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Majid GhayoomiView profile

Professor

• Geotechnical Engineering
• Earthquake Engineering
• Unsaturated Soil Mechanics
• +4 more

Majid Ghayoomi is a Professor and Civil Engineering Undergraduate Coordinator in the Department of Civil and Environmental Engineering at the University of New Hampshire's College of Engineering and Physical Sciences. His research focuses on geotechnical engineering and geomechanics, particularly unsaturated soil mechanics and geotechnical earthquake engineering. He teaches courses such as Soil Mechanics, Engineering Behavior of Soils, and Geotechnical Modeling. Dr. Ghayoomi holds a Ph.D. from the University of Colorado at Boulder, an M.S. from Sharif University, and a B.S. from the University of Tehran. His research interests include bioremediation, hazards mitigation, soil-structure interaction, and materials testing. He leads the Geotechnical Modeling and Innovation lab, advancing bio-inspired solutions and remote sensing applications in geotechnical systems. His recent work emphasizes climate change impacts on seismic resilience, microbial stabilization of soils, and satellite-based soil moisture monitoring. Key contributions include studies on liquefaction mitigation, seismic site response, and infrastructure vulnerability in dynamic environments. His research spans theoretical, experimental (centrifuge modeling), and computational approaches to address complex geotechnical challenges.

• Computational Social Science
• Network Science
• Data Visualization
• +7 more

Yu-Ru Lin is an Associate Professor at the School of Computing and Information, University of Pittsburgh, and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber). She leads the Pitt Computational Social Dynamics Lab (PICSO Lab) and holds secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program. PhD in Computer Science from Arizona State University Postdoctoral research at Harvard University and Northeastern University Her research focuses on computational approaches for: Networked social dynamics High-dimensional social information summarization Trust and distrust propagation Misinformation detection Policy diffusion analysis Recent publications span 2014-2020, covering: Social media crisis response Graph visualization techniques Policy diffusion patterns Misinformation mitigation Temporal topic modeling Scientific support includes: National Science Foundation (NSF) awards Minerva/ONR funding AFOSR grant for distrust modeling DARPA Understanding Group Biases program As director of PICSO Lab, she leads interdisciplinary research teams on: Digital accountability Urban mobility patterns Health informatics via crowdsourcing Trust-influence dynamics

Martin T. WellsView profile

Professor

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +7 more

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.

Barbara CaputoView profile

Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +3 more

Barbara Caputo is a Full Professor at Politecnico di Torino, leading the VANDAL Laboratory and directing the AI@PoliTo Interdepartmental Lab. She holds a double affiliation with the Italian Institute of Technology (IIT) and has held roles at Idiap-EPFL and Sapienza University. Her research focuses on AI, computer vision, domain adaptation, and federated learning. She contributes to national AI policy, including the Italian Strategy on AI and the National PhD on AI for Industry 4.0. She is an ERC Laureate and ELLIS Fellow, co-founding ELLIS society. Her work spans visual place recognition, action recognition, and cross-domain learning. Education: PhD in Computer Science from KTH Royal Institute of Technology (2005). Major roles include Rector’s Advisor on AI at PoliTo, Board Member of ELLIS, and coordinator of the AI & Industry 4.0 vertical in the National PhD program. Awards include ERC Laureate (2017), ELLIS Fellow (2019), and Inspiring Fifty Italy (2018). Her research emphasizes federated learning, domain adaptation, and AI ethics. Recent articles explore domain generalization, resource-efficient federated models, and AI-environment interactions. She collaborates with institutions like MUR, CNR, and the European Commission on AI policy and tech initiatives.

