Dr. Kaya de Barbaro is an Associate Professor in the Department of Psychology at the University of Texas at Austin (College of Liberal Arts). She holds a Ph.D. from the University of California San Diego. Her research focuses on bridging computer science and developmental/clinical psychology, particularly maternal mental health and infant social-emotional development. She directs the Daily Activity Lab, which uses mobile/wearable sensors and machine learning to analyze real-world interactions, aiming to develop just-in-time interventions for new mothers. Key research areas include maternal-infant dynamics, physiological synchronization, and the impact of environmental chaos on development. She has pioneered methods for analyzing high-density data, such as Granger causality and machine learning algorithms to detect behaviors like crying and holding. Recent work emphasizes leveraging ecological momentary assessment surveys and 24-hour LENA audio recordings to understand proximal mechanisms of development. Dr. de Barbaro teaches Psychology 333D (Introduction to Developmental Psychology) and has developed curricula for both in-person and online formats. She is actively involved in training students through the Eureka program at UT Austin. Her lab collaborates on tools like chatbots for postpartum mental health and has published extensively on sensor-based methodologies in developmental science.
Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .
Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .
Jeff Huang is an Associate Professor and Associate Chair of Computer Science at Brown University. His research focuses on Human-Computer Interaction (HCI), with emphases on personalized systems using behavioral data, health informatics, and user interfaces. Huang holds a PhD from the University of Washington and degrees from the University of Illinois at Urbana-Champaign. His work is funded by NSF, NIH, and ARO, and he has received awards including the NSF CAREER Award and ARO Young Investigator Award. Education: PhD in Information Science, University of Washington (2013) MS in Computer Science, University of Washington (2013) BS and MS in Computer Science, University of Illinois at Urbana-Champaign (2007) Research Interests: His work spans health informatics (e.g., sleep tracking systems like Self-E and SleepCoacher), user interface design, and leveraging behavioral data for personalized systems. Notable projects include WebGazer (webcam eye tracking), filtered.ink (creative illustration tools), and Sochiatrist (mental health analytics). Teaching: He teaches courses on user interfaces, HCI, and research methods, including CSCI 0130/1300 (User Interfaces), CSCI 2300 (HCI Seminar), and CSCI 2000 (Research Methods). Awards: NSF CAREER Award Facebook Fellowship ARO Young Investigator Award Grants & Advising: Huang leads a research group advising over 50 students, many of whom have pursued academic and industry roles. His grants support projects in HCI, health tech, and collaborative systems.
Pasquale Bottalico serves as Associate Professor in the Department of Speech and Hearing Science at the University of Illinois, with dual appointments as Associate Professor at the Center for Latin American and Caribbean Studies and Affiliate Faculty in the School of Music. His unique interdisciplinary profile bridges engineering, music performance, and speech science, reflecting his dual academic training and professional artistry. His educational foundation includes: Bachelor's in Telecommunications Engineering from Univeristà Mediterranea di Reggio Calabria, Italy Concurrent Opera Singing degree from F. Cilea Music Academy, Reggio Calabria Master's in Telecommunications Engineering from Politecnico di Torino, Italy Ph.D. in Metrology specializing in acoustics measurement uncertainty and classroom acoustics Dr. Bottalico's research centers on vocal load quantification and professional voice techniques , with significant contributions to understanding vocal fatigue in teachers and singers. His work spans Speech Intelligibility in educational environments, Room Acoustics for performance and learning spaces, and Musical Acoustics of historical vocal styles. A distinctive thread throughout his research examines how acoustic conditions modulate voice production and perception, increasingly incorporating virtual reality and bone conduction technologies for innovative assessment and intervention approaches. His Colombian vocal health study demonstrates cross-cultural applications of his work. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) The impact of noise and dysphonia on children's speech processing in educational settings, using multimodal assessment including EEG; (2) Virtual reality applications for voice production research and therapeutic intervention; (3) Cross-cultural validation of vocal fatigue metrics and development of biofeedback systems. His work consistently bridges engineering precision with clinical applicability, particularly for professional voice users in challenging acoustic environments. No scientific awards were documented in the available information. While specific advising relationships aren't detailed, his research collaborations span international institutions including Colombian and Italian universities, suggesting graduate mentorship in interdisciplinary projects. No grant information was provided, though his systematic reviews and cross-cultural studies imply externally funded research activities. Though no dedicated laboratory is specified, his virtual reality voice studies and acoustic parameter assessments suggest affiliations with audio engineering facilities and voice clinics, likely through the Speech and Hearing Science department's research infrastructure.
