Dr. Ginger Mills is a board-certified bilingual (English/Spanish) clinical neuropsychologist and Assistant Professor of Neurology at Yale School of Medicine. She specializes in assessing neurological disorders and neurodegenerative conditions through comprehensive cognitive and emotional evaluations that integrate developmental and cultural contexts. Specializes in neuropsychological evaluations for diagnostic clarity Focuses on memory disorders and cognitive rehabilitation Active in clinical research on prospective memory mechanisms Her research spans prospective memory assessment across diverse populations including children with epilepsy, brain injury patients, and healthy adults. Publications highlight methodological innovations like the MISTY testing framework and visual imagery interventions for memory disorders. Clinical expertise includes: Dementia and Alzheimer's disease Developmental neuropsychology Psychological assessment of children Dr. Mills emphasizes patient-centered care, stating: 'You are the expert on you. Trust your instinct and gut.' She maintains active clinical practice at Yale Medicine Neurology while contributing to translational research through peer-reviewed publications in journals like Child Neuropsychology and Clinical Neuropsychologist .
Anita Hubley is a Professor at the University of British Columbia's Faculty of Education, Department of Educational and Counselling Psychology, and Special Education (ECPS). She serves as MERM Program Coordinator and directs the Adult Development and Psychometrics Lab. Her work focuses on psychometric test development/validation and quality of life research across adult populations. Education: Ph.D. in Psychology (Human Assessment specialization), Carleton University (1995) M.A. in Psychology (Lifespan Development and Aging), University of Victoria (1991) Pre-doctoral training at Geriatric Assessment Unit (Ottawa) and Neuropsychological Assessment Unit (Ottawa) Her research integrates psychometric theory with practical applications in aging populations, homeless/vulnerably housed individuals, and neuropsychological assessment tools. Key contributions include developing the Memory Test for Older Adults (MTOA), Hubley Depression Scale for Older Adults (HDS-OA), Quality of Life in Homeless and Hard-to-House Individuals (QoLHHI), and Subjective Age Identity Scale (SAIS). Scientific Awards: Killam Teaching Prize (2017) Distinguished Reviewer, Buros Institute of Mental Measurements (2013) She has taught graduate courses in Psychological Assessment, Measurement Principles, Scale Development, and Applied Neuropsychology, emphasizing ethical testing practices and response process research. Her lab's work on test adaptation for marginalized populations has informed international measurement standards.
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)
Robert E. (Rob) Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, holding joint appointments in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. His research spans Bayesian statistics, neural data analysis, and computational neuroscience. Kass earned a B.A. in Mathematics from Antioch College, a Ph.D. in Statistics from the University of Chicago, and has been at CMU since 1981. He has served as Department Head of Statistics (1995–2004) and Interim Co-Director of the CNBC (2015–2018). His work focuses on statistical methods for neuroscience, particularly analyzing spike train data and identifying cross-brain interactions. Notable contributions include co-authoring Analysis of Neural Data and foundational articles on Bayesian inference. Kass has received prestigious awards such as the National Academy of Sciences membership and COPSS Distinguished Achievement Award. Research interests include computational neuroscience, statistical modeling of neural systems, and interdisciplinary education. He has advised numerous students and co-organized major workshops like the Statistical Analysis of Neuronal Data series. Kass’s work emphasizes the interplay between statistical rigor and scientific insight, bridging theoretical and applied domains. Education: B.A. in Mathematics, Antioch College Ph.D. in Statistics, University of Chicago Postdoctoral Fellow, Princeton University Scientific contributions include advancements in spike train analysis, Bayesian model assessment, and statistical methods for brain connectivity. His work on neural synchrony and population coding has influenced both theoretical and applied neuroscience.
Suvi Saarikallio is a Professor of Music Education at the University of Jyväskylä, Finland , affiliated with the Faculty of Humanities and Social Sciences and the Department of Music, Art and Culture Studies . She leads interdisciplinary research bridging music psychology, education, and therapy, with a focus on youth development, emotion regulation, and well-being. Research Groups: Centre of Excellence in Music, Mind, Body and Brain (2022-2029), Musiconnect (2022-2027) Key Projects: Music and You, Stress & music listening, MPACT (Music and Sports), Music and Cross-modal Associations, SOSUS (Social Sustainability for Children) Research Trends: Her recent publications explore music's role in emotional regulation, cross-modal perception, health outcomes, and educational applications. Themes include AI's impact on music evaluation, rhythm's connection to cognitive skills, and music's influence on stress and social-emotional development. Contact: suvi.saarikallio@jyu.fi
Zohreh Sharafi is an Assistant Professor of Software Engineering in the Department of Computer and Software Engineering (GIGL) at Polytechnique Montréal. Previously, she served as a Senior Research Fellow in the Department of Electrical and Computer Engineering at the University of Michigan, Ann Arbor, where she worked with Dr. Westley Weimer and was awarded the prestigious NSERC Postdoctoral Fellowship. Prior to her academic career, she worked as a software engineer at Morgan Stanley, contributing to the firm's electronic trading platform and serving as principal architect of SURF, a market data simulator. Her educational background includes a Ph.D. in Computer Engineering from École polytechnique de Montréal under the supervision of Dr. Giuliano Antoniol and Dr. Yann-Gaël Guéhéneuc, a Master of Applied Science in Software Engineering from Concordia University, and a Bachelor of Computer Engineering from the University of Tehran. Dr. Sharafi leads the SENSE Lab, a multidisciplinary software engineering research laboratory focused on understanding problem-solving strategies developers use during software development, with particular attention to human factors such as gender and native language. Her research combines human-centric design with experimental methodologies, investigating