جستجوی استاد راهنمای دانشگاهی

Kuldeep S. Meelمشاهده پروفایل

دانشیار

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +۳ مورد دیگر

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at Georgia Institute of Technology's School of Computer Science and an Associate Professor at the University of Toronto (currently on leave). His research focuses on the intersection of Formal Methods and Artificial Intelligence, emphasizing scalable automated reasoning techniques. He holds prestigious awards including the 2022 ACP Early Career Researcher Award and the 2019 NRF Fellowship for AI. His work has been recognized with multiple best paper awards at conferences like ICLP, CAV, and IJCAI. Meel's research spans automated reasoning, formal methods, and their applications in AI. He has developed influential tools like ApproxMC and UniGen, advancing model counting and uniform sampling. His academic journey includes roles at NUS and collaborations with institutions globally. Teaching excellence is highlighted by NUS Annual Teaching Awards (2022, 2023). Awards include Distinguished Paper Awards at CAV-23 and CAV-24, and 1st place in Model Counting Competitions. His lab has produced notable advisees securing tenure-track positions worldwide. Current projects explore distribution testing, probabilistic reasoning, and AI verification.

Cindy Xiongمشاهده پروفایل

استادیار

• Data Visualization
• Visual Perception
• Cognition
• +۳ مورد دیگر

Cindy Xiong is an Assistant Professor in the School of Interactive Computing at Georgia Tech. She holds a Ph.D. in Cognitive Psychology from Northwestern University and undergraduate degrees in Applied Mathematics and Psychology from UCLA. Her research focuses on the intersection of visual perception, cognition, and data visualization, aiming to understand how humans interpret and make decisions from visualized data. She has pioneered work on mitigating confirmation bias, perceptual illusions in charts, and the impact of visualization design on decision-making. Her research has been recognized with an NSF CAREER Award (2023) and an NSF Medium Award (2024), as well as best paper awards at IEEE PacificVis (2024) and ACM CHI (2022). She co-founded VISxVISION, an initiative bridging visualization researchers and perceptual psychologists. She also serves on the Human Factors Task Group at NIST to evaluate forensic lab standards. Teaching: She designed a UX Research course at UMass Amherst, emphasizing user-centered design, experimental methods, statistical analysis, and effective communication of research findings. The course involves group projects analyzing UX challenges and presenting data-driven solutions. Grants/Awards: NSF funding totaling $1.2M supports her work on visual analytic decision-making and mitigating confirmation bias. Her honors include VGTC Dissertation Award recognition (2022) and IEEE CG&A Best Paper Runner-Up (2022). Research Labs/Teams: Active in NIST's Human Factors Task Group and leading the VISxVISION collaboration. Her work often involves interdisciplinary teams combining psychology and computational methods.

Trang Vuمشاهده پروفایل

مدرس

• Natural Language Processing (NLP)
• Machine Learning
• Trustworthy AI
• +۵ مورد دیگر

Trang Vu is a Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology. Her research focuses on trustworthy NLP methods, cultural-aware machine translation, and efficient ML techniques like active and transfer learning. She holds a PhD in AI and Machine Learning from Monash University, awarded in 2022. Education: Doctoral of Philosophy (AI and Machine Learning) - Monash University (2022) Research Interests: Safe and trustworthy NLP methods for LLM alignment and hallucination mitigation Cultural-aware machine translation systems Efficient NLP techniques including active learning and semi-supervised methods Recent Projects: TMLGenAI (2023-2026): Developing safe and aligned foundation models Knowledge-Intensive Multimodal ASR research (2024) Collaborations: International collaborations in generative AI and multilingual NLP Team leader roles in multiple large-scale AI projects

