Larissa Samuelson is a Professor in the School of Psychology at the University of East Anglia, specializing in developmental cognitive science with a focus on early word and category learning. She holds administrative roles including Director of Research (2018–present). Her research integrates neural network models with empirical studies to understand how children process information and learn language. Education: BS (Honors) in Psychology, Indiana University (1993) PhD in Psychology & Cognitive Science, Indiana University (2000) Research Interests: Cognitive development in early childhood Word learning mechanisms and neural models Executive function development Cross-cultural language processing Awards: American Psychological Association Distinguished Scientific Award (2010) European Research Council Grant (2025–2031) Recipient of Leverhulme Trust funding (2025–2029) Projects: System of shape representations in cognition Neural process theory of vocabulary variability Language processing in deaf individuals Lab & Teams: Leads the Developmental Dynamics Laboratory , focusing on precision science of word learning and ensuring equitable early language potential for toddlers.
Dani S. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania with primary appointment in the Department of Bioengineering (School of Engineering and Applied Science) and secondary appointments in Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry. They serve as an external professor at the Santa Fe Institute and lead a research group focused on complex systems and network science. B.S. in Physics, Penn State University (2004) Ph.D. in Physics, University of Cambridge as Churchill Scholar and NIH Health Sciences Scholar (2009) Postdoctoral position at UC Santa Barbara and Junior Research Fellow at Sage Center for the Study of the Mind Their research integrates complex systems science, statistical mechanics, and applied mathematics to study network dynamics in physical and biological systems. Key areas include brain connectivity mechanisms, cognitive processes, neurological disease modeling, granular matter physics, and collective human curiosity. Bassett employs advanced methodologies including algebraic topology, network control theory, and multilayer network analysis to investigate how network architecture influences system function across diverse domains. Recent publications reveal a strong trend toward interdisciplinary network science applications, particularly in modeling human curiosity through Wikipedia navigation patterns and analyzing brain network reconfiguration during cognitive development. Their work bridges physics, neuroscience, and behavioral science with emphasis on topological network properties and dynamical processes. American Psychological Association's Rising Star (2012) MacArthur Fellow Genius Grant (2014) Lagrange Prize in Complex Systems Science (2017) Erdos-Renyi Prize in Network Science (2018) American Physical Society Fellow (2021) Web of Science Highly Cited Researcher (3 consecutive years) Bassett's research is supported by major agencies including NSF, NIH, DoD, ONR, and private foundations (MacArthur, Sloan, Paul Allen). Their lab actively recruits students from physics, engineering, neuroscience, and computer science backgrounds, emphasizing diversity in academic perspectives. Current projects include the 'Curious Minds' initiative exploring collective knowledge building and network-based models of neurological disorders. Bassett co-authored the MIT Press book 'Curious Minds: The Power of Connection' with philosopher Perry Zurn.
Dr. Sarah L. Friedman is a Research Professor in the Department of Psychological & Brain Sciences, specializing in developmental psychology. Her work focuses on child development, antisemitism, and family contexts. She holds a Ph.D. from the George Washington University (1975), an M.A. from Cornell University (1971), and a B.A. from the Hebrew University of Jerusalem (1969). Her research encompasses preterm birth effects on development, brain-cognition-education interfaces, and longitudinal follow-up strategies. Dr. Friedman has received prestigious awards, including NIH Merit Awards and multiple fellowships from professional associations. She serves on advisory boards for organizations like the Military Child Education Coalition and the Longitudinal Study of Australian Children. Her current research examines antisemitism's impact on Jewish children and interventions against bias. Advisory Roles: Child Research Net (Japan), APA Division 7 Executive Committee, and the Association of Jewish Psychologists Research Committee. Leadership: APA Division 1 President (2020–2021), APA Council Member (2024–2026).
Chet C. Sherwood is a Professor of Anthropology at George Washington University (GW) and a core faculty member of the Center for the Advanced Study of Human Paleobiology (CASHP). He also directs the National Chimpanzee Brain Resource and is affiliated with the GW Mind-Brain Institute. His research focuses on evolutionary neuroscience, particularly brain evolution in primates and other mammals, emphasizing how brain structure relates to behavior, development, and genetics. Education: Ph.D. (2003), M.A. (1998, 1996), and B.A. (1995) from Columbia University, with an additional M.A. from New York University (1996). Teaches courses such as ANTH 1001: Biological Anthropology and ANTH 3413: Evolution of the Human Brain. Research interests include comparative neuroanatomy of the cerebral cortex, human brain evolution relative to other primates, and the molecular and cellular mechanisms underlying cognitive evolution. He explores how brain differences across species correlate with ecological and behavioral traits, leveraging neuroimaging, transcriptomics, and fossil reconstruction techniques. Recent work investigates aging-related brain changes in primates and elephants. Notable achievements include membership in the National Academy of Sciences (2021) and the AAAS Fellowship (2022). His lab’s studies on chimpanzee brain plasticity and the genetic basis of primate cognition have advanced understanding of human uniqueness and shared evolutionary traits. Chet’s interdisciplinary collaborations span paleontology, genomics, and neuroscience, with a focus on bridging evolutionary and medical insights. His leadership in the National Chimpanzee Brain Resource underscores his commitment to advancing comparative neurobiology through resource development and ethical research practices.
