John Paisley is an Associate Professor of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, and a member of Columbia's Data Science Institute (DSI). He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Duke University (2004-2010), followed by postdoctoral research in Computer Science at Princeton University and UC Berkeley. His research focuses on Bayesian models, posterior inference techniques for Big Data, and applications in data analysis, recommendation systems, information retrieval, and compressed sensing. He has pioneered methods like Bayesian Gaussian Process ODEs and Double Normalizing Flows, with recent work emphasizing uncertainty quantification in environmental modeling and neuroimaging analysis. His collaborative workflows (e.g., bneR ) address air pollution exposure and PM2.5 concentration uncertainties, combining Bayesian nonparametric ensembles with geospatial data. He has also developed frameworks for neural network interpretability, image denoising, and compressed sensing MRI. Paisley's work bridges statistical theory and applied machine learning, with applications in healthcare, environmental science, and geophysics. His academic contributions include over 50 publications since 2016, spanning topics like deep metric learning, adversarial learning, and variational inference optimization. He maintains an active research group and serves on editorial boards for machine learning and signal processing journals.
Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC), with a PhD in Electrical Engineering from the University of Southern California (USC) and a postdoctoral background in Computer Science at USC's Integrated Media Systems Center (IMSC). His research focuses on machine learning, data mining, and their applications in biomedical informatics, social network analysis, and multimedia data mining. Key contributions include survival analysis models using pseudo values and federated learning frameworks for healthcare data. He has received awards including the Best Paper Award at SIGSPATIAL 2014 and a Best Poster Runnerup at SCMLS 2016. Education: PhD in Electrical Engineering (USC), Postdoc in Computer Science (USC) His work spans interdisciplinary areas such as domain adaptation for remote sensing, thermal face translation, and interpretable neural networks for medical applications. Recent projects include federated survival analysis models and climate-informatics frameworks for cloud property retrieval. He teaches courses in artificial intelligence, healthcare informatics, and statistical learning at UMBC. Research highlights include developing MedFuseNet for multimodal medical question answering and VDAM for multi-sensor cloud data analysis. His work on fair survival analysis models addresses algorithmic bias in healthcare predictions. Current grants include a NSF CAREER award for trustworthy federated learning in computational healthcare.
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.
Elif BAŞTÜRK is an Associate Professor at Kırşehir Ahi Evran University's Faculty of Arts and Sciences, Department of Archaeology, specializing in Settlement Archaeology, Hittite studies, and Early Bronze Age ceramics. With a PhD from Ege University (2014), she has led excavations at Tanır Yassıhöyük since 2022 and participated in major projects across Anatolia. Education: PhD in Protohistory and Near Eastern Archaeology, Ege University (2014) MA in Protohistory and Near Eastern Archaeology, Ege University (2006) BA in Archaeology, Ege University (2003) Research Focus: Her work examines archaeological transitions in Anatolia and the Near East, particularly ceramic traditions, settlement patterns, and Assyrian influence during the Bronze and Iron Ages. She employs statistical and technological methods to analyze material culture. Projects: Notable projects include the Tanır Yassıhöyük excavations (2021–present), Assyrian trade influence studies in Afşin-Elbistan plain, and documentation of Early Bronze Age ceramics in Siirt Başur Höyük. She has also led regional surveys threatened by the Ilısu Dam. Publications: Recent outputs include 2025 studies on Iron Age ceramics and Assyrian mill technology, alongside excavation reports from 2016–2024. Her work bridges field archaeology with material analysis, contributing to understanding cultural dynamics in ancient Anatolia.
Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, specializing in machine learning and statistics. Her research focuses on developing methods for uncertainty quantification in data analysis, Bayesian nonparametrics, and scalable inference algorithms. She leads a research group advising PhD students and postdocs in statistical machine learning. Her work spans Bayesian modeling, variational inference, spatial statistics, and applications in epidemiology and environmental science. Recent projects involve uncertainty-aware forecasting, robustness analysis of statistical methods, and efficient algorithms for high-dimensional inference. Broderick teaches Bayesian Modeling and Inference and contributes to MIT's statistics and data science initiatives.
