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Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.

• Machine Learning
• Deep Learning
• Signal Processing
• +5 more

Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.

• Computational Physics
• Machine Learning
• Applied Mathematics
• +7 more

Meng Li is the Noah Harding Associate Professor of Statistics at Rice University's School of Engineering. He specializes in Bayesian analysis, machine learning, and statistical theory. His research bridges methodological development and applications in biomedical sciences, materials informatics, and neuroimaging. Li holds a Ph.D. from North Carolina State University and a B.S. from Sun Yat-sen University. He has been recognized with awards including the 2020 Rice Engineering Excellence Award and the Ralph E. Powe Junior Faculty Enhancement Award. Li's research focuses on probabilistic modeling of complex data such as images, functional data, and networks. His funded projects include AI frameworks for pancreatic cancer biomarkers and Bayesian spatiotemporal modeling of marine ecosystems. He collaborates with institutions like Houston Methodist and Baylor College of Medicine on medical applications. His teaching includes advanced courses like Bayesian Statistics and Advanced Bayesian Inference. He advises over 30 students, many of whom have pursued academic and industry roles. Li serves as an associate editor for Bayesian Analysis and the new ACM Transactions on Probabilistic Machine Learning.

• Bayesian Analysis
• Machine Learning
• Functional Data Analysis
• +8 more

Ben Seiyon Lee is an Assistant Professor in the Department of Statistics at George Mason University's College of Science. His work bridges computational statistics, climate modeling, and environmental risk assessment. Education: PhD in Statistics, Pennsylvania State University (2020) Lee specializes in computational methods for high-dimensional spatiotemporal data and uncertainty quantification in climate models. His research explores climate change impacts on extreme hydrological events, wildfire emissions, and medical decision-making. Recent publications focus on Bayesian spatiotemporal frameworks for extreme precipitation analysis, zero-inflated spatial models, and multisector uncertainty quantification. His work addresses challenges in flood risk assessment, agricultural yield projections, and healthcare compliance metrics.

• Computational Statistics
• Spatiotemporal Modeling
• Climate Change Impact Analysis
• +3 more

May Yuan is the Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas (UT-Dallas), affiliated with the School of Economic, Political and Policy Sciences. She directs the Geospatial Analytics and Innovative Applications (GAIA) Lab. Her research focuses on space-time representation, GIS analytics, and environmental/social problem-solving (e.g., disaster risk, pollution, crime mapping). She holds a Ph.D. in Geography from SUNY Buffalo (1994) and B.S. from National Taiwan University (1987). Previously, she was Brandt Professor and Director of the Center for Spatial Analysis at the University of Oklahoma (1994–2014). Education: Ph.D. in Geography, State University of New York at Buffalo, 1994 M.A. in Geography, State University of New York at Buffalo, 1992 B.S. in Geography, National Taiwan University, 1987 Research Interests: Her work integrates space-time GIS databases with cognitive science, environmental modeling, and social dynamics. Key areas include: - Spatiotemporal query and analytics for geographic processes - GIS-based disaster risk assessment (wildfires, tornadoes) - Urban air quality modeling - Neurogeography and Alzheimer’s disease prediction using environmental complexity metrics - Deep mapping and spatial narratives. Grants & Partnerships: Supported by NSF, NASA, DoD, DHS, NOAA, EPA, and state agencies. Her GAIA Lab explores 'place' concepts in space-time analytics. Awards & Roles: Fellow, AAAS and AAG Editor-in-Chief, International Journal of Geographical Information Science (2017–present) Former President, Cartography and Geographic Information Society (2020–2021) and UCGIS (2011–2012) Member, NOAA Environmental Information Services Working Group (2016–2022) Labs/Teams: Leads the GAIA Lab, collaborating on geospatial AI, environmental health, and urban analytics.

• Geospatial Information Science
• Space-Time Representation
• GIS Analytics
• +5 more

Venkatesh Merwade is a Professor in the Lyles School of Civil Engineering at Purdue University , where he has been since 2007. His research focuses on GIS applications in water resources engineering , surface water hydrology , river hydraulics , and cyberinfrastructure for hydrology , with regional interests in water management in developing countries . PhD in Civil Engineering (University of Texas at Austin, 2004) MSc in Engineering Hydrology (National University of Ireland, Galway, 2000) B.Engg. in Environmental Engineering (Shivaji University, India, 1997) His work includes floodplain mapping , hydrological modeling , and cyber-enabled hydrology education via tools like RWater and SWATShare . He has received awards such as the 2022 Purdue Individual Graduate Student Mentorship Award and 2018 Seed for Success for securing major research grants. Merwade collaborates with students and colleagues on flood prediction , soil moisture assimilation , and hydro-climatic impact analysis . His projects emphasize data-driven modeling , uncertainty quantification , and GIS integration for watershed management. He is affiliated with the Burke Hydraulics and Hydrology Lab and Purdue's Center for the Environment .