Judy HoffmanView profile

Associate Professor

• Computer Vision
• Machine Learning
• Domain Adaptation
• +7 more

Judy Hoffman is an Associate Professor in the College of Computing at Georgia Institute of Technology, with a joint appointment in the School of Interactive Computing and affiliation to the Machine Learning Center . She received tenure in April 2025 after joining Georgia Tech as an Assistant Professor. Her research focuses on enabling AI systems that are reliable, fair, and resource-efficient. PhD in Electrical Engineering and Computer Science (2016, UC Berkeley) Postdoctoral Fellowships at Stanford (2017) and UC Berkeley (2018) Former Research Scientist at Facebook AI Research Her work intersects computer vision and machine learning , with specialization in domain adaptation , adversarial robustness , and algorithmic fairness . She has published over 40 peer-reviewed articles, including the award-winning DeCAF (ICML 2024 Test of Time Award) and co-founded Women in Computer Vision (2015), which has sponsored ~40 women annually to premier conferences. ICML Test of Time Award (2024) NSF CAREER Award (2022) PAMI Distinguished Young Researcher (2023) Samsung AI Researcher of the Year (2021) Dr. Hoffman has served as Program Chair for CVPR 2023, Associate Editor for T-PAMI (2021-2023), and co-organizer of workshops at major AI conferences. She has delivered over 70 invited talks and contributes to open-source projects like cycada_release (567 stars) and lsda (47 stars).

Ronald McGarveyView profile

Professor

• Operations Research
• Robust Optimization
• Sustainable Systems
• +3 more

Ronald McGarvey is a Full Professor at IÉSEG School of Management since 2022, with prior roles including Associate Professor with Tenure at the University of Missouri (2013-2022) and Senior Operations Researcher at RAND Corporation (2002-2022). He holds a Ph.D. in Industrial Engineering and Operations Research from Pennsylvania State University and an H.D.R. in Industrial Engineering from the University of Paul Sabatier, France. Education : H.D.R. (2024), Industrial Engineering, University of Paul Sabatier Ph.D. (2002), Industrial Engineering and Operations Research, Pennsylvania State University His research focuses on Operations Research and Robust Optimization applied to diverse domains: Healthcare Logistics : Modeling access disparities in rural obstetric care for American Indian populations Environmental Sustainability : Analyzing biomass energy systems, forest carbon impacts, and waste management frameworks Network Design : Developing optimization models for supply chain resilience, military logistics, and urban transit safety Policy Analysis : Evaluating multi-state environmental collaborations and localized food systems His publications demonstrate strong interdisciplinary applications of mathematical programming and spatial optimization to real-world challenges in healthcare access, energy sustainability, and transportation systems. Key Collaborations : Working with researchers in public health (Thorsen, Noble), environmental science (Aguilar), and engineering (Mirzaee, Gutierrez-Lopez)

Praneet PrakashView profile

Researcher

• Biophysics
• Soft Matter Physics
• Statistical Mechanics
• +3 more

Dr. Praneet Prakash is a Researcher in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge, working under Prof. Raymond Goldstein. His research focuses on interdisciplinary approaches combining microfluidics, microscopy, and theoretical physics to study biological systems. Key areas include microbial motility, active matter dynamics, and the interplay between physics and living systems. He utilizes experimental techniques such as microfluidics and advanced microscopy to investigate phenomena like bacterial swimming, nutrient exchange in microbial communities, and growth oscillations in filamentous fungi. His work bridges Soft Matter Physics, Statistical Mechanics, and Biophysics, with applications ranging from understanding microorganism behavior to developing biosensor technologies. Recent studies explore phototactic algae behavior, ciliary surface interactions in animalcules, and adaptive motility in marine microorganisms. Publications highlight contributions to microbial ecology, active matter systems, and biophysical modeling. While no formal awards are listed, his research has been published in high-impact journals like Journal of the Royal Society Interface and Physical Review Fluids . He is affiliated with the Biological Physics and Mechanics research group and maintains an active presence on academic platforms like Twitter and LinkedIn.

Elmira HassanzadehView profile

Associate Professor

• Hydrology
• Water Resources Engineering
• Climate Change Impact Assessment
• +4 more

Elmira Hassanzadeh is an Associate Professor at the Department of Civil, Geological and Mining Engineering , Polytechnique Montréal, with a focus on integrated water resource modeling and management under climate and anthropogenic changes. She is affiliated with the Global Institute for Water Security (University of Saskatchewan) and the Experimental and Digital Water Flow Engineering Group (GENIE EAU) . Education: PhD in Civil Engineering, University of Saskatchewan Postdoctoral Fellow, McGill University Research interests include climate change impact assessment, socio-hydrological modeling, stakeholder engagement in water management, and sustainable development of water systems in cold regions. Her work emphasizes decision-making under uncertainty and adaptive strategies for ungauged basins. Recent publications (2024–2020) span coastal flood hazard modeling, climate-driven hydrological changes in tropical and Canadian watersheds, evaluation of agricultural practices under warming climates, and integrated approaches to water quality and system dynamics. Keywords include Hydrology , Climate Change , Water Security , and Socio-Hydrology . Supervision: She has mentored 15+ graduate students in projects related to Lake Urmia, Saskatchewan River Basin, and climate adaptation in Africa and the Pacific.