Xi Gong is an Associate Professor in the Department of Biobehavioral Health and the Institute of Computing and Data Sciences at Pennsylvania State University, where he leads the Gong Lab. His research focuses on Geospatial Data Science, integrating GIScience, computational methods, and statistical analysis to study environmental health and social dynamics. He holds a PhD in Geographic Information Science from Texas State University, an M.Sc. from the University of Chinese Academy of Sciences, and a B.Eng. from Wuhan University. Dr. Gong's research interests include spatio-temporal data mining, environmental exposure modeling, and visual analytics for big data. His work bridges Environmental Health Science (EHS) and Spatially Integrated Social Science (SISS), addressing public health concerns through geospatial modeling and interdisciplinary collaboration. He currently accepts graduate students and postdoctoral scholars for projects in Geospatial Data Science and EHS. Education: PhD (2016, Texas State University), M.Sc. (2011, University of Chinese Academy of Sciences), B.Eng. (2008, Wuhan University) Labs/Teams: Director of Gong Lab, focused on geospatial big data analysis for health and environmental studies Advising: Mentors PhD and MS students in Biobehavioral Health and related fields
Pierre-Philippe Combes is a CNRS Research Professor and Head of Doctoral Studies in Economics at Sciences Po since 2021. He also serves as Co-Editor of the Journal of Urban Economics and Director of Graduate Programs in Economics at Sciences Po. Previously, he held positions at Aix-Marseille University (2003-2016), Boston University (2001-2003), and CERAS-École des Ponts et Chaussées (1998-2001). He holds a PhD in Economics from CREST (Paris School of Economics). Education PhD in Economics, Paris School of Economics (CREST), 1996 Habilitation à Diriger des Recherches (HDR), Aix-Marseille University, 2004 Research Interests : Urban economics and economic geography, focusing on local labor markets, housing markets, firm and household location choices, and resulting spatial disparities. His work uses machine learning for historical data recovery and examines agglomeration economies, migration externalities, and discrimination in urban contexts. Scientific Output Trends : Recent publications analyze urbanization in Sub-Saharan Africa, agglomeration economies, real income inequality in China, and historical land use through machine learning. Collaborative work spans France, China, and international institutions, addressing housing costs, peer effects in research, and urban poverty metrics. Scientific Awards : NATO Advanced Fellowship Grant (2001-2002) PhD Thesis Award - Association Française de Science Économique (AFSE) PhD Thesis Award - Association de Science Régionale de Langue Française (ASRDLF) Mention très honorable avec félicitations du jury (PhD thesis) Teaching & Editorial Roles : Teaches urban economics at Sciences Po and ENS Lyon. Serves on editorial boards of the Journal of Economic Geography , Regional Science and Urban Economics , and Journal of Urban Economics . Participates in evaluation committees for PhDs and habilitations across Europe. Policy Contributions : Advises on urban poverty measurement for the World Bank and contributes to French regional policy reports, including the Grand Paris Express project and CNIS (National Statistical Council).
Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.
Christophe Ancey is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he serves in the School of Architecture, Civil and Environmental Engineering (ENAC), specifically in the Institute of Civil Engineering (IIC) and the Environmental Hydraulics Laboratory (LHE). He also holds teaching appointments in SGC-Teaching and EDME-Teaching departments at EPFL. His office is located at GC A1 401, Station 18, 1015 Lausanne, Switzerland. Dr. Ancey holds both a PhD and engineering degree from Ecole Centrale de Paris and Grenoble National Polytechnic Institute. After completing his doctoral work (1994-1997) on rheology of granular flows under Pierre Evesque, he worked as a researcher at Cemagref before joining EPFL in 2004. He directs the Environmental Hydraulics Laboratory and serves as associate editor for Water Resources Research, a leading journal in hydrology. Dr. Ancey's research spans fluid dynamics, rheology, and hydraulics with emphasis on geophysical flows and natural hazards. His work focuses on three interconnected themes: rheology of concentrated suspensions (particularly granular flows in avalanches and mudflows), inverse problems in rheology (determining microscopic behavior from macroscopic measurements), and particle entrainment in turbulent suspensions (erosion and sediment transport processes). His recent publications (2022-2025) demonstrate continued innovation in sediment transport modeling, experimental techniques like PIV for complex flows, and integration of machine learning approaches. The research shows progression from fundamental fluid mechanics to practical applications in natural hazard assessment and river engineering, with particular attention to granular segregation, avalanche-obstacle interactions, and river morphodynamics. Associate Editor, Water Resources Research Co-founder of Toraval, an engineering consulting firm specializing in avalanche risk management Dr. Ancey teaches courses on Fluid Mechanics, Flood and Dam Break Waves, Hydrological Risks and Structures, and Similarity and Transport Phenomena in Fluids. He has supervised numerous PhD students whose work spans environmental hydraulics, granular flows, and sediment transport, with current students including Chen Yanan, Farazande Sofi, and Giboulot Axel Loïc among others.
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.
