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Katherine MoenView profile

Associate Professor

• Cognitive Psychology
• Memory
• Attention
• +3 more

Dr. Katherine Moen is an Associate Professor in the Department of Psychology at the University of Nebraska at Kearney (UNK). Her research focuses on cognitive psychology with particular emphasis on memory, attention, and visual imagery, utilizing eye-tracking technology to explore how real-world factors influence these processes. She regularly teaches courses including General Psychology, Research Methods, Sensation and Perception, and Memory and Cognition. Education Background : Not explicitly stated in text. Awards : Received the 2024 UNK CAS Faculty Mentoring of Student Research Award. Her research interests revolve around intentional forgetting mechanisms, attentional control, and the interplay between visual working memory and environmental factors. Recent work has examined neural correlates of memory suppression, multimedia learning effectiveness, and spatial reasoning development through mental rotation training. Dr. Moen’s publications span cognitive neuroscience, educational psychology, and applied human factors, demonstrating expertise in both basic research and pedagogical innovation. Her lab employs eye-tracking and neuroimaging techniques to investigate cognitive processes in real-world contexts, with findings contributing to improved instructional strategies and safety-related behavioral models.

Aaron P. BlaisdellView profile

Professor

• Animal Cognition
• Behavioral Neuroscience
• Associative Learning
• +6 more

Aaron P. Blaisdell is a Professor and Area Chair of Behavioral Neuroscience in the Department of Psychology at the University of California, Los Angeles (UCLA). He has been a faculty member at UCLA since 2001, following two years as a Postdoctoral fellow in Psychology at Tufts University. His research is conducted through his laboratory, which can be found at pigeonrat.psych.ucla.edu . Dr. Blaisdell received his educational training at several institutions: a B.A. in Anthropology from SUNY Stony Brook, an M.A. in Anthropology from Kent State University, and a Ph.D. in Psychology with a Behavioral Neuroscience focus from SUNY Binghamton. His research focuses on the intersection between associative and cognitive processes in animal behavior. Specifically, he investigates how animals build and use representations of their world using Pavlovian and instrumental conditioning procedures. His work addresses questions about causal inferences in rats, rationality in rat behavior, spatial map learning in pigeons, sources of behavioral variability, and the role of variability in problem solving. More recently, he has expanded his research to examine human health and disease from evolutionary and ecological perspectives. Dr. Blaisdell's research program spans multiple species including rats, pigeons, and hermit crabs, with particular emphasis on comparative approaches to understanding cognition. His laboratory employs various methodologies including traditional conditioning paradigms, touchscreen-operated chambers, and the Automated Remote Environmental Navigation Apparatus (ARENA) for cross-species comparisons. Throughout his career, Dr. Blaisdell has maintained an active publication record with numerous papers in leading journals across psychology, neuroscience, and animal behavior. His work demonstrates how animal models can inform our understanding of fundamental cognitive processes that may have implications for human cognition and health.

Barbara A. SpellmanView profile

Professor

• Psychology and Law
• Forensics
• Cognitive Psychology
• +3 more

Barbara A. Spellman is a Professor of Law and Professor of Psychology at the University of Virginia. She holds dual appointments in the School of Law and the Department of Psychology. Her research focuses on the intersection of psychology and law, including judicial reasoning, forensics, and the replication crisis in science. Spellman earned her J.D. from NYU School of Law and a Ph.D. in cognitive psychology from UCLA. She has authored numerous publications, including the influential book The Psychological Foundations of Evidence Law (2016) with Michael Saks. She has been recognized with awards such as the Leadership Award from Women in Cognitive Science and is a Fellow of the American Association for the Advancement of Science. Her teaching includes courses on psychology for lawyers and empirical methods in law. Spellman has held visiting professorships at institutions like UCLA and Columbia University. Her work frequently addresses challenges in forensic science communication, legal decision-making, and improving scientific rigor. She has contributed to national committees on intelligence analysis and serves on editorial boards for journals like Perspectives on Psychological Science . Key research themes include the cognitive foundations of legal evidence, the role of counterfactual reasoning in law, and the reform of scientific practices. Spellman’s interdisciplinary approach bridges legal theory with psychological insights, emphasizing practical applications in judicial and policy contexts.

