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Joseph Tao-yi WangView profile

Professor

• Experimental Economics
• Neuroeconomics
• Game Theory
• +3 more

Joseph Tao-yi Wang is a Distinguished Professor in the Department of Economics at National Taiwan University (NTU). He holds a PhD from UCLA and previously served as a Postdoctoral Scholar and Visiting Associate at Caltech. His research spans experimental economics, neuroeconomics, game theory, and behavioral economics, with a focus on strategic decision-making, market design, and learning in games. Wang directs the Taiwan Social Sciences Experimental Laboratory (TASSEL), which hosts large-scale experimental research and conferences like the 2017 APESA. His work integrates eye-tracking, pupillometry, and machine learning to study cognitive processes in economic decisions. Wang is also active in educational innovation, developing flipped classroom models with experiments for economics courses. His publications consistently explore behavioral deviations from game-theoretic predictions, such as overcommunication in sender-receiver games and learning patterns in auctions. Recent work emphasizes reproducibility in management science and AI applications in education. Wang’s research uses diverse methodologies—from neuroimaging to field experiments—to test economic theories in real-world contexts. Wang mentors through NTU’s Berkeley Economics Student Assistant Program (BESAP) and organizes mini-courses for high school students. He has not received scientific awards per the available data.

• Consumer Behavior
• Social Media Marketing
• Digital Marketing
• +3 more

Natalie T. Wood, PhD (Auburn University, 2002), is a Professor of Marketing and Associate Dean at Saint Joseph’s University’s Haub School of Business. Her expertise spans consumer behavior and social media marketing, with a focus on digital engagement and virtual environments. PhD, Auburn University (2002) GradDip in Marketing, Edith Cowan University (1999) BBus in Marketing, Edith Cowan University (1996) Her research explores social media’s influence on consumer trends, political marketing, themed hospitality authenticity, and virtual education. Key journals include Journal of Consumer Behaviour and Business Horizons . Recent publications analyze microblogging’s role in customer service and brand promotion. Notable awards: Saint Joseph’s University Faculty Merit Award for Teaching (2013) and Research (2007). Grants from Arrupe Center (2019) and Direct Selling Education Foundation (2010-2011) highlight her funded work. Professional roles include Assistant Director, Center for Consumer Research (2007-2011).

Nicolas Federico MartinView profile

Associate Professor

• Precision Agriculture
• Crop Science
• Machine Learning Applications in Agriculture
• +7 more

Nicolas Federico Martin is an Associate Professor in the Department of Crop Sciences at the University of Illinois at Urbana-Champaign, with additional appointments as Associate Professor in the Center for Latin American and Caribbean Studies, Center for Digital Agriculture, and the National Center for Supercomputing Applications (NCSA). His interdisciplinary work bridges traditional agricultural science with cutting-edge computational approaches. Dr. Martin's research focuses on the intersection of agriculture and data science, with particular emphasis on: Precision agriculture and on-farm experimentation methodologies Machine learning applications for crop management and yield prediction Nitrogen and nutrient management optimization Soybean and corn breeding and production systems Remote sensing and UAV applications in agriculture Sustainable agricultural practices including cover crop management His publication record demonstrates a clear trajectory toward increasingly sophisticated integration of artificial intelligence with agricultural science. Recent work shows heavy emphasis on using machine learning algorithms (particularly reinforcement learning, convolutional neural networks, and generalized additive models) to solve practical farming challenges related to crop management decisions, yield prediction, and resource optimization. This research has significant implications for both scientific understanding of crop-environment interactions and practical farm management. Dr. Martin actively collaborates across disciplines and institutions, as evidenced by his extensive co-authorship network spanning agronomy, computer science, environmental science, and economics. His work has garnered attention from numerous news outlets and social media platforms, indicating its relevance to current agricultural challenges. He is a key contributor to the Data-Intensive Farm Management project, which aims to transform agronomic research through on-farm precision experimentation. His affiliation with NCSA provides access to high-performance computing resources essential for processing large agricultural datasets. Additionally, his work in Latin American agriculture (particularly in Mexico and Argentina) reflects his commitment to addressing global food security challenges.

