Find Academic Supervisors

• Hydrometeorology
• Hydroclimatology
• Climate Science
• +4 more

Dr. Benjamin Hatchett is a Research Professor in the Department of Atmospheric Sciences at the Desert Research Institute (DRI). His research focuses on hydrometeorology and hydroclimatology, particularly in dryland and alpine regions across past, present, and future contexts. He is actively involved in projects related to snow drought dynamics, climate change impacts, and environmental risk communication. His work bridges gaps between scientific data and public understanding, such as improving heat risk messaging and visualizing probabilistic forecast information. Key affiliations include the Western Regional Climate Center and collaborations with state agencies like Nevada's Department of Transportation. Research interests include snowpack evolution, rain-snow transition zone analysis, wildfire-climate interactions, and citizen science applications. Hatchett has contributed to developing real-time snow drought tracking tools and assessing the societal implications of climate extremes. His interdisciplinary approach integrates hydrology, atmospheric science, and public health to address challenges in climate adaptation and disaster resilience.

Anne HeggliView profile

Research Professor

• Hydrometeorology
• Snow Science
• Atmospheric Sciences
• +3 more

Dr. Anne Heggli is an Assistant Research Professor in Mountain Hydrometeorology at the Desert Research Institute (DRI). Her research focuses on developing decision support systems for climate responders, emphasizing co-production with operational agencies. She specializes in rain-on-snow events, snowpack monitoring technologies, and improving probabilistic forecast communication. Heggli holds a Ph.D. in Atmospheric Sciences from the University of Nevada, Reno, alongside degrees in Water Resource Management and International Relations/Spanish. Education: Ph.D. in Atmospheric Sciences, University of Nevada, Reno (2023) M.S. in Water Resource Management, Fresno State University (2017) B.A. in International Relations and Spanish, San Francisco State University (2009) Research Interests: Heggli’s work integrates field observations and decision support tools to address climate challenges. Key areas include: Real-time snowpack monitoring and runoff prediction Probabilistic forecast visualization for stakeholders Citizen science applications in precipitation phase analysis Climate adaptation for water resource management Grants & Awards: Recipient of Desert Research Institute’s 1st Place Graduate Student Poster Competition (2023) Jerry and Betty Wilson Scholarship ($4,000, 2022) Long Now Foundation Science Fellowship ($35,000, 2022) Lab & Collaborations: Heggli leads projects at DRI’s Atmospheric Sciences division, partnering with National Weather Service offices, Bureau of Reclamation, and Nevada Department of Transportation. She co-founded Alpine Hydromet to advance snowpack monitoring technologies.

Xun HuanView profile

Associate Professor

• Uncertainty Quantification
• Data-Driven Modeling
• Numerical Optimization
• +3 more

Xun Huan is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor. He holds a PhD in Computational Science and Engineering from MIT (2015), an SM in Aeronautics and Astronautics from MIT (2010), and a B.A.Sc. in Engineering Science (Aerospace) from the University of Toronto (2008). His research focuses on uncertainty quantification, data-driven modeling, numerical optimization, Bayesian analysis, and machine learning applications across engineering systems. Key research areas include optimal experimental design, stochastic modeling for manufacturing processes, and interdisciplinary applications in aerospace, energy systems, and biomedical engineering. He leads a research group conducting weekly meetings, emphasizing collaborative problem-solving and student independence. His work has been supported by grants such as the Keck Foundation award for cancer cell research (2022). He actively mentors PhD students, emphasizing continuous literature engagement, flexible work hours, and strategic internship planning. Education : Ph.D., MIT (2015) S.M., MIT (2010) B.A.Sc., University of Toronto (2008) Awards/Grants : Keck Foundation Collaborative Research Award (2022) Regents-approved promotion to Associate Professor (2024) Advising Philosophy : Weekly one-on-one meetings transitioning from hands-on to independent guidance Encourages academic career exploration through teaching opportunities Supports strategic internships, particularly in later PhD stages Current research emphasizes uncertainty-aware AI for digital twins, Bayesian methods in complex systems, and optimal experimental design frameworks. His group collaborates on projects ranging from material flow analysis to space weather prediction, leveraging interdisciplinary methods to address real-world challenges.

• Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +4 more

Colin Cotter is a Professor of Computational Mathematics in the Department of Mathematics at Imperial College London , part of the Faculty of Natural Sciences . His research focuses on numerical analysis, scientific computing, and geophysical fluid dynamics, with applications in weather forecasting, ocean modeling, and climate simulation. He is Head of the Applied Mathematics and Mathematical Physics Section and an Associate Editor for the SIAM Journal of Scientific Computing and Foundations of Data Science. Education : PhD in Mathematics, Imperial College London (2000–2004) BA and Part III in Mathematics, Cambridge University (1996–2000) Research Interests : Dr. Cotter develops compatible finite element methods and time-parallel algorithms for geoscientific models. His work includes: Design of geometric variational numerical methods for fluid equations Probabilistic forecasting and Bayesian data assimilation techniques Parallel-in-time algorithms using ParaDiag methods Awards & Roles : EPSRC Peer Review College member Co-author of "Probabilistic Forecasting and Bayesian Data Assimilation" Grants & Labs : Involved in projects funded by EPSRC, NERC, and ExCALIBUR. Active in the EPSRC Centre for Maths of Precision Healthcare and the Mathematics of Planet Earth initiative.

David N. BreschView profile

Professor

• Climate Risk Management
• Weather and Climate Impacts
• Socio-Economic Systems
• +5 more

Prof. Dr. David N. Bresch is a Full Professor at ETH Zurich's Department of Environmental Systems Science, specializing in weather and climate risk modeling. His research integrates probabilistic methods with socio-economic analysis to enhance resilience against climate impacts. He leads the CLIMADA open-source risk modeling framework and collaborates with institutions like MeteoSwiss and the UN Green Climate Fund. Previously, he held roles at Swiss Re, focusing on sustainability and catastrophe risk assessment. Education: PhD in Physics from ETH Zurich (Switzerland). Research interests include climate risk adaptation, probabilistic modeling, and interdisciplinary integration for policy. Key projects include global tropical cyclone risk analysis, mangrove ecosystem resilience studies, and flood impact modeling using open data. His work emphasizes actionable insights for decision-makers, such as impact-based forecasts for tropical cyclones and cascading risk assessment in Europe. Contributions to climate policy include advising the Swiss UNFCCC delegation (2009–2012) and serving on the UN GCF’s Private Sector Advisory Group (2014–2016). Publications highlight advancements in climate risk quantification, including uncertainty analysis in future cyclone projections and the economic effects of recurrent disasters. He chairs the Climada project, an open-source tool used globally for natural hazard risk assessment.

• Climate Science
• Atmospheric Science
• Climate Change
• +5 more

Dr. Jan Rajczak is a Lecturer at the Department of Environmental Systems Science at ETH Zurich and a Senior Scientist at MeteoSwiss's Climate Department, where he leads development of Swiss Climate Scenarios. His roles involve academic teaching and applied research in climate science, focusing on regional climate modeling for impact assessments. His research emphasizes climate change in Alpine regions, particularly heavy precipitation patterns, climate scenario development, and ensemble modeling techniques. Specializations include bias correction methods, user-tailored climate data products, and operational forecasting systems. He collaborates with the Institute for Atmospheric and Climate Sciences at ETH Zurich to advance applied climate research. Key research trends include methodological advancements in climate modeling, assessment of precipitation extremes in complex topographies, and translating scientific outputs into actionable climate scenarios for policy and environmental decision-making. His work bridges academia and operational meteorology, addressing both fundamental climate science and practical applications. No scientific awards explicitly mentioned. Advising and grant details are not provided in available information. He contributes to MeteoSwiss's climate scenario initiatives and maintains active involvement in collaborative projects on regional climate modeling and forecasting.

