جست‌وجوی استاد و پژوهشگر

Wilka Carvalhoمشاهده پروفایل

پژوهشگر ارشد

• deep reinforcement learning
• human learning and generalization
• computational cognitive science
• +۵ مورد دیگر

Wilka Carvalho is a Research Fellow at Harvard University's Kempner Institute for the Study of Natural and Artificial Intelligence, where she works closely with Sam Gershman. Her research focuses on developing deep reinforcement learning theories for human learning and generalization. Dr. Carvalho earned her Ph.D. at the University of Michigan, where she studied deep reinforcement learning with Satinder Singh, Honglak Lee, and Richard Lewis, and was supported by the NSF GRFP and Rackham Merit Fellowship. During her PhD, she spent significant time at DeepMind working with Murray Shanahan, Daniel Zoran, and Danilo Rezende. She completed an M.S. in Computer Science at USC working with Yan Liu on machine learning for healthcare, and earned her B.S. in Physics at Stony Brook University working with Axel Drees on computational nuclear physics. Her research interests center on how predictions about future rewards drive human thoughts and behaviors: Deep reinforcement learning Human learning and generalization Computational cognitive science Artificial intelligence Neuroscience applications Dr. Carvalho's recent publications examine how people generalize to new tasks via counterfactual simulation, building agents that coordinate with humans without human data, and computational approaches to naturalistic experimental paradigms. Her work bridges theoretical machine learning with practical cognitive science applications. She has received prestigious fellowships including: National Science Foundation Graduate Research Fellowship Program (NSF GRFP) Rackham Merit Fellowship Dr. Carvalho is highly active in the academic community, regularly presenting her work at leading institutions including Harvard, MIT, Stanford, UC Berkeley, Columbia, NYU, UCLA, and Caltech. She maintains valuable academic resources for graduate students through her PhD Resources website and is open to collaboration opportunities with researchers across neuroscience and cognitive science disciplines.

Kristin Vickersمشاهده پروفایل

دانشیار

• Anxiety Disorders
• Panic Attacks
• Stress Response
• +۷ مورد دیگر

Kristin Vickers is an Associate Professor and Undergraduate Program Director in the Department of Psychology at Toronto Metropolitan University (formerly Ryerson University), Faculty of Arts. She earned her PhD from Harvard University and has been a faculty member since 2005. Her educational background includes: PhD, Harvard University Dr. Vickers' research centers on anxiety disorders, particularly the etiology and correlates of unexpected panic attacks. She investigates how biological stress can be provoked—such as through carbon dioxide challenges—to study individual differences in stress and panic responses. Her work also explores resilience and risk factors in psychological stress, contributing to a deeper understanding of anxiety pathophysiology. She teaches courses in Introductory and Abnormal Psychology, demonstrating a strong commitment to pedagogy. Her recent publications reflect a sustained focus on panic disorder, stress reactivity, emotional regulation, and innovative teaching methods. Themes across her work include laboratory models of panic, social anxiety, media influences on self-perception, and parenting stress. The use of experimental and survey methodologies is prominent, with applications in both clinical and educational contexts. Scientific recognition includes: Slobodan Drakulic Cross-Disciplinary Teaching Award, Faculty of Arts, Ryerson University (2012) Dr. Vickers actively mentors students in her research lab and encourages undergraduate and graduate involvement in her projects. While specific grant details are not mentioned, her sustained publication record suggests ongoing research funding. She has collaborated with researchers across psychology subfields, including trauma, developmental psychology, and clinical interventions. She leads a research lab focused on anxiety and stress, accessible via http://psychlabs.ryerson.ca/vickers/ , where students engage in empirical studies on panic and emotional regulation.

• Decision-Making
• Emotion and Cognition
• Cognitive Psychology
• +۵ مورد دیگر

Kevin Thomas is an Associate Professor and Deputy Head of Department in Psychology at Bournemouth University . His research explores the interplay between emotion and decision-making, bias in task duration predictions, and mental well-being among healthcare professionals. Education: PhD in Bias in Predictions of Task Duration (University of Plymouth, 2004) MSc in Psychological Research Methods (University of Plymouth, 2000) BSc (Hons) in Psychology (University of Plymouth, 1999) Research Interests span emotional influences on reasoning, counterfactual thinking in depression, probabilistic reasoning, and surgeon well-being. Current projects include studies on depression-related counterfactual thinking and task duration prediction accuracy. Publications highlight collaborations with NHS professionals and focus on surgical mental health, cognitive biases in time estimation, and emotional processing across cultures. Key themes include healthcare resilience, decision theory, and temporal cognition. Grants include ESRC funding for task duration bias research (2000, 2004), an Irish Research Council grant on depression and counterfactual thinking (2008), and a BU Fusion Investment Fund project (2020s) on depression-related cognitive processes.