Nancy M. Wells is a Professor in the Department of Human Centered Design and serves as the Senior Associate Dean for Research and Graduate Education in the College of Human Ecology at Cornell University. Her work bridges environmental psychology and public health, examining how built and natural environments impact human health and behavior across the lifespan. Dr. Wells received a joint PhD in Psychology and Architecture from the University of Michigan and completed National Institute of Mental Health (NIMH) post-doctoral training at the University of California, Irvine. Her educational background uniquely positions her to investigate the intersection of physical environments and human wellbeing through both psychological and architectural lenses. Her research focuses on how environmental contexts—from homes to neighborhoods to schools—affect health outcomes, cognitive functioning, and resilience. A central theme examines nature's role in human resilience, particularly how natural environments buffer against adversity and challenge. She has conducted influential work on school gardens and their effects on children's physical activity, dietary habits, and learning. More recently, she has pioneered research on nature and autism, conducting a randomized controlled trial examining natural environments' effects on autistic youth wellbeing. Analysis of Dr. Wells' recent publications reveals consistent focus on nature's health benefits, with strong emphasis on experimental methodologies including randomized controlled trials and natural experiments. Her work spans environmental psychology, public health, and education, with particular attention to vulnerable populations including low-income communities and children of color. NSF Funding for environmental health research USDA Funding for school garden studies Robert Wood Johnson Foundation Funding MacArthur Foundation Funding Senior Associate Editor of Environment and Behavior (4 years) Research Advisory Board of Children and Nature Network Board of Directors for reDirect Dr. Wells actively mentors undergraduate and graduate students through the Wells Lab, involving them in all research stages from data collection to publication. Her research has been supported by multiple significant grants examining housing quality effects on mental health, neighborhood design impacts on physical activity, and school garden interventions. She leads interdisciplinary research teams that integrate perspectives from environmental psychology, public health, landscape architecture, and education. The Wells Lab operates as an integrated research-teaching-outreach unit that develops practical yet rigorous studies to assess causal relations between environment and health, creates valid measurement tools, and identifies causal pathways. Current projects include examining nature's effects on autistic youth wellbeing in partnership with The Center for Discovery in New York, and investigating multi-sensory design in urban public spaces.
Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.
Professor Emily So serves as Deputy Head of the School of Arts and Humanities at the University of Cambridge and directs the Cambridge University Centre for Risk in the Built Environment (CURBE). A chartered civil engineer with extensive field experience, she holds leadership roles in the Open-Oxford-Cambridge AHRC Doctoral Training Partnership and chairs the Faculty EDI Committee. Her research focuses on urban risk and resilience , particularly in earthquake-prone regions. Combining structural engineering with epidemiological approaches, she develops innovative casualty estimation models and engages directly with affected communities worldwide. Her work spans seismic safety, disaster epidemiology, and remote sensing applications for rapid damage assessment. Professor So's publication trends reveal strong emphasis on machine learning for disaster risk modeling , with recent work featuring graph neural networks, deep clustering for urban morphology, and LSTM-based population forecasting. Her research bridges engineering, social sciences, and data science to address resilience in developing nations. 2010 Shah Family Innovation Prize (Earthquake Engineering Research Institute) Fellow of the Institution of Civil Engineers (FICE) Scientific Advisory Group for Emergencies (SAGE) member advising UK government As Director of CURBE, she leads interdisciplinary collaborations with EEFIT, Global Earthquake Model (GEM), World Bank, and USGS. Her field investigations following major earthquakes inform practical solutions for vulnerable communities, notably contributing to the 2017 World Building of the Year design in China. Current work includes sabbatical research for 2025-2026 focused on decolonizing architectural approaches to disaster resilience. Professor So maintains active roles in professional organizations and international disaster response frameworks, with her CURBE team developing methodologies now implemented globally for seismic safety improvements.