Kai PolstererView profile

Research Professor

• Astroinformatics
• Machine Learning
• Data Science
• +4 more

Dr. Kai Polsterer is the Scientific Director of the Heidelberg Institute for Theoretical Studies (HITS) since January 2025, succeeding Tilmann Gneiting. He has been a leading researcher at HITS since 2013, heading Europe's first astroinformatics research group. His work focuses on developing machine learning methods to handle exponential data growth in astrophysics, including projects like the ERC Synergy Grant for universe mapping and exascale computing initiatives (SPACE). He collaborates across disciplines with statisticians and physicians to advance weather forecasting and cardiological analysis. Polsterer is actively involved in international organizations such as the International Astroinformatics Association (IAIA, President since 2023), IEEE Task Force on Mining Complex Astronomical Data, and the German Physical Society's AI working group. His research spans astroinformatics, data science, and interdisciplinary applications of AI. His expertise includes photometric redshift estimation, galaxy classification, and unsupervised learning techniques. Polsterer has pioneered tools like PINK (Parallelized Kohonen Maps) and contributed to projects like the LUCIFER instrument for near-infrared spectroscopy. He emphasizes open science and collaborative frameworks, such as the HiPS ecosystem for astronomical data visualization. His current role as Scientific Director reflects his leadership in advancing theoretical studies through computational and data-driven approaches. Polsterer’s contributions include over 50 peer-reviewed articles, spanning astroinformatics, machine learning applications in astronomy, and computational methodologies. He advocates for interdisciplinary research, bridging astrophysics, computer science, and medical data analysis. His ERC Synergy Grant project exemplifies his commitment to large-scale, collaborative science addressing fundamental questions in cosmology and data science.

Tilmann GneitingView profile

Professor

• Computational Statistics
• Probability Theory
• Spatial and Spatio-Temporal Modeling
• +5 more

Prof. Tilmann Gneiting is a renowned academic holding dual roles as Group Leader of Computational Statistics at the Heidelberg Institute for Theoretical Studies (HITS) and Professor of Computational Statistics at the Karlsruhe Institute of Technology (KIT). He specializes in statistical methodology, probability theory, and forecasting applications across environmental, atmospheric, and earth sciences. His research bridges theoretical foundations with practical challenges in weather prediction, climate modeling, and uncertainty quantification. Education: PhD in Mathematics (1997, University of Bayreuth) MA in Mathematics (1992, Boston University) Diplom in Mathematics (1993, University of Bayreuth) Key Roles: Scientific Director of HITS (2023–2024) Affiliate Professor at University of Washington (since 2010) His research interests focus on spatial/spatio-temporal modeling, probabilistic forecasting, and statistical post-processing. He has contributed to advancements in ensemble forecasting, uncertainty quantification, and the evaluation of probabilistic predictions in meteorology and epidemiology. Prof. Gneiting has received prestigious awards, including the 2024 Ulf Grenander Prize in Stochastic Theory and Modeling, recognizing his seminal work in probabilistic forecasting and environmental modeling. He has also held editorial roles in top journals like the Annals of Applied Statistics and the Journal of the Royal Statistical Society. His recent work emphasizes methodological developments for forecasting evaluation, climate science applications, and interdisciplinary collaborations with ECMWF and other institutions.

Lachlan AstfalckView profile

Research Fellow

• Statistics
• Spectral Analysis
• Oceanography
• +3 more

Lachlan Astfalck is a Research Fellow at the University of Western Australia's School of Physics, Mathematics and Computing, working on the TIDE ARC ITRH project. He specializes in statistical methodologies for oceanography, hydrodynamics, and paleoclimate modeling. His research focuses on spectral analysis, uncertainty quantification, and developing statistical methods for environmental applications. He holds a PhD (2019) and Bachelor of Engineering in Mechanical Engineering (2014) from the University of Western Australia. Dr. Astfalck's publications demonstrate a strong focus on developing novel statistical approaches for environmental systems. His recent work spans spectral analysis techniques, ocean turbulence modeling, ice sheet reconstruction, and Bayesian methods for climate applications. He actively develops open-source statistical tools and maintains several GitHub repositories related to spectral estimation methods. He is accepting PhD students in statistics and oceanography and has capacity for research collaborations.

