Dr. Dan White is a Senior Lecturer in Musicology at the Department of Media, Humanities and the Arts, School of Arts and Humanities, University of Huddersfield. His research focuses on fantasy worldbuilding through music in film, theme parks, and transmedia contexts. As a member of the Centre for Research in Music and its Technologies, he explores cinematic sound design and audience emotional engagement with musical motifs. Specializes in fantasy film music analysis Expert in diegetic/non-diegetic sound dynamics Investigates nostalgia through musical cues Recent publications examine Harry Potter's musical universe, Jurassic Park's sonic environments, and Amazon's Rings of Power TV adaptations. His work contributes to understanding how music shapes immersive fictional worlds across multiple media platforms.
Jessica Mayhew is an Associate Professor in the Department of Anthropology at Central Washington University. She specializes in primate behavior, biological anthropology, and cognitive ethology, with a focus on social play, dominance hierarchies, and communication patterns in primates such as chimpanzees, macaques, lemurs, and gorillas. Her research integrates field studies, captive observations, and interdisciplinary approaches to understand primate social dynamics and conservation challenges. Her work extends to historical analysis, such as interpreting Bronze Age wall paintings depicting primates, and modern contexts like examining the impact of human caregivers on captive primates and the portrayal of nonhuman primates in digital media. She has conducted studies in diverse locations including China, Madagascar, and sanctuaries in the United States. Mayhew’s publications span topics like lateralization in lemurs, dominance behavior in Tibetan macaques, and paternal care in orangutans. She teaches courses on biological anthropology, primate origins, nonverbal communication, and primatology, reflecting her expertise in both academic instruction and applied research. Her advising and grants focus on primate behavior studies, with notable projects including camera trap deployments in China and collaborative work at sanctuaries. No specific labs or teams are listed, though her collaborative nature is evident through joint publications with institutions like Duke Lemur Center and international field collaborations.
Jennifer Pfeifer is a Professor at the University of Oregon and co-Director of the Center for Translational Neuroscience within the College of Arts and Sciences, Department of Psychology. Her research spans developmental cognitive neuroscience, focusing on adolescence, puberty, self-concept, social cognition, emotion, motivation, and mental health. Her work integrates neuroimaging with behavioral and hormonal data to examine normative and atypical brain development, particularly how social processes and early adversity influence neurobiological models of adolescence. She has secured funding from NIMH, NICHD, NIDA, NSF, and other institutions. Recent publications analyze adolescent social reorientation, pubertal timing, self-disclosure mechanisms, and affective reactivity. Awards and student lists are not explicitly mentioned in the provided text. Her lab emphasizes translational neuroscience applications for mental health prevention and well-being promotion across the lifespan.
Dr. Eiko Fried is an Associate Professor at Leiden University's Faculty of Social and Behavioural Sciences, where he works at the intersection of clinical psychology, psychiatry, epidemiology, methodology, and complexity science. His research focuses on improving psychological science through open science practices and innovative measurement approaches. PhD in clinical psychology, Free University of Berlin Postdoctoral training at KU Leuven and University of Amsterdam Promoted to Associate Professor at Leiden University in 2021 Key research areas include: Psychopathology measurement and classification Network analysis in mental health research Ecological momentary assessment (EMA) methodology Open science advocacy and implementation Dynamic systems modeling in psychology Transdiagnostic approaches to mental disorders Recent publications demonstrate expertise in: Symptom network analysis across disorders Improving depression measurement standards Transdiagnostic assessment protocols Mental health data integration challenges Psychological theory construction Methodological innovations in clinical research
Nils Köbis is a Professor at the University of Duisburg-Essen, leading the chair Human Understanding of Algorithms and Machines . He is also an affiliated researcher at the Center for Humans and Machines (Max Planck Institute for Human Development). His work bridges psychology , social sciences , and artificial intelligence , focusing on corruption, unethical behavior, and human-AI interaction. Education : Ph.D. in Social Psychology from VU Free University Amsterdam, Post-Doc at CREED, University of Amsterdam. Research interests span behavioral ethics , social norms , anti-corruption strategies , and the psychological implications of AI . His articles explore topics like the dual role of AI in corruption mitigation, synthetic relationships, and moral dynamics in human-machine interactions. Key projects include the Interdisciplinary Corruption Research Network and the KickBack - Global AntiCorruption Podcast . His work employs experimental methods to analyze how algorithms shape dishonesty, trust, and social behavior.
Mark d'Inverno is a Professor in the Department of Computing at Goldsmiths, University of London, where he has established himself as a leading researcher at the intersection of artificial intelligence, multi-agent systems, and creative applications. His academic journey began with foundational work in formal methods and agent-based systems, culminating in his 1998 PhD thesis 'Agents, Agency and Autonomy: A Formal Computational Model' from University College London, and has evolved toward practical applications in music technology and ethical AI systems. Professor d'Inverno's research interests span multiple interconnected domains, with a particular focus on computational creativity, multi-agent systems, and the application of AI in musical contexts. His work explores how artificial intelligence can enhance creative processes, particularly in music composition and performance, while maintaining ethical considerations in social AI systems. He has made significant contributions to understanding how agents can interact meaningfully in social contexts, how ethical frameworks can be embedded in online systems, and how technology can support creative learning experiences. His recent scholarly output demonstrates a clear trajectory toward applied research with social impact, as evidenced by his 2021-2024 publications which increasingly address ethical considerations in AI, human-AI collaboration in creative domains, and educational applications of technology. These works reveal a researcher deeply engaged with both theoretical foundations and practical implementations, bridging the gap between abstract computational models and real-world creative and educational applications. Professor d'Inverno maintains an extensive collaborative network, frequently working with Matthew Yee-King on music technology applications, with Pablo Noriega on ethical AI frameworks, and with Jon McCormack on computational creativity. His research has been supported through various projects that connect theoretical computer science with practical creative applications, particularly in the development of systems that facilitate human-AI creative collaboration.