Tracey Galloway is an Associate Professor in the Department of Anthropology at the University of Toronto Mississauga (UTM), where she conducts critical research on Indigenous health disparities and policy interventions in northern Canada. Her work bridges medical anthropology, public health, and community-based participatory research to address systemic inequities affecting circumpolar populations. Education: PhD, McMaster University, 2008 MA (institution unspecified) BA (institution unspecified) BScN (institution unspecified) Dr. Galloway's research program centers on chronic disease risk assessment and health system improvement in Indigenous communities, with specific expertise in nutrition transition, food security, child growth patterns, and public health policy evaluation. She examines the impact of federal programs like Nutrition North Canada while developing community-led solutions for health equity. Her methodological approach combines quantitative analysis of health outcomes with qualitative community engagement, emphasizing Indigenous research sovereignty and decolonizing methodologies. Her publication record reveals consistent thematic focus across 15 recent articles, demonstrating interdisciplinary collaboration between anthropology, epidemiology, and health economics. Key trends include rigorous evaluation of colonial impacts on Indigenous food systems, innovative analysis of subsidy program effectiveness, and centering Indigenous patient experiences in healthcare design. Her work consistently prioritizes community-defined research questions and actionable policy recommendations. Dr. Galloway actively mentors graduate students including Darci Belmore, Carly Checholik, Neda Maki, and Hiliary Monteith, guiding research on Indigenous health determinants and policy interventions. While specific grant details aren't publicly enumerated, her collaborative projects involve partnerships with Indigenous communities across Northern Canada and interdisciplinary teams addressing complex health system challenges. She maintains strong community partnerships for her applied research, particularly in Nunavut and Northwestern Ontario, working directly with Anishinabeck and Inuit communities to translate findings into culturally safe health initiatives and policy reforms that address the root causes of health inequities.
Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.
Professor Oliver Razum at Bielefeld University's Faculty of Health Sciences leads Working Group 3 in Epidemiology & International Public Health. He serves on multiple commissions including the Faculty Conference and Teaching Commission, and contributes to the Center for Statistics and Bielefeld Center for Data Science. His work focuses on Epidemiology of migration and health Refugee health inequalities Medical rehabilitation equity Othering processes in public health Gender-sensitive health research Recent articles explore 2026 medical rehabilitation for people with disabilities, 2025 refugee mental health frameworks, and 2024 pandemic equity research. Key scientific contributions include the 2025 Georg Forster Fellowship and leadership in diversity-sensitive healthcare initiatives. His research spans urban neighborhood health effects , digital rehabilitation , and structural racism in healthcare . Current projects address intersectional inequalities , post-migration stress , and gender as social determinant . Professor Razum maintains active collaborations with institutions like the German Research Foundation and DRV Westphalia.
Dr. Baijian "Justin" Yang serves as the Associate Dean for Research at Purdue Polytechnic Institute and is a Professor in the Department of Computer and Information Technology at Purdue University. He earned his Ph.D. in Computer Science from Michigan State University, with Master's and Bachelor's degrees in Automation (EECS) from Tsinghua University. Dr. Yang has established himself as a leader in multiple interdisciplinary research domains. Dr. Yang's educational background includes: PhD in Computer Science, Michigan State University (2002) MS in Automation (EECS), Tsinghua University (1998) BS in Automation (EECS), Tsinghua University (1995) His research interests span multiple cutting-edge domains with practical applications: Cybersecurity : Developing novel approaches for threat intelligence, security education, and network defense Big Data : Creating innovative algorithms for dimension reduction, regression with categorical variables, and tensor decomposition Applied Machine Learning : Implementing AI solutions in healthcare, manufacturing, and forestry applications Digital Forestry : Using UAV imagery and remote sensing for forest management and tree species classification Dr. Yang's publication record demonstrates significant impact across multiple disciplines, with recent work focusing on spatial transcriptomics analysis (SiGra), delirium detection using limited-lead EEG, and visual localization technologies. His research bridges theoretical advances with practical applications in healthcare, manufacturing quality control, and environmental monitoring. The interdisciplinary nature of his work is evident in collaborations spanning computer science, healthcare, forestry, and manufacturing domains. His scientific achievements have been recognized with numerous awards: 2023 HRSA Building Bridges to Better Health Competition Winner (Phase 1) and 2nd place ($100,000 prize) in Phase 3 2023 Outstanding Faculty Award in Engagement, Department of Computer and Information Technology, Purdue University 2021 Leadership in Manufacturing Award, Manufacturing Times Digital (MxD) 2021 Good to Great Award, Purdue Polytechnic 2020 Outstanding Faculty Award in Discovery, Department of Computer and Information Technology 2019 University Faculty Scholars, Purdue University As an educator and mentor, Dr. Yang has advised numerous graduate students through their PhD and Master's research. His leadership extends to significant service roles including serving as Faculty Champion for the Holistic Safety and Security research impact area at Purdue Polytechnic from 2018 to 2021, board membership with ATMAE (2014-2016), and participation in the IEEE Cybersecurity Initiative Steering Committee (2015-2017). He holds valuable industry certifications including CISSP, MCSE, and Six Sigma Black Belt, demonstrating his commitment to bridging academic research with industry practice. Dr. Yang leads multiple research projects including "Digital Forestry" for developing tools to quantify forest function, "CHEESE" (Cyber Human Ecosystem of Engaged Security Education), and "CICI" (Supporting Controlled Unclassified Information with a Campus Awareness and Risk Management Framework). His work on "Applied Machine Learning" focuses on solving real-world problems, while his "Dimension Reduction and Memory Amnestic Big Data Regression" project innovates computational algorithms for large-scale data analysis.
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.
