 best paper awards. He advises industry partners including Google, Microsoft, and Toyota while maintaining a lab focused on real-world impact. Research interests center on accelerating knowledge acquisition via systems that scaffold sensemaking (e.g., Selenite for web exploration) and fostering analogical innovation through crowdsourced/AI hybrid approaches. Notable contributions include CrowdForge (human-machine workflows) and Kinetica (touch-based data visualization). Awards include NSF CAREER Award, Allen Newell Award, and CHI Academy membership. His lab's Skeema tool has achieved 79% 30-day retention in beta, reflecting impactful user-centered design principles. Current projects emphasize LLM integration for composite cognition, aiming to create systems where 'LLMs + Humans > Either Alone.' Funding来自NSF, NIH, ONR, and industry partners like Bosch and Wikimedia. Teaching includes PhD bootcamps and user-centered research courses. Over 100 students have contributed to his projects, many advancing to tech leadership roles.

• Data Science
• Computational Social Science
• Misinformation
• +۳ مورد دیگر

Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.

Lukas Engelmannمشاهده پروفایل

مدرس ارشد

• History of Epidemiology
• History of Science and Medicine
• Digital Health
• +۷ مورد دیگر

Lukas Engelmann is a Senior Lecturer at the University of Edinburgh , specifically within the Science, Technology and Innovation Studies department under the School of Social and Political Science . His research focuses on the history and sociology of biomedicine , with particular interest in epidemiological reasoning , visual cultures of disease , digital epidemiology , and decolonial approaches to medical history . The Epidemy Lab , which he founded, explores the historical development of epidemiology and its contemporary influence on data-driven public health and pandemic policy-making . Engelmann's work has been funded by prestigious grants including an ERC Starting Grant (2021-2025) for his research on the history of epidemiological reasoning, and support from the Wellcome Trust for projects examining the social dimensions of digital health . His book 'Mapping AIDS' (2018) established him as a leading scholar in medical visualization , while 'Sulphuric Utopias' (2020) with Christos Lynteris explores the technological history of maritime sanitation and its political implications. Recent publications emphasize the visual and data practices that have shaped epidemiology, including works on epidemic modeling during the COVID-19 pandemic , the history of plague mapping , and the ethical implications of digital phenotyping . He has also contributed to interdisciplinary discussions on syndemics , co-infection epistemology , and the commercialization of bacteriology in the early 20th century. His scientific contributions have earned recognition through editorial roles in journals like Big Data and Society , and collaborative projects such as 'Working with Diagrams' (2022) which investigates the epistemological role of visual tools in medical knowledge production. Scientific Awards and Funding: ERC Starting Grant (2021-2025) Wellcome Trust Institutional Support Fund British Academy/Leverhulme Small Research Grant Chancellor's Fellowship (University of Edinburgh) 'Sulphuric Utopias' listed in The Guardian's 30 Books to Understand the World (2020)

• Software Engineering
• Reverse Engineering
• Program Understanding
• +۷ مورد دیگر

Ken Wong is an Associate Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. He also serves as Associate Chair within the same department. Holding a PhD in Computer Science from the University of Victoria (1999), his research focuses on software engineering challenges such as reverse engineering, program understanding, and software visualization. He emphasizes improving software evolution through tools like architecture recovery and root cause analysis, with applications in web/mobile platforms and diverse system understanding. Teaching highlights include developing Massive Open Online Courses (MOOCs) via Coursera, including the 'Software Product Management Specialization' and courses on Agile practices, client needs analysis, and software metrics. His recent publications (2023–2025) span AI-driven healthcare innovations (e.g., medical imaging, photoacoustic tomography) and advanced computer vision techniques (e.g., diffusion models, video inpainting). Notable collaborations include EVAREST studies on heart failure management and lung transplantation outcomes. His work bridges software engineering theory and practical applications in healthcare technology, with contributions to federated learning frameworks (e.g., FedLPPA) and AI-augmented clinical decision support systems. Research also extends to autonomous driving (DriveGPT4-V2) and 3D human avatar generation (DreamAvatar), showcasing interdisciplinary impact.

Cynthia D. Rudinمشاهده پروفایل

استاد

• Interpretable Machine Learning
• Sparse Models
• Causal Inference
• +۷ مورد دیگر

Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in the Departments of Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics. She directs the Interpretable Machine Learning Lab and has held previous positions at MIT, Columbia, and NYU. Her educational background includes: Undergraduate degree from the University at Buffalo PhD from Princeton University (2004) Research Interests: Dr. Rudin's research focuses on interpretable machine learning and its applications across multiple domains. Her work emphasizes creating machine learning models whose reasoning processes people can understand, which includes algorithms for extremely sparse models, interpretable neural networks, interpretable matching methods for causal inference, and dimension reduction for data visualization. She applies these techniques to critical societal problems in healthcare, criminal justice, materials science, and other domains. Her lab has developed practical code for sparse models such as decision lists, decision trees, and additive models that provably optimize accuracy and sparsity. Dr. Rudin's recent publications (2024-2025) demonstrate a strong focus on interpretable AI applications across diverse fields including healthcare (mortality risk scores, breast cancer prediction), materials science (metamaterials design), and environmental justice (location-based health analysis). Her work consistently emphasizes practical implementations with real-world impact, particularly in high-stakes decision-making domains where model transparency is critical. Scientific Awards: Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (2022) - often described as the "Nobel Prize of AI" INFORMS Society on Data Mining Prize (2024) Guggenheim Fellowship (2022) Three-time winner of the INFORMS Innovative Applications in Analytics Award (2013, 2016, 2019) Winner of the 2023 John M. Chambers Statistical Software Award for PaCMAP Winner of the 2024 Award for Innovation in Statistical Programming and Analytics Dr. Rudin has advised numerous PhD students and postdocs who have co-authored significant publications with her. Her lab has received substantial funding for projects applying interpretable machine learning to healthcare (seizure prediction in ICU patients), criminal justice (crime series analysis), and energy infrastructure (underground electrical distribution networks). Her work on the Series Finder algorithm has been adapted by the NYPD and has been running live in NYC since 2016. She directs the Interpretable Machine Learning Lab at Duke, which includes the Almost-Matching-Exactly Lab focused on interpretable causal inference. Her team develops practical code implementations for all their research, emphasizing usability and real-world application in critical domains.

John Zeleznikowمشاهده پروفایل

استاد

• Artificial Intelligence in Law
• Dispute Resolution Systems
• Data Mining in Sports
• +۲ مورد دیگر

John Zeleznikow is Professor in both Management and Information Systems at Victoria University, affiliated with the Institute for Sustainable Industries & Liveable Cities. With 48 years of academic experience across Australian, European and US institutions, he has published extensively including 4 monographs and 88 refereed journal articles. His research develops AI applications for decision-making in legal disputes and sports analytics. Notable innovations include negotiation support systems for divorce mediation adopted by Victoria Legal Aid. Professor Zeleznikow has secured over A$8 million in competitive grants and supervised 20 PhD candidates. His educational background includes: PhD from Monash University (1980) Graduate Diploma in Computing from University of Melbourne (1987) BSc (Hons) from Monash University (1973)

Lingming Zhangمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Machine Learning
• +۶ مورد دیگر

Lingming Zhang is an Associate Professor at the Department of Computer Science, University of Illinois Urbana-Champaign, affiliated with the Grainger College of Engineering. His research focuses on the intersection of Software Engineering, Programming Languages, and Machine Learning, with a particular emphasis on automated program repair, compiler testing, and large language model (LLM) applications in software engineering. He has published over 100 papers, achieving an h-index of 50+, and holds an ACM Distinguished Member status. Research Interests: LLM-based software testing, repair, and synthesis Fuzzing of deep-learning libraries and compilers Open-source code LLMs (e.g., StarCoder2, Magicoder) with over 1M downloads Automated program repair systems (e.g., AlphaRepair, ChatRepair, Agentless) Recent Contributions: Developed TitanFuzz for coverage-guided compiler fuzzing Released Agentless , an LLM-based coding tool adopted by OpenAI and DeepSeek Proposed SWE-RL to enhance LLM reasoning via reinforcement learning Service Roles: Program Co-Chair for ASE 2025 and LLM4Code 2025 Associate Chair for OOPSLA 2024 and Area Chair for ICSE 2025/2026 Recipient of NSF CAREER Award and ACM SIGSOFT Early Career Award Lab/Teams: Develops open-source tools like UniAPR for efficient patch validation Active in releasing industry-adopted LLM-based software engineering tools

• Information Access
• Information Retrieval
• Natural Language Processing
• +۳ مورد دیگر

Tetsuya Sakai is a Professor at the School of Fundamental Science and Engineering within Waseda University's Faculty of Science and Engineering. His work focuses on information access, retrieval, and natural language processing, with a particular emphasis on evaluation frameworks for search systems. Affiliations: Waseda University (Faculty of Science and Engineering, School of Fundamental Science and Engineering) Academic Rank: Professor Research Interests : Dr. Sakai's research spans four key areas: (1) Information Access —designing systems for direct and immediate information delivery, (2) Search Evaluation —developing metrics like Height-Biased Gain and hierarchical intent-based diversity measures, (3) Fairness in IR —pioneering frameworks for group fairness in conversational search, and (4) Statistical Reform —advocating Bayesian methods and robust experimental design. His work also addresses privacy inconsistencies in mobile apps and cognitive biases in LLMs. Scientific Awards : Notable recognitions include induction into the SIGIR Academy (2023) , ACM Distinguished Member (2018) , ACM Senior Member (2016) , and multiple DEIM/FIT/CSS Best Paper Awards . He has received teaching honors like the Waseda Presidential Teaching Award (2016) and WASEDA e-Teaching Award (2018) . Article Trends : Recent publications highlight: Advancements in LLM-assisted relevance assessments and hallucination diagnostics for tool-augmented models Conversational search fairness through multi-level evaluation frameworks and group diversity metrics Innovations in 3D medical reconstruction from clinical data and multimodal uncertainty modeling Statistical rigor via randomization tests , credible intervals , and topic set design Privacy analysis in mobile app descriptions and cognitive bias studies in search interaction

FENG Menglingمشاهده پروفایل

دانشیار

• Biostatistics
• Healthcare Data Analytics
• Medical AI
• +۳ 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.