Xiao Wuمشاهده پروفایل

استادیار

• Biostatistics
• Causal Inference
• Climate and Health Research
• +۷ مورد دیگر

Xiao Wu is an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University. He is also a verified member of the Data Science Institute (DSI) and affiliated with the Computational Social Science Committee, Foundations of Data Science, and Health Analytics initiatives. His work bridges statistical methodology and public health applications, particularly in climate and environmental health. Education: Ph.D. in Biostatistics, Harvard University (advised by Dr. Francesca Dominici and Dr. Danielle Braun) Postdoctoral Fellow in Data Science, Stanford University (Department of Statistics, with Dr. Trevor Hastie, 2021–2022) Xiao Wu's research focuses on developing advanced statistical and causal inference methods to assess the health impacts of environmental factors amid climate change. His work addresses methodological challenges in handling error-prone and time-series exposures, with applications in air pollution, extreme weather, and global health. He integrates data science techniques with public health questions, emphasizing real-world evidence, Bayesian modeling, and meta-analysis. His methodological innovations support rigorous evaluation of environmental policies and health interventions. His recent publications span high-impact journals such as Science Advances , The Lancet Planetary Health , and Annals of Applied Statistics . Collectively, his articles emphasize causal frameworks, scalable algorithms, and integrative modeling for environmental health. Key trends include the use of satellite data, hierarchical Bayesian models, and real-world data to estimate health risks under climate variability. Topics frequently involve exposure-response relationships, measurement error correction, and health disparity assessment. Scientific Awards: Forbes 30 Under 30 Xiao Wu has advised doctoral students and postdoctoral researchers in biostatistics and data science. His collaborative projects include designing Bayesian clinical trials and conducting meta-analyses using real-world evidence. While specific grants are not listed, his affiliations and research scope suggest active involvement in federally and foundation-funded studies at the intersection of climate, health, and data science. He is actively involved in interdisciplinary research teams, including the Data Science Institute and Health Analytics Center at Columbia. His work leverages large-scale datasets and computational methods to address pressing public health challenges related to climate change. Through these collaborations, he contributes to building robust statistical frameworks for policy-relevant environmental health research.

Philip Dawidمشاهده پروفایل

استاد

• Foundations of probability and statistics
• evidential reasoning
• causal inference
• +۵ مورد دیگر

Professor Philip Dawid is a distinguished faculty member in the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics at the University of Cambridge. His academic career spans several decades with significant contributions to statistical theory and methodology. His research has established him as a leading authority in foundational statistical concepts with applications across multiple disciplines. Professor Dawid's research interests focus on the foundations of probability and statistics , evidential reasoning , causal inference , graphical models , forecasting systems , and DNA profiling . His work bridges theoretical statistics with practical applications, particularly in forensic science and legal contexts where statistical evidence plays a critical role. His research program has been remarkably consistent in exploring how probability and statistics can provide rigorous frameworks for reasoning under uncertainty across diverse domains. An analysis of his recent publications reveals a continued emphasis on causal inference methodology, with increasing focus on individual causation, decision-theoretic foundations, and applications in legal contexts. His work demonstrates sophisticated integration of graphical models with causal reasoning frameworks, particularly exploring the limitations and proper applications of Directed Acyclic Graphs (DAGs). The publications also show expanding applications in public health, obesity research, and pandemic modeling, reflecting responsiveness to contemporary challenges while maintaining theoretical rigor. Professor Dawid maintains an active research program with publications extending into 2025, demonstrating continued scholarly productivity. His work frequently engages with methodological debates in the statistical community, as evidenced by numerous discussion contributions and responses to other prominent researchers in the field.