Martin HalveyView profile

Professor

• Information Retrieval
• Human Computer Interaction (HCI)
• Multimodal Interaction
• +3 more

Professor Martin Halvey is a leading academic in the Department of Computer & Information Sciences at the University of Strathclyde, serving as Head of Department. His expertise lies in information retrieval (IR), human-computer interaction (HCI), and multimodal interaction, with a focus on innovative technologies for search and information access. He teaches modules across faculties, including information retrieval, machine learning, big data, and business analysis. Education: PhD in Computer Science from University College Dublin (2007), BSc from University College Dublin (2003) His research emphasizes novel uses of multimodal technology to enhance search experiences, particularly in areas like conversational agents, driver awareness systems, and user effort evaluation. Key projects include the DoSSIER initiative for domain-specific information systems and studies on thermal feedback in mobile devices. Publications span topics such as conversational breakdown recovery, search result ranking, and user experience in automated vehicles. His work has been recognized with awards like the Best Paper Award at ACM CUI 2024 and the CSTG Mark Resnick Best Paper Award. Professional Activities: Organized workshops on Applied AI, presented on AI’s role in discovery, and served as an external examiner for computing programs. Grants/Projects: Principal Investigator on projects like EmergeAdapt TDL QuickStart and co-investigator on EU-funded DoSSIER. Labs/Teams: Active in the Scottish Informatics and Computing Science Alliance (SICSA) and Strathclyde iSchool.

Oriel FeldmanHallView profile

Associate Professor

• Cognitive Neuroscience
• Social Psychology
• Affective Neuroscience
• +8 more

Oriel FeldmanHall is an Associate Professor of Cognitive, Linguistic, and Psychological Sciences at Brown University. She holds a Doctorate from the University of Cambridge and a Bachelor of Arts from Cornell University. Her research focuses on the cognitive and neural mechanisms underlying social behavior, including trust, cooperation, moral decision-making, and social network dynamics. She employs interdisciplinary methods from cognitive neuroscience, behavioral economics, and network science, using tools like neuroimaging and computational modeling. Her work has been recognized with numerous awards, including the APA Distinguished Scientific Award for Early Career Contribution (2022), the Cognitive Neuroscience Society Young Investigator Award (2022), and the NARSAD Young Investigator Award (2017). She teaches courses such as 'Social Psychology' (CLPS 700) and 'The Moral Brain' (CLPS 1760). Funding for her research comes from the National Science Foundation, NIH, and the Brain & Behavior Research Foundation. FeldmanHall's lab investigates how humans navigate social environments, emphasizing the role of emotion and uncertainty in decision-making. Recent studies explore cognitive maps of social networks, emotion prediction errors, and the neural basis of political polarization. She emphasizes the integration of theoretical and empirical approaches to advance understanding of complex social cognition.

Andrzej JanuszView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Data Science
• +4 more

Dr Andrzej Janusz is a Lecturer in the School of Information Systems at Queensland University of Technology (QUT), within the Faculty of Science. He holds a Doctor of Mathematical Sciences in Informatics from Warsaw University. His research focuses on artificial intelligence, machine learning, and data science with particular emphasis on explainable AI, rough sets, and decision support systems. He currently serves as ECR Co-leader for the Fundamental Methods in Data Science & AI Theme at QUT's Centre for Data Science. His work spans innovative areas such as automatic data annotation for machine learning improvement, diagnostic AI models for error analysis, and semantic categorization of large datasets. Key contributions include applications of rough sets theory in cluster analysis and uncertainty modeling using evidence theory. Janusz has led several projects related to data science competitions, including organizing IEEE Big Data Cup challenges. His publications demonstrate a strong track record in top journals like International Journal of Applied Mathematics and Computer Science and conferences such as IEEE Big Data and FedCSIS. While not explicitly highlighted, his work reflects a commitment to advancing both theoretical foundations and practical applications of AI technologies. Janusz is actively involved in the QUT research ecosystem, contributing to initiatives that bridge fundamental data science methods with real-world applications. His research group collaborates internationally, evidenced by co-authored works with institutions across Europe and Australia.