• GIS applications in water resources engineering
• Surface water hydrology
• River hydraulics
• +4 more

Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging. PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research interests center on: Bayesian hierarchical models for documents, sequences, and medical images Optimization algorithms for approximate inference Model fairness and interpretability in clinical contexts Semi-supervised learning for medical diagnostics Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research. Scientific awards include: NIH R01 Award (PI) for heart valve disease detection (2025) NSF CAREER Award (2024) NSF GCR Grant (2024) Best Poster Award at Time Series Workshop (ICML 2021) Top 10% Reviewer Awards at AISTATS (2023, 2022) Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.

• Statistical Machine Learning
• Bayesian Inference
• Variational Methods
• +5 more

Dr. XiaoYue Cathy Liu is an Associate Professor in the Department of Civil & Environmental Engineering at the University of Utah. She holds a PhD in Transportation Engineering from the University of Washington and advanced degrees in Transportation Planning and Electronics Engineering. Her research focuses on sustainable transportation systems, shared mobility, public transit optimization, managed lanes, and intelligent transportation systems. She actively serves on committees including the Transportation Research Board's Highway Capacity Quality of Service (HCQS) Committee and chairs its Technology Transfer Subcommittee. She also advises the Utah Model Advisory Committee and previously served on Salt Lake City’s Transportation Advisory Board. Dr. Liu is a licensed professional engineer in Utah. Education: PhD, Transportation Engineering, University of Washington (2013) MA, Transportation Planning & Management, Texas Southern University BS, Electronics & Electrical Engineering, Beijing Jiaotong University Graduate Certificate, Global Trade & Logistics, University of Washington (2011) Research Interests: Electric vehicle infrastructure and charging networks Smart transportation systems and agent-based modeling GIS-based asset management for transportation infrastructure Snowplowing operations optimization and winter maintenance Managed lanes and high-occupancy toll (HOT) lane analysis Public transit equity and accessibility Her recent publications emphasize data-driven approaches to transportation challenges, including EV demand forecasting, drone delivery networks, and resilience in interdependent transit systems. She collaborates on projects addressing Utah’s unique transportation needs, such as optimizing snowplow routes and integrating renewable energy into public transport systems. Professional Contributions: Board member, Utah Model Advisory Committee Former Chair, Salt Lake City Transportation Advisory Board (2013-2016) TRB Managed Lane Committee member TRB Transit Capacity & Quality of Service (TCQS) Committee member (2016-2019) Dr. Liu’s work bridges transportation engineering with computational methods, focusing on equity, sustainability, and operational efficiency. She leads research initiatives involving large-scale modeling, geospatial analytics, and interdisciplinary solutions for modern mobility challenges.

• Sustainable Transportation Systems
• Shared Mobility
• Public Transit Planning
• +7 more

David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.

• Bayesian Statistical Modeling
• Uncertainty Quantification
• Space-Time Modeling
• +7 more

Guofeng Cao is an Associate Professor in the Department of Geography at the University of Colorado . His research integrates GIScience , GeoAI , geostatistics , and remote sensing to develop advanced methodologies for analyzing heterogeneous geospatial data and modeling complex spatiotemporal patterns. Focus areas: Uncertainty-aware geographic knowledge discovery, land cover/land use dynamics, spatiotemporal bias analysis, geospatial cyberinfrastructure development Applications: Natural hazards, environmental science, public health, global change studies Recent publications emphasize generative adversarial networks for climate downscaling, neural processes for uncertainty modeling, and fusion transformers for disaster assessment. His work combines deep learning with Bayesian inference to address scalability challenges in geospatial data processing. Scientific Recognition : NASA and USDA grants for spatiotemporal research Advising : Mentoring graduate students in geospatial data science Laboratory : Leads the STAR lab (Spatiotemporal Pattern Analysis & Research)

• GIScience
• GeoAI
• Geostatistics
• +10 more

Emily Cooper is an Associate Professor of Optometry & Vision Science at the Herbert Wertheim School of Optometry & Vision Science, University of California, Berkeley. She serves as the Chair of the Vision Science PhD Program and is a co-Director of the Center for Innovation in Vision & Optics. Additionally, she is a member of the Helen Wills Neuroscience Institute and a Visiting Faculty Researcher at Google. Dr. Cooper's research focuses on 3D vision, perceptual graphics, AR/VR, computational neuroscience, visual encoding, and display system design. Her work investigates how the visual system processes information to create our perception of the 3D world, with applications in computer graphics, virtual reality, and assistive technologies for people with low vision. Analysis of Dr. Cooper's recent publications (2023-2025) reveals a strong focus on the intersection of vision science and emerging technologies, particularly in augmented reality and assistive vision systems. Her work spans fundamental research on visual perception mechanisms to applied research developing practical technologies for low vision rehabilitation. A significant portion of her recent work addresses visual discomfort in XR displays, perceptual guidelines for AR/VR systems, and innovative approaches to assistive vision technologies that enhance mobility and independence for visually impaired individuals. Dr. Cooper leads an active research laboratory at UC Berkeley's 391 Minor Hall, where she mentors students and collaborators in vision science research. Her lab investigates both basic questions about how vision works and translational questions about improving visual technologies. She has developed perceptual guidelines for optimizing field of view in stereoscopic augmented reality displays and created assistive technologies such as an augmented reality sign-reading assistant for users with reduced vision. Dr. Cooper is also involved in professional activities including co-organizing the Computational Neuroscience: Vision summer course at Cold Spring Harbor Laboratory and working with Community Resources For Science to promote science education.