Hayriye CagnanView profile

Senior Lecturer

• Neuroscience
• Neuromodulation
• Brain Stimulation
• +4 more

Dr. Hayriye Cagnan is a Senior Lecturer (equivalent to Associate Professor) at Imperial College London and Associate Unit Member at the MRC Brain Network Dynamics Unit. She holds a PhD in Neuroscience from the University of Amsterdam and Philips Research Laboratories (2010), an MSc in Engineering and Physical Science in Medicine from Imperial College London, and a BS in Electrical and Electronics Engineering from Cornell University as a Fulbright Scholar. Her research develops theory-driven strategies for selective modulation of brain rhythms using: Theoretical disease models and stimulation paradigms Dynamic neuromodulation approaches Wearable devices for remote therapy optimization Advanced signal processing techniques Her recent publications (2015-2025) demonstrate consistent focus on tremor pathophysiology, neural oscillations in Parkinson's disease, and advanced brain stimulation technologies. Research employs computational modeling, EEG, magnetoencephalography, and both invasive/non-invasive stimulation techniques. Awards & Fellowships: Fulbright Scholarship (2000-2004) British Chevening Scholarship (2004) MRC Skills Development Fellowship (2015) MRC Career Development Award (2018) Dr. Cagnan leads an independent research group originally established at the MRC Brain Network Dynamics Unit in 2018, maintaining close collaborations with clinical neuroscientists and engineers to translate basic research into therapeutic applications.

Maarten de RijkeView profile

Professor

• Information Retrieval
• Recommender Systems
• Retrieval Models and Ranking
• +8 more

Maarten de Rijke is a Professor at the University of Amsterdam and affiliated with the Innovation Center for Artificial Intelligence (ICAI) . He is a leading expert in Information Retrieval , Recommender Systems , and Machine Learning , with over 500 publications and 10,000 citations. His work spans theoretical and applied domains, including conversational recommender systems , domain generalization , and neural ranking models . Research Pillars: Information retrieval, e-commerce search, learning to rank, and empathetic AI systems Awards: Best Paper (2x), Best Student Paper Community Roles: Organized workshops (MANILA25, SIGIR editions) His recent publications focus on robust recommendation systems , cross-domain contract extraction , and brain signal integration for query refinement. He leads the AIRLab (Amsterdam) and collaborates with institutions like Shandong University and the University of Chinese Academy of Sciences. Scientific Contributions : Over 500 publications in ACM Transactions, SIGIR proceedings, and journals Developed novel frameworks for learning-to-rank and user satisfaction modeling Pioneered research on conversational AI and adversarial attacks in retrieval He actively engages in community service, including organizing conferences and advocating for epilepsy research through initiatives like Emma’s collection box (over €24,448 raised). His work bridges theoretical rigor with real-world impact in search and recommendation technologies.

• Machine Learning
• Artificial Intelligence
• Distributed Systems
• +3 more

Ameet Talwalkar is an Associate Professor in the Machine Learning Department at Carnegie Mellon University and Chief Scientist at Datadog. He holds a PhD from the Courant Institute at NYU (2010) where he received the Janet Fabri Prize for Best Thesis. His professional achievements include co-founding Determined AI (acquired by HPE), creating MLlib in Apache Spark, co-authoring the textbook 'Foundations of Machine Learning,' and spearheading the MLSys conference. Talwalkar's research focuses on fundamental challenges in machine learning systems, including distributed ML, federated learning, neural architecture search, and human-AI interaction. His work bridges theoretical foundations with practical applications across domains like computational biology, PDE solving, and code generation. Current interests include AI for science, specialized model development, and agent-based systems. His publications demonstrate strong focus on ML systems optimization, foundation model evaluation, and interpretable AI. Recent works investigate specialized foundation models, PDE-solving frameworks, code generation tools, and human-AI interaction paradigms. The research consistently targets efficiency, scalability, and practical deployment challenges. Best Paper Award at EAAMO 2023 Best Student Paper at NYAS ML Symposium 2009 Runner-up for Best Real-world Application at Socal ML Symposium 2017 Janet Fabri Prize for Best PhD Thesis (2010) Talwalkar leads the CMU MLSys Lab focused on scalable ML systems and has served as Board President for the MLSys conference series. His educational contributions include developing courses like 'Machine Learning with Large Datasets' and creating the LEAF benchmark for federated learning and NAS-Bench-360 for neural architecture search.