Joe Koopmeinersمشاهده پروفایل

استاد

• Biostatistics
• Cancer Prevention
• Cancer Detection
• +۶ مورد دیگر

Joseph Koopmeiners, Ph.D., is a Professor and Division Head of Biostatistics & Health Data Science at the University of Minnesota's School of Public Health. He also serves as a Member of the Masonic Cancer Center and Scientific Co-director of the RapidEVAL Program at the Center for Learning Health Systems Sciences. Education: Ph.D. in Biostatistics, University of Washington (2009) M.S. in Biostatistics, University of Minnesota (2004) B.A. in Mathematics, Saint John's University (2004) Koopmeiners specializes in developing advanced statistical methodologies for cancer research and clinical trials. His work focuses on cancer prevention , detection , and treatment optimization , particularly through biomarker validation and Bayesian adaptive methods . His research has significant applications in public health and health data science. His publications demonstrate expertise in clinical trial design , cancer epidemiology , and biostatistical modeling , with recent work on adaptive allocation in sepsis trials, oncolytic virus therapy for osteosarcoma, and virtual twins methodology for personalized medicine. Awards and Recognitions: Delta Omega Honorary Society in Public Health Professional Memberships: American Statistical Association International Biometrics Society Society for Clinical Trials

Arie van Deursenمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Trustworthy AI
• +۵ مورد دیگر

Arie van Deursen is a Professor in Software Engineering at Delft University of Technology, where he is affiliated with the Department of Software Technology within the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS). He previously served as head of department from 2016-2023 and currently chairs the Software Engineering Research Group (SERG). He is also the scientific co-director of AI4SE, a five-year collaboration between JetBrains and TU Delft investigating novel uses of artificial intelligence in software engineering. His research interests span software engineering, software testing, trustworthy AI, language models for code, software architecture, human aspects of software engineering, and digitalization in the financial and public sector. His work often bridges academic research with practical industry applications, as evidenced by his co-founding of two companies: The Software Improvement Group (2000) and PerfectXL (2010/2013). Recent publications show a strong focus on large language models in software engineering contexts, with multiple 2024-2025 papers examining code generation quality, multilingual code comments, contamination-free code datasets, and license infringement concerns in LLM training data. His research demonstrates significant engagement with the challenges and opportunities presented by AI in software development. AIWare 2024 ACM SIGSOFT Distinguished Paper Award ASE 2024 ACM SIGSOFT Distinguished Paper Award CAIN 2023 Distinguished Paper Award CSCW 2015 ACM SIGCHI Best Paper Award EASE 2016 Best Paper Award Professor van Deursen has (co-)supervised 50 PhD students and 100 MSc students throughout his career. He serves on the Advisory Council on IT Assessment (AcICT) of the Dutch government (since 2022) and the Advisory Board of ING Bank Netherlands (since 2024). He was elected as a fellow of the Netherlands Academy of Engineering (NAE) in 2023. He leads the AI4SE lab, which hosts 10 PhD students, dozens of MSc and BSc students, and collaborates with many TU Delft faculty members and JetBrains specialists. The lab focuses on investigating novel uses of artificial intelligence in software engineering, with particular attention to practical applications and industry relevance.

William Hedgcockمشاهده پروفایل

استاد

• Neuromarketing
• Decision Neuroscience
• Judgment and Decision Making
• +۲ مورد دیگر

William Hedgcock is a Professor in the Marketing Department at the Carlson School of Management, University of Minnesota, specializing in consumer behavior and decision neuroscience. Previously, he served as Associate Professor and Director of the Marketing PhD Program at the Tippie College of Business, University of Iowa. His research employs physiological measures (facial expressions, heart rate, eye tracking, skin conductance) and functional brain imaging to investigate neural correlates of consumer decision biases. Educational background: BA in Economics, Macalester College (1996) BA in Psychology, Macalester College (1996) PhD in Business, Carlson School of Management, University of Minnesota (2008) Hedgcock's research expertise centers on Neuromarketing and Decision Neuroscience , with focused streams in judgment/decision-making processes, choice biases, and age-related decision differences. His interdisciplinary approach bridges marketing theory with cognitive psychology and neuroscientific methods to decode irrational consumer behavior through objective physiological markers. Analysis of his 2016-2022 publications reveals consistent exploration of decision biases across contexts—from Olympic medalist emotions to virtual reality haptic effects—with growing emphasis on digital marketing environments and aging populations. The work demonstrates methodological sophistication spanning behavioral experiments, physiological monitoring, and neural imaging. No specific scientific awards were documented in the source text, though his publication record in premier journals like Journal of Marketing Research and Psychological Science indicates significant scholarly impact. Hedgcock has directed PhD programs (University of Iowa) and mentors graduate researchers, but no individual students or grants are listed. His methodological toolkit suggests collaboration with biometric/neuroimaging labs, though no specific laboratory affiliations are named in the provided text.

