Find Academic Supervisors

• Civil Engineering
• Climate Science
• Machine Learning
• +6 more

Brennan Bean is an Assistant Professor in the Mathematics and Statistics Department at Utah State University's College of Arts & Sciences. His work focuses on geospatial modeling, statistical methods for extreme weather analysis, and machine learning applications in structural and environmental engineering. Recent publications highlight expertise in snow load prediction, Bayesian entropy, and interdisciplinary data science. Notable contributions include optimizing design methods for insulated concrete wall panels and addressing deployment challenges for ML models in engineering contexts. Research trends span geospatial data integration, climate change impact assessments, and educational interventions in STEM. Key subfields include ground snow load mapping, extreme value statistics, climate downscaling, and high-dimensional ecological modeling.

Aleksey ZaytsevView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Predictive Modeling
• +4 more

Aleksey Zaytsev is a Senior Lecturer at Skoltech, where he leads the Applied Research Laboratory «Skoltech-Sberbank» at the Center for Applied Artificial Intelligence. He holds a PhD in Physics and Mathematics and is a laureate of the Moscow Government Prize for Young Scientists. Education: PhD in Physics and Mathematics His research focuses on artificial intelligence, particularly on developing predictive models for geospatial analysis, drilling safety, and aerospace engineering. He has created practical AI solutions for drought prediction, accident prevention, and aircraft quality monitoring. His work also emphasizes model reliability and safe deployment in industrial contexts. The recent studies highlighted his contributions to neural network accuracy in banking applications, algorithmic hesitation for improved decision-making, and geospatial data optimization. These works demonstrate interdisciplinary applications of AI in environmental, financial, and engineering domains. Scientific Awards: Moscow Government Prize for Young Scientists As head of the Artificial Intelligence Center's applied research lab, Zaytsev bridges academic research with industry needs, particularly in AI safety and real-world implementations. He also engages in public education through lectures like 'How to Deceive Artificial Intelligence' at the Yeltsin Center, addressing AI vulnerabilities and security implications.

• Energy Storage
• Solid-State Batteries
• Electrochemical Engineering
• +3 more

Bairav Sabarish Vishnugopi is a Researcher at Purdue University's College of Engineering, Department of Mechanical Engineering. He holds a PhD from Purdue University and previously served as a Research Scientist at the same institution. His research focuses on energy storage systems, particularly solid-state and lithium-ion batteries, with emphasis on interfacial stability, thermal management, and electrochemical safety analytics. PhD, Purdue University Previous Role: Research Scientist, Purdue University His research spans mechanistic analysis of electrode/electrolyte interactions, data-driven battery safety, and multiscale degradation modeling. Recent work examines thermal gradients, chemo-mechanical coupling, and dynamic discharge behaviors for applications in electric vertical take-off aircraft (eVTOL). Articles highlight alloy anode stability, SEI engineering, and hydrogen's role in steel electrification. Key scientific contributions include: Thermo-electrochemical instability fingerprinting Microstructural evolution in conversion cathodes Pressure-driven stability optimization He received the H. H. Dow Memorial Student Achievement Award (2023) for his work on interface stability analysis. Current collaborations involve PEM electrolyzer integration and energy-climate analytics frameworks.

Zhaoxia PuView profile

Professor

• Numerical Weather Prediction
• Data Assimilation
• Machine Learning
• +5 more

Dr. Zhaoxia Pu is a Professor in the Department of Atmospheric Sciences at the University of Utah and an Adjunct Professor at the School of Computing . Recognized as a Fellow of both the American Meteorological Society and the Royal Meteorological Society, she serves on the NOAA Science Advisory Board and has led 38 federally funded projects from agencies including NOAA, NASA, NSF, DOE, and ONR. Specializes in numerical weather prediction , data assimilation , and AI/machine learning for high-impact weather systems Developed advanced methods integrating satellite/radar data (GOES-R, CYGNSS, TROPICS) with Earth system models (UFS, E3SM, WRF) Recipient of the 2024 Excellence in Research Award and 2023 Provost's Banner Project recognition Research Trends : Her recent publications focus on: Machine learning approaches for precipitation retrieval using GOES-R data Cold fog microphysics and visibility parameterization in complex terrain Tropical cyclone dynamics through radar and lidar data assimilation Boundary layer turbulence in landfalling storms Drought mechanisms linked to synoptic-scale circulation New particle formation in mountainous regions Scientific Leadership : Lead scientist for CFACT NSF field campaign (2021–2025) Editorial board member of leading journals Active reviewer for NSF, DOE, NOAA, and NASA Teaching & Mentorship : Teaches Numerical Weather Prediction , Atmospheric Dynamics , and Introduction to Atmospheric Sciences courses. Has supervised 28 graduate students to completion.