Prof. George Magoulas is a Professor of Computer Science at the University of London's School of Computing and Mathematical Sciences and Director of the Birkbeck Knowledge Lab. He specializes in machine intelligence, machine learning algorithms, and AI system architectures, with applications in healthcare (e.g., neurodegenerative disease diagnosis) and educational technologies. His research has received awards from IEEE, ACM, and others. He holds a PhD in Nonlinear Optimization for Neural Networks and a PGCE in Higher Education. Education: BEng/MEng (Integrated Master's in Systems & Control Engineering), University of Patras, Greece PhD in Nonlinear Optimization for Neural Networks Learning, University of Patras, Greece PGCE in Teaching and Learning (Higher Education) Research & Leadership: He leads the Birkbeck Knowledge Lab, focusing on AI's impact on learning and communication. His work includes designing learning algorithms for psychophysiological data modeling and developing the cloudUPDRS app for Parkinson's disease assessment. He has supervised over 12 PhD students and contributed to 200+ publications. Awards & Recognition: Stanford’s “World’s top 2% of Scientists” (2024) Best Paper Awards at IEEE, ACM, and EUNITE Keynote speaker at major AI and e-learning conferences Honorary membership in the Hellenic Artificial Intelligence Society Administrative Roles: Director of Teaching & Learning Quality (2016–2023) Chair of Postgraduate Programmes Exam Board (2010–2022) Editor-in-Chief, International Journal on Artificial Intelligence Tools Teaching: He teaches courses on Artificial Intelligence, Neural Networks, and Project Management at both undergraduate and postgraduate levels. Labs & Collaborations: He directs the Birkbeck Knowledge Lab and is a member of the Data Science and AI Research Group. His projects include analyzing violent cycles using AI and collaborating on EU-funded initiatives.
Professor Ahmad Hariri is a faculty member in the Department of Psychology & Neuroscience at Duke University, part of the Trinity College of Arts & Sciences. He holds affiliations with the Duke-UNC Brain Imaging and Analysis Center, the Duke Institute for Brain Sciences, and the Duke Initiative for Science & Society. His research integrates neuroimaging, pharmacology, and molecular genetics to study biological pathways underlying individual differences in behavior and psychopathology risk. Education: Ph.D. in Psychology from UCLA (2000), M.S. and B.S. in Psychology from the University of Maryland, College Park (1997, 1994). Research interests include understanding how genetic and environmental factors influence brain function and behavior, with a focus on psychopathology, stress, and resilience. His work explores neural correlates of traits like psychopathy, childhood adversity effects on brain structure, and biomarkers of aging. Recent studies highlight links between lead exposure and neurodegeneration, neighborhood disadvantage and dementia risk, and the role of brain connectivity in self-regulation. Key achievements include over 200 publications, including high-impact papers in Nature Aging , Neuron , and Biological Psychiatry . Honors include the APA Distinguished Scientific Award for Early Career Contribution (2009) and being named a Highly Cited Researcher (2014). Grants include leadership roles in the Duke-NCCU Postdoctoral Training Program in Child Psychiatric Conditions and the Duke Psychiatry Physician-Scientist Residency Program. He has contributed to editorial boards of journals like Cortex and Biology of Mood and Anxiety Disorders . Professional activities include mentoring in the Summer Neuroscience Program and serving as a Bass Connections Faculty Team Leader.
Lyndsey Juliane Chong is a Research Fellow at the Yale Child Study Center, Yale School of Medicine. Her work focuses on neurobiological and environmental risk markers of childhood anxiety, employing methodologies such as eye-tracking, EEG, and behavioral observations. She is mentored by Drs. Wendy Silverman, Eli Lebowitz, and Michael Crowley and holds a T32 Fellowship in Childhood Neuropsychiatric Disorders. Chong earned her Ph.D. in Clinical Psychology from Florida State University (2023), supported by a National Science Foundation (NSF) Graduate Research Fellowship, and completed a clinical psychology internship at the University of Texas Health Science Center at Houston. Education: B.A. in Psychology and Economics, University of Texas at Austin (2015) M.S. and Ph.D. in Clinical Psychology, Florida State University (2019, 2023) Clinical Psychology Internship, UTHealth Houston (2023) Research Interests: Her research integrates neurobiological and environmental factors influencing childhood anxiety, with a focus on neural correlates (e.g., error-related negativity, late positive potential) and parent-child interactions. She develops computerized interventions targeting anxiety-related brain activity in young children. Awards: National Science Foundation (NSF) Graduate Research Fellowship (2019–2021) T32 Fellowship in Childhood Neuropsychiatric Disorders Departmental Graduate Research Development Awards (Florida State University) Grants & Advising: No formal advisees listed. Her work is supported by institutional fellowships and grants. Labs/Teams: Affiliated with the Child Study Center at Yale, collaborating with interdisciplinary teams in developmental psychopathology and clinical neuroscience.
Professor Liwei Zhang, M.D., serves as Director of Neurosurgery Department and Vice President of Beijing Tiantan Hospital, with Doctoral Supervisor appointments at Beijing Capital Medical University and Tsinghua University. He is Associate Director of China National Clinical Research Center for Neurological Diseases, Chairman of the Chinese Congress of Neurological Surgeons, and Committee member of the World Federation of Neurosurgical Societies. His research pioneers skull-base and brainstem tumor treatment, focusing on brainstem glioma genetics and brain function protection. He established the National Brain Tumor Registry of China (NBTRC) —now the world's second-largest brain tumor database with 110,000+ clinical records—and developed non-invasive CSF ctDNA diagnostics for molecular profiling. His work includes identifying PPM1D mutations in brainstem glioma and creating patient-derived DIPG cell models. Major recognitions include: 2018 National Science and Technology Progress Award of China (Second Prize) 2017 Beijing Science and Technology Progress Award (Second Prize) 2017 Chinese Medical Science Prize (Third Prize) 2017 Chinese Medical Science and Technology Progress Award (Third Prize) 2015 Beijing Medical Science and Technology Progress Award (Third Prize) 2015 Chinese Medical Science Prize (Third Prize) 2014 Chinese Medical Science Prize (Third Prize) 2006 Beijing Science and Technology Progress Award (Third Prize) 2006 Chinese Medical Science Prize (Second Prize) He has mentored 30+ graduate students, secured 40+ million RMB in research funding, and established China's Multidisciplinary Cooperation System for Skull-based Tumors. As Chief of Beijing Key Laboratory of Brain Tumor Research, he leads NBTRC's global data-sharing initiatives for brain health studies.
Roman Kuc is a Professor of Electrical Engineering at Yale University, affiliated with the School of Engineering & Applied Science. He directs the Intelligent Sensors Laboratory, focusing on biomimetic sensors for robotics and bioengineering. His research explores brain-based devices (BBDs), sonar sensing, and neuromorphic processing inspired by biological systems. He holds a BSEE from Illinois Institute of Technology and a PhD from Columbia University. Dr. Kuc’s work bridges signal processing, robotics, and bioengineering, with applications in autonomous systems and clinical diagnostics. He has published over 200 papers and authored textbooks like Electrical Engineering in Context and The Digital Information Age . Notable honors include an honorary doctorate from the Glushkov Institute of Cybernetics and the Yale Sheffield Distinguished Teaching Award. His research themes include cognitive mapping via sonar echoes, neural network-based classification of environmental features, and biomimetic approaches to echolocation. Recent work emphasizes sensorimotor integration and robust performance in uncertain environments. Scientific awards highlight his contributions to robotics, signal processing, and education. His lab develops systems that emulate biological sensory mechanisms, aiming to advance robotics, medical applications, and assistive technologies.
