جستجوی استاد راهنمای دانشگاهی

Christopher Conway serves as Associate Professor of Psychology at Fordham University's College of Arts and Sciences, where he directs the Bronx Personality (B-PER) Lab. His research investigates borderline personality disorder, anxiety, depression, and distress tolerance using experience sampling methods and longitudinal designs. The lab examines personality development across key transitions such as romantic breakups and financial strain. 2007 BS in Psychology and Spanish, Duke University 2009 MA in Clinical Psychology, University of California, Los Angeles 2013 PhD in Clinical Psychology, University of California, Los Angeles Conway's work centers on distress tolerance as a protective factor against self-injurious behaviors, momentary personality processes using ecological assessment, and the HiTOP consortium 's dimensional classification of psychopathology. His lab develops quantitative models linking personality dimensions to clinical outcomes, with emphasis on how stressors trigger symptom changes. Recent publications reveal neuroticism's specific association with broadband internalizing symptoms rather than narrowband anxiety or anhedonia. His publications demonstrate consistent focus on transdiagnostic mechanisms and dimensional classification systems . Key trends include validating the HiTOP framework across cultures, examining distress tolerance in substance use contexts across four continents, and developing within-person models of self-injury using registered report methodology. Professional affiliations include: Society for Research on Psychopathology Association for Psychological Science Association for Behavioral and Cognitive Therapies Association for Research in Personality Conway advises multiple graduate students in the B-PER Lab and leads several active studies including MOMENT (Measuring Our Momentary Emotions and Negative Thoughts), DENEM (Daily Experiences of Negative Emotions), and READI (Responses to Emotions And Daily Interactions). His lab participates in the multinational Cross-cultural Addictive Behaviors Study examining distress tolerance across seven countries. All research materials follow open science principles through his OSF repository. The B-PER Lab maintains active research programs examining personality development through: 3-year longitudinal Multiyear Adult Personality Project (MAPP) Cross-cultural Addictive Behaviors Study (CABS) Daily emotion regulation projects using smartphone-based assessments

• Borderline Personality Disorder
• Clinical Psychology
• Distress Tolerance
• +۶ مورد دیگر

Professor George Siemens is a leading academic in the field of learning analytics and AI-driven education, serving as Professor and Director of the Centre for Change and Complexity in Learning at UniSA Education Futures, University of South Australia. His work focuses on advancing educational practices through data analytics, artificial intelligence, and understanding online learning dynamics. His research spans MOOCs, social and emotional learning analytics, and the ethical integration of AI in education. Notable contributions include the development of frameworks like the MOOC Replication Framework (MORF) and the DAIR infrastructure for educational AI research. Key publications include studies on student agency in AI environments, practicum effectiveness in teacher education, and synthetic data fairness in learning analytics. He collaborates internationally, with affiliations previously including the University of Texas Arlington. As a Research Degree Supervisor, he guides students in transformative educational technology research. His work emphasizes actionable intelligence for educators and scalable solutions for lifelong learning in the digital age.

• Learning Analytics
• Artificial Intelligence in Education
• MOOCs (Massive Open Online Courses)
• +۵ مورد دیگر

Miguel Nacenta is a Professor in the Department of Computer Science at the University of Victoria (UVic), Canada, and a founding member of the Victoria Interactive eXperiences with Information (VIXI) research group. Previously affiliated with the University of St Andrews (UK), his work bridges Human-Computer Interaction (HCI), Information Visualization, and Cognitive Science. He specializes in designing interactive systems that enhance human cognition, with a focus on Infotypography (using typography to encode data), collaborative problem-solving tools, and perceptual input/output devices. Research Interests: His key areas include cognitive augmentation, visualization techniques for complex tasks, multi-display environments, and tools for constraint problem-solving. Notable projects include the WriteReason tool for essay writing, InfoTypography studies on perceptual typographic parameters, and Solvi for visual constraint modeling. Grants & Collaborations: He collaborates internationally, including with the University of St Andrews on PhD scholarship programs. His work is supported by grants focusing on HCI innovations and accessibility. He actively mentors students (e.g., Adam Binks, Johannes Lang) and supervises postdoctoral researchers. Affiliations: Member of the VIXI group,他曾是St Andrews计算机科学学院的教授, 并参与多个学术服务活动, including conference program committees and journal reviews. Labs & Teams: Leads the VIXI lab at UVic, focusing on interactive technologies for cognitive tasks. Collaborates with industry partners on projects like TypoCartographer for infoTypographic maps and HaptiQ for accessible graph exploration.