Shiqiang YanView profile

Professor

• Wave/current/wind interaction
• Structure responses in extreme sea conditions
• Renewable energy and low carbon technologies
• +2 more

Professor Shiqiang Yan is an ocean engineering expert at City St George's, University of London , with over 25 years of academic experience. He obtained his PhD in Hydraulics (2007) from City University London, following MSc (2004) and BEng (1999) from Dalian Maritime University. His career includes Postdoctoral Research Fellow (2007-2012) and Lecturer (2012–present) positions at City University. Specializes in Wave-Structure Interaction and Extreme Sea Condition Modeling Developed QALE-FEM and ISPH-GNN hybrid numerical methods Research spans Wave Energy , Offshore Wind , and Marine Pollution Control His 15 most recent publications (2021-2025) demonstrate expertise in Wave-Structure Interaction (40% of articles), Hybrid Modeling (30%), and Renewable Energy Systems (25%). Key subfields include Wave-WEC Coupling , Ice Floe Dynamics , and Multi-Scale Hydrodynamic Modeling . He has advised PhD student Hao Yang (2013–present) on submerged oil spill research. Professional memberships include the International Society of Ocean & Polar Engineering (2013–present).

Abigail HathwayView profile

Senior Lecturer

• Building Physics
• Indoor Airflow Dynamics
• Sustainable Building Systems
• +7 more

Dr Abigail Hathway is a Senior Lecturer at the School of Mechanical, Aerospace and Civil Engineering , University of Sheffield, specializing in Building Physics and Indoor Airflow Dynamics . Her work bridges energy efficiency with occupant health , focusing on human-building interactions. Education: PhD in CFD modeling of bioaerosols (University of Leeds) Research interests include: Computational Fluid Dynamics (CFD) for indoor environments Human activity impacts on airflow and infection risk Sustainable drainage systems (SuDS) for urban climate mitigation Smart building controls integrating machine learning Her recent publications address ventilation strategies and airborne transmission mitigation across healthcare, hospitality, and urban settings. She leads research on SuDS microclimate impacts and battery storage optimization in buildings. Current PhD opportunities in her group focus on: Urban stormwater-climate interactions Low-energy ventilation systems Occupant-driven building performance

• Probability Theory
• Futurology
• Existential Risk
• +4 more

Olle Häggström is a Professor of Mathematical Statistics at Chalmers University of Technology, specifically in the Department of Applied Mathematics and Statistics. His academic career spans several decades with a significant shift in research focus over time. Häggström's research interests have evolved from traditional probability theory to encompass broader future-oriented topics. Initially focused on mathematical statistics and probability theory, including percolation theory and stochastic processes, he has increasingly turned his attention to futurology, existential risk, and AI safety in recent years. His work demonstrates a unique interdisciplinary approach, bridging rigorous mathematical analysis with philosophical considerations about humanity's technological trajectory. The trends in Häggström's publications reveal a clear evolution from purely mathematical research toward interdisciplinary studies examining the societal implications of emerging technologies. His recent work focuses heavily on AI safety, existential risk assessment, and long-term futures thinking, while still maintaining connections to his mathematical foundations. This shift is evident in publications ranging from technical mathematical papers to broader philosophical discussions about technology's impact on civilization. Häggström has received research funding from notable sources including the FTX Foundation Future Fund for his project "Topics in the theory of xrisk and longtermism" (2022-2025), indicating recognition of the importance of his work in the existential risk community. His book "Here Be Dragons: Science, Technology and the Future of Humanity" (2016) represents a significant synthesis of his thinking on these topics. While specific details about his advising activities are not provided in the source material, his research projects suggest engagement with interdisciplinary teams working at the intersection of mathematics, computer science, and future studies. His work appears to influence both academic and policy discussions regarding technological risk and long-term planning.

Juan Carlos Linares CalderónView profile

Professor

• Ecology
• Climate Change
• Forest Management
• +5 more

Juan Carlos Linares Calderón is a Full Professor at Pablo de Olavide University in the Department of Physical, Chemical and Natural Systems. His research focuses on Mediterranean relict forests' response to climate change through multidisciplinary approaches combining dendrochronology , ecophysiology , genomics , and epigenetics . PhD in Science (2008) from University of Jaén Principal Investigator in 10+ national/international projects Research Themes Specializing in climate change impacts on forest ecosystems , Linares leads the Forests Ecology and Global Change Lab . His work spans from individual tree responses to large-scale biogeographical patterns, challenging CO 2 -induced growth paradigms and emphasizing forest management as climate modulator. Scientific Contributions Over 162 publications with 82,704 reads and 9,074 citations Highly Cited Author status (Ioannidis et al., 2020) Developed SAPFLUXNET global transpiration database Email: jclincal@upo.es