Karin Allor Pfeiffer is a Professor in the Department of Kinesiology at Michigan State University (MSU) and Director of the Institute for the Study of Youth Sports. She holds additional membership in the Center for Physical Activity and Health. With a Ph.D. from MSU, her research focuses on physical activity measurement methodologies and population health interventions, particularly among children and adolescents. Her work addresses obesity prevention, environmental design impacts on activity levels, and sociocultural factors influencing youth sport participation. Education: Ph.D. in Kinesiology from Michigan State University Her research interests emphasize: - Quantitative methods for physical activity assessment - Schoolyard redesign strategies and their health impacts - Cardiometabolic risk factors in pediatric populations - Longitudinal tracking of physical fitness and health outcomes Recent work explores accelerometer fragmentation metrics, GPS-linked activity tracking, and disparities in sedentary behavior across demographic groups. She has pioneered interdisciplinary approaches integrating spatial analysis, wearable technology, and policy evaluation. Key contributions include developing the Observational System for Recording Physical Activity in Children and advancing consensus methods for accelerometer data interpretation. Her studies frequently highlight socioeconomic and environmental determinants of health behaviors. Dr. Pfeiffer collaborates with public health agencies and urban planners to translate research into actionable policies. Her lab focuses on scalable interventions for underserved communities, leveraging community-engaged methods to address greenspace accessibility and safety concerns.
Dhruv Jain is an Assistant Professor in the Computer Science and Engineering Department at the University of Michigan , with affiliations in the School of Information and Michigan Medicine . He leads the Soundability Lab , focusing on transforming hearing into a programmable interface through human-centered AI systems. PhD from University of Washington MS from MIT Media Lab Formerly worked at Microsoft Research, Google, and Apple Research Interests intersect Human-Computer Interaction (HCI) , Audio AI , Accessibility , and Hearing Health . His work develops systems like SoundWatch (smartwatch-based sound awareness) and HomeSound (IoT sound visualization) to expand auditory access for DHH individuals and other domains. Recent Publications (2023–2025) span top venues like CHI , ASSETS , and ICMI , with trends including generative AI for real-time captioning , adaptive soundscapes , and clinical communication tools . SIGCHI Outstanding Dissertation Award (2023) William Chan Memorial Dissertation Award (2023) Best Poster Award at ASSETS 2024 Google Academic Research Award (2024) NIH Grant ($450k, 2023) William Demant Foundation Grant ($740k, 2025) Teaching includes Accessible Computing (undergraduate) and Advanced Accessibility (graduate), alongside global DIY workshops in five countries. The Soundability Lab collaborates with neuroscientists, clinicians, and Deaf scholars to create deployable systems like clinical communication tools now at Michigan Medicine and features integrated into Apple devices.
Kelly Arnold is an Associate Professor in the Department of Biomedical Engineering at the University of Michigan. Her research integrates systems engineering principles with immunology to investigate variability in immune responses across infection, vaccination, and injury, with a focus on computational modeling and clinical translation. Research Focus Systems-level immune response modeling Vaccination and antibody functionality Vaginal microbiome-host interactions Chronic lung disease progression Computational serology and proteomics Recent Work Her 2025 studies examine SARS-CoV-2 vaccination responses in cancer patients and computational frameworks for vaginal probiotics. Earlier works (2024-2007) span COPD progression, lupus fibrosis, HIV susceptibility, and tissue engineering for fertility preservation. Methodologies include proteomic profiling, network modeling, and microfluidic systems.
Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.