Sanjeev KhudanpurView profile

Associate Professor

• Information Theory
• Speech Recognition
• Machine Translation
• +3 more

Sanjeev Khudanpur is an Associate Professor at Johns Hopkins University (JHU), holding dual appointments in the Department of Electrical and Computer Engineering and the Department of Computer Science. He is affiliated with the Center for Language and Speech Processing (CLSP) and the Human Language Technology Center of Excellence (HLTCOE). Khudanpur received his B.Tech. from IIT Bombay (1988) and his Ph.D. from the University of Maryland, College Park (1997), under Prof. Prakash Narayan, focusing on information theory and data compression. His research integrates information theory with human language technologies, including speech recognition, machine translation, and natural language processing. Key contributions include statistical modeling of language structure, cross-lingual adaptation techniques, and sparse recovery algorithms. He organizes the CLSP Summer Workshops to advance the field and has advised numerous PhD and MSE students. Khudanpur’s work spans academic roles since 1996, including visiting appointments at the IMA (University of Minnesota) and editorial roles in journals. His research has been published in top-tier venues such as IEEE Transactions on Information Theory, Speech Communication, and Computer Speech & Language.

Tom McCoyView profile

Assistant Professor

• Computational Linguistics
• Cognitive Science
• Artificial Intelligence
• +4 more

Tom McCoy is an Assistant Professor of Linguistics at Yale University, affiliated with the Department of Computer Science and the Wu Tsai Institute. He holds a PhD in Cognitive Science from Johns Hopkins University (2022) and was a postdoctoral fellow in Computer Science at Princeton University (2022–2023). His research bridges computational linguistics and artificial intelligence, focusing on how language is learned and represented in humans and neural networks. Key interests include syntax, inductive biases in AI systems, and connections between linguistic theory and neural network architectures. Education: PhD in Cognitive Science (Johns Hopkins University, 2022); Postdoctoral Fellow in Computer Science (Princeton University, 2022–2023). Research explores computational mechanisms underlying human language, including syntactic representations in neural networks and mechanisms for efficient language learning. He investigates how AI systems can be informed by linguistic insights and vice versa. Active in organizing the North American Computational Linguistics Open competition (NACLO) for high school students. Key affiliations include Yale’s Linguistics Department, Computer Science Department, and Wu Tsai Institute. Current activities include advising PhD students/postdocs and hosting research opportunities for Yale undergraduates.

• Cognitive Psychology
• Judgment and Decision Making
• Behavioral Economics
• +11 more

Ben Newell is Professor of Cognitive Psychology and Deputy Head of the School of Psychology at the University of New South Wales (UNSW Sydney). He was appointed Director of the interdisciplinary UNSW Institute for Climate Risk and Response in 2023. His research spans judgment, decision-making, and cognitive processes, with applications in environmental, medical, financial, and forensic contexts. Research Interests: Ben Newell’s work investigates the cognitive mechanisms behind decision-making under uncertainty, particularly focusing on overcoming myopic thinking in long-term decisions such as climate action and retirement planning. His research integrates behavioral economics, cognitive psychology, and real-world policy applications. Key themes include risk perception, trust in algorithms, information seeking, mental effort, and the psychological barriers to sustainable behavior. Publications and Trends: His recent publications (2022–2024) reflect a strong focus on experimental and computational modeling approaches. Topics include non-instrumental information seeking, income volatility and saving behavior, the impact of interruptions on decision-making, and the role of uncertainty in learning. His work frequently appears in top journals such as Cognition , Psychological Review , Decision , and Journal of Behavioral and Experimental Finance , demonstrating a consistent contribution to both theoretical and applied decision science. Editorial Roles: Editorial Board, Psychological Science ; Perspectives on Psychological Science ; Thinking & Reasoning ; Decision ; Journal of Behavioral Decision Making ; Experimental Psychology ; Consulting Editor, Judgment & Decision Making . Professional Affiliation: Member, Academic Advisory Panel, Behavioural Economics Team of the Australian Government. Books: Lead author of Straight Choices: The Psychology of Decision Making and Open Minded: Searching for Truth about the Unconscious Mind . Leadership and Outreach: As Director of the UNSW Institute for Climate Risk and Response, he leads interdisciplinary research on climate communication and behavioral responses. He actively engages in public discourse, as seen in his talk titled “From ‘why should I bother?’ to ‘Yes, we can!’: Risk, willingness, and cooperation in action on climate change.”