Changjing ShangView profile

Academic

• Artificial Intelligence
• Fuzzy Systems
• Robotics
• +5 more

Dr. Changjing Shang is a Senior Research Fellow at the Department of Computer Science, Aberystwyth University. He holds a BSc from National University of Defense Technology (NUDT) and a PhD from Heriot-Watt University. His primary research focuses on fuzzy systems, rule interpolation, robotics control, and machine learning applications in computer vision and autonomous systems. Key research interests include: Fuzzy Rule Interpolation and its applications in robotics Unsupervised learning and deep learning techniques Unmanned aerial vehicle (UAV) navigation and tracking Data compression and neural representation optimization Medical imaging analysis and environmental forecasting systems Recent work emphasizes advancements in AI-driven solutions for: Dynamic fuzzy rule interpolation frameworks Person re-identification through contrastive learning UAV motion planning and active interaction control Efficient neural network compression techniques He has secured significant research funding including: Newton International Fellowship (2022-2024) Welsh Government funded projects (2017-2020) focusing on robotics control and big data applications Dr. Shang was recently elected as a Fellow of the Learned Society of Wales (2025) recognizing his contributions to computational intelligence and robotics research.

Valerian KwigizileView profile

Professor

• Traffic Safety
• Transportation Engineering
• Intelligent Transportation Systems (ITS)
• +4 more

Valerian Kwigizile is a Professor of Civil and Construction Engineering and Associate Director of the Transportation Research Center for Livable Communities (TRCLC) at Western Michigan University. He holds a Ph.D. in Civil and Environmental Engineering from the University of Nevada, Las Vegas (UNLV), a M.S. from Florida State University, and a B.Sc. from the University of Dar Es Salaam, Tanzania. His expertise spans motorized/non-motorized traffic safety, intelligent transportation systems (ITS), GIS applications, transportation planning, and statistical modeling in civil engineering. Education: Ph.D., Civil and Environmental Engineering, University of Nevada, Las Vegas M.S., Civil Engineering, Florida State University B.Sc., Civil Engineering, University of Dar Es Salaam, Tanzania His research focuses on improving transportation safety through advanced analytics, ITS, and GIS, with over $9M in external funding. He has authored 85+ peer-reviewed papers and 20 research reports. Areas of impact include traffic enforcement, micro-mobility safety, and highway cost indices. He is a registered Professional Engineer with 22 years of experience and active in the Transportation Research Board, ASCE, and ITE. Key Contributions: Developed frameworks for highway cost indices and safety metrics Advanced ITS applications for vulnerable road users Explored NLP/CNN for crash data analysis Advocated for multimodal infrastructure safety His work bridges academia and practice, addressing real-world challenges in transportation systems planning and implementation.

Mohamed IskandaraniView profile

Professor

• Oceanography
• Climate Modeling
• Data Assimilation
• +3 more

Mohamed Iskandarani is a Professor in the Department of Ocean Sciences at the Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami. His research focuses on oceanographic modeling, data assimilation, and uncertainty quantification in geophysical fluid dynamics. He specializes in developing advanced numerical methods for climate and ocean circulation studies, including polynomial chaos frameworks, Gaussian process regression, and spectral element models. His work integrates machine learning techniques with traditional oceanography to improve predictions of submesoscale structures, storm surges, and hurricane impacts. Iskandarani has contributed to understanding Loop Current dynamics in the Gulf of Mexico, fish larval connectivity in Florida Keys, and the propagation of uncertainties in coupled atmosphere-wave-ocean systems. Key research areas include: (1) High-resolution ocean velocity field reconstruction using Lagrangian drifter data, (2) Quantifying uncertainties in biophysical and climate models, (3) Developing statistical interpolation codes for ocean forecasting, and (4) Exploring multiscale ocean-atmosphere interactions. His methods are applied to real-world challenges like hurricane forecasting, oil spill trajectory modeling, and coastal engineering design criteria. Collaborations involve institutions such as NOAA, NASA, and international oceanographic consortia. Published extensively in journals like Journal of Marine Science and Engineering and Monthly Weather Review , his work bridges computational science with environmental applications. Recent projects analyze submesoscale turbulence, drifter-based velocity field reconstructions, and ensemble-based statistical emulators for ocean current predictions.