Anjali Mazumderمشاهده پروفایل

پژوهشگر

• Responsible AI
• Algorithmic Fairness
• Data Science
• +۶ مورد دیگر

Anjali Mazumder is a Theme Lead at the Alan Turing Institute, focusing on AI and Justice & Human Rights. Her work emphasizes responsible and inclusive data/AI methodologies to safeguard vulnerable populations and promote equitable systems. She holds a doctorate in Statistics from the University of Oxford and two masters' degrees from the University of Toronto. Her research spans Bayesian decision support systems, causal reasoning, bias detection, and algorithmic fairness. Notable roles include membership on Canada’s National DNA Databank Advisory Committee (2012–2018), the UK Forensic Science Regulator’s fingerprint interpretation subgroup, and the senior management board of the UK’s Policy and Evidence Centre for Modern Slavery and Human Rights. Her projects include developing tools for modern slavery prevention and advancing AI for human rights. She contributes to interdisciplinary collaborations across academia, policy, and practice in the UK, US, and Canada.

Kevin Buzzardمشاهده پروفایل

استاد

• Pure Mathematics
• Computation Theory and Mathematics
• Formal Proof Verification
• +۳ مورد دیگر

Kevin Buzzard is a Professor of Pure Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His work focuses on formal proof verification and the integration of computers into mathematical research. He leads projects formalizing mathematical theorems using Lean, including a 21st-century proof of Fermat’s Last Theorem. Notable talks include the 2024 Stanford MRC Public Lecture on AI in mathematics and the 2022 International Congress of Mathematicians plenary lecture on formalism trends. His research spans Pure Mathematics and Computation Theory, emphasizing collaboration between mathematicians and computer scientists. He co-maintains Mathlib, a formalized mathematical library, and teaches Lean to undergraduates. Awards include the Whitehead Prize (2023) and Senior Berwick Prize (2023) from the London Mathematical Society. Buzzard’s career includes research positions at the University of Cambridge, Harvard, and the Institute for Advanced Study. He explores how AI and theorem provers will transform mathematical practice, advocating for formalization as a foundational tool for future mathematics.

Katie Steeleمشاهده پروفایل

استاد

• Rational Choice and Inference
• Ethics and Risk
• Climate-Change Policy
• +۴ مورد دیگر

Katie Steele is a Professor in the School of Philosophy at the Australian National University (ANU), where she holds an ANU Futures Scheme grant. She previously served at the London School of Economics (2008–2016) and conducted postdoctoral research at the University of Sydney's Australian Centre of Excellence for Risk Analysis. Her roles include co-editing Economics & Philosophy and co-investigator on major grants like the 'Ethics and Risk' ARC Discovery Project and the 'Climate Change and Future Generations' grant. Education: BSc in Mathematics, University of Queensland (1999) MA in Philosophy, University of Queensland (2003) PhD in Philosophy, University of Sydney (2007) Research Interests: Her work focuses on rational choice theory, ethics of climate policy, intergenerational fairness, and moral uncertainty. She explores decision-making under risk, the implications of climate change for future generations, and the philosophical foundations of belief revision and Bayesian reasoning. Grants & Advising: She leads the ANU Futures Scheme grant (2018–2025) and advises on projects addressing ethics in risk analysis. Her advisory work includes mentoring students in decision theory and climate ethics. Labs/Teams: Active in interdisciplinary collaborations at ANU and international institutions like the Institute for Futures Studies in Stockholm.

Ernesto Jiménez-Ruizمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +۵ مورد دیگر

Dr. Ernesto Jiménez-Ruiz is a Senior Lecturer in Artificial Intelligence and Director of Research at City, University of London. He holds a PhD from Jaume I University, Spain (2010), and has held academic positions since 2010. His research focuses on knowledge graphs, ontology alignment, semantic web technologies, and neuro-symbolic AI. Currently, he teaches modules on semantic web technologies and research methods. He leads the Research Center affiliated with City St George's, University of London. Administrative Roles: Director of Research (2024–present), Senior Tutor for Research (2023–2024) Research Interests: AI, knowledge representation, ontology alignment, semantic integration, and biomedical informatics. His work bridges machine learning and symbolic reasoning, with contributions to ontology matching and knowledge graph embeddings. He supervises PhD students in areas like knowledge graph alignment and neuro-symbolic systems. Awards: SWSA Ten-Year Award (2021) for influential ontology alignment work. He is a Fellow of AdvanceHE (FHEA). Grants & Collaborations: Leads projects on semantic industrial data modeling and collaborates with Samsung Research UK, Bosch, and Statoil. Active in initiatives like the Turing Institute's Knowledge Graph Interest Group.