Dr. Zhi-Ping Feng is a Bioinformatician at the John Curtin School of Medical Research (JCSMR), Australian National University (ANU). Her research focuses on integrating omics data with protein structure-function relationships to study interactions between macromolecules. She has expertise in analyzing genomic and transcriptomic data (e.g., RNA-Seq, ChIP-Seq) and protein structure determination via nuclear magnetic resonance (NMR) spectroscopy. Previously, she held a Senior Research Fellow position at the Walter and Eliza Hall Institute (WEHI) from 2009, working on quality control in omics research and genomic data analysis. Her postdoctoral work at WEHI (2002–2005) involved structural biology of malaria-related proteins, supported by an Australian Postdoctoral Fellowship. She holds a PhD in protein bioinformatics from China and a physics background from Peking University. Education: PhD in Protein Bioinformatics (China) Bachelor’s in Physics, Peking University Research Interests: Her work bridges computational biology and structural biology, with emphasis on: Intrinsically unstructured proteins (IUPs) and their applications in malaria proteomics Omics data integration for disease modeling (e.g., cancer, diabetes, neurodegeneration) Protein-protein interaction networks and structural bioinformatics Publications: Recent work spans cancer immunotherapy, miRNA regulation in retinal degeneration, and T cell biology, with contributions to understanding Wnt signaling in joint replacement complications and genetic fusions in pediatric brain tumors. Awards: Australian Postdoctoral Fellowship (2005) Grants/Teams: Currently affiliated with ANU Bioinformatics Consultancy, supporting translational medical research in immunology, cancer, and genomics. Labs/Teams: Collaborates with the JCSMR’s multidisciplinary teams focusing on biomedical informatics and translational research.
Professor Mohan Lal Kolhe is a distinguished academic at the University of Agder , serving as a Full Professor in Smart Grid and Renewable Energy within the Faculty of Engineering and Science and the Department of Engineering Sciences . With over three decades of international academic experience, he has held positions at prestigious institutions including University College London, University of Dundee, and Hydrogen Research Institute in Canada. His career spans technical innovation, policy development (e.g., as a member of South Australia’s Renewable Energy Board), and extensive research leadership in sustainable energy systems. Research Leadership : Focus on Smart Grid integration, Electric Vehicles, Hydrogen Energy, Solar/Wind Systems, and Techno-Economic Energy Analysis. Global Recognition : Listed in the top 2% of scientists worldwide (2020-2023) by Stanford University, with 10 publications averaging 200+ citations. Recent publications emphasize advanced optimization techniques for renewable integration, EV charging infrastructure, hydrogen production, and power system stability. His work has secured competitive funding from entities like the Norwegian Research Council and EU programs. Awards and Expert Roles : Top 2% Global Scientist (Stanford, 2020-2023) Highly Cited Researcher (Top 10 publications, 200+ avg. citations) Expert evaluator for European Commission, Royal Society London, EPSRC, and Cyprus Research Foundation He actively contributes to international conferences as keynote speaker and editorial board member, with leadership roles in research groups like Autonomous and Cyber-Physical Systems and Energy Systems .
Olof Stephansson is a senior researcher and group leader at the Clinical Epidemiology Unit (KEP) , Karolinska Institutet , focusing on reproductive, perinatal, and pediatric epidemiology. His work utilizes Swedish healthcare registries and quality registers to investigate risk factors, interventions, and medical management during pregnancy and childbirth. Key research areas: maternal obesity, preeclampsia prediction, climate change impacts, neonatal neurodevelopment, IBD in pregnancy, and labor management Funded by: Swedish Research Council, NIH, FORTE, NordForsk, and Karolinska Institute Collaborates with: Stanford, Oregon Health, University of British Columbia, London School of Hygiene, University of Witwatersrand His group (2014-present) manages the Swedish Pregnancy Registry and conducts randomized trials like the Oneplus study on midwifery models. Recent publications (2024-2025) analyze maternal BMI effects on offspring sleep, labor duration risks, and vaccine safety in pregnancy.