Professor Masooda Bano serves as William Golding Senior Research Fellow at Brasenose College and Professor of Development Studies at the University of Oxford's Department of International Development. She leads the five-year ERC-funded Changing Structures of Islamic Authority (CSIA) project examining Islamic scholarly debates and their societal impacts. Her academic credentials include an MPhil in Development Studies from Cambridge and a DPhil from Oxford. These qualifications underpin her expertise in development theory and field research methodologies. Bano's research centers on the interplay between ideas, beliefs, and material incentives in development processes . She investigates how psycho-social factors shape individual choices and collective outcomes, with particular focus on Islamic authority structures, female education movements, and aid effectiveness. Her work combines large-scale comparative studies using ethnographic and survey data across Muslim-majority contexts. Recent publications reveal a concentrated focus on education policy in developing nations , especially Pakistan, Nigeria, and Indonesia. Key themes include decentralized education planning, low-fee private tuition markets, community participation models, and Islamic education systems. The research consistently examines religion-development intersections and institutional responses to social change. Her scientific recognition includes: Two competitive ESRC Fellowships A 1.4 million euro ERC Starting Grant for the CSIA project ESRC Best Social Science Impact award for Nigeria education interventions Bano supervises MPhil/DPhil theses at Oxford and has advised major development agencies, notably designing DfID's education reforms in northern Nigeria. Her CSIA project coordinates a multinational team conducting textual analysis, surveys, and ethnographic fieldwork to map evolving Islamic authority structures.
Jack J. Blanchard, Ph.D., is an Associate Provost for Enterprise Resource Planning and Professor in the Department of Psychology at the University of Maryland, College Park. He is affiliated with the Brain and Behavior Institute and serves as Academic Director of the Master of Professional Studies program in Clinical Psychological Science. Previously, he held roles as Department Chair and Director of Clinical Training. Education: Ph.D. in Clinical Psychology, State University of New York at Stony Brook (1991) B.S. in Psychology, Arizona State University (1984) NIMH Postdoctoral Fellow at Medical College of Pennsylvania (1990-1992) Research Interests: Dr. Blanchard’s work focuses on emotion-behavior interactions in psychotic disorders, particularly schizophrenia. His laboratory (LEAP) employs fMRI, smartphone-based ecological assessments, and actigraphy to study: Social affiliation deficits and paranoia Neural correlates of motivation and reward processing Impact of sleep on symptom severity Clinical and behavioral assessment innovations Publication Trends: Recent articles (2020-2025) demonstrate a strong emphasis on transdiagnostic approaches, integrating neuroimaging with real-world behavioral tracking to examine social reward processing, pandemic-related mental health impacts, and sleep-psychosis comorbidities. Methodological themes include fMRI, smartphone assessments, and longitudinal designs. Awards and Honors: Joel and Kim Feller Professorship (2016) Excellence in Teaching Mentorship Award Fellow, Association for Psychological Science Mentoring and Grants: Actively trains graduate/undergraduate researchers through LEAP lab. Secured major funding including: NIMH grants for neural correlates of social affiliation/paranoia University of Maryland Brain and Behavior Initiative awards for wearable tech integration Laboratory and Collaborations: Directs the Laboratory of Emotion and Psychopathology (LEAP), collaborating with psychiatry departments and neuroscientists. Focuses on minority-representative samples and integrates clinical, behavioral, and technological methodologies.
Eli Ben-Michael is an Assistant Professor jointly appointed in the Heinz College of Information Systems and Public Policy and the Department of Statistics & Data Science at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC), contributing to cutting-edge research at the intersection of statistics, policy analysis, and artificial intelligence. His educational background includes a PhD in Statistics from U.C. Berkeley and undergraduate studies at Columbia University where he earned a dual degree in Computer Science and Statistics. Prior to his current position, he completed a postdoctoral fellowship at Harvard University's Institute for Quantitative Social Science and Department of Statistics. Ben-Michael's research focuses on developing innovative statistical and computational methods for causal inference and policy evaluation, with particular emphasis on integrating machine learning techniques to address complex problems in public policy and social science. His work bridges theoretical statistics with practical applications in healthcare, criminal justice, education, and social policy. Current research directions include safe policy learning, sensitivity analysis for clustered data, and methodological innovations for the synthetic control method. His publication record shows a strong trajectory in top-tier journals including Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Proceedings of ICML. Recent work demonstrates increasing focus on policy-relevant applications including abortion legislation impacts, pre-trial risk assessment, and healthcare disparities, while maintaining methodological rigor in causal inference frameworks. Ben-Michael has developed open-source software tools including augsynth and multical R packages, which implement his methodological contributions for synthetic controls and multilevel calibration weighting. These packages have been adopted by researchers in multiple disciplines for causal inference applications.
