جست‌وجوی استاد و پژوهشگر

Naresh Neupaneمشاهده پروفایل

عضو هیئت علمی

• Climate Change Impacts
• Ecological Forecasting
• Butterfly Conservation
• +۷ مورد دیگر

Naresh Neupane is a Research Assistant Professor at Georgetown University, where he has been actively contributing to climate and ecological research since 2015. His work integrates climate science, ecological modeling, and conservation biology, with a strong focus on understanding the impacts of global warming on biological systems, particularly butterfly populations. His research interests center on climate change impacts on species distribution , ecological forecasting , and spatio-temporal modeling . He investigates how global warming affects the population dynamics and migratory patterns of insects, especially monarch butterflies, and explores the broader implications for biodiversity and ecosystem stability. His interdisciplinary approach combines remote sensing, citizen science data, and advanced statistical models to forecast ecological changes under future climate scenarios. Analysis of his recent publications reveals a consistent focus on insect conservation under climate change , with particular emphasis on monarch butterflies, Arctic and boreal species, and disease vectors. His work spans diverse ecosystems across North America and integrates data from satellites, field observations, and community science. He frequently publishes in top-tier journals such as Science , Nature Ecology & Evolution , and PNAS , indicating high scientific impact. Dr. Neupane collaborates extensively with leading ecologists, including Leslie Ries and Elise Zipkin, on large-scale projects related to butterfly monitoring and climate responses. While no formal students are listed, his role suggests active mentorship within research teams. He has also contributed to public health research through modeling Lyme disease dynamics in Virginia. His work is supported by robust computational and modeling frameworks, and he has developed novel methods for predicting forest green-up and classifying insect developmental stages using AI. These innovations highlight his technical expertise and commitment to advancing conservation through technology.

Junho Hongمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Cybersecurity
• Smart Grids
• +۷ مورد دیگر

Junho Hong is an Associate Professor at the University of Michigan–Dearborn's Department of Electrical and Computer Engineering, College of Engineering and Computer Science. He holds a PhD in Electrical Engineering from Washington State University (2014), with prior roles at ABB (2014–2019) and Ford Motor Company (2021 sabbatical). His research spans cybersecurity of energy delivery systems, AI applications in power systems, and cyber-physical systems. Doctor of Philosophy in Electrical Engineering, Washington State University, USA (2014) Master of Science in Electrical Engineering, Myongji University, South Korea (2010) Bachelor of Science in Electrical Engineering, Myongji University, South Korea (2008) Junho's research focuses on securing critical energy infrastructure through machine learning, anomaly detection, and advanced cybersecurity frameworks. His work includes projects on substation automation, HVDC systems, smart grids, and high-power EV chargers. He has secured grants from the U.S. Department of Energy, NSF, Ford, and South Korean institutions. Recent publications highlight his contributions to generative AI for anomaly detection, SDN-based cyