Find Academic Supervisors

Kaiyu GuanView profile

Professor

• Ecohydrology
• Crop Remote Sensing
• Agroecosystem Modeling
• +7 more

Kaiyu Guan is a Professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois at Urbana-Champaign (UIUC) , with affiliations in the Siebel School of Computing and Data Sciences, College of Agricultural, Consumer and Environmental Sciences (ACES), Institute for Sustainability, Energy, and Environment (iSEE), and the National Center for Supercomputing Applications (NCSA). He serves as the Founding Director of the Agroecosystem Sustainability Center (ASC) and holds the Levenick Endowed Professorship in Agroecosystem Sensing and Modeling . Education : PhD in Environmental Engineering from Princeton University (2013), B.S. in Geography and Geoinformatics from Nanjing University (2008). His research integrates satellite remote sensing , land surface models , and machine learning to address climate change impacts on agriculture , soil carbon dynamics , and water resource sustainability . His group develops scalable frameworks for field-level carbon budgeting , irrigation monitoring , and crop yield forecasting . Recent publications focus on knowledge-guided AI , N2O emissions , and plant hydraulic responses to drought . His work is funded by NASA , NSF , USDA , and DOE . Scientific Awards : Blavatnik National Award Finalist (2021, 2024) AGU James Macelwane Medal (2023) NSF CAREER Award (2019) Clarivate Highly Cited Researcher (2024) Guan actively mentors graduate students and leads 20+ federal projects , emphasizing science-to-policy solutions for global food security and environmental sustainability .

Daniel Sanz-AlonsoView profile

Associate Professor

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

Daniel Sanz-Alonso is an Associate Professor in the Department of Statistics at the University of Chicago and a member of the Committee on Computational and Applied Mathematics. Prior to this, he was a postdoc at Brown University's Division of Applied Mathematics and Data Science Initiative (2016-2018), following a PhD in Mathematics and Statistics (2016) at the University of Warwick under Andrew Stuart and Gareth Roberts. His research focuses on inverse problems, data assimilation, and scientific machine learning, aiming to integrate complex predictive models with large datasets through theoretical and computational frameworks. His work intersects data science, machine learning, partial differential equations, and calculus of variations, with applications in weather forecasting, geophysical sciences, and machine learning. Scientific Awards José Luis Rubio de Francia Prize (2020) – Recognized as the best Spanish mathematician under 32 by the Spanish Royal Society of Mathematics NSF CAREER award (2023) He has received research support from the National Science Foundation, National Geospatial-Intelligence Agency, Department of Energy, and BBVA Foundation (€35,000 grant over three years). Since 2025, he serves as Associate Editor for the SIAM/ASA Journal on Uncertainty Quantification.

Uwe Freiherr von LukasView profile

Professor

• Maritime Graphics
• Underwater Image Processing
• Machine Learning for Computer Vision
• +4 more

Prof. Dr.-Ing. Uwe Freiherr von Lukas is Professor of Maritime Graphics at the University of Rostock , Faculty of Computer Science and Electrical Engineering, and heads the Rostock site of the Fraunhofer Institute for Computer Graphics Research IGD . Since 2010 he has also been Honorary Professor for Virtual Product Development and is a member of the Interdisciplinary Faculty’s Department of Maritime Systems. Education: Diplom in Computer Science, TU Darmstadt (1994) Doctorate (Promotion), University of Rostock Research Focus: His research concentrates on three pillars: Machine Learning for automated image and video analysis with a strong emphasis on underwater imagery , interactive visualization and immersive analytics of large-scale maritime data , and technology-oriented networks for innovation transfer and regional development . These topics converge in applications such as digital twins for the Baltic Sea, VR-based maritime training, and AI-driven ecosystem monitoring. Scientific Output & Trends: Recent publications (2022-2025) reveal an intensified integration of deep learning and computer vision techniques to solve domain-specific challenges in marine and environmental sciences. Studies range from transformer-based seagrass segmentation and unsupervised clustering of ROV imagery to digital-twin frameworks for ocean research, reflecting a clear trend toward AI-supported, data-centric maritime analytics . Professional Service & Networks: Board member, Subsea Monitoring Network e.V. Speaker, BMBF Future Cluster “Ocean Technology Campus Rostock” Managing Director, competence network “OceanTechnologies@Fraunhofer” Member of advisory boards of Society for Maritime Technology, Maritime Future Advisory Board Mecklenburg-Vorpommern, and others Labs & Teams: At Fraunhofer IGD Rostock he leads the Maritime Graphics group, coordinating more than thirty collaborative R&D projects that bridge computer graphics, VR/AR, and maritime engineering. The group operates state-of-the-art VR laboratories, underwater vision testbeds, and high-performance visualization facilities serving both academic and industrial partners.

Marc LelargeView profile

Professor

• Generative AI
• Interactive Theorem Proving
• Deep Learning
• +4 more

Marc Lelarge is a Researcher at INRIA and a part-time Professor in the Department of Computer Science at École Normale Supérieure. He received his PhD in Applied Mathematics from École Polytechnique in 2005, following engineering qualifications from École Nationale Supérieure des Télécommunications and undergraduate studies at École Polytechnique. His research focuses on generative AI for coding and interactive theorem proving, deep learning for graphs and structured data, machine learning, and programming languages. Recent publications emphasize graph neural networks, theorem proving, optimization algorithms, and statistical learning theory. He has received multiple awards including the NetGCoop Best Paper Award (2011), SIGMETRICS Rising Star Award (2012), and Best Publication in Applied Probability Award (2015). He developed the 'Deep Learning: Do It Yourself' course and maintains active educational resources on GitHub. Current affiliations include collaborations with NVIDIA (hardware donations) and Google (cloud computing grants). He advises PhD students including E. Coupechoux and maintains research teams focused on deep learning implementations.

Claude R. DuguayView profile

Professor

• Remote Sensing
• Numerical Modeling
• Cryosphere
• +5 more

Claude R. Duguay is a Professor and University Research Chair in Cryosphere & Hydrosphere from Space at the University of Waterloo , specifically within the Department of Geography and Environmental Management . He also serves as the founding director of the Interdisciplinary Centre on Climate Change , where he leads research on cold region studies and remote sensing technologies. Current research focuses on Arctic hydro-climatology and remote sensing of cryospheric processes. Teaches courses like Earth from Space Using Remote Sensing , The Cryosphere , and Remote Sensing of Cold Regions . His research involves developing remote sensing algorithms and numerical models to study lake/land-atmosphere interactions in cold environments. Key areas include lake ice dynamics , permafrost monitoring , microwave backscatter analysis , and climate change impact assessments . Publications emphasize satellite-based climate products and machine learning applications for ice and hydrology studies. Recent publications highlight trends in SAR and microwave remote sensing for ice thickness, snow properties, and climate feedback mechanisms. His work bridges environmental physics and data science to improve predictive models for cold regions. Scientific recognition includes the University Research Chair in Cryosphere & Hydrosphere from Space . Collaborative projects involve institutions like the Water Institute and Waterloo-Laurier Graduate Program in Geography . Graduate supervision includes Jaya Sree Mugunthan (MSc radar altimetry thesis). Research facilities utilize X/Ku/C-band microwave systems and 1-D lake models .

• Physical Oceanography
• Climate Modeling
• Ocean Circulation
• +4 more

Markus Jochum , Professor at the Niels Bohr Institute , University of Copenhagen, specializes in Physical Oceanography within the Ice, Climate and Geophysics section. With extensive experience in climate modeling and ocean dynamics, his work bridges theoretical oceanography with practical observations. PhD in Physical Oceanography (MIT, 2002) Professor at University of Copenhagen since 2012 Adjunct Assistant Professor at ESSIC, University of Maryland since 2007 Research Focus covers Atlantic Meridional Overturning Circulation (AMOC), tropical ocean dynamics, and climate system interactions. His recent work examines rogue wave forecasting, carbon removal impacts, and mixed layer parameterizations using advanced ocean models. Specialized in ICON-O and FESOM model comparisons Developed empirical frameworks for extreme ocean event prediction Investigates tipping points in ocean circulation systems Scientific Recognition : Rossby Award for best PhD thesis (2002) Excellence in Reviewing (Geophysical Research Letters, 2005) Academic Leadership includes: Associate Editor for Journal of Geophysical Research since 2003 Coordinator of SUNNY project since 2008 Key member of CLIVAR panels and NASA SST initiatives