Sao Mai Nguyenمشاهده پروفایل

مدرس

• Robotics
• Artificial Intelligence
• Cognitive Science
• +۷ مورد دیگر

Sao Mai Nguyen is an Enseignante-Chercheuse (Lecturer-Researcher) at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS). Her research bridges robotics, artificial intelligence, and cognitive science, focusing on cognitive developmental robotics, intrinsic motivation in learning, and human-robot interaction. She explores how robots can adapt to social and physical environments, particularly in physical rehabilitation and smart home applications. Research Interests: Nguyen’s work integrates machine learning, robotic embodiment, child psychology, and neuroscience. Key areas include human-robot interaction, assistive robotics for chronic low back pain rehabilitation, activity recognition in smart homes using IoT sensors, and intrinsic motivation-driven learning frameworks. Recent Contributions: Her 2024 HDR thesis on reinforcement and imitation learning for sequential tasks underscores her expertise in strategic learning systems. Recent publications address bio-inspired robotics models, hierarchical reinforcement learning, and benchmark environments like 'Open the Chests' for activity recognition. Collaborations include projects like the R-COOL randomized trial for robot-coached physical exercises. Labs & Teams: She contributes to the U2IS lab, advancing interdisciplinary research in AI and robotics. Her work spans experimental platforms for sensorimotor learning and healthcare robotics applications.

• Machine Learning
• Social Media Analysis
• Public Health
• +۳ مورد دیگر

Lydia Manikonda is an Assistant Professor in the Department of Information Systems and Web Science at the Lally School of Management, Rensselaer Polytechnic Institute (RPI). She is also affiliated with the AIRC (Artificial Intelligence Research Center) at RPI. Her research focuses on developing intelligent decision-making models using data from social media, online forums, and news articles to address challenges in business, public health, and technology. Her work has been recognized with awards, including the Best Reviewer Award at ICWSM 2016 and the Outstanding Demonstration Award at ICAPS 2014. Dr. Manikonda holds a PhD in Computer Science from Arizona State University (2019). Her research interests span interpretable machine learning, privacy in AI systems, social media analytics, and the societal impacts of technology. She has published extensively on topics like misinformation detection, ethical AI design, and pandemic-related behavioral shifts. Education: PhD in Computer Science, Arizona State University (2019) Her recent work emphasizes interdisciplinary approaches to tackle real-world problems, such as predicting intimate partner violence using machine learning and analyzing anti-Asian hate discourse on Reddit during the pandemic. She collaborates with interdisciplinary teams to bridge gaps between technical innovation and societal needs. Key Awards: Best Reviewer Award at ICWSM 2016 Outstanding Demonstration Award at ICAPS 2014 Dr. Manikonda's research also explores anthropomorphism in voice assistants, temporal privacy concerns, and the role of emotional cues in medical crowdfunding platforms. She actively participates in academic leadership roles, such as organizing the Web Science 2024 conference. Her lab focuses on actionable domain models and data-driven decision-making, with applications in healthcare, technology ethics, and public policy. She mentors students in these areas and is committed to advancing AI for societal good.