Daniela M Witten is a Professor of Statistics and Biostatistics at the University of Washington, holding the Dorothy Gilford Endowed Chair in Mathematical Statistics. Her research focuses on developing statistical machine learning methods for high-dimensional data, with a particular emphasis on unsupervised learning and theoretical foundations. Witten earned her BS in Math and Biology with Honors and Distinction from Stanford University in 2005 and her PhD in Statistics from Stanford University in 2010 under Robert Tibshirani. Her academic journey established her expertise in bridging mathematical theory with biological applications. Her research program centers on high-dimensional statistical learning , where she develops methods for unsupervised learning and graphical modeling when features outnumber observations. She pioneers statistical models for neural activity through collaborations with the Allen Institute for Brain Science and Princeton University, addressing functional connectivity and neuron sub-population identification. Her groundbreaking work on selective inference solves the "double-dipping" problem in hypothesis generation and testing, enabling valid inference after hierarchical clustering and regression trees. Additionally, she advances multi-view data analysis to integrate complementary data sources like clinical and genomic measurements. Applications span genomics, neuroscience, microbial ecology, and pathology, demonstrating her commitment to solving real-world biomedical challenges. Her 2025 publications reveal a cohesive trend toward developing theoretically rigorous inference frameworks for high-dimensional settings, with emphasis on linear regression validity, semi-supervised efficiency, Gaussian decomposition, and PCA variance quantification—showcasing her signature blend of methodological innovation and practical applicability. Witten's exceptional contributions are recognized through extensive honors: Presidents’ Award, Committee of Presidents of Statistical Societies (COPSS) (2022) Mortimer Spiegelman Award, American Public Health Association (2019) Simons Investigator Award (2018-2023) Sloan Research Fellowship (2013-2015) NSF CAREER Award (2013-2018) NIH Director’s Early Independence Award (2011-2016) 23 major awards including named lectureships, fellowships, and editorial leadership As a dedicated mentor, she has guided students like Olivia McGough (NSF GRFP winner), Dwight (Zichun) Xu (ASA Nonparametrics Student Paper Award winner), Yiqun Chen (Hopkins Biostat faculty), and Anna Neufeld (Williams College faculty). Her research is sustained by major grants from NIH, NSF, and Simons Foundation. Witten co-authored the seminal textbook "Introduction to Statistical Learning" and currently serves as Joint Editor of the Journal of the Royal Statistical Society, Series B (2023-2025), shaping the field through both scholarship and community leadership.
Andrea H. Brand serves as the Frederick L. Ehrman Professor of Cell Biology and Professor of Neuroscience at NYU Grossman School of Medicine, New York University, where she chairs the Department of Cell Biology. Her dual appointments reflect an interdisciplinary research program spanning molecular mechanisms of stem cell regulation and neural development. Dr. Brand earned her PhD from the University of Cambridge followed by prestigious postdoctoral fellowships: a Leukemia Society Special Fellowship at Harvard Medical School and a Helen Hay Whitney Fellowship at Harvard University. These foundational experiences established her expertise in genetic model systems. Her research integrates stem cell biology and neuroscience through innovative work with Drosophila and mouse models. Key investigations focus on chromatin dynamics in stem cell quiescence, Notch/insulin signaling pathways, neural progenitor reprogramming, and blood-brain barrier formation. Current projects explore obesity-related gene function and CHD8 genomic targets relevant to neurodevelopmental disorders, emphasizing translational potential for regenerative medicine. Recent publications (2020-2022) reveal consistent themes in stem cell niche organization and disease mechanisms, with notable contributions to understanding tumorigenesis through neural progenitor studies and metabolic influences on barrier development. Her work demonstrates strong interdisciplinary convergence between developmental genetics and systems neuroscience. No scientific awards were documented in the provided profile information. While the profile indicates Professor Brand's leadership as Department Chair, specific details regarding student mentorship, grant funding, or laboratory structure were not included in the available text. Her position suggests active supervision of research teams and potential involvement in major collaborative initiatives.
Andrew K. Przybylski is a Professor of Human Behaviour and Technology at the University of Oxford's Oxford Internet Institute (OII). His research bridges psychology and digital technology studies, focusing on how virtual environments like social media and video games influence motivation, health, and well-being. He advocates for open, reproducible science and collaborates with policymakers to address digital-age challenges. Recent appointments include Honorary Professor at The Educational University of Hong Kong’s Centre for Psychosocial Health. Education: Undergraduate, postgraduate, and doctoral degrees from the University of Rochester (United States). Research Interests: His work explores digital well-being, online platform data donation, social media's psychosocial effects, and video game engagement. Key themes include open science, meta-science, and the intersection of technology with adolescent mental health. Article Trends: Recent studies analyze digital harms, social media's impact on youth, and reproducibility in tech research. His projects span neuroscience (e.g., screen time's effect on brain organization), behavioral analysis (e.g., gaming and affective responses), and policy-oriented work (e.g., multiverse approaches to internet use). Collaborations with institutions like the Ashmolean Museum highlight his interest in digital culture's therapeutic potential. Scientific Awards: Honorary Professor at The Educational University of Hong Kong’s Centre for Psychosocial Health Grants & Collaborations: Funded by the Huo Family Foundation, UK Research and Innovation (UKRI), Economic and Social Research Council (ESRC), The British Academy, The Leverhulme Trust, Barnardo’s, and the University of Oxford’s John Fell Fund. He contributes as a scientific advisor to the Sync Digital Wellbeing Program. Labs & Projects: Leads initiatives like the Programme on Adolescent Well-Being in the Digital Age, Capturing Digital Footprints of Video Game Play, and Understanding Video Game Play and Mental Health. These projects emphasize open-source data collection and cross-disciplinary collaboration.