Xu ZhangView profile

Research Fellow

• Climate Tipping Elements
• Quaternary Climate Variability
• Antarctic Climate Dynamics
• +3 more

Xu Zhang is a Research Fellow affiliated with the British Antarctic Survey (BAS) under the Cambridge NERC Doctoral Landscape Awards (C-CLEAR DTP) at the University of Cambridge. Their work focuses on climate science, particularly abrupt and nonlinear climate changes such as Dansgaard-Oeschger events and glacial cycles. Research Area: Dynamics of past climate changes Institution: University of Cambridge and British Antarctic Survey Research interests include: Global core climate tipping elements and cascade effects Quaternary millennial-to-orbital time scale climate variability Antarctic forcings and feedbacks in climate changes Tropical hydroclimate variability Paleoclimate data assimilation Marine carbon cycle Xu Zhang leads a modeling protocol in the PAGES Quaternary Interglacial working group to study sea level-CO2 evolution during Marine Isotope Stage 11c and Termination V. Their interdisciplinary approach combines proxy records, numerical models, statistical methods, and machine learning.

Firat Y. TestikView profile

Professor

• Environmental Fluid Mechanics
• Rainfall Microphysics
• Coastal Engineering
• +3 more

Firat Y. Testik is a Professor in the Civil and Environmental Engineering and Construction Management department at the University of Texas at San Antonio's Klesse College of Engineering and Integrated Design. Contactable at firat.testik@utsa.edu, he leads the Flow Physics Lab and has published extensively on rainfall microphysics and coastal engineering challenges. Ph.D. in Aerospace Engineering from Arizona State University His research focuses on environmental fluid mechanics , particularly: Rainfall microphysics, including raindrop collision dynamics and shape-velocity distribution under wind shear Coastal bridge vulnerability to hurricane wave impacts Gravity current propagation through aquatic vegetation Atmospheric boundary layer turbulence interactions Instrument validation for precipitation measurement Recent publications show a trend toward advanced measurement techniques (high-speed imaging, sUAS) and hazard modeling for hurricanes. He actively contributes to operational rainfall estimation algorithms and infrastructure resilience studies.

Yiqi LuoView profile

Professor

• Ecosystem Ecology
• Biogeochemistry
• Carbon Cycle Modeling
• +3 more

Yiqi Luo is a Liberty Hyde Bailey Professor at Cornell University's School of Integrative Plant Science, Soil and Crop Sciences Section. His research focuses on advancing predictive understanding of ecosystem ecology and biogeochemistry under global environmental change through data-model integration. Academic Rank: Professor Affiliation: Cornell University Research Focus: Ecosystem carbon cycling, climate mitigation strategies, microbial dynamics, and knowledge-guided AI applications Research Interests: Dr. Luo investigates terrestrial ecosystem responses to global change, including carbon residence time optimization, climate feedback mechanisms, and nature-based carbon dioxide removal (CDR) strategies. His work spans experimental analysis, process-based modeling, and machine learning approaches. His recent publications highlight climate change mitigation through soil carbon dynamics and terrestrial ecosystem modeling . Key themes include microbial efficiency, nutrient cycling, and ecosystem resilience to droughts and warming. Scientific Awards: Fellow of the American Geophysical Union Fellow of the Ecological Society of America Fellow of AAAS Web of Science Highly Cited Researcher (2018-2023) Collaborative Grants: Current projects include DOE-funded peatland warming studies, NSF grants for LTER site research, and USDA-NIFA's AI-LEAF institute. He leads data assimilation algorithm development and contributes to global carbon cycle model intercomparisons. Contact: yl2735@cornell.edu

Elie Bou-ZeidView profile

Professor

• Atmospheric Science
• Urban Environmental Modeling
• Air Flow Dynamics
• +5 more