• Spatial Analysis
• Geographic Information Systems (GIS)
• Environmental Management
• +5 more

Colin John McClean is a Senior Lecturer in the Department of Environment and Geography at the University of York , with over 25 years of experience in spatial analysis and GIS applications for environmental management. His career spans post-doctoral research at Cardiff and Newcastle, followed by academic roles at York since 1996. Education: BSc in Geography from the University of Edinburgh (1986); PhD in Geography from Durham University (1991). McClean’s research focuses on leveraging spatial datasets to address conservation ecology, climate change impacts on biodiversity, and land use modeling. He has pioneered work using machine learning methods like genetic algorithms and simulated annealing for reserve selection, with applications in the Sahel region (e.g., Shea Tree climate modeling) and marine conservation. Recent publications highlight interdisciplinary themes, including urban green-blue spaces and health , tropical peatland restoration , and oil palm sustainability . His work has been funded by NERC, Defra, EU, and the Darwin Initiative, often involving cross-institutional collaborations. He supervises PhD students in topics such as water chemistry prediction , soil nitrogen dynamics , and protected area effectiveness under climate change . No scientific awards are documented, but his research on climate change impacts has influenced major reports like the Stern Review and the 2007 IPCC assessment.

Amr OmarView profile

Researcher

• Solar Thermal Systems
• Desalination Processes
• Water Treatment Technologies
• +4 more

Dr. Amr Omar is a researcher at the University of New South Wales , affiliated with the School of Mechanical and Manufacturing Engineering and Department of Chemical Engineering. His work focuses on integrating renewable energy systems—particularly solar thermal technology—with advanced water treatment processes to address global sustainability challenges. PhD in Solar Thermal Systems & Desalination (UNSW, 2017-2020) Postdoctoral Researcher (UNSW Chemical Engineering, 2020-2023) Omar specializes in decarbonizing water processes through solar-driven membrane distillation , multi-effect distillation , and hydrogen production cooling systems . His research bridges laboratory innovation with real-world applications, targeting United Nations' SDG6 (Clean Water) and SDG7 (Affordable Clean Energy). Recent publications emphasize techno-economic modeling of hybrid solar-desalination systems, machine learning applications for water filtration optimization, and geospatial analyses of global desalination site feasibility. Key projects include developing hollow fiber vacuum membrane distillation modules and improving flocculant strength assessment for water catchment monitoring. Scientific Recognition : 2022 Malcolm Chaikin Prize for Research Excellence 2020 UNSW Postgraduate Council Research Student Award 2020 3MT Competition Winner (School of Mechanical & Manufacturing Engineering) 2017 Australian Postgraduate Award Scholarship Omar actively supervises PhD and Master’s projects related to solar-driven water treatment and green hydrogen development. He serves as a journal reviewer and conference panel member, advocating for nanobubble technologies in agricultural irrigation systems.

TCHUENTE DieudonnéView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Sustainability
• +7 more

Dr. TCHUENTE Dieudonné is an Associate Professor in the Department of Information, Operations and Decision Sciences at TBS Education. His research bridges artificial intelligence, sustainability, and operations management through practical applications in energy efficiency, supply chain resilience, and social media analysis. Department: Information, Operations and Decision Sciences University: TBS Education Email: d.tchuente@tbs-education.fr Research Focus : Dr. Tchuente's work explores AI-driven solutions for sustainable development, including building energy retrofits , real estate valuation , and vaccine supply chains . His studies on explainable AI provide actionable insights for corporate emissions reduction and aeronautics optimization. Publications Trends : His recent work (2022-2024) emphasizes AI ethics , climate accountability , and industry transformation across domains like real estate, aviation, and public health. Key methodologies include SHAP analysis , meta-learning , and crowdsourcing frameworks . Scientific Awards : FNEGE Grant - for research on connected vehicle infrastructure

Rebecca YangView profile

Visiting Professor

• Building Integrated Photovoltaics
• Solar Energy Applications in Buildings
• Construction Innovation
• +2 more

Rebecca Yang is a Visiting Professor at RMIT University's School of Property, Construction and Project Management, specializing in building, construction, and distributed renewable energy research. She integrates theoretical knowledge with cutting-edge technologies to advance sustainable urban development. Her research focuses on solar energy applications in buildings, construction innovation, and international energy policy frameworks through her leadership roles in the International Energy Agency's Photovoltaic Power Systems Programme (PVPS) Task 15 and Solar Heating and Cooling Programme (SHC) Task 66. She established RMIT's Solar Energy Application Lab and has 8 years of BIPV expertise. Notable achievements include: Australian representative in international BIPV standardization (IEC 63092) 2019 Facilitator Prize for BIPV Tool development She supervises research projects related to: Solar building envelope optimization Machine learning for energy systems Fire safety in BIPV installations Blockchain-enabled energy trading Circular economy for PV waste