Snigdha Chaturvediمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۸ مورد دیگر

Snigdha Chaturvedi is an Associate Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. She previously held faculty positions at the University of California, Santa Cruz, and has conducted postdoctoral research at the University of Pennsylvania and University of Illinois, Urbana-Champaign. PhD in Computer Science from University of Maryland, College Park Bachelor's degree in Computer Science and Engineering from Indian Institute of Technology (IIT) Kanpur Her research spans Natural Language Processing with emphasis on Narrative Understanding , Text Summarization , and Socially Aware Language Generation . She advances Fairness in AI through ethical NLP applications in Mental Health and Educational Technology . Recent work focuses on 2025 publications in ACL and NAACL journals, alongside 2024 contributions to EMNLP Findings and ICLR . Earlier projects include the NarraSum dataset (2022) and MOOC forum analysis (2020). Scientific recognitions include: ACM Student Research Competition First Place (2014) IBM PhD Fellowship (2014-2015, renewed in 2015) Kulkarni Summer Research Fellowship (2015) WPI STEM Faculty Launch Program Participant (2015) Her team has advised 13 PhD and Master's students with notable placements at Bloomberg, AI2, and University of Southern California. Research integrates Accessibility challenges through collaborations with Google and IBM labs.

Margreet Vogelzangمشاهده پروفایل

مدرس

• Psycholinguistics
• Language Processing
• Cognitive Neuroscience
• +۸ مورد دیگر

Dr. Margreet Vogelzang is a Lecturer in Psychology at Newcastle University and an affiliated researcher at Theoretical and Applied Linguistics within the Faculty of Modern and Medieval Languages and Linguistics at the University of Cambridge. Based at the English Faculty Building, 9 West Road, Cambridge, CB3 9DP, and affiliated with Trinity College at Cambridge, she maintains a dual institutional presence that reflects the interdisciplinary nature of her work. Her research program combines neuroscientific, psychological, linguistic, and statistical methodologies to investigate language and cognitive processing across the lifespan. She specializes in multilingual syntactic and semantic processing, employing both written and spoken language paradigms with offline (comprehension, judgments) and online (eye-tracking, EEG, fMRI) measures. Her work examines how hearing loss, socio-linguistic variables, and developmental conditions like autism spectrum disorder influence language processing mechanisms. Analysis of her recent publication record reveals three dominant research trajectories: (1) bilingualism effects on cognitive flexibility in autistic children, (2) neural and cognitive mechanisms of sentence processing in individuals with hearing loss, and (3) computational modeling of pronoun interpretation across diverse populations. These strands demonstrate her consistent focus on bridging theoretical linguistics with cognitive neuroscience to address practical questions about language processing variability. Dr. Vogelzang actively supervises several graduate students including Alexander Cairncross, Andromachi Tsoukala, and Chara Triantafyllidou, and serves as course contact for Li16: Psychology of language processing and learning at Cambridge. Her teaching portfolio includes psycholinguistics, neurolinguistics, cognitive science, and research methods, reflecting her methodological expertise across experimental and computational approaches.

• Artificial Intelligence
• Decision-Making Systems
• Fuzzy Logic
• +۵ مورد دیگر

Núria Agell Jané is a Full Professor at ESADE Business School , Universitat Ramon Llull, specializing in Artificial Intelligence and Decision-Making Systems. She leads the JUICE (Judgements and Decisions in the Market Place) research group and the ESADE D3 - Institute for Data-Driven Decisions . Doctorate in Applied Mathematics (Qualitative Reasoning Modelling), UPC-BarcelonaTech Bachelor's in Mathematics, University of Barcelona Her research focuses on Artificial Intelligence , Decision-Making Systems , and Fuzzy Logic , with applications in Business, Marketing, and Sustainability. Recent publications emphasize Hesitant Fuzzy Linguistic Term Sets , Consensus Modeling , and AI in Sustainable Development . She coordinates multiple publicly and privately funded projects applying AI to Business and Marketing challenges. As PhD Programme Director (2005-2013) and current Department Director of Operations, Innovation and Data Sciences , she has shaped academic and research strategies at ESADE. Her work spans collaborations with institutions like LAAS-CNRS (France) and University of Edinburgh Business School , with over 40 journal publications and 50 conference contributions. She has directly supervised 11 PhD students in AI and Decision Sciences.