Gordon DobieView profile

Professor

• Ultrasonics
• Non-Destructive Evaluation (NDE)
• Robotics
• +7 more

Professor Gordon Dobie is a full-time faculty member in the Department of Electronic & Electrical Engineering within the Faculty of Engineering at the University of Strathclyde. He holds the academic rank of Professor and is actively engaged in research, teaching, and supervision of PhD students. MEng in Electrical and Mechanical Engineering with Distinction, University of Strathclyde (2000–2005) PhD in Ultrasonic Sensor Platforms for Non-Destructive Evaluation, University of Strathclyde (2006–2009) His research focuses on applied engineering solutions in ultrasonics, non-destructive evaluation (NDE), robotics, automation, and embedded systems. Key areas include automated inspection, signal processing, computer vision, and photogrammetry, with applications in structural integrity and medical imaging. His work supports industrial and medical advancements through real-time, intelligent inspection systems. The recent publications reflect a strong trend in integrating robotics and sensor technologies for robust inspection systems, particularly in challenging environments such as pipelines and medical diagnostics. The research combines ultrasonic sensing with computer vision, machine learning, and tactile feedback, demonstrating interdisciplinary innovation in engineering for safety-critical applications. Professor Dobie has received recognition through active engagement in major research projects and collaborations, including KTP partnerships and Innovate UK-funded initiatives. While specific awards are not listed, his role as Principal Investigator on multiple high-impact projects highlights his leadership and contribution to technological innovation. He advises several research students and leads or contributes to numerous grants, including projects such as RAICo - Next Generation Remote Inspection of Confined Spaces and KTP - Cokebusters , focusing on improving ultrasonic inspection pigs. His work is supported by strong industrial partnerships and contributes to UN Sustainable Development Goals through technological advancement. He is affiliated with the Centre for Ultrasonic Engineering (CUE) and participates in external collaborations with institutions such as the National Robotarium. His research group works on developing next-generation inspection systems using mobile robotics, sensor fusion, and AI-driven data analysis.

Cosma ShaliziView profile

Associate Professor

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +15 more

Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.

Xin LiView profile

Associate Professor

• Theory of Computation
• Randomness Extractors
• Pseudorandomness
• +6 more

Xin Li is an Associate Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. He is a member of the theory group and conducts research in the theory of computation, with a focus on randomness in computation, complexity theory, coding theory, and cryptography. He has advised several Ph.D. students and is actively involved in organizing seminars and serving on program committees of major theoretical computer science conferences. Ph.D., University of Texas at Austin, 2011 B.S. and M.S., Tsinghua University, Beijing, China His research interests lie broadly in theoretical computer science, particularly in pseudorandomness , randomness extractors , and explicit combinatorial constructions . He has made significant contributions to the construction of two-source extractors and non-malleable codes. His earlier work includes research in quantum computing and human-computer interaction. The most recent publications show a strong trend in coding theory (especially insertion-deletion codes), extractors (two-source, affine, non-malleable), and streaming algorithms. His work combines deep combinatorial insights with applications in cryptography and complexity theory. Scientific Awards and Honors: Simons Postdoctoral Fellowship NSF CAREER Award CCF-1845349 Invited to Theory of Computing Special Issue (RANDOM 2018) Invited to SICOMP Special Issue (FOCS 2013) SICOMP Special Issue (FOCS 2011) Advising and Grants: Xin Li has advised multiple Ph.D. students including Kuan Cheng, Zhengzhong Jin, Yu Zheng, Songtao Mao, and Yan Zhong. He has served as a postdoctoral mentor and research assistant supervisor. He is supported by several grants including NSF Award CCF-1617713, NSF CAREER Award CCF-1845349, and the Johns Hopkins Catalyst Award. He co-organizes the theory seminar at CS@JHU and has served on the program committees of STOC, RANDOM, and other top conferences. Labs and Teams: Xin Li is a member of the theory group at the Johns Hopkins Department of Computer Science, which focuses on foundational aspects of computation, algorithms, and complexity.

