Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.
Elizabeth A. Simpson is an Associate Professor in the Department of Psychology at the University of Miami's College of Arts and Sciences. She serves as Associate Director of the Child Division and directs the Social Cognition Laboratory, focusing on infant social and cognitive development through interdisciplinary methodologies. Education: Ph.D. in Psychology (unspecified institution) Key Research Areas: Developmental Psychology, Autism, Social Cognition, Primate Studies, Visual Attention, Infant Behavior Her research investigates individual differences in infant visual attention, social motivation, and physiological markers like salivary oxytocin. Recent studies demonstrate: Stability of attentional patterns from newborn to 14 months Sex differences in early face detection Links between oxytocin levels and social affect in infants Neurophysiological markers in newborns later diagnosed with ASD Scientific awards include the NSF CAREER grant for neonatal imitation research. She contributes to open science initiatives through the ManyBabies consortium and develops remote eye-tracking methods for broader accessibility. Current lab work explores: Parent-infant affective interactions during still-face episodes Predictive power of early attentional biases Development of pathogen avoidance behaviors Cross-species comparisons of social cognition
Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.
Soroush Saghafian is an Associate Professor of Public Policy at Harvard Kennedy School, specializing in applying operations research and machine learning to address public health challenges. He leads the Public Impact Analytics Science Lab (PIAS-Lab), focusing on analytics-driven solutions for societal problems. His research spans healthcare delivery optimization, emergency department efficiency, and public health policy. Notable collaborations include Massachusetts General Hospital and Harvard's Center for Health Decision Science. Awards include the INFORMS MSOM Responsible Research Award and the Pierskalla Award for healthcare research. Recent work includes studies on hospital closures' impacts, predictive analytics for bipolar disorder using Fitbit data, and policy implications of race in disease risk models. He teaches courses on machine learning and big data for public impact.
Pinar Keskinocak serves as the H. Milton and Carolyn J. Stewart School Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. She co-founded and directs the Center for Health and Humanitarian Systems, demonstrating leadership in both academic administration and research innovation. Her career includes prior roles as College of Engineering ADVANCE Professor and interim associate dean for faculty development, alongside industry experience at IBM T.J. Watson Research Center. Her educational foundation includes a Ph.D. in Operations Research from Carnegie Mellon University and M.S. and B.S. degrees in Industrial Engineering from Bilkent University. This rigorous training underpins her interdisciplinary approach to complex societal challenges. Dr. Keskinocak's research pioneers operations research applications for societal impact , with dual emphases on health systems and humanitarian logistics . She develops advanced models for infectious disease dynamics (including COVID-19, malaria, and Guinea worm), vaccination strategies, disaster response, and supply chain optimization. Her work uniquely bridges engineering analytics with real-world implementation through partnerships with the CDC, American Red Cross, Carter Center, and Children's Healthcare of Atlanta. Analysis of her 2023-2025 publications reveals intensifying focus on eradication program modeling for polio and Guinea worm, with increasing integration of machine learning for predictive accuracy in vaccine distribution. A pronounced trend addresses health equity through targeted interventions for under-immunized populations in Sub-Saharan Africa, emphasizing resource allocation under constraints and cross-border coordination challenges. Her scientific recognition includes: INFORMS Fellow (2015) NSF CAREER Award (2001) Outstanding Professional Education Award, Georgia Tech (2018) Denning Award for Global Engagement, Georgia Tech (2016) Women in Engineering Excellence Teaching Award (2005) Moving Spirit Award, INFORMS (2005) Dr. Keskinocak has secured extensive project funding through collaborations with federal agencies and NGOs, directing research on pandemic response systems, vaccine logistics, and disaster debris management. While specific advisees aren't publicly cataloged, her leadership in the Center for Health and Humanitarian Systems mentors numerous graduate researchers through interdisciplinary projects. She significantly influences national policy via National Academies committees addressing supply chain resilience and vaccine distribution infrastructure. The Center for Health and Humanitarian Systems operates as a dynamic hub where engineering rigor meets humanitarian action, coordinating multi-institutional teams to develop deployable solutions for global health crises and disaster response systems.
