Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.
Suresh Krishna is an Associate Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on the neurophysiological and computational basis of sensory processing, attention, and eye movements, with applications to brain-machine interfaces and human health. He works with human subjects, non-human primates, and open datasets using in-vivo electrophysiology, eye-tracking, and computational modeling. Research interests include visual attention mechanisms, saccadic eye movement control, neural coding of motion perception, and the interplay between attention and decision-making. His work bridges basic neuroscience with translational applications such as improving neural prosthetics and understanding perceptual disorders. Recent work highlights how neural remapping processes during saccades underlie spatial perception, and how attention modulates neural activity patterns in visual cortex. The lab's publications reveal critical insights into the temporal dynamics of attentional shifts and their neural substrates, particularly in areas MT and MST. Dr. Krishna's team also investigates auditory temporal processing in the inferior colliculus, exploring correlations between neuronal responses to sound modulation. Their findings contribute to understanding how sensory systems encode temporal information across modalities. Research is conducted in the M2B3 Lab (http://m2b3.lab.mcgill.ca), which integrates experimental and computational approaches to study brain mechanisms underlying perception and action. No specific awards are listed, but ongoing work involves major contributions to primate neurophysiology and translational neuroscience.
Nathan W. Hudson is an Associate Professor in the Department of Psychology at Southern Methodist University (SMU) in Dallas, Texas. Previously, he received his Ph.D. in Psychology from the University of Illinois at Urbana-Champaign in 2016. His research focuses on personality psychology, particularly how people's personalities change across time and how individuals function in romantic relationships. He leads the Δ Lab at SMU, which investigates individual differences in human personality. Education: 2016 – Ph.D., Psychology, University of Illinois at Urbana-Champaign 2011 – M.A., Psychology, University of Illinois at Urbana-Champaign 2009 – B.A., Purdue University Nathan Hudson's primary research area is volitional personality change—people's desires and attempts to change their own personality traits. His groundbreaking research has shown that approximately 90% of people want to change aspects of their personality traits, and moreover, they may be able to find some degree of success in doing so. He also studies adult attachment styles and how people function in close relationships. Hudson's work suggests that attachment anxiety is linked to false memories, and that people's attachment styles can change over time. His research integrates both theoretical and applied perspectives, with implications for therapeutic interventions and personal development. Hudson's recent publications demonstrate a clear trajectory toward understanding the mechanisms of personality change and developing effective interventions. His work spans across multiple domains of personality psychology, with a particular emphasis on the Big Five personality traits, attachment theory, and well-being. A consistent theme throughout his research is examining how people's desires for change translate into actual personality growth. His work has increasingly focused on the practical applications of personality science, including developing measures and interventions that can help people achieve their desired personality changes. Scientific Awards: Goss-Lucas Award for Excellence in Teaching James Davis Fellowship Foundation for Personality and Social Psychology Heritage Dissertation Award Hudson actively mentors students in the Δ Lab at SMU, where they conduct research on personality change, attachment, and well-being. He has secured multiple grants to support his research on volitional personality change and the development of personality assessment tools. His lab has developed several psychological measures, including the Change Goals Big Five Inventory (C-BFI2) and its variants, which are freely available for academic research. Hudson also engages with the public through his TEDx talk "You Can Change Your Personality" and has been featured in Olga Khazan's book "Me, But Better," which applies his research to real-life settings. The Δ Lab, led by Hudson, is dedicated to understanding individual differences in personality and how people can intentionally change aspects of themselves. The lab values all individual differences among humans, recognizing that variance in human thoughts, feelings, and behaviors is adaptive and makes humanity resilient. The lab wholeheartedly supports the LGBTQ+ community and emphasizes that individual differences between humans are what personality science is all about.
Zion Zibly, MD, MBA is an Associate Professor in the Department of Neurosurgery at Yale School of Medicine . He holds multiple leadership roles including Director of the Center of Neuromodulation , Director of the Center of Neurosurgical Cancer Pain , and Head of Stereotactic & Functional Neurosurgery and the Focused Ultrasound Institute . Previously served as Chair of Neurosurgery at Sheba Medical Center after graduating from Technion’s Faculty of Medicine (MD) and Coller School of Management (MBA). Research Interests: Specializes in Neuromodulation for movement disorders (Parkinson’s, tremors, dystonia), Deep Brain Stimulation , Gene Therapy for pediatric neurodegenerative conditions, Oncological Neurosurgery , and Neurological Pain Management . Combines Functional Neurosurgery with Focused Ultrasound technology. Scientific Contributions: Participated in pioneering Alzheimer’s brain stimulator procedures and Gene Therapy applications. Active member of the North American Association of Functional Neurosurgery and Israeli Neurosurgical Society . Clinical Expertise: Implantation of electrostimulators for Parkinson’s and essential tremor, treatment of Benign/Malignant CNS Tumors , and management of Neurological Pain Conditions . Affiliated with Yale Cancer Center and Center for Brain & Mind Health .