cognitive processes involved in software development using biometric measures including eye tracking and neuroimaging. Current active projects include evaluating trustworthiness perceptions of software artifacts and studying the role of creativity in software engineering tasks. She has made significant contributions to understanding how gender influences program comprehension and code review processes. Her publication record demonstrates a strong focus on empirical methods in software engineering, particularly eye tracking and neuroimaging techniques to study developer cognition. Her work spans program comprehension, code review, requirements engineering, and the impact of human factors on software development processes. She has developed methodological frameworks for conducting eye tracking studies in software engineering and has made notable contributions to understanding how visualization techniques affect software development tasks. NSERC Postdoctoral Fellowship NSERC Discovery Grant Program and Launch Supplements (Sep 2024-Sep 2029) IVADO Startup & Operation Fund (Jan 2022-Jan 2023) Scholarship for Doctoral Studies from Fonds de Recherche du Quebec Distinguished Reviewer Awards from IEEE ICPC 2020 and ACM FSE 2024 Dr. Sharafi actively mentors students including Mahta Amini (PhD Candidate, IVADO Scientifique en résidence 2024 Laureate), Cameron Cherif (PhD Candidate), Sara Yabesi (Master's Student), and Anthonia Njoku (Graduate research intern). She serves on numerous conference organizing committees including as Local Arrangement Chair for SANER 2025, Program Co-chair for SEMLA 2024, and as a reviewer for top-tier journals including IEEE Transactions on Software Engineering and ACM Computing Surveys. Her research is supported by multiple grants focused on understanding human factors in software engineering through empirical methods. At Polytechnique Montréal, Dr. Sharafi directs the SENSE Lab which brings together computer scientists, cognitive scientists, and software engineering researchers to investigate the cognitive aspects of software development. The lab employs advanced methodologies including eye tracking, functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) to study how developers comprehend, navigate, and modify software systems. Current projects examine trustworthiness perceptions in code review, the role of creativity in software engineering tasks, and gender differences in software development processes.
Xenophon Papademetris is a Professor of Biomedical Informatics & Data Science and Radiology & Biomedical Imaging at Yale School of Medicine. He serves as Associate Director of Biomedical Imaging Data Sciences at Yale Biomedical Imaging Institute and directs the Medical Software and Medical Artificial Intelligence Certificate Program. PhD in Electrical and Information Sciences from Yale University (2000) BA from Cambridge University (1994) Postdoctoral Fellowship at Yale University (2002) His research focuses on medical image analysis, machine learning, and biomedical software development. He has developed tools like BioImage Suite Web and contributed to standards committees at the Association for the Advancement of Medical Instrumentation (AAMI). His work spans modalities including MRI, CT, PET, and optical imaging. Recent publications emphasize neuroimaging analysis, explainable AI in healthcare, and multimodal data integration across species. He leads NIH-funded research under the BRAIN Initiative (R24 MH114805) and has authored a textbook on Medical Software published by Cambridge University Press. IEEE Senior Member Yale Brown-Coxe Postdoctoral Fellowship Harding Bliss Prize for Excellence in Engineering He directs the BioImage Suite Project, creating web-based image analysis tools using JavaScript and WebAssembly. His teaching includes both academic courses and a Coursera program on Medical Software with over 14,000 enrollments.
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.
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.
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
Doç. Dr. Muhammed Aras is an Associate Professor in the Department of Mechanical Engineering at Baskent University (Başkent Üniversitesi), with a research focus on sustainable machining processes, tool wear monitoring, and surface roughness optimization. His work spans advanced manufacturing technologies, energy storage systems, and biomedical device design. PhD in Manufacturing Engineering (2018), Gazi Üniversitesi MSc in Mechanical Education (2013), Gazi Üniversitesi BSc in Mechanical Engineering (2010), Tabriz Islamic Azad University Research interests include sustainable machining (dry/hard turning, cooling-lubrication strategies), tool wear analysis (CBN, ceramic and coated inserts), and surface integrity optimization using AI-based methods (firefly algorithm, TOPSIS, Grey Relational Analysis). His 15 most recent articles (2017-2024) examine topics like: Surface roughness prediction in dry hard turning Energy storage technology viability assessments Cutting parameter optimization for various steels Acoustic/vibration monitoring in machining He has received scientific awards including the Teşvik Ödülü (Encouragement Award, 2015). His patent on an automatic orthognathic surgery articulator and book chapters on tool monitoring systems demonstrate his multidisciplinary impact.
Dr. Anna Bobak is a Senior Lecturer in Psychology at the University of Stirling, UK. She holds a PhD from Bournemouth University (2016) and joined Stirling as a Research Assistant on an EPSRC project under Peter Hancock before transitioning to her current role. Her primary research focuses on individual differences in unfamiliar face recognition, particularly developmental prosopagnosia, and the reliability of face-processing assessments. She also investigates neurodiversity in women, emphasizing lived experiences of autism and ADHD, including camouflaging behaviors and societal awareness. Research Interests: Face Recognition: Examines perceptual strategies, diagnostic criteria (e.g., Balanced Integration Score), and technological applications in forensic contexts. Neurodiversity: Explores gender-specific manifestations of autism and ADHD, societal perceptions, and support mechanisms. Cognitive Methodology: Advances psychometric rigor in face-processing studies and critiques measurement validity. Her work bridges theoretical research and real-world applications, such as evaluating automated face recognition technology’s biases and collaborating on initiatives like #ScienceForUkraine to aid displaced academics. She is affiliated with the Cognition in Complex Environments research group and contributes to global security and resilience themes at Stirling.