Elie Bou-Zeid is a Professor of Civil and Environmental Engineering at Princeton University and serves as the Director of the Program in Environmental Engineering and Water Resources . He previously directed the Metropolis Initiative for urban technology until 2022. His research integrates theory, numerical simulations, and field observations to study atmospheric flows, heat/pollutant transport, and sustainability in cities . Bachelor's : American University of Beirut Master's : American University of Beirut and Johns Hopkins University PhD : Johns Hopkins University He leads the Thermofluids of Urban and Natural Environments (TUNE) Lab , focusing on urban meteorology, renewable energy integration, and climate resilience . His work addresses inequitable urban environmental conditions and develops technologies for sustainable cities , including the CityReader Project . Recent publications explore urban heat islands, blue infrastructure, machine learning for climate, and boundary layer dynamics . Awards include the 2024 STAC Award , Beyond Bauhaus Prototyping the Future Award , and Fondation Latsis Internationale Prize . Awards : STAC 2024, Beyond Bauhaus 2024, EPFL Latsis Prize 2022 Editorial Roles : Editor of Journal of the Atmospheric Sciences (2020–2025) He advises five Ph.D. students and collaborates with postdocs like Einara Zahn and Young Paul Yi . His lab investigates kirigami-inspired ventilation, agrivoltaic farms, and downscaling climatic variables using Bayesian deep learning .

Bruce GluckmanView profile

Professor

• Data Assimilation
• Seizure Prediction
• Epilepsy Research
• +5 more

Bruce Gluckman is a Professor in Engineering Science and Mechanics, Department of Neurosurgery, and Biomedical Engineering at Penn State University. He also holds affiliations with the Materials Research Institute (MRI), Huck Institutes of the Life Sciences, and Penn State Neuroscience Institute. Dr. Gluckman serves as Director of the Center for Neural Engineering, leading interdisciplinary research at the intersection of engineering and neuroscience. Dr. Gluckman's research focuses on several key areas including: Data Assimilation and Mechanistic Machine Learning Seizure prediction and epilepsy research Neural dynamics and brain-computer interfaces Sleep research, particularly REM sleep Neurovascular coupling and brain hemodynamics His recent work demonstrates a strong trend toward developing advanced imaging technologies for brain monitoring, particularly combining ultrasound and photoacoustic techniques. Gluckman's research also emphasizes computational modeling of neural systems, with applications to understanding seizure dynamics and developing neural engineering solutions. His work bridges fundamental neuroscience with clinical applications, particularly in epilepsy and neurodegenerative conditions. Dr. Gluckman has secured significant research funding, including: Impaired Vasoreactivity, Sleep Degradation, and Impaired Clearance in the APOE4 Brain (National Institute on Aging, 2022-2027) as Co-PI Cross-Disciplinary Neural Engineering (CDNE) Training Program (National Institute of Neurological Disorders and Stroke, 2021-2026) as PI Mechanistic Machine Learning (U.S. National Library of Medicine, 2017-2021) as Co-PI Imaging and Modeling Fluid Mechanics of Metabolite Transport in the Brain Interstitium (National Science Foundation, 2017-2021) as Co-PI 7th International Workshop on Seizure Prediction (National Institute of Neurological Disorders and Stroke, 2014-2015) as PI As Director of the Center for Neural Engineering, Dr. Gluckman leads a multidisciplinary team working at the intersection of engineering, neuroscience, and medicine. His center fosters collaboration between engineers developing novel technologies and clinicians applying these advances to neurological conditions. The research environment he has created supports innovation in neural engineering with applications to understanding and treating brain disorders.

• Medical Image Processing
• Bioinformatics
• Computational Biology
• +4 more

Twan Maintz is a Lecturer at the Department of Biology , Faculty of Science , Utrecht University . His roles include being Senior Policy Officer and Secretary to the Board for academic governance bodies. PhD in Medical Image Processing (1992-1996) MSc in Mathematics (1987-1992) His research spans Medical Image Processing , Integrative Bioinformatics , Image Science , and Game Research . His work focuses on Mutual Information techniques for Medical Image Registration , 3D Modeling , and Computer Vision applications in Epilepsy Research and Aortic Endograft Analysis . He received the Young Teacher Talent Award (Best Young Lecturer Award) in 2000 from Utrecht University. His publications from 1996-2006 cover topics in Medical Imaging , Computer Vision , and Algorithm Development . Teaches Introduction to Biological Modeling Teaches System Tipping Points: Bifurcations Involved in the Bioinformatics and Biocomplexity master's program Dr. Maintz's career includes roles as Associate Professor (1997-2009), Postdoctoral Fellow at the Image Sciences Institute , and Executive Policy Officer at Utrecht University's Graduate Schools.