California Institute of Technology (Caltech)United States
Dr. Konstantin (Kostia) M. Zuev serves as Teaching Professor in the Computing + Mathematical Sciences Department at California Institute of Technology , where he has made significant contributions to network science and computational statistics since 2016. His dual PhDs in Mathematics (Moscow State University, 2008) and Civil Engineering (HKUST, 2009) underpin his interdisciplinary research spanning differential geometry, stochastic simulation, and network dynamics. Education PhD in Mathematics, Lomonosov Moscow State University (2008) PhD in Civil Engineering, Hong Kong University of Science & Technology (2009) His research focuses on network science , particularly course-prerequisite networks and complex financial systems , with recent work extending to network navigability in cosmological models and rare event simulation. Over his career, he has developed innovative Bayesian inference methods and geometric preferential attachment theories while maintaining active collaborations across mathematics, physics, and biomedical domains. Recent publications highlight network analysis in education ( 2023 ), hyperbolic graph theory ( 2024 ), and pandemic-informed cancer mortality studies ( 2023 ). His 15 most recent articles demonstrate methodological innovations across disciplines including statistics, physics, finance, and cosmology. Scientific recognition includes Humboldt Research Fellowship (2021) Carver Mead Seed Fund Grant (2023) ASCIT Teaching Award (2018, 2023) Northrop Grumman Teaching Excellence Prize (2019) As Graduate Option Representative for Information and Data Sciences at Caltech and faculty advisor for multiple student organizations including the Caltech Karate Club and Caltech Chess Club , he actively bridges academic rigor with community engagement through outreach initiatives like the virtual math education channel and university math circles for K-12 students.
Dr. Shubham Misra is a Research Fellow in the Department of Neurology at Yale University School of Medicine. He holds a Ph.D. in Neurology from the All India Institute of Medical Sciences (2022) and advanced degrees in Biotechnology from Amity University (2014). His research focuses on biomarker discovery, proteomics, and clinical data analysis in neurological disorders, particularly stroke and post-stroke epilepsy. Dr. Misra has authored over 50 peer-reviewed publications, including work in JAMA Neurology and Neurology , and leads research on omics-based approaches in stroke at Frontiers in Neurology. He collaborates globally, including with the WHO's Brain Health Unit to address neurological aspects of pandemics like COVID-19. Research Interests: Biomarker discovery using proteomics and machine learning, outcomes research in stroke, genetic risk factors for neurological disorders, and systematic reviews/meta-analyses. Dr. Misra's work bridges clinical neurology with advanced data science, aiming to improve diagnostics and patient outcomes. Awards: Young Investigator Award (World Stroke Organization), DST-INSPIRE Fellowship (India), and multiple international travel grants for collaborative research. Grants & Collaborations: Leads the International Post-stroke Epilepsy Research Repository (IPSERR) and collaborates with institutions worldwide. His work on post-stroke epilepsy and stroke subtyping biomarkers has been widely cited (over 1,200 citations). Labs & Teams: Mishra Lab at Yale, Rangaraju Lab, and global networks through the WHO's Global Forum and IPSERR consortium.
Chris Darimont is a Professor and Raincoast Research Chair in Applied Conservation Science within the Department of Geography at the University of Victoria's Faculty of Social Sciences. His work bridges natural and social sciences to address urgent conservation challenges, with a geographic focus on British Columbia's Central Coast (Great Bear Rainforest) but designed for global relevance. His research spans three primary domains: landscape ecology at the marine-terrestrial interface, conservation biology of harvest management, and conservation ethics. Darimont maintains deep collaborations with First Nations communities and conservation organizations like the Raincoast Conservation Foundation (where he previously served as Science Director) and Hakai Institute. His work frequently integrates Indigenous Knowledge with scientific methods, exemplified by projects like the Nuxalk Sputc (Eulachon) initiative and Heiltsuk bear monitoring. Darimont's publication record shows consistent focus on human-wildlife interactions, particularly bear-salmon ecosystems, trophy hunting ethics, and Indigenous-led conservation. His highly cited 2009 PNAS paper "Human predators outpace other agents of trait change in the wild" and 2015 Science paper "The unique ecology of human predators" established foundational frameworks in conservation science. Recent work increasingly emphasizes decolonial approaches and Two-Eyed Seeing methodologies. As an educator, he teaches GEOG 391 (Contemporary Topics in Coastal Conservation) and GEOG 353 (Coastal and Marine Resources), prioritizing student mentorship as his "favourite form of outreach." His research is regularly featured in high-profile media including National Geographic and The New York Times, reflecting his commitment to science communication and public engagement.
Zhiling Gu is a Research Fellow at Yale School of Public Health, having earned her Ph.D. in Statistics at Iowa State University. Her work integrates statistical theory with applications in public health and medicine. Her research spans Functional Data Analysis Network Analysis Spatiotemporal Modeling Statistical AI Foundations Nonparametric Learning applied to neuroimaging, electronic health records, and environmental health studies. Recent publications focus on Adaptive spatiotemporal models Neuroimaging data processing Pandemic forecasting frameworks Environmental exposure modeling with methodological rigor and practical implementation. Scientific achievements include Runner-up in SMI 2023 Student Paper Competition She has taught STAT 305: Engineering Statistics (ISU) STAT 226: Business Statistics Statistical Computing Statistical Learning and actively engages in academic presentations at conferences like SMI 2024 and CMStatistics 2022.