Jeff Heflinمشاهده پروفایل

دانشیار

• Semantic Web
• Ontology Development
• Artificial Intelligence
• +۳ مورد دیگر

Jeff Heflin is an Associate Professor in the Department of Computer Science and Engineering at Lehigh University, and Director of the Cognitive Science Program. He holds a PhD from the University of Maryland, College Park, and a B.S. from The College of William and Mary. His research focuses on AI for Data Management, Scalable Reasoning, and Data Exploration, with significant contributions to Semantic Web technologies like SHOE, DAML+OIL, and OWL. Education: Ph.D. in Computer Science, University of Maryland, 2001 M.S. in Computer Science, University of Maryland, 1999 B.S. in Computer Science, The College of William and Mary, 1992 Research Interests: Heflin’s work advances Semantic Web infrastructure, including ontology development, scalable reasoning systems, and data integration. His contributions include foundational benchmarks like LUBM and innovations in entity matching (DAME), table search (StruBERT), and neural-augmented logical reasoning. Awards: Winner of the Billion Triple Challenge (ISWC 2012) Best Paper Award, ISWC 2004 Ruth and Joel Spira Award for Excellence in Teaching (2017) Service & Leadership: Served as Vice-President of the Semantic Web Science Association (SWSA), Editorial Board member of the Artificial Intelligence Journal , and General Chair of ISWC 2017. Active in conference organization (CIKM 2020, COLING 2022) and journal special issues on Semantic Web evaluation. Labs & Collaborations: Leads the Semantic Web and Agent Technologies (SWAT) Lab at Lehigh, focusing on AI-driven data management and scalable reasoning systems. Collaborates on NSF-funded projects like CRISPS (cell-centric image similarity search) and Domain-Agnostic Dataset Search.

• Database Systems
• Data Management
• Graph Databases
• +۵ مورد دیگر

Alin Deutsch is a Professor of Computer Science at the University of California, San Diego (UCSD), specializing in database systems, graph databases, and formal verification. He has contributed significantly to research areas including query optimization, data integration, and privacy-preserving systems. His work spans theoretical foundations and practical implementations, such as the Linked Data Benchmark Council (LDBC) and the TigerGraph database system. He co-authored over 100 papers and has been involved in major conferences like SIGMOD and VLDB. Research interests include graph query processing, parallel computing, data-centric business processes, and automated system verification. Recent work focuses on scalable hybrid analytics and graph databases. Deutsch is also active in database education, co-authoring a paper on UCSD's database curriculum. He has led projects in privacy-aware systems, such as policy-aware location-based services, and contributed to tools like CLIDE for interactive query formulation in service-oriented architectures. His collaborations involve industry partners like TigerGraph and academic institutions globally.

Scott Weinsteinمشاهده پروفایل

استاد

• Logic
• Philosophy of Mathematics
• Cognitive Science
• +۳ مورد دیگر

Scott Weinstein is a Professor of Philosophy, Mathematics, and Computer Science at the University of Pennsylvania, where he also serves as Director of the Logic, Information, and Computation Program. His academic appointments span multiple departments within the College of Arts and Sciences, reflecting his interdisciplinary expertise. He holds a Ph.D. from Rockefeller University. Education: Ph.D. in Philosophy, Mathematics, and/or Computer Science from Rockefeller University Research Interests: Dr. Weinstein’s work focuses on foundational aspects of logic, philosophy of mathematics, cognitive science, and formal learning theory. His research bridges theoretical computer science, mathematical logic, and ancient philosophical paradoxes such as Zeno’s. He explores topics like truth detection models, inductive inference systems, complexity in finite structures, and the philosophical implications of theoretical terms in scientific inquiry. Articles Trends: His publications reflect a sustained engagement with formal methods in science and philosophy. Recent work (e.g., 2022) re-examines classical paradoxes through modern mathematical lenses, while earlier contributions (e.g., 1988, 1989) investigate inductive reasoning and computational models of scientific discovery. Key themes include the interplay between logic and empirical inquiry, the structure of scientific knowledge, and foundational questions in mathematics and computing. Awards: No scientific awards are explicitly mentioned in the provided text. Advising & Grants: While no formal advisees or grant details are listed, his role as Director of the Logic, Information, and Computation Program indicates leadership in interdisciplinary academic initiatives. His publications often involve collaborations with colleagues like Daniel Osherson and Michael Stob. Labs/Teams: As Director of the Logic, Information, and Computation Program, he oversees a collaborative research environment at the University of Pennsylvania, integrating philosophical, mathematical, and computational disciplines.