• Atmospheric Science
• Meteorology
• Infrasound
• +4 more

Dr. Ekaterina Vorobeva is a researcher at the Department of Physics, Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, with additional affiliation to NORSAR in Kjeller, Norway. Her research focuses on applying machine learning techniques to analyze infrasound data for studying stratospheric dynamics, particularly the polar vortex. Dr. Vorobeva's research interests center on atmospheric science, with special emphasis on stratospheric dynamics, polar vortex analysis, and the application of machine learning to meteorological data. She bridges physics, meteorology, and computational science to develop novel methods for monitoring upper atmospheric phenomena using naturally occurring infrasound signals. Her recent work demonstrates how microbarom infrasound data from the International Monitoring System can be used with stochastic machine learning models to estimate stratospheric polar vortex strength. This approach provides potential for near-real-time monitoring of upper stratospheric winds, which could improve subseasonal-to-seasonal weather forecasting models. Dr. Vorobeva has made significant contributions to the field of atmospheric monitoring through her innovative application of machine learning to infrasound data. Her work offers a promising alternative approach to measuring upper atmospheric conditions where traditional observational methods are limited. Her research methodology combines expertise in physics, atmospheric science, and advanced computational techniques, positioning her at the intersection of multiple scientific disciplines working toward improved understanding and prediction of atmospheric phenomena.

• Hydrology
• Machine Learning
• Groundwater Remediation
• +5 more

Tianfang Xu is an Assistant Professor at the School of Sustainable Engineering and the Built Environment at Arizona State University, with affiliations to the Water Institute and Global Futures Scientists and Scholars . She holds a B.S. in Geotechnical Engineering (Nanjing University, 2010), and M.S. and Ph.D. in Civil Engineering (University of Illinois at Urbana-Champaign, 2012 & 2016). Research Focus: Numerical simulation of groundwater systems, uncertainty quantification, and machine learning applications in hydrology. Labs: Leads a research group integrating process-based and data-driven models to enhance water resource predictions under global change. Teaching: Instructs courses on groundwater hydrology, fluid mechanics, and advanced research topics in civil engineering. Publication Trends: Recent work emphasizes hybrid physics-ML modeling for complex watersheds, remote sensing in irrigation monitoring, and structural error quantification in groundwater predictions. Her research bridges computational modeling with real-world sustainability challenges. Key Projects: Snow-dominated karst watershed modeling Agricultural managed aquifer recharge assessment Crop irrigation monitoring using satellite data Urban-agricultural water use in megacities

Roland PotthastView profile

Professor

• Numerical Weather Prediction (NWP)
• Data Assimilation
• Inverse Problems
• +9 more

Prof. Dr. Roland Potthast is a Full Professor for Applied Mathematics at the University of Reading, UK, and Director of the Meteorological Analysis and Modeling Department (FE1) at the German Weather Service (DWD) since 2020. He leads ~110-120 researchers focused on operational numerical weather prediction (NWP), Earth system modeling, and data assimilation for the ICON model. Key Institutions: University of Reading, German Weather Service (DWD), Max-Planck-Institute for Meteorology (MPI-M), Karlsruhe Institute of Technology (KIT), ETH Zurich. Research: Data assimilation, inverse problems, ensemble/particle filtering, high-resolution NWP systems (ICON-global, ICON-EU, ICON-D2), and integration of machine learning with weather modeling. Scientific Contributions: Developed hybrid ensemble-variational data assimilation (EnVar) schemes, 4D-LETKF for convective-scale systems, and ultra-rapid data assimilation methods. His work impacts air traffic control, severe weather warnings, and energy supply applications. Awards/Grants: ESM Project (2022-2030), BeCoM (2022-2025), CONTRAILS (2022-2025). DFG grants, EPSRC funding, EU projects, and international fellowships. Leadership: Steering Committee member for ICON Consortium, COSMO Consortium, and EUMETNET AI program. He also contributes to supercomputing initiatives at HLRS and Gauss Centre.