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.
Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.
Dr. Yang Liu is the Chair and Gangarosa Distinguished Professor in the Gangarosa Department of Environmental Health at Emory University's Rollins School of Public Health. His research focuses on satellite remote sensing applications for public health, climate change impacts, and air pollution exposure modeling. He leads federally funded projects integrating satellite data into health studies and serves as a PI for the NIH-funded Climate & Health Actionable Research and Translation (CHART) Center. Education: Ph.D. (Harvard University), M.S. (University of California), B.S. (Tsinghua University). Professional Affiliations include NASA mission science teams and the ORISE Faculty Fellowship at CDC. Research interests span satellite aerosol retrieval, climate-health linkages, and machine learning in environmental statistics. Notable work includes advancing wildfire smoke health impacts, PM2.5 modeling, and climate dashboard development for Georgia. Awards include Gangarosa Distinguished Professorship, ORISE CDC Fellowship, and leadership in NIH/NASA initiatives. His lab develops innovative tools for environmental health monitoring and policy translation.
Ryan M Hebert, MD, is an Assistant Professor of Neurosurgery at the Yale School of Medicine, affiliated with the Neurosurgery department. He specializes in neurovascular disorders, including open and endovascular treatments for intracranial aneurysms, arteriovenous malformations, carotid disease, and dural arteriovenous fistulas. He also focuses on interventions for ischemic and hemorrhagic strokes. His practice locations include Yale New Haven Hospital and Lawrence and Memorial Hospitals in Connecticut. Education & Training: Bachelor’s in Molecular, Cellular, and Developmental Biology from the University of Colorado – Boulder (Howard Hughes Undergraduate Research Grant recipient) MD from Yale School of Medicine (NIH-funded thesis on CCM3 in cerebral cavernous malformations) 7-year Neurosurgery Residency at Yale New Haven Hospital, including enfolded neuro-interventional training CAST-accredited fellowship in Endovascular Neurosurgery and Neurovascular Critical Care at Thomas Jefferson University Hospital (2016) Research Interests: Dr. Hebert’s work centers on emerging surgical and endovascular techniques for neurovascular disorders, risk stratification in carotid disease, vasospasm prediction/post-subarachnoid hemorrhage treatment, and outcomes of minimally invasive spine surgery. His studies often integrate clinical practice with translational research, emphasizing patient outcomes and procedural optimization. Key topics include flow diverters, cranial nerve palsies, hospital frailty risk scores, and immune mechanisms driving aneurysm rupture. Publications Trends: His recent articles emphasize clinical outcomes of endovascular procedures, stroke interventions, and immune mechanisms in aneurysm rupture. He frequently collaborates on meta-analyses and systematic reviews to evaluate treatment efficacy and complication rates, particularly involving cranial nerve recovery and thrombectomy volumes. Awards & Grants: He secured an NIH-funded thesis and a Howard Hughes Undergraduate research grant. No explicit scientific awards are listed, but his work has been recognized via peer-reviewed publications and contributions to clinical guidelines. Advising & Grants: While no students are listed, he collaborates with prominent researchers like Charles Matouk, Kevin Sheth, and Joseph Antonios on NIH-funded projects. His research addresses healthcare resource utilization and procedural volume impacts on mortality, indicating a focus on translational and clinical studies. Labs & Teams: Active in interdisciplinary teams at Yale School of Medicine, particularly in neurovascular critical care and endovascular surgery. Collaborates with the Stroke Center and Janeway Society for physician-scientist development.
David Steinsaltz is an Associate Professor of Statistics at the University of Oxford, affiliated with Worcester College. His research focuses on stochastic processes, biodemography, survival analysis, and Bayesian methods, with applications to aging, mortality, and population dynamics. He holds a PhD in probability theory from Harvard University, followed by postdoctoral work at UC Berkeley. His work bridges theoretical probability and applied statistics, addressing questions in demography, ecology, and epidemiology. Education: PhD in Mathematics (Probability Theory), Harvard University (1996); Postdoctoral Research, UC Berkeley (Departments of Demography and Statistics). Research interests include stochastic flows, Markov processes, and statistical methods for longitudinal data. He contributes to interdisciplinary projects, such as earthquake impact modeling and vaccine efficacy analysis. His collaborations span fields like biostatistics, ecology, and machine learning. He advises students on topics including survival analysis and demographic modeling.