Yongfeng Zhangمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Information Retrieval
• +۸ مورد دیگر

Yongfeng Zhang is an Associate Professor in the Department of Computer Science at Rutgers University. He is also the Director of the AIOS Foundation . His research focuses on Machine Learning, Data Mining, Recommender Systems, and Explainable AI , with notable contributions to fair and personalized AI, AI for science, and social good. Education and Experience: PhD in CS (2011–2016) from Tsinghua University Postdoc at UMass Amherst (2016–2017) Joined Rutgers as Assistant Professor (2018), promoted to Tenured Associate Professor (2024) Research Interests: Machine Learning, Data Mining, Information Retrieval, Recommender Systems, Natural Language Processing, ML Systems, AI Agents, Explainable AI, Fairness, and AI for Science. Recent Achievements: Received the 2024 ACM SIGIR Test of Time Award, 2024 Presidential Teaching Excellence Award, and multiple NSF grants. He has advised over 15 PhD students and led projects on trustworthy AI, generative recommendation, and causal inference. Awards: 2024 ACM SIGIR Test of Time Award 2021 NSF CAREER Award 2015 Microsoft PhD Fellowship Grants & Labs: Led NSF grants on explainable AI, conversational recommendation, and neural-symbolic AI. Active in the AIOS platform development and multiple research labs.

Liane Gaboraمشاهده پروفایل

استاد

• Creativity
• Cultural Evolution
• Cognitive Science
• +۵ مورد دیگر

Liane Gabora is a Full Professor in the Department of Psychology at the University of British Columbia's Okanagan Campus, affiliated with the Irving K. Barber Faculty of Arts and Social Sciences. She holds additional research affiliations with the Australian National University's School of Music, University of Waterloo's Institute for Complexity & Innovation, and Free University of Brussels' Centre Leo Apostel for Interdisciplinary Studies. Her educational background includes a BSc from the University of Western Ontario, PhD from the Free University of Brussels, and postdoctoral positions at UC Berkeley and the Free University of Brussels. Gabora's research program explores creativity, cultural evolution, and cognitive transitions through interdisciplinary approaches. Her work examines: How concepts combine and adapt to new contexts The origins of modern cognition and human creativity Computational models of cultural evolution Quantum formalisms for concept interactions Self-Other Reorganization (SOR) theory of cultural evolution Honing theory of creativity integrating complex systems and memory Her publications demonstrate strong cross-disciplinary integration spanning psychology, physics, anthropology, and computer science. Major research awards include the 2011 Berlyne Award from the American Psychological Association for outstanding research by a junior scholar. She has secured over $1 million in funding from NSERC, SSHRC, and private donors. Gabora supervises graduate students in Psychological Science and Digital Humanities programs and leads research on cognitive transitions underlying cultural evolution. Her laboratory focuses on mathematical and computational modeling of creativity mechanisms, with ongoing projects exploring AI creativity, quantum models of cognition, and the evolution of behavioral modernity through autocatalytic network frameworks.

• Cybersecurity
• Explainable AI (xAI)
• Machine Learning
• +۵ مورد دیگر

Aleksandra Pawlicka is a prominent researcher specializing in cybersecurity , explainable AI , and disinformation detection . Her work bridges ethical considerations with technical innovations in network security and AI applications , particularly focusing on the intersection of human-robot collaboration , fake news analysis , and IoT vulnerabilities . Key research areas: Cybersecurity Ethics, AI Explainability, Disinformation Mitigation Notable collaborations: Marek Pawlicki, Rafal Kozik, Michal Choras Her publications analyze xAI challenges in intrusion detection, machine learning for network security, and ethical dilemmas in cybersecurity. She contributes to projects like ULTIMATE (robotic AI) and SWAROG (fake news detection), emphasizing transparency and practical implementation. Recent work explores adversarial attacks against AI systems , few-shot learning for cybersecurity, and neuro-symbolic reasoning for trustworthy AI. Her 2023 study on ChatGPT's impact on scientific communication highlights evolving technological and ethical landscapes in academic practices.