Catherine TuckerView profile

Professor

• Digital Privacy
• Marketing
• Artificial Intelligence
• +7 more

Catherine Tucker is the Sloan Distinguished Professor of Management and Professor of Marketing at the MIT Sloan School of Management. She serves as the faculty director of the EMBA program and has chaired the MIT Sloan PhD Program. Her research focuses on the intersection of technology, marketing, and public policy, with expertise in digital privacy, online advertising, and blockchain applications. She co-founded the MIT Cryptoeconomics Lab and has testified before Congress on digital privacy issues. Tucker holds a PhD in Economics from Stanford University and a BA from the University of Oxford. Education: PhD in Economics, Stanford University Bachelor of Arts, University of Oxford Research Interests: Algorithmic bias and fairness Privacy regulation and consumer data Blockchain technology applications Healthcare technology and digital health Marketing strategy in digital ecosystems Awards: NSF CAREER Award (2010s) William F. O’Dell Award (Long-term Impact in Marketing) Garfield Economic Impact Award (2020) Editorial Roles: Senior editor at Marketing Science , former co-editor at Quantitative Marketing and Economics , and associate editor roles at Management Science and Journal of Marketing Research . Labs & Initiatives: Co-director of the NBER Digital Economics and AI program, and co-founder of the MIT Cryptoeconomics Lab studying blockchain applications.

• Statistical Machine Learning
• Deep Learning
• Computational Biology
• +2 more

Rui Li is an Associate Professor in the Ph.D. program at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. She directs the Lab for Use-inspired Computational Intelligence (LUCI), focusing on AI applications in computational biology and medical imaging. Education includes: B.Sc. in Computer Science, Harbin Institute of Technology M.Sc. in Computer Science, Tianjin University of Technology Ph.D. in Computing and Information Sciences, RIT Research integrates statistical machine learning with computational biology, medical image analysis, and human visual attention modeling. Current projects include deep learning for histopathology, multimodal medical image registration, and gene network inference. Publications demonstrate consistent focus on medical AI applications, with recent advances in unsupervised image registration, interactive segmentation, and multimodal fusion techniques. Key trends include self-supervised learning, uncertainty-aware models, and human-AI collaboration frameworks. Awards include the NSF CAREER Award for developing adaptive machine intelligence systems. Advises multiple PhD students on projects spanning deep learning architectures, biomedical image analysis, and biological network modeling. Leads several NSF-funded projects including human-centered image understanding systems and gene-protein network inference tools. Directs LUCI lab investigating machine learning for healthcare applications and teaches graduate courses in Statistical Machine Learning and Deep Learning.

Richard BalmeView profile

Professor

• Environmental Governance
• Climate Policy
• EU-China Relations
• +3 more

Richard Balme is a Professor at the Centre for European Studies and Comparative Politics (CEE) at Sciences Po, specializing in environmental governance, EU-China relations, and public policy. He serves as Director of the Master in International Governance and Diplomacy at PSIA and the Executive Master in Development Policy and Management (Potentiel Afrique) at Sciences Po Executive Education. His research focuses on climate governance, biodiversity policy in the EU, and China's environmental administration. He is a Fellow at several institutions, including Hong Kong Baptist University and the Centre des Politiques de la Terre in Paris. His academic contributions span environmental policy transfers, EU-China energy dynamics, and institutional reforms in Francophone Africa. He has taught at Columbia, Tsinghua, and Fudan Universities and served on key French advisory councils. Balme's work bridges comparative politics, international relations, and environmental studies, with a focus on transnational policy frameworks and governance capacity. Research Themes: Climate policy implementation, EU-China environmental cooperation, authoritarian governance of environmental issues. Recent Activities: Convenor of Sciences Po's AIRE environmental research platform; editorial board member of Environmental Policy and Governance . Awards: Ranked first in France's national political science professor recruitment (1995), recipient of grants from EU, CNRS, and Hong Kong RGC. His publications analyze China's environmental policy evolution, EU climate diplomacy, and the intersection of soft power and human rights in Sino-European relations. He frequently engages in policy dialogues on water security, urbanization risks, and global climate governance.