Dr. Naseem Choudhury is a Professor of Psychology and Neuroscience at Ramapo College of New Jersey, affiliated with the School of Social Science and Human Services (SSHS). She holds a Ph.D. in Experimental Psychology from the University of Vermont. Her research focuses on the neural basis of infant information processing, particularly how perceptual abilities influence typical and atypical development, with an emphasis on familial and sociocultural factors. Her work spans auditory processing in infants at risk for developmental language disorders, electrophysiological studies in children with autism, and cross-cultural analyses of artistic perception. She directs the Palestroni Integrated Neuroscience Lab, exploring neural mechanisms underlying cognitive development. Dr. Choudhury has published extensively in journals like Journal of Neuroscience and Developmental Cognitive Neuroscience , with over 50 peer-reviewed articles since 2002. Her studies often involve ERP and EEG methodologies to track developmental milestones and intervention efficacy in high-risk populations. Her research demonstrates that early auditory experiences shape prelinguistic acoustic mapping and that neuroplasticity interventions improve outcomes for language-impaired children. She collaborates internationally on projects linking sensory perception to linguistic outcomes, particularly in Italian and bilingual populations. Dr. Choudhury’s contributions bridge basic neuroscience and clinical applications, emphasizing early screening and preventive strategies for developmental disorders.
Stephen Bach is an Assistant Professor in the Computer Science Department at Brown University, where he leads the BATS (Bach's Awesome Team of Students) research group. His research focuses on improving how humans teach computers through programmatic weak supervision and methods for learning from fewer examples like zero-shot and few-shot learning. His primary research interests include weak supervision, data programming, probabilistic soft logic (PSL), statistical relational learning (SRL), information extraction, zero-shot learning, and few-shot learning. Bach's work often focuses on exploiting high-level, symbolic or semantically meaningful domain knowledge, with applications in information extraction, image understanding, scientific discovery, and data science. Bach's recent publications show a strong focus on language models, weak supervision techniques, and multimodal learning, particularly examining the capabilities and limitations of models like CLIP. His research has increasingly emphasized practical applications in low-resource settings and cross-lingual scenarios. Best Paper Award at NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 2023 Larry S. Davis Doctoral Dissertation Award Selected for oral presentation at ICLR 2024 Best of VLDB 2018 paper selection Bach advises numerous Ph.D., Master's, and undergraduate students, many of whom have gone on to positions at leading tech companies, research institutions, and graduate programs. His research group has developed several influential frameworks including Snorkel (for weak supervision), PSL (Probabilistic Soft Logic), T0 (for zero-shot task generalization), ZSL-KG (for zero-shot learning with knowledge graphs), TAGLETS (for semi-supervised learning with auxiliary data), and WISER (for programmatic weak supervision in sequence tagging).
Dr. Lisa Libby is a Professor of Social Psychology at The Ohio State University, affiliated with the Department of Psychology. Her research focuses on how subjective perceptions shape cognition, emotion, and behavior, with a particular emphasis on mental imagery and visual perspective. She explores how manipulating these processes can improve decision-making, emotional well-being, and social harmony. Libby holds a PhD from Cornell University (2003), an MA from Simmons College (1997), and a BA from Williams College (1996). Education: PhD in Psychology, Cornell University, 2003 MA in Psychology, Simmons College, 1997 BA in Psychology, Williams College, 1996 Her research investigates how visual perspective in mental imagery influences self-perception, memory, and goal pursuit. Key themes include the interplay between imagery, identity, and decision-making, with applications to personal and interpersonal contexts. Libby's work highlights how shifting perspectives (e.g., first- vs. third-person imagery) can alter emotional responses, reduce bias in self-assessment, and enhance adaptive behaviors. Her advising contributions include mentoring students like Valenti G. and Shaeffer E.M., who have co-authored influential studies on imagery perspective and memory. Libby's lab at Ohio State University continues to advance understanding of how subjective experiences are constructed and manipulated through psychological mechanisms. Her research spans cognitive and social psychology, addressing topics such as autobiographical memory, regulatory fit, and the psychological effects of motion on conflict resolution. She has contributed to major academic volumes, including The Oxford Handbook of Social Cognition and Advances in Experimental Social Psychology .