Harjit Bhogalمشاهده پروفایل

دانشیار

• Philosophy of Science
• Metaphysics
• Metaethics
• +۷ مورد دیگر

Harjit Bhogal is an Associate Professor of Philosophy at the University of Maryland, College Park. His primary research areas include philosophy of science, metaphysics, and metaethics, with specific interests in causation, time, Humean supervenience, interlevel metaphysics, and laws of nature. His recent work explores the nature of coincidences, Humean approaches to metaphysics, and the intersection of scientific explanation with ethical disputes. He has published extensively in top-tier journals such as Mind , Philosophical Studies , and Noûs . 2018: Sanders Prize in Metaphysics Current projects examine the theoretical role of coincidences and how scientific explanation can resolve classical ethical debates. His methodological focus on unification and reductive accounts bridges metaphysics and philosophy of science.

• Artificial Intelligence
• Human-Computer Interaction
• User Modeling
• +۵ مورد دیگر

Cristina Conati is a Professor at the University of British Columbia, Canada, with an extensive research portfolio spanning artificial intelligence, human-computer interaction, and educational technology. Her work focuses on developing intelligent systems that adapt to user characteristics and behaviors, particularly through the analysis of eye-tracking data and other implicit user signals. Her research interests include user-adaptive visualizations, intelligent tutoring systems, explainable AI, and eye-tracking analysis. She investigates how AI can be used to personalize user experiences, particularly in educational contexts, by modeling user cognitive abilities, emotional states, and learning behaviors. Her work often bridges theoretical AI research with practical applications in education and decision support. Analysis of her recent publications (2023-2025) shows a strong focus on explainable AI in educational contexts, adaptive visualization techniques, and the use of eye-tracking to understand and support user interactions. Her research demonstrates a consistent trajectory toward making AI systems more transparent, adaptive, and effective for diverse users across various domains including education, healthcare, and decision-making. Extensive work on user modeling through eye-tracking data Development of adaptive visualization techniques Research on explainable AI for educational applications Studies on how individual differences affect user-system interaction Her research has significant implications for designing more effective educational technologies, decision support systems, and user interfaces that can adapt to individual user needs and characteristics. She has been instrumental in advancing the field of user-adaptive interfaces through both theoretical contributions and practical implementations.

• Supply Chain Management
• Advanced Manufacturing
• Mathematical Optimization Modelling
• +۲ مورد دیگر

Amir Hossein Ordibazar is a PhD candidate at the School of Business, UNSW Canberra. He holds a Bachelor's and Master's degree in Industrial Engineering from the University of Tehran, Iran (2016 & 2018), and has experience as a teaching assistant and software teacher at the University of Tehran. His research focuses on developing an intelligent framework for real-time Supply Chain Risk Management (SCRM) using Mathematical Optimization Modelling , Artificial Intelligence , and Machine Learning techniques. Educational Background Bachelor's of Industrial Engineering (University of Tehran, 2016) Master's of Industrial Engineering (University of Tehran, 2018) Research Interests Amir's work bridges Supply Chain Management , Advanced Manufacturing , and Mathematical Optimization Modelling with a focus on real-time risk mitigation. His application of Artificial Intelligence and Machine Learning to SCRM demonstrates an interdisciplinary approach to solving complex industrial problems. Publications Trends Amir's publications span Supply Chain Management , Mathematical Optimization , and Project Scheduling , reflecting his expertise in integrating technical methods with industrial applications. His work includes a 2022 paper on risk mitigation via counterfactual explanations, a 2021 book chapter on project scheduling, and a 2020 journal article on sustainable supply chain network design.

• compulsivity
• impulsivity
• Hoarding Disorder
• +۷ مورد دیگر

Sharon Morein-Zamir is a Professor in the School of Psychology, Sport and Sensory Science at Anglia Ruskin University (ARU) , with a focus on understanding cognitive control, compulsivity, and impulsivity across clinical and healthy populations. She co-directs the ARU Centre for Mind and Behaviour , leads the ARU Possessions and Hoarding Collective , and contributes to the Cambridge Neuroscience network. Her research integrates experimental psychology , psychiatry , and cognitive neuroscience to explore mental health conditions like Hoarding Disorder , Obsessive Compulsive Disorder (OCD) , and Attention Deficit Hyperactivity Disorder (ADHD) . Education: PhD in Psychology, University of British Columbia, Canada MA in Psychology, University of British Columbia, Canada BA and MA in Cognitive Psychology, University of Ben-Gurion, Israel Sharon’s research investigates behavioral and neural mechanisms of action control , response inhibition , and executive functioning . She employs transdiagnostic approaches to study compulsivity and impulsivity across disorders such as OCD, hoarding, and ADHD. Her work spans cross-sectional and cohort studies , neuroimaging , and behavioral paradigms , aiming to improve interventions and prevention strategies. Her recent publications highlight interdisciplinary trends in digital compulsions , hoarding behaviors , and neurocognitive mechanisms in ADHD and OCD. She has contributed to journals like British Journal of General Practice , Biological Psychiatry: Cognitive Neuroscience and Neuroimaging , and PLoS One , often collaborating with leading researchers such as Trevor Robbins and Gideon Anholt . Scientific Awards: Senior Fellow of the Higher Education Academy Sharon supervises PhD and MSc students in areas including hoarding (physical and digital), compulsive behavior, and cognitive flexibility. She teaches Quantitative Research Methods and Issues in Scientific Research at the postgraduate level, alongside modules in Clinical Child Psychology and Cognitive Neuroscience . Her lab, the Morein Lab , is part of ARU’s Safe and Inclusive Communities research theme.