Philip ThomasView profile

Associate Professor

• Artificial Intelligence
• Reinforcement Learning
• AI Safety
• +3 more

Philip Thomas is an Associate Professor and Doctoral Program Director at the Manning College of Information and Computer Sciences, University of Massachusetts Amherst. He leads the Autonomous Learning Lab (ALL) and co-founded the Reinforcement Learning Conference (RLC). His research focuses on reinforcement learning, AI safety, and algorithms that ensure safety guarantees for high-risk applications like healthcare and digital marketing. Education: PhD in Computer Science, University of Massachusetts Amherst (2015) MSc in Computer Science, Case Western Reserve University (2009) BSc in Computer Science, Case Western Reserve University (2008) Research Interests: Thomas specializes in designing biologically plausible reinforcement learning algorithms and ensuring safety through frameworks like Qualia Optimization and Seldonian Algorithms . His work emphasizes off-policy evaluation, fairness guarantees, and ethical AI. Recent projects include developing benchmarks for medical decision-making (e.g., ICU-Sepsis) and analyzing adversarial robustness in speech denoising models. Articles Trends: His recent work spans high-confidence policy evaluation, fairness metrics, and algorithmic safety. Key themes include improving benchmarking practices, rethinking eligibility traces, and leveraging state abstraction for consistent off-policy evaluation. Awards & Grants: Armstrong Award Co-PI on Army Research Grant (IoBT), NSF grant (FMitF) Significant funding from Adobe Research Advising & Grants: Thomas has overseen grants totaling millions and mentored students in reinforcement learning and AI safety. His current focus includes exploring qualia optimization for doctoral applications (2026-2027). Labs & Teams: He directs the Autonomous Learning Lab and collaborates on interdisciplinary projects at the Center for Data Science, emphasizing ethical AI and safe machine learning systems.

• Artificial Intelligence in Education (AIED)
• User Modeling
• Human-Computer Interaction (HCI)
• +5 more

Professor Judy Kay is a leading academic in Computer Science at the University of Sydney, holding a Chair in Human-Centred Technology Research within the Faculty of Engineering and IT. Her work focuses on designing personalized systems that empower users to control their data, particularly in lifelong learning and health contexts. She leads the Human Centred Technology Research Cluster and is Editor-in-Chief of the International Journal of Artificial Intelligence in Education (IJAIED). Education: Studied at the University of Sydney, inspired to pursue academia due to its multidisciplinary environment and student collaboration opportunities. Research interests include user modeling, scrutable AI systems, and interfaces for personal informatics. Current projects span virtual reality learning environments, health data dashboards, and ethical AI transparency. Her work has led to commercialized technologies like the Personis system. Publications include over 200 peer-reviewed works in top venues like CHI, UbiComp, and AIED conferences. Awards highlight her contributions to computing education and AIED, including the 2023 Lifetime Achievement Award. Advises 4 current PhD/Master’s students focusing on topics like Open Learner Models in VR and health decision-making tools. Collaborates with multidisciplinary teams across computer science, health sciences, and education. Labs/Teams: Leads research groups in the School of Computer Science and collaborates with the Charles Perkins Centre on health-related projects.

Amalia FokaView profile

Assistant Professor

• Artificial Intelligence Applications in Arts
• Computer Science for Creative Practices
• Digital Interaction
• +4 more

Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.