• Life-Cycle Behavior
• Applied Microeconometrics
• Empirical Health Economics
• +6 more

Hans-Martin von Gaudecker is a Professor of Applied Microeconomics in the Department of Economics at the University of Bonn. He holds multiple significant research positions including Cluster Faculty Member at ECONtribute, Principal Investigator of the Collaborative Research Center TR/224, Speaker of the Transdisciplinary Research Area 'Individuals, Institutions and Societies,' and Associated Member at the IZA Institute of Labor Economics. He is also a research fellow at the Reinhard Selten Institute, CESifo, and Netspar. His research focuses on life-cycle behavior of households, particularly examining how people manage risks over their life-cycles and how public policy can reduce human inequality. His work spans several key areas including household finance, labor market dynamics, health economics, and empirical microeconometrics. He combines innovative data with economic models and modern econometric methods to address questions about stock market participation, retirement decisions, disability schemes, and the relationship between health and labor market outcomes. Professor von Gaudecker has developed software tools to enhance research reproducibility in economics and maintains templates for reproducible research projects. His work on computational methods for economists reflects his commitment to rigorous, replicable scholarship. IZA Institute of Labor Economics Institute for Applied Microeconomics Institute for Financial Economics and Statistics Institute of Macroeconomics and Econometrics Institute of Microeconomics CRC TR 224 EPoS Professor von Gaudecker teaches a range of courses at the University of Bonn, including Applied Microeconomics (PhD/MSc), Applied Data Analytics (BSc), Effective Programming Practices for Economists (PhD/MSc), and Public Finance and Social Policy (BSc). He emphasizes combining economic theory with practical computational and data analysis skills, particularly using Python. His teaching approach includes flipped classroom models and hands-on case studies to develop students' abilities in data analysis, statistical interpretation, and critical thinking about empirical relationships. He maintains an active research group focused on computational economics and reproducible research practices, contributing significantly to methodological advancements in economic research. His work with CentERdata on COVID-19 impacts demonstrates his ability to respond to emerging research needs with rigorous empirical analysis.

Anqi LiuView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Distributionally Robust Learning
• +5 more

Anqi Liu (Angie) is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She is affiliated with the Data Science and AI Institute, Mathematical Institute for Data Science (MINDS), and Institute for Assured Autonomy (IAA). Her research focuses on developing principled machine learning algorithms for reliable, trustworthy, and human-compatible AI systems in high-stakes applications. PhD in Computer Science from University of Illinois Chicago Postdoctoral Research at Caltech's Department of Computing and Mathematical Sciences Her work emphasizes robustness to changing data environments, uncertainty quantification, and human-AI interaction. Key methodologies include distributionally robust learning, active learning, safe exploration, fair machine learning, and conformal prediction. Applications span healthcare (NIA/NIH-funded), robotics, and computational social science. Amazon Research Award Johns Hopkins + Amazon Initiative for AI Faculty Research Johns Hopkins Discovery Award Institute for Assured Autonomy Challenge Grant She advises PhD candidates in AI safety and fairness, with students co-advised by faculty in Human-Robot Interaction and Computational Linguistics. Collaborations include Center for Language and Speech Processing (CLSP) and Laboratory for Computational Sensing and Robotics (LCSR).

Thomas DohmenView profile

Professor

• Behavioral Economics
• Labor Economics
• Experimental Economics
• +4 more

Thomas Dohmen is Professor of Applied Microeconomics at the University of Bonn and Professor of Education and the Labour Market at Maastricht University. He holds a PhD in Economics from Maastricht University (2003) and an MSc from the University of Warwick. His affiliations include Research Fellowships at CESifo, DIW Berlin, ROA (Research Centre for Education and Labour Market), where he chairs the Scientific Advisory Board, and IZA (Institute of Labor Economics), where he serves as Project Coordinator. Research Focus: Dohmen investigates behavioral foundations of economic decision-making through interdisciplinary approaches. His work spans: Life-cycle development of cognitive/non-cognitive skills Psychology of incentives in organizational settings Experimental validation of economic preferences Labor market dynamics and personnel economics Cross-cultural studies of risk attitudes and patience Publication Trends: His recent articles (2021-2025) demonstrate strong emphasis on behavioral measurement methodologies, experimental validation of incentive structures, and cross-cultural analyses of economic preferences. Recurring themes include risk perception optimization, stress responses to competitive environments, and intergenerational transmission of economic behaviors. Awards: Leading House Best Paper Award (2021) for work on intergenerational preference transmission Academic Leadership: Dohmen coordinates research initiatives at IZA, serves as co-editor for Labour Economics , and leads the Cluster of Excellence ECONtribute as Bonn Spokesperson. His laboratory utilizes experimental economics protocols combined with physiological measurements to study incentive effects.