Justin Dresselمشاهده پروفایل

دانشیار

• Quantum Information Science
• Quantum Computation
• Foundations of Quantum Physics
• +۴ مورد دیگر

Dr. Justin Dressel is an Associate Professor in the Department of Physics at Schmid College of Science and Technology , Chapman University. His research spans quantum information science, quantum computation, and foundational quantum physics, with a focus on superconducting circuits, weak measurements, and geometric algebraic methods. Ph.D. and M.A. in Physics from the University of Rochester Co-Program Director of Physics at Schmid College Leader of the Spin Group at Chapman, fostering interdisciplinary collaboration with global affiliates Dr. Dressel's work addresses the intersection of quantum technology and mathematics, including quantum measurement theory, machine learning applications, and spacetime geometry in electromagnetism. His recent publications explore Kerr-cat qubit design, counterfactual communication, and quantum Zeno effects in superconducting systems. Notable collaborations include experimental teams at institutions like the Hebrew University of Jerusalem, University of California, Berkeley, and Tel Aviv University. His group utilizes tools such as neural networks for quantum state estimation and investigates phenomena like optical ventriloquism and acoustic spin emergence.

Jian Peiمشاهده پروفایل

استاد

• Data Science
• Data Mining
• Databases
• +۴ مورد دیگر

Jian Pei is the Arthur S. Pearse Distinguished Professor and Chair of the Department of Computer Science at Duke University's Trinity College of Arts & Sciences. He also holds professorships in Duke's Department of Biostatistics & Bioinformatics (School of Medicine) and Electrical & Computer Engineering (Pratt School of Engineering). His work bridges data science, data mining, databases, information retrieval, computational statistics, and applied machine learning/AI. Ph.D. from Simon Fraser University (2002) Research spans data markets, statistical paradox detection, spatial-temporal analysis, causality, and data mining applications in healthcare, economics, climate, and social good His recent publications focus on data markets, signed graph analysis, medical NLP, serverless collaborative learning, and causal inference. He has secured major NSF grants for projects on polyhedral geometry knowledge discovery and data storytelling paradoxes. Notable awards include Fellowships from the Royal Society of Canada, Canadian Academy of Engineering, ACM, and IEEE. He actively mentors Ph.D. students and has co-authored over 150 refereed publications with collaborators across institutions.

• Causation
• Artificial Intelligence
• Metaphysics
• +۲ مورد دیگر

Professor Javen Shi is the Founding Director of the Causal AI Group at the University of Adelaide and a director at the Australian Institute for Machine Learning (AIML). His research spans causation, artificial intelligence, metaphysics, and mind theories, with global recognition as Google Scholar ranks him 4th in causation and 7th in probabilistic graphical models. Active contributor to AI ecosystem as a panellist for Responsible AI Think Tank (2022-2024) and AI Industry Forum (2024 onward) Research applications across material discovery, agriculture, mining, sports, manufacturing, bushfire prediction, health, and education Developed NOBURN bushfire prediction app (2023) with over 50 media coverages Research Focus : Causal AI methods that identify root causes, discover latent variables, and build immunity from spurious correlations. His work enables generalization across domains, models intervention consequences, answers counterfactual questions, and determines optimal intervention sequences for outcome optimization. Scientific Recognition : Open Catalyst Challenge 2023 (AI for Science) - 1st place AUS/NZ Bushfire Data Quest 2020 - Winner Gawler Challenge 2020 - Finalist (judged as 'most innovative modeling') Explorer Challenge 2019 - 2nd place SAIC Volkswagen Logistics Innovation Day 2019 - 1st place