Xugui ZhouView profile

Assistant Professor

• Cyber-Physical Systems
• Security
• Dependability
• +7 more

Xugui Zhou is an Assistant Professor jointly appointed in the Department of Electrical and Computer Engineering and the Department of Computer Science at Louisiana State University (LSU). He holds a Ph.D. in Electrical and Computer Engineering from the University of Virginia (2024), where he worked under Prof. Homa Alemzadeh. Prior to his Ph.D., he earned an M.Eng in Control Science and Engineering and a B.Eng in Automation from Shandong University. Before joining UVA, he was a Senior Engineer at NR Research Institute, State Grid of China. His research focuses on the intersection of system dependability, security, and control theory, with applications in autonomous systems and healthcare. Key interests include engineering resilient autonomous vehicles, mitigating adversarial attacks on cyber-physical systems (CPS), and enhancing safety through formal methods and machine learning. He leads a research lab with openings for Ph.D. students and interns. Zhou is actively involved in academic service, serving on program committees for DSN'26, AsiaCCS'26, and ICCPS'25, among others. He also chairs workshops like DSML'25 and organizes conferences such as ICCPS'24. His recent awards include the UVA Engineering Endowed Graduate Fellowship (2023) and selection as a Rising Star in Cyber-Physical Systems (2023). His teaching includes courses like AI-Driven Autonomous Driving Systems and ML for CPS Security at LSU. He has extensive experience in teaching and research supervision, emphasizing hands-on projects and interdisciplinary collaboration.

Shari SabetiView profile

Senior Lecturer

• Arts Education
• Cultural Heritage Education
• Decolonizing Approaches in Education
• +4 more

Dr. Shari Sabeti is a Senior Lecturer in Arts and Humanities Education at the Moray House School of Education and Sport, University of Edinburgh. She holds a PhD in English Literature from the University of Cambridge and is a Senior Fellow of the Higher Education Academy (SFHEA). Her research focuses on arts, cultural heritage, and decolonizing education, with projects in museums, schools, and Pacific Island communities. She has led initiatives in the Marshall Islands, Samoa, and Hawaii, emphasizing participatory and arts-based methodologies. Education: MA (Hons) English (Cambridge, 1994), MA English and American Literature (UCL, 1995), PhD English Literature (Cambridge, 1999), PGCE Secondary English (IOE, 2002). She has taught at the University of Stirling and the University of Edinburgh, where she currently organizes courses on anthropology of education, creative pedagogies, and literacy. Research interests include museum education, decolonizing approaches, Pacific Island education, and creative writing pedagogy. Her work bridges anthropology, ethnography, and arts-based methods, exploring community resilience and identity formation. Recent projects include 'Remediating Stevenson' (AHRC-funded, 2022–2025) and studies on Marshallese displacement through arts education. Awards and grants include a £1M AHRC Standard Grant for decolonizing Pacific literature and multiple GCRF awards for education in the Marshall Islands. She has supervised over a dozen PhD students on topics like museum pedagogy, diaspora education, and creative writing practices. Labs/Networks: Centre for Creative Relational Inquiry, Digital Cultural Heritage Network, Atelier Creative Arts and Social Sciences Network. Her work often involves partnerships with museums, NGOs, and international communities.

Ananth GramaView profile

Professor

• Parallel Computing
• Distributed Systems
• Machine Learning
• +7 more

Ananth Grama is the Samuel D. Conte Distinguished Professor of Computer Science and Associate Director of the Center for Science of Information at Purdue University. He holds a faculty position in the Department of Computer Science, College of Science. His research focuses on parallel computing, distributed systems, machine learning, and their applications in complex systems such as materials modeling and clinical analytics. He teaches advanced courses like CS525 (Parallel Computing) and CS314 (Numerical Methods). Research interests span parallel algorithms, fault-tolerant learning, quantum machine learning, and data-driven healthcare analytics. Recent work addresses fundamental limits of generative models, online learning under noisy conditions, and clinical outcome predictions. His projects include DOE-funded research on critical element recovery and NIH grants for hearing assessment technologies. Notable contributions include over 50 peer-reviewed publications since 2022, with recent papers appearing at ICLR, NeurIPS, and ICML. Current postdocs include Changlong Wu (collaborating with Wojciech Szpankowski) and Luopin Wang (with Nadia Atallah). He advises seven graduate students and oversees multidisciplinary research teams.