• Human-Computer Interaction
• Information Visualization
• Infotypography
• +۵ مورد دیگر

Daniel Bolt is the Nancy C. Hoefs Bascom Professor of Educational Psychology at the University of Wisconsin-Madison’s School of Education. His research focuses on psychometric methodologies in educational, social, and health sciences, including latent variable models, computational methods, and assessment of individual differences. He also collaborates on biostatistics projects at the Waisman Center. Education: PhD in Educational Psychology, University of Illinois at Urbana-Champaign (1999) MS in Statistics, University of Illinois at Urbana-Champaign (1995) BA in Psychology/Mathematics, Calvin College (1992) Research Interests: Bolt’s work bridges psychometrics and educational data science, addressing topics like response style modeling, computer-based testing, and measurement validation. His recent projects explore the intersection of IRT models with modern assessment challenges, including rating scale confusion and item complexity effects. Awards: Kellett Mid-Career Award (2019) Vilas Associates Award (2015, 2017) Chancellor’s Distinguished Teaching Award (2009) Outstanding Reviewer Awards (Journal of Educational and Behavioral Statistics, 2011/2020) Teaching & Leadership: Bolt teaches advanced courses in test theory and hierarchical linear modeling. He served as President of the Psychometric Society (2019–2021) and is a Teaching Academy Fellow at UW-Madison.

• Psychometrics
• Quantitative Methods
• Educational Data Science
• +۷ مورد دیگر

Professor Daniel A. McFarland at Stanford University's Graduate School of Education holds courtesy appointments in Sociology and Organizational Behavior, with over two decades of academic service (2000-Present). As Director of the Stanford Center for Computational Social Science (2012-2016, 2018-2020) and Chair of Social Sciences (2023-Present), he bridges educational systems with computational sociology. His research spans Scientific innovation dynamics Adolescent social structures Computational methods Knowledge diffusion Recent publications in Social Networks and American Sociological Review examine tie fitness metrics, interdisciplinary career progression, and epistemic constraints. With 81 total publications, his work synthesizes material, cultural, and institutional network effects. Awards include the Gould Award (American Journal of Sociology) and Bessel Award (Humboldt Foundation). As Doctoral Dissertation Advisor for Taylor LiCausi and Nick Sherefkin, and Master's Program Advisor for Jason Zhang, he fosters next-generation scholarship. His computational sociology courses (EDUC 317, SOC 317W) integrate network methods and data science.

• Sociology of Science
• Computational Social Science
• Social Network Analysis
• +۳ مورد دیگر

John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.

• Cognitive Neuroscience
• Cognitive Science
• Computational Psychology
• +۳ مورد دیگر

Frank Christian Stephan is a Professor at the National University of Singapore (NUS), with joint appointments in the Department of Mathematics (primary) and School of Computing (secondary). His research spans mathematical logic, theoretical computer science, and computational complexity. Research Interests: Recursion theory and Kolmogorov complexity Inductive inference and learning theory Automata theory and automatic structures Parity games and algorithmic randomness Selected Publications include works on quasipolynomial time algorithms for parity games (STOC 2017 Best Paper) and semi-automatic structures. His scientific awards include the STOC 2017 Best Paper Award and the EATCS-IPEC Nerode Prize 2021. He teaches courses such as Computational Complexity (AY 2023/2024 Sem 2, AY 2024/2025 Sem 2), Advanced Automata Theory (multiple editions), and Mathematical Logic (undergraduate). He co-organizes the Logic Seminar at NUS.

• Mathematical Logic
• Theoretical Computer Science
• Recursion Theory
• +۵ مورد دیگر

Catherine Guastavino is a Professor at McGill University's School of Information Studies (within the Faculty of Arts) and an Associate Member at the Schulich School of Music. She also contributes to the Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT). Her roles include leading research projects and supervising graduate students in music technology and related fields. She holds a PhD in Psychoacoustics from Université Pierre et Marie Curie (Paris VI), an MSc from IRCAM (France), and a BSc in Mathematics from McGill University. Her research focuses on auditory perception, soundscape analysis, human-computer interaction, and urban acoustics. Notable projects include the 'Sounds in the City' partnership addressing urban noise policy and the development of immersive soundscape simulation tools. She leads or co-leads grants from SSHRC, NSERC, and MITACS, emphasizing interdisciplinary approaches to sound in urban environments. Guastavino's work bridges academic research with practical applications, such as designing sound installations for public spaces and improving acoustic environments in transportation and performance venues. She collaborates with city planners, musicians, and engineers, reflecting her commitment to making cities sound better through evidence-based strategies. Key Projects: SSHRC Partnership Grant for urban soundscape management (2018-2023) NSERC Discovery Grant on auditory localization (2019-2024) MITACS projects on Montreal festival soundscapes and noise policy Labs & Partnerships: Multimodal Interaction Laboratory (MIL) Collaborations with Ville de Montréal and Quartier des Spectacles Education: PhD: Psychoacoustics (Paris VI) MSc: Computer Science & Music Technology (IRCAM) BSc: Mathematics (McGill) Her teaching includes courses like Music Information Retrieval, integrating computational methods with artistic and scientific applications.

• Auditory perception & cognition
• Urban soundscapes
• Environmental noise management
• +۳ مورد دیگر

Patrick Sturt is a Reader in Psychology at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. His research focuses on syntactic processing in language comprehension, computational models of incremental parsing, anaphor resolution, and eye movements in reading. With over 100 publications and more than 3,300 citations, he is a recognized expert in psycholinguistics and language processing. Dr. Sturt's research interests span multiple areas of language processing. He investigates how humans comprehend sentences in real-time, with particular focus on syntactic structures, agreement phenomena, and anaphoric reference. His work often employs eye-tracking methodologies to examine the moment-by-moment processing of linguistic information. He has made significant contributions to understanding how readers handle syntactic ambiguities, garden-path sentences, and the role of prediction in language comprehension. His recent publications demonstrate a strong focus on cross-linguistic studies, particularly examining language processing in Mandarin Chinese and Korean. Many of his studies investigate how syntactic and semantic information interact during comprehension, and how linguistic structures like honorifics, classifiers, and non-canonical word orders are processed. His work bridges theoretical linguistics with experimental psycholinguistics, providing empirical evidence for models of sentence processing. Dr. Sturt actively supervises PhD students including Carine Abraham, Wenjia Cai, Chiuchou Hao, Ruomeng Zhu, and Christy Gu. He teaches Psychology of Language 1 and 2 at the MSc level, as well as Data Analysis for Psychology in R for first-year undergraduates. His teaching reflects his research expertise, providing students with both theoretical knowledge and practical analytical skills. Based in Room G29 of the Psychology Building at 7 George Square, Edinburgh, Dr. Sturt maintains regular office hours on Tuesdays from 3-4pm, providing accessibility to students and colleagues. His email address is patrick.sturt@ed.ac.uk.

• Psycholinguistics
• Syntactic processing
• Language comprehension
• +۵ مورد دیگر

Amitai Shenhav is an Associate Professor at the University of California, Berkeley, specializing in Cognitive Neuroscience. His research explores the neural and computational mechanisms underlying motivation, affect, decision-making, and cognitive control, as detailed on the Shenhav Lab website . Ph.D., Harvard University Key research themes include: Explaining motivated behavior through affective gradients Modeling decision-making with mutual inclusivity and value integration Investigating cognitive control allocation under varying motivational contexts Understanding neural dynamics in target-distractor interactions Recent publications (2025–2024) highlight his work on value-based decision-making, effort allocation, and computational models of cognitive control. These studies often bridge behavioral experiments with neural recordings and theoretical frameworks. Scientific contributions include: NSF CAREER Award (2021) for research on motivation in cognition He mentors students and collaborators in his lab, focusing on psychophysiological experiments, computational modeling, and neuroeconomic paradigms. His work intersects with psychology, neuroscience, and artificial intelligence, particularly in attention training applications.

• Cognitive Neuroscience
• Motivation
• Decision-Making
• +۴ مورد دیگر

Dr. Sharon K. Sagiv is an Adjunct Assistant Professor of Epidemiology at UC Berkeley's School of Public Health and an investigator at the Center for Environmental Research and Children’s Health. Her research focuses on prenatal and early-life environmental toxin exposures, particularly persistent organic pollutants, heavy metals, pesticides, and consumer chemicals, and their impacts on child neurodevelopment. She has extensively studied associations between environmental toxicants and neurodevelopmental disorders like ADHD and autism, alongside innovative work using functional neuroimaging to assess neural activity in exposed populations. Dr. Sagiv holds a PhD in Epidemiology from UNC Chapel Hill (2004), an MPH from Boston University (1998), and a BA in Biology from Franklin and Marshall College (1993). She has contributed to major cohorts such as CHAMACOS and Project Viva, examining long-term effects of pollutants like PFAS, organophosphates, and PBDEs. Her work bridges epidemiology, toxicology, and neuroimaging to advance understanding of environmental health risks. Research interests emphasize translational environmental health science, with a focus on pesticide mixtures, air pollution, and endocrine disruptors. Key themes include exposure assessment frameworks, functional neuroimaging outcomes, and developmental origins of health disparities. She teaches grant-writing seminars and collaborates globally on pesticide neurotoxicity studies in farmworker populations. Recent publications highlight links between prenatal PFAS exposure and metabolic outcomes, pesticide exposure and brain connectivity, and traffic pollution’s role in neurocognitive decline. Her research portfolio includes systematic reviews on air pollution health impacts and methodological innovations for assessing chemical mixtures in pregnancy. Dr. Sagiv’s work has policy implications for environmental regulations and public health interventions targeting vulnerable populations.

• Environmental Epidemiology
• Neurodevelopment
• Prenatal and Early Life Toxicant Exposures
• +۴ مورد دیگر

Maizie Zhou is an Assistant Professor in Biomedical Engineering and Computer Science at Vanderbilt University’s School of Engineering. She holds dual PhDs in Computer Science (Stanford University) and Neuroscience (Wake Forest School of Medicine), with additional degrees from Wake Forest University and Huazhong University of Science and Technology. Her research focuses on computational genomics, bioinformatics, and machine learning applied to problems in cancer genomics, single-cell and spatial transcriptomics, and computational neuroscience. She leads the Zhou Lab, which develops algorithms for structural variant detection, neural circuit analysis, and integrative omics approaches. Recent work includes tools like VolcanoSV and stDyer, and she has received grants from NIH, Vanderbilt Brain Institute, and industry partnerships. Key achievements include VUSE Best Paper Awards, Global Engagement Travel Grants, and mentoring students in prestigious programs like the Provost’s Pathbreaking Discovery Award. Her lab also explores the neural underpinnings of cognitive maturation in primates, combining computational and experimental neuroscience. Education: PhDs in Computer Science (Stanford) and Neuroscience (Wake Forest), MS (Computer Science, Wake Forest), BS (Biotechnology, Huazhong). Research interests span computational genomics (e.g., structural variant detection, haplotype phasing), spatial transcriptomics (clustering, integration), and computational neuroscience (neural circuit dynamics, prefrontal cortex plasticity). Her lab’s tools address challenges in precision medicine, cancer genomics, and understanding adolescent brain development. Recent projects include NIH-funded work on spatial transcriptomics and collaborations with Dr. Meltzer’s lab on cancer genomics. Publications highlight advancements in bioinformatics tools and neural mechanisms, with trends toward multi-omics integration and algorithmic innovation in genomics. Awards include the Global Engagement Travel Grant and CCSB Accelerator Fund. Students under her mentorship have excelled in qualifying exams and travel grants, reflecting her impactful training program.