Xiaowei JiaView profile

Assistant Professor

• Machine Learning
• Data Mining
• Physics-Guided Neural Networks
• +5 more

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China (USTC) and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal and environmental challenges, such as climate modeling, hydrology, and fairness in AI. Education: Ph.D., University of Minnesota (2020) M.S., State University of New York at Buffalo B.S., University of Science and Technology of China (USTC) Research Interests: Knowledge-Guided Machine Learning Spatiotemporal Data Mining Fairness in AI for Social Good Applications in Environmental Science and Healthcare Publications showcase his work on physics-integrated neural networks, spatiotemporal modeling (e.g., water temperature prediction), and fairness-aware algorithms. His work has been recognized with Best Paper awards at SIAM SDM (2022, 2023). Awards include the Best Applied Data Science Paper Award at SIAM SDM in 2022 and 2023. He teaches advanced machine learning courses, emphasizing theory integration with real-world applications.

Olle JärvView profile

Researcher

• Human mobility
• Big data analysis
• Spatial segregation
• +7 more

Dr. Olle Järv is an Academy Research Fellow at the University of Helsinki's Department of Geosciences and Geography. As Docent in Human Geography and holder of a joint PhD from University of Tartu and Ghent University, his research focuses on leveraging big data sources like mobile phone records and social media for analyzing human mobility patterns across European cross-border regions. Key research areas: Spatial segregation, urban diversity, transnationalism, and pandemic-induced mobility changes Current projects: BORDERSPACE (2020-2025) – Mapping transnational spaces through mobility data MOBI-TWIN (2023-2026) – Regional mobility patterns in twin transitions WinWin4WorkLife (2024-2027) – Sustainable cross-border remote work His methodological innovations include developing tools for analyzing social media data and mobile phone records to study urban dynamics. Recent recognitions include the 2024 Research Council of Finland Award and 2021 Best Paper Award from Journal of Location Based Services.

Kevin ManaughView profile

Associate Professor

• Sustainable Transportation
• Spatial Justice
• Decision Making Processes
• +3 more

Kevin Manaugh is an Associate Professor at McGill University, specializing in Urban Planning with a focus on sustainable transportation and spatial justice. His research examines how decision-making processes in urban development balance environmental, economic, and social equity goals, particularly through transport systems. He holds a PhD in Urban Planning from McGill University (2013). His key research interests include integrating social equity into land use policies, analyzing accessibility impacts on socioeconomically diverse populations, and studying behavior change related to sustainable transportation. Current projects explore equitable transit accessibility, responses to local mobility policies, and health/well-being effects of sustainable transport choices. Manaugh has published extensively on transportation equity, urban form analysis, and GIS applications. His work bridges theoretical frameworks with practical policy implementation, emphasizing participatory approaches to urban planning. Notable contributions include studies on pandemic street reallocation equity and cycling infrastructure prioritization strategies. His research has been supported by interdisciplinary collaborations and applied to Canadian urban contexts. Despite prolific output, Manaugh maintains an active engagement with public discourse on urban mobility challenges and equitable urban futures.

Fahui WangView profile

Professor

• Spatially-Integrated Social Sciences
• Geographic Information Systems (GIS)
• Public Policy and Planning
• +3 more

Fahui Wang is the Cyril & Tutta Vetter Alumni Professor in the Department of Geography & Anthropology at Louisiana State University (LSU). He holds a B.S. in Geography from Peking University (1988), an M.A. in Economics (1993), and a Ph.D. in City & Regional Planning (1995), both from The Ohio State University. His research focuses on spatially-integrated social sciences, public policy, and GIS applications in healthcare, urban planning, and public safety. He has authored/co-authored multiple influential books and peer-reviewed articles, including works on computational methods in GIS and healthcare accessibility analysis. Professor Wang’s work has been supported by grants from the National Institutes of Health, National Science Foundation, and U.S. Departments of Housing and Urban Development and Justice. His accolades include LSU’s Distinguished Research Master Award (2023) and CPGIS Distinguished Scholar (2023). He teaches advanced courses in GIS, urban geography, and quantitative methods, and leads research initiatives in the LSU Geoscience department. His lab focuses on geospatial technologies for addressing societal challenges in healthcare access, urban planning, and crime analysis.