Jonathan Santo is a Professor of Psychology at the University of Nebraska at Omaha (UNO), where he serves as Director of the Graduate Developmental Psychology Program and as a faculty member in the Office of Latino/Latin-American Studies. His research explores peer relations, cultural differences in self-esteem, and classroom-level influences on child and adolescent development, with a focus on improving school environments and fostering positive social interactions. ORCA Faculty Fellow His research spans broad areas including Developmental Psychology , Social Support Systems , and Cross-Cultural Peer Dynamics , with specific attention to peer victimization , self-continuity , school climate , and developmental trajectories in Brazilian and Colombian youth . Recent publications analyze topics like hikikomori experiences , friendship security , and HPA axis dysregulation in adolescents. Scientific awards include the ORCA Faculty Fellow recognition. His work often involves longitudinal assessments of peer relationships cross-cultural comparisons (Brazil, Colombia, China, Nigeria, Singapore, U.S.) policy-focused blog posts on family separations and school interventions
Christine Clark, MD, MSc is an attending laryngologist at Weill Cornell Medicine’s Sean Parker Institute for the Voice and Assistant Professor in the Department of Otolaryngology-Head and Neck Surgery at Weill Cornell Medical College. She earned her B.A. from the University of Pittsburgh (2011) and her M.D. from Pennsylvania State University College of Medicine (2017), followed by residency at Georgetown University Medical Center and a fellowship in Laryngology at Weill Cornell, where she also obtained a master’s degree in Clinical and Translational Science. B.A. - University of Pittsburgh (2011) M.D. - Pennsylvania State University College of Medicine (2017) Residency - Georgetown University Medical Center Fellowship - Sean Parker Institute for the Voice (Laryngology) Master’s - Clinical and Translational Science at Weill Cornell Dr. Clark specializes in evaluating and treating swallowing, voice, and airway disorders. Her research focuses on chronic cough, laryngeal hypersensitivity, and benign phonotraumatic vocal fold lesions. She has developed clinical tools like the 3D-printed injection laryngoplasty simulator and quality improvement initiatives for surgical airway management. Her clinical work and research span topics such as vocal fold hemorrhage management, pharyngeal residue quantification in dysphagia, and granuloma as markers of malignancy. Recent publications emphasize global otolaryngology capacity building and performer-specific voice injury care. Scientific awards include: Teaching awards for excellence in medical student education during residency Dr. Clark is affiliated with the Sean Parker Institute for the Voice at Weill Cornell Medicine and serves as an Assistant Professor of Otolaryngology.
Anne H Schistad Solberg is a Professor in the Department of Informatics at the University of Oslo's Faculty of Mathematics and Natural Sciences. She leads research in digital signal processing and image analysis, with a focus on machine learning applications across multiple domains. As co-director of SFI Visual Intelligence, she oversees research on interpretable deep learning models, uncertainty quantification, contextual learning, and self-supervised learning approaches. Her research spans medical imaging (particularly cardiovascular ultrasound), environmental monitoring using satellite imagery, and seabed mapping with sonar technology. Professor Solberg's work demonstrates a consistent trajectory from foundational signal processing techniques to cutting-edge deep learning applications. Her recent publications show increasing specialization in medical image analysis, particularly in echocardiography enhancement and cardiac structure segmentation, while maintaining strong contributions to remote sensing and geophysical applications. She teaches several popular courses including IN2070, IN3310, and IN5400 (Machine Learning for Image Analysis), which is noted as the most popular master's/PhD course on deep learning at the University of Oslo. Professor Solberg serves as principal investigator for the Intelligent Cardiovascular Ultrasound Scanner (INCUS) project, collaborating with GE Vingmed Ultrasound to develop AI-enhanced cardiac imaging systems that improve diagnostic accuracy and productivity in echocardiography. Co-director of SFI Visual Intelligence research center Principal Investigator for the INCUS project (Intelligent Cardiovascular Ultrasound Scanner) Member of the Digital Signal Processing and Image Analysis (DSB) research group Member of the Strategic Research Initiative: Multimodal Medical Imaging and Image Analysis (MEDIMA) Her research group develops algorithms that address real-world challenges in medical diagnostics and environmental monitoring, with a particular emphasis on making deep learning models more interpretable and reliable for critical applications. The INCUS project, funded through User-driven Research-based Innovation (BIA), aims to reduce the time wasted during cardiac ultrasound examinations by implementing intelligent algorithms that learn from expert users and historical data.