• Fatigue and Fracture Mechanics
• Probabilistic Computational Mechanics
• Risk Assessment and Management
• +7 more

Yongming Liu is a Professor of Aerospace and Mechanical Engineering at Arizona State University's School for Engineering of Matter, Transport and Energy. He leads the Prognostic Analysis and Reliability Assessment Laboratory (PARA) and has been at ASU since 2012. His research focuses on fatigue/fracture mechanics, probabilistic computational methods, risk assessment, multi-physics damage modeling, and uncertainty quantification across scales. Key interests include experimental testing, diagnostics/prognostics, and structural durability. Education: Ph.D. Civil Engineering (Vanderbilt University, 2006); M.S. and B.S. Structural Engineering (Tongji University, Shanghai, 2002 and 1999). Research activity includes NSF-funded projects on fatigue prognosis for coastal bridges, DOE-supported studies on creep-fatigue analysis of alloys, and industry collaborations on pipeline diagnostics. Recent work emphasizes AI-driven approaches for structural health monitoring, aerospace safety, and energy systems. Courses taught span mechanics of materials, probabilistic methods in engineering design, and graduate supervision. Key contributions include physics-informed machine learning frameworks, fatigue life prediction under uncertainty, and multi-agent reinforcement learning for air traffic management. His lab develops advanced diagnostics/prognostics tools bridging structural and biomedical applications.

Stefan BrucknerView profile

Professor

• Biomedical Visualization
• Volume Rendering
• Visual Data Exploration
• +1 more

Stefan Bruckner is Professor of Visualization at the University of Bergen, specializing in biomedical visualization, volume rendering, and visual data exploration. His work develops novel techniques for analyzing complex scientific datasets across meteorology, medicine, and materials science. Dr. Bruckner's research group develops interactive visual analytics tools for weather forecasting, medical diagnostics, and ensemble data analysis. His methodological innovations include GPU-accelerated rendering, visual parameter exploration, and uncertainty visualization. He received the 2011 Eurographics Young Researcher Award for contributions to illustrative visualization. Professional service includes program committee roles for IEEE VIS, Eurographics, and ECRTS conferences. His pedagogical contributions span visualization, computer graphics, and programming languages at institutions including École normale supérieure and École polytechnique.

Erik NielsenView profile

Academic

• Operational Forecasting
• Numerical Weather Prediction
• Extreme Rainfall Processes
• +4 more

Erik Nielsen is an Instructional Assistant Professor at Texas A&M University's Department of Atmospheric Science. His research focuses on operational forecasting, extreme rainfall processes, and social aspects of weather warnings. He holds a Ph.D. and M.S. in Atmospheric Science from Colorado State University and a B.S. in Meteorology from Texas A&M University. Key research interests include mesoscale convective systems, concurrent hazards (e.g., tornado-flash flood overlaps), and improving forecast communication. Notable awards include the NSF GRFP Fellowship (2014-2018) and American Meteorological Society Graduate Fellowship (2013-2014). His recent work emphasizes understanding the dynamics of extreme precipitation events, such as the Houston Tax Day Flood (2016), and leveraging social science to enhance disaster response. Publications span journals like Weather and Forecasting , Monthly Weather Review , and Human-Computer Interaction . Education: Ph.D., Atmospheric Science, Colorado State University M.S., Atmospheric Science, Colorado State University B.S., Meteorology, Texas A&M University Awards: 2020 Bulletin of the AMS “Paper of Note” 2013 Thomas S. Gathright Academic Excellence Award Grants and collaborations include NSF-funded research on compound hazards and field campaigns like PECAN. He actively engages in public outreach through social media analysis of disaster communication patterns.