• 3D vision
• perceptual graphics
• AR/VR
• +4 more

Daniel J. Sorin is a Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he also serves as Associate Chair of Education. He holds joint appointments in both the Electrical and Computer Engineering department and Computer Science department, and is recognized as a Bass Fellow for his contributions to education and research. His research focuses on computer architecture with specific expertise in memory systems, cache coherence protocols, fault tolerance, and verification-aware design. Dr. Sorin's work bridges theoretical computer architecture with practical implementations, often incorporating coding theory to solve architectural challenges. His research group has made significant contributions to automated protocol generation, hardware acceleration, and robot motion planning systems. Dr. Sorin's publications reveal a consistent focus on memory consistency models, cache coherence protocols, and verification techniques. His recent work has expanded into robot motion planning acceleration, FPGA resource management, and novel error correction techniques for emerging memory technologies. The trend shows increasing interdisciplinary work connecting computer architecture with robotics and machine learning applications. Program Chair of HiPEAC 2017 Co-chair of IEEE Micro's Top Picks selection committee (2016) Lois and John L. Imhoff Distinguished Teaching Award (2011) NSF CAREER Award recipient IEEE Micro Top Pick awards (2011, 2015) ACM Senior Member As an advisor, Dr. Sorin has mentored numerous PhD students who have gone on to successful careers at leading technology companies including Google, Microsoft, Oracle, and Nvidia. His research group maintains strong industry connections and has produced influential work in cache coherence protocols, memory systems, and fault-tolerant architectures. He has also authored the widely-used textbook 'A Primer on Memory Consistency and Cache Coherence' (2nd edition). Dr. Sorin leads an active research laboratory focused on next-generation computer architecture challenges, with ongoing projects in hardware acceleration, memory systems, and robot motion planning. His group collaborates with researchers across multiple disciplines including robotics, coding theory, and semiconductor design.

• Computer Architecture
• Memory Systems
• Cache Coherence Protocols
• +7 more

Jun Tu is a Professor in the Department of Geography and Anthropology at Kennesaw State University (KSU). He specializes in Environmental Geography, Health Geography, and Spatial Analysis, with a focus on integrating GIS, spatial statistics, and modeling to study environmental and health impacts of urbanization and climate change. His research spans water quality in Georgia and Massachusetts, air pollution in China and Iran, and public health issues like preterm birth and lung cancer in Georgia. Education: Ph.D. and M.Phil. in Earth and Environmental Sciences (Geography Specialization) from The City University of New York; M.E. and B.S. in Earth Sciences from Nanjing University, China; Certificate in Urban Environmental Management from the Institute for Housing and Urban Development Studies, The Netherlands. Research interests include GIS applications in environmental health, urbanization effects on natural environments, and climate resilience. Collaborations include projects funded by The Partnership for Inclusive Innovation, focusing on community preparedness in Athens-Clarke County. He has published over 35 peer-reviewed articles and reviewed for 60+ journals. Teaching includes courses on Weather and Climate, Environmental Studies, GIS in Public Health, and Geography of Asia. His work has been cited over 2500 times globally.

• Environmental Geography
• Health Geography
• Spatial Analysis
• +5 more

Lijing Wang is an Assistant Professor of Data Science at the New Jersey Institute of Technology (NJIT). She specializes in interdisciplinary research at the intersection of artificial intelligence, epidemiology, and public health. Her work emphasizes data-driven approaches to forecasting infectious disease dynamics, integrating machine learning with theoretical epidemiological models. Education: Ph.D. in Computer Science, University of Virginia (2021) M.S. in Computer Science, Chinese Academy of Sciences (2013) B.S. in Software Engineering, Dalian University of Technology (2010) Research focuses on epidemic forecasting using ensemble modeling, graph neural networks, and causal inference. Key topics include: COVID-19 and influenza prediction using mobility data AI-driven disease surveillance systems Policy impact analysis of pharmaceutical/nonpharmaceutical interventions Cross-national epidemic modeling Publications consistently address forecasting challenges through innovative methodological combinations - e.g., Bayesian ensemble techniques, causal graph approaches, and multi-scale human mobility analysis. Recent work emphasizes real-time prediction accuracy improvements for public health decision-making. No scientific awards explicitly noted in text. Active in collaborative research involving public health agencies and international institutions.

• Epidemic Forecasting
• Artificial Intelligence
• Machine Learning
• +3 more

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +4 more