• Computational Genomics
• Bioinformatics
• Computational Neuroscience
• +۶ مورد دیگر

Marcelo Mattar is an Assistant Professor of Psychology and Neural Science at New York University, leading the Mattar Lab. His research focuses on the neural computations underlying memory, decision-making, and reinforcement learning. He holds a Ph.D. in Psychology from the University of Pennsylvania and has held academic positions at NYU, UC San Diego, and postdoctoral roles at Princeton University and the University of Cambridge. His work bridges computational neuroscience and artificial intelligence, aiming to model how the brain uses internal models for planning and decision-making. Education: Ph.D. in Psychology (Computational and Cognitive Neuroscience), University of Pennsylvania, 2016 M.A. in Statistics, University of Pennsylvania, 2016 B.A. in Electronics Engineering, Instituto Tecnologico de Aeronautica, Brazil, 2010 Research Interests: The lab develops mathematical models of learning and decision-making, leveraging reinforcement learning, Bayesian statistics, and neural networks. Experiments involve human behavioral studies and neuroimaging, with collaborations in animal electrophysiology and computational psychiatry. Key Contributions: His work explores how episodic memory and hippocampal replay support flexible decision-making. Recent studies highlight parallels between human cognition and AI systems, such as language models' metacognitive abilities and brain-inspired algorithms. Awards: Newton International Fellowship, Royal Society (2018–2019) Lab Team: The lab includes postdocs, PhD students, and undergraduates from diverse fields like cognitive science, neuroscience, and computer science. Current members are listed on the lab's website. Lab Location: Meyer Hall, 6 Washington Place, New York, NY 10003.

• Computational Neuroscience
• Decision-Making
• Reinforcement Learning
• +۳ مورد دیگر

Ingo Bojak is a Professor at the School of Psychology and Clinical Language Sciences , University of Reading. His research focuses on computational neuroscience, neurodynamics, and neural population models. He explores topics such as biological mistake-making, EEG analysis, and the effects of anesthesia on brain activity. Bojak serves as an associate editor for Neurocomputing and related journals. His work bridges theoretical frameworks with experimental data, emphasizing cross-scale biological phenomena and neural network dynamics. Bojak’s research interests include understanding spontaneous neural oscillations, cortical activity modeling, and the integration of EEG/fMRI data. He has contributed to advancements in neural field theory and Bayesian uncertainty quantification. His studies on biological mistakes highlight adaptive mechanisms across biological systems. Related affiliations include collaborations with the School of Biological Sciences at the University of Reading. Recent publications emphasize theoretical biology, computational neuroscience, and interdisciplinary approaches to understanding neural systems. His work often addresses functional adaptation through error-driven mechanisms and explores the interplay between neural excitability and inhibition.

• Computational Neuroscience
• Neurodynamics
• Neural Population Models
• +۳ مورد دیگر

Chandan J Vaidya is a Professor in the Department of Psychology at Georgetown University, directing the Developmental Cognitive Neuroscience Laboratory (DCNL). His research focuses on cognitive neuroscience mechanisms underlying adaptive behaviors, particularly implicit learning, executive control, and their dysfunction in ADHD, ASD, and other developmental disorders. Using multidisciplinary methods including fMRI, behavioral testing, and genetic analysis, he investigates how dopamine systems, brain connectivity, and environmental factors influence cognitive processes. Primary appointment: Professor, College of Arts and Sciences - Department of Psychology Education: Ph.D. from Syracuse University Research interests include neurodevelopmental disorders, neuroimaging of cognitive control, and translational neuroscience. Recent work examines striatal connectivity changes in ADHD due to stimulant use, executive dysfunction subtypes in autism, and brain correlates of reward processing in obesity. Key findings highlight hyperconnectivity in ASD, dopamine genotype influences on executive function, and age-related changes in default mode networks. Ongoing studies explore transdiagnostic models of psychopathology and precision medicine approaches in neurodevelopmental disorders. Lab activities focus on translational research bridging basic neuroscience with clinical applications. Collaborations involve pediatric neurology, psychiatry, and computational modeling.

• Cognitive Neuroscience
• Developmental Disorders
• ADHD
• +۵ مورد دیگر