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Joe KoopmeinersView profile

Professor

• Biostatistics
• Cancer Prevention
• Cancer Detection
• +6 more

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 DeursenView profile

Professor

• Software Engineering
• Software Testing
• Trustworthy AI
• +5 more

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 HedgcockView profile

Professor

• Neuromarketing
• Decision Neuroscience
• Judgment and Decision Making
• +2 more

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 BhogalView profile

Associate Professor

• Philosophy of Science
• Metaphysics
• Metaethics
• +7 more

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
• +5 more

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
• +2 more

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
• +7 more

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 DresselView profile

Associate Professor

• Quantum Information Science
• Quantum Computation
• Foundations of Quantum Physics
• +4 more

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 PeiView profile

Professor

• Data Science
• Data Mining
• Databases
• +4 more

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
• +2 more

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

Chien-Jer Charles LinView profile

Associate Professor

• Chinese Linguistics
• Psycholinguistics
• Cognitive Science
• +7 more

Chien-Jer Charles Lin is an Associate Professor at the Department of East Asian Languages and Cultures and Adjunct Associate Professor in the Department of Linguistics and Cognitive Science Program at Indiana University Bloomington . As director of the Language and Cognition Lab , he investigates the interface of grammar, cognition, and society, with a focus on Mandarin Chinese and cross-linguistic comparisons. Education: PhD in Linguistics from the University of Arizona (2006) His research integrates psycholinguistic experiments (eye-tracking, EEG), corpus analysis , and sociolinguistic fieldwork to study: Processing of head-final structures (e.g., Chinese relative clauses) Cognitive effects of tone sandhi and phonetic orthography Discourse relations (causals, concessives, counterfactuals) Linguistic bias and sociopolitical identity in language variation His 15 most recent articles (2015–2024) reflect interdisciplinary focus on: Mandarin phonology (tone sandhi, neutral tones) Syntactic typology and parsing universals Translation syntax and corpus authenticity metrics Lexical ambiguity and metaphorical polysemy Discourse coherence and cognitive load Scientific Awards : Trustees’ Teaching Award (IU, 2016) Young Scholar Award (IACL, 2010) PACLIC-19 International Young Scholar Award (2005) Fulbright Scholarship (2001–2003) He advises graduate students across IU’s Cognitive Science Program , Psychological and Brain Sciences , and Second Language Studies , and has secured grants from IU’s Research Equipment Fund, Chiang Ching-Kuo Foundation, and Mellon Innovating International Research Fellowship.

Wei PanView profile

Professor

• Statistical Genetics
• Bioinformatics
• Computational Biology
• +5 more

Wei Pan is a Professor in the Division of Biostatistics & Health Data Science at the School of Public Health, University of Minnesota. He is also affiliated with the Graduate Program in Bioinformatics, Biomedical Genomics Center, and Masonic Cancer Center. PhD in Statistics (UW-Madison, 1997) MS in Computer Science (UW-Madison, 1996) BEng & BS in Computer Engineering & Applied Math (Tsinghua University, 1989) Research Interests Dr. Pan specializes in statistical genetics , focusing on genetic association testing for GWAS and sequencing data. His work in bioinformatics includes integrative analysis of multi-omics data and microarray gene expression analysis. He develops methods in statistical learning (supervised, unsupervised, semi-supervised), penalized/regularized models, and survival analysis for clinical trials. Publication Trends His recent publications (2021-2023) emphasize causal machine learning (Mendelian randomization, IV regression, causal networks) and deep learning (CNNs for genomic data, interpretable neural networks). Earlier work (2008-2014) focused on graphical models and clustering algorithms for biomedical data. Scientific Recognition Fellow, American Statistical Association (2010-- ) Member, Delta Omega Honorary Society in Public Health Editorial Board, Statistics in Medicine (2002-2006) Associate Editor, JASA (2003-2006) Advising & Grants Dr. Pan advises students in statistical genetics and machine learning, with trainees participating in the NIGMS T32 Biostatistics Training in Genetics and Genomics program (2020-2025). His research involves collaborations with biomedical scientists and clinicians, supported by NIH grants including the T32 training grant. Labs & Teams He leads a research group meeting weekly for collaborative projects, with subgroups focusing on network analysis , deep learning , and semi-supervised learning . The group participates in the BD2K Guide to the Fundamentals of Data Science Series and hosts post-doctoral researchers in statistical genetics and neuroimaging.

George MaldonadoView profile

Associate Professor

• Epidemiology
• Causal Inference
• Environmental Health
• +2 more

George Maldonado, PhD, MSPH, is an Associate Professor in the Division of Environmental Health Sciences at the University of Minnesota School of Public Health. With a strong academic foundation from UCLA, where he earned his PhD and MSPH in Epidemiology and Environmental Health, Dr. Maldonado has established himself as a leading researcher in causal inference methods within epidemiology. His research focuses on methodological approaches to causal inference from epidemiologic data, with particular expertise in uncertainty analysis, quantitative bias assessment, and the application of counterfactual theory. Dr. Maldonado's work bridges environmental health, infectious disease epidemiology, and methodological innovation, contributing significantly to the theoretical foundations of epidemiological science. His publication record demonstrates a consistent contribution to epidemiological methodology, with recent work exploring the intersection of artificial intelligence and epidemiological methods, including investigations into how AI and chatbots might assist in epidemiological research and manuscript preparation. His scholarly output spans from foundational theoretical work to practical applications in risk analysis and environmental health. Member, Delta Omega Honorary Society in Public Health Dr. Maldonado's research program addresses critical methodological challenges in epidemiology, with particular focus on improving how researchers assess causality, quantify uncertainty, and address various forms of bias in observational studies. His work has implications for environmental health research, infectious disease modeling, and evidence-based public health decision-making.

• Database Systems
• Data Mining
• Machine Learning
• +7 more

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.

Robin VandendriesscheView profile

Researcher

• Competition Law
• Data Protection Regulation
• Digital Market Competition
• +4 more

Robin Vandendriessche is a Doctoral Researcher at the Brussels Center for Competition Policy within the Faculty of Economic and Social Sciences and Solvay Business School at Vrije Universiteit Brussel. His work focuses on the intersection of EU Competition Policy and Data Protection, examining how digital economy developments challenge traditional competition frameworks. His research interests include: Digital market competition dynamics Data protection considerations in antitrust cases Foreign subsidies regulation and market distortion EU State Aid policy in environmental contexts Merge control in data-intensive industries Vandendriessche's recent publications (2023-2025) reveal a strong focus on emerging competition challenges in digital ecosystems, with particular attention to data-induced market power, Android Auto antitrust doctrine, and the effectiveness of the EU Foreign Subsidies Regulation. His work bridges legal and economic perspectives through mixed-methods approaches including document analysis, empirical studies, and policy toolkits. His scientific recognition includes: Certificate for outstanding master thesis (2023) Nomination for best competition law theses at Belgian universities (2023) Nomination for Concurrences Antitrust Writing Award 2025 As an active supervisor, he has guided four master's theses on topics ranging from semiconductor acquisitions to tax reforms and sports business models. His research activities include conference chairing (ASCOLA 2025), workshop presentations, and extensive peer-review work. The OZRIFTM17 project (2023-2027) serves as the foundation for his ongoing investigation into competition policy adaptation for the digital age.