Juergen Schmidhuber is Associate Professor at the Faculty of Informatics of Università della Svizzera italiana and a leading researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). He is also Chief Scientist at NNAISENSE, a company dedicated to building practical general-purpose AI. His work has profoundly influenced modern artificial intelligence, particularly through the development of Long Short-Term Memory (LSTM) networks in 1991, now deployed across billions of devices for speech recognition, machine translation, and virtual assistants. His research interests span Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Universal AI, Meta-Learning, Algorithmic Information Theory, Artificial Curiosity, Robotics , and Low-Complexity Art . He has pioneered mathematically rigorous frameworks for self-improving AI systems and formal theories of creativity and beauty. His work bridges theoretical foundations with real-world applications in computer vision, natural language processing, and autonomous robotics. The recent articles reflect a consistent trajectory of innovation, combining deep theoretical insights with scalable machine learning architectures. His publications emphasize sequence modeling, universal learning, intrinsic motivation, and computational creativity , demonstrating both foundational contributions and industrial impact. From LSTM to Goedel machines, his work consistently targets the long-term goal of self-improving general AI. Scientific Awards: Numerous awards in AI and machine learning (specific names not listed) Schmidhuber leads a research group at IDSIA, where he mentors students and researchers in advancing the frontiers of AI. His lab has secured significant recognition and industrial collaboration, though specific grants are not detailed. He promotes the 'New AI'—general, sound, and relevant to physics—and continues to explore the convergence of intelligence, computation, and the universe. Labs and Teams: Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) NNAISENSE (as Chief Scientist)
Marc G. Berman is a Professor and Chair of the Department of Psychology at the University of Chicago. His research focuses on understanding how environmental factors interact with human cognition, emotion, and behavior, particularly through the lens of environmental neuroscience. He leads the Environmental Neuroscience Lab (ENL), investigating how natural and urban environments influence brain function, memory, attention, and mental health. Dr. Berman holds a B.S.E. in Industrial and Operations Engineering from the University of Michigan and a Ph.D. in Psychology and Engineering from the same institution. His postdoctoral training was at the Rotman Research Institute in Toronto. Prior to Chicago, he was an Assistant Professor at the University of South Carolina. His research interests include the cognitive and affective benefits of natural environments, brain network efficiency, and the neurobiological underpinnings of self-control and emotion regulation. Recent work explores how urban design elements (e.g., greenspace, street activity) relate to crime rates and mental health outcomes, leveraging big data from social media and geospatial tools. Key contributions include demonstrating that natural environments improve memory and attention by ~20%, and that city characteristics like population diversity and segregation correlate with implicit racial biases. His lab employs fMRI, neuroimaging, computational modeling, and ecological data to quantify brain-environment interactions. Dr. Berman collaborates across disciplines, integrating neuroscience, psychology, urban planning, and data science to inform evidence-based environmental design for public health. Current projects include analyzing social media data to map gang networks and investigating how heat and greenspace influence emotional states in urban populations.
Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.
Shannon Johnson is an Associate Professor in the Department of Psychology and Neuroscience at Dalhousie University, concurrently affiliated with the Departments of Pediatrics and Psychiatry. She serves as Director of Clinical Training and Co-Director of the Dalhousie Centre for Psychological Health, which provides mental health services to underserved populations while training clinical psychology students. Dr. Johnson holds a BA from Kalamazoo College, MSc and PhD from the University of Victoria, and a Postdoctoral Fellowship from Indiana University. Her research focuses on enhancing well-being through nature connection interventions, understanding resilience mechanisms in pediatric populations, and improving diagnostic practices for neurodevelopmental disorders. She investigates the physical and cognitive benefits of nature exposure, barriers to nature connection, and behavioral change strategies. Her work bridges clinical and environmental psychology, with recent studies examining nature-based interventions for stress reduction, pain adaptation in youth with juvenile idiopathic arthritis, and moral foundations in autistic children. She has pioneered the concept of Indoor Nature Exposure (INE) as a health-promotion framework. Dr. Johnson’s lab collaborates with healthcare providers to develop scalable mental health interventions, particularly for underserved communities. Her training programs emphasize evidence-based practices and culturally responsive care. Key contributions include validating the role of nature in cognitive restoration and challenging clinical biases in autism assessment.
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.