• Operations Management
• Supply Chain Management
• Sharing Economy
• +6 more

Ming Hu is a Professor and University of Toronto Distinguished Professor of Business Operations and Analytics at Rotman School of Management, University of Toronto. He serves as Area Coordinator for the Operations Management & Statistics Area and holds editorial leadership roles including Editor-in-Chief of Naval Research Logistics and Associate Editor for multiple top journals. MS in Applied Mathematics, Brown University (2003) PhD in Operations Research, Columbia University (2009) His research focuses on sharing economy , social operations , and platform economics , examining how operational decisions can maximize societal benefit. Key areas include crowdfunding , two-sided markets , crowdsourcing , and group buying , with applications to DEI , sustainability , and AI-empowered operations . Recent work analyzes spatial operations in delivery systems, algorithmic fairness , and climate change adaptation in agricultural supply chains. His publications span top journals like Management Science and Operations Research , covering topics from blockchain traceability to quantum-inspired optimization . Scientific recognitions include: Wickham Skinner Early-Career Research Award (2016) Best Operations Management Paper in Management Science (2017) 2018 Poets & Quants Best 40 Under 40 MBA Professors As an Amazon Scholar (2022–) and Chair of Chain Analytics Institute (2023–), he bridges academic research with industry applications in AI-driven logistics and sustainable operations.

Daniel Sanz-AlonsoView profile

Assistant Professor

• Inverse Problems
• Data Assimilation
• Scientific Machine Learning
• +3 more

Daniel Sanz-Alonso is an Assistant Professor in the Department of Statistics at the University of Chicago since 2018, affiliated with the Committee on Computational and Applied Mathematics. He previously held a postdoctoral position in Brown University’s Division of Applied Mathematics and contributed to their Data Science Initiative. His research focuses on integrating predictive mathematical models with large datasets, addressing challenges in inverse problems, data assimilation, and scientific machine learning. Education: BSc from University of Valladolid (2012), PhD in Mathematics and Statistics from University of Warwick (2016). Awards include the José Luis Rubio de Francia Prize (2020) for Spanish mathematicians under 32 and an NSF CAREER Award (2023). He has been an Associate Editor of the SIAM/ASA Journal on Uncertainty Quantification since 2025. Funding support comes from the National Science Foundation, National Geospatial-Intelligence Agency, Department of Energy, and BBVA Foundation. His interdisciplinary work bridges data science, machine learning, and partial differential equations, with applications in weather forecasting and geophysical sciences.

Stan AhaltView profile

Professor

• Data Science
• High-Performance Computing
• Biomedical Informatics
• +4 more

Dr. Stan Ahalt serves as the inaugural Dean of the UNC School of Data Science and Society (SDSS) and holds a professorship in the Department of Computer Science at UNC-Chapel Hill. He previously directed the Renaissance Computing Institute (RENCI) and currently acts as its executive advisor. Additionally, he is the associate director of informatics and data science at NC TraCS, the translational research institute at UNC. His academic journey includes a 22-year tenure as professor of electrical and computer engineering at Ohio State University and leadership roles at the Ohio Supercomputer Center. Ahalt’s research focuses on applying data science to societal challenges such as healthcare, climate, and judicial reform. Key initiatives include the National Consortium for Data Science and the iRODS project, which advanced enterprise data management systems. Ahalt has secured over $34.5M in grants to lead high-impact projects like the NHLBI BioData Catalyst (unifying biomedical data), the NCATS Translator (accelerating clinical discovery), and the NIH HEAL Initiative (ensuring FAIR data standards). He mentors students across doctoral and master’s programs, teaching data science fundamentals in courses like COMP 116: Introduction to Scientific Programming. His work emphasizes interdisciplinary collaboration, evidenced by roles in RENCI, NC TraCS, and the SDSS. Ahalt’s contributions span cyberinfrastructure development, educational innovation, and translational research, positioning UNC as a national leader in data science.