• Environmental Systems Analysis
• Water-Food-Energy Nexus
• Climate Change Impact Assessment
• +3 more

Gordon Huang is an Adjunct Professor in the Civil Engineering department at McMaster University , specializing in environmental systems analysis and sustainable resource management. His research focuses on quantitative methods for addressing uncertainties in climate change, water-food-energy nexus planning, and contaminant remediation. Primary Affiliation : Civil Engineering, McMaster University His work integrates advanced computational models like Bayesian neural networks, factorial optimization, and copula-based downscaling to analyze complex environmental interactions. Recent projects examine CO2 emission pathways, microplastic impacts, and climate-driven drought risks. Key methodologies include stochastic programming , ecological network analysis , and machine learning for predictive modeling. Publications span topics from membrane technology for water treatment to large-scale hydropower socio-economic effects. Email : huangg31@mcmaster.ca

Lesley De CruzView profile

Research Fellow

• Climate Modeling
• Regional Climate Downscaling
• AI for Environmental Systems
• +3 more

Lesley De Cruz is a Research Fellow at Vrije Universiteit Brussel's Electronics and Informatics department within the School of Electronics and Computer Science, with concurrent research affiliation at the Royal Meteorological Institute of Belgium since 2012. Current projects include climate-resilient urban modeling through BRGEOZ464 and flood prediction systems under FWOAL1127. Research focuses on AI-driven climate modeling with expertise in nonlinear dimension reduction (DIRESA framework), urban climate resilience, and renewable energy meteorology. Key methodologies integrate deep learning with environmental physics for applications including real-time flood prediction offshore wind farm optimization LEGO-based urban climate prototyping regional climate downscaling Recent publications (2023-2025) demonstrate strong trends in geospatial AI for environmental systems, with 64% focusing on climate change impact analysis, 54% on urban applications, and 51% on control measure development. The DIRESA framework represents a significant contribution to nonlinear dimension reduction in climate informatics. Awards include: Matìère Grise Science Communication Trophy (2024) Major media recognition for breakthrough rainfall prediction model (2021) Supervises multiple PhD candidates and leads public engagement initiatives including CurieuCity and Dag Van de Wetenschap. Current grants total 12 active projects (2021-2029) with €2.8M+ funding, primarily focused on climate adaptation and sustainable technology development. Labs and teams include the Climate Informatics Group at VUB and collaborative networks with Ghent University and the Flemish Institute for Carbon-Aware Technologies.

• Artificial Intelligence
• Natural Language Processing
• Computational Linguistics
• +6 more

Ayşenur Akyüz Birtürk serves as a Lecturer in the Department of Computer Engineering at Middle East Technical University (METU), Ankara, where she has taught since February 1994. Her academic career spans foundational programming courses to advanced graduate seminars in AI and Computational Linguistics, reflecting 30+ years of institutional commitment. She earned all her degrees from METU, culminating in a 1998 Ph.D. focused on Turkish language computational analysis. Her educational journey includes: B.S. in Computer Engineering (1985) M.S. in Computer Engineering (1988) with thesis on “A Model for Representing Concepts: Conceptual Dependency Theory” Ph.D. in Computer Engineering (1998) with thesis on “A Computational Analysis of Turkish using the Government-Binding Approach” Dr. Birtürk’s research centers on Artificial Intelligence and Natural Language Processing , with pioneering work in Turkish language parsing evolving into modern Recommender Systems . She integrates semantic relations and multi-domain data to build hybrid engines for movies, books, and music, emphasizing user modeling through knowledge representation and data mining techniques. Analysis of her 2010-2015 publications reveals two dominant threads: adaptive recommender systems (80% of output) using semantic similarity and dynamic clustering, and renewable energy analytics (20%) applying machine learning to wind/hydrological data. This pivot from NLP to energy forecasting demonstrates methodological versatility while maintaining core AI expertise. Her scientific recognition includes: TUBITAK scholarships throughout education (1977-1988) Multiple national contest awards in high school (1979-1980) Leadership in TUBITAK-funded energy and healthcare projects Dr. Birtürk has supervised 17 Master’s theses in NLP and recommender systems while securing competitive grants including METU-ISTEC #17435 (2006-2008; 504,000 YTL) and HASAT (2010-2013; 601,637 YTL). Her industry consultancy spans medical form design (FormAnalitik), question-answering systems, and retail intelligence platforms, translating academic research into real-world tools.