Stuart Hargreaves is an Associate Professor at the Faculty of Law , The Chinese University of Hong Kong . His research focuses on information and privacy law and constitutional law and legal theory , reflecting his prior legal practice and academic background. Education: SJD, University of Toronto (2013) BCL, Oxford University (2008) JD, Osgoode Hall Law School (2006) BA in Politics & Sociology, McGill University (2000) His work examines the intersection of artificial intelligence and legal education , with projects like “Teaching Avatars” using AI for educational content creation. He also investigates surveillance technologies , privacy law , and constitutional rights in the context of Hong Kong’s political and legal environment. Scientific Awards and Grants: CUHK Teaching Excellence Award (2017) CUHK Direct Grant for Research University Grants Committee Teaching Development Grant Research Grants Council General Research Fund Joseph-Armand Bombardier Canada Graduate Scholarship Google Policy Fellowship He has contributed to AI in legal pedagogy through multiple conference presentations (e.g., “Teaching Avatars” at the 2024 CUHK Teaching & Learning Innovation Expo) and serves on the Board of Advisors for Teach for Hong Kong . His external roles include journal reviewing and LAWASIA membership.
Steven Andrew Culpepper is a Professor of Statistics at the University of Illinois at Urbana-Champaign, holding additional appointments as Professor in the Beckman Institute for Advanced Science and Technology, Psychology, and Educational Psychology. He specializes in quantitative methods for social sciences, focusing on psychometric models, latent class analysis, and statistical computing. Education: PhD, Educational Psychology, University of Minnesota, 2006 BS, Economics, Bowling Green State University, 2001 Research interests include advanced statistical methodologies such as latent class models, high-stakes testing analysis, and applications of Bayesian computing in education and organizational research. His work emphasizes improving large-scale assessment systems through innovative modeling approaches. His publications consistently address latent structure modeling, cognitive diagnosis frameworks, and methodological advancements in educational and behavioral statistics. While no scientific awards are explicitly listed, his contributions to psychometric theory and statistical software development are notable. Steven has grants and consulting projects related to statistical methodologies but specific grant details are not provided in the texts. He has no listed advisees/PhD students in the provided information. He collaborates across disciplines through affiliations with the Beckman Institute and maintains active software development projects, including R packages like 'rrum' and 'pathmodelfit'.
Kay James is an Associate Professor of Neuroscience and Education at Teachers College, Columbia University, and serves as Director of the Graduate Program in Neuroscience and Education and the Neurocognition of Language Lab. Their work focuses on neural mechanisms underlying language disorders, second language acquisition, and cognitive processes in schizophrenia. Key affiliations include Biobehavioral Sciences, Neuroscience and Education, Human Development, and Cognitive Science in Education. Research interests emphasize the neural basis of language processing in pathological contexts such as developmental speech disorders and schizophrenia, alongside second language acquisition in adults. Their interdisciplinary approach bridges cognitive neuroscience with clinical and educational interventions. Publications span studies on mismatch negativity in speech disorders, syntactic development in Arabic diglossia, and brain-behavior asymmetry in schizophrenia. Ongoing work explores voice-related cortical potentials and emotional face processing through electrophysiological methods. Labs and teams include the Neurocognition of Language Lab, focusing on language neurobiology and clinical applications. Grants and advising roles are not explicitly detailed in the provided materials.
Chris B. Schaffer is a Professor in the Meinig School of Biomedical Engineering at Cornell University, specializing in developing advanced optical techniques to study neurovascular dynamics in neurological diseases. His lab focuses on Alzheimer’s disease mechanisms, leveraging multiphoton microscopy and in vivo imaging to explore capillary stalling, cerebral blood flow deficits, and their cognitive impacts. He holds a Ph.D. in Physics from Harvard University and postdoctoral training in neuroscience at UC San Diego. Research interests include biomedical imaging instrumentation, neurodegenerative disease modeling, and science education innovation. Awards include AAAS Fellowship (2021), AIMBE Fellowship (2019), and multiple teaching accolades. His work bridges engineering and medicine, with contributions to spinal cord injury studies, epilepsy, and vascular contributions to dementia (VCID). Notable discoveries include identifying neutrophil-induced capillary stalls as a key Alzheimer’s disease mechanism and demonstrating cerebral blood flow improvements can restore memory in mouse models. His lab also develops educational tools emphasizing science as a discovery process, used in K-12 and university settings.
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
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.
Irene McMullin is a Professor at the University of Essex within the School of Philosophical, Historical, and Interdisciplinary Studies, affiliated with the Department of Philosophy. She joined the university in 2013 after postdoctoral work at Bergische Universität Wuppertal and six years teaching at the University of Arkansas. Her academic background includes a PhD from Rice University, an MA from the University of Toronto, and a BA Hons from St. Francis Xavier University. Her research examines questions of personhood, agency, and self-becoming, particularly emphasizing the role of others in these processes. Drawing from both Continental and Analytic traditions, McMullin explores: Existentialism and Phenomenology Virtue ethics and Kantian ethics Moral psychology and social relations Current investigations focus on the phenomenology of ideality and how encounters with 'the good' shape practical agency. Her publications demonstrate sustained engagement with themes of moral deliberation, trust, and rationality across philosophers including Heidegger, Kant, and Løgstrup. Analysis reveals consistent interdisciplinary bridges between ethics, phenomenology, and social theory, with recent work increasingly addressing normative foundations of shared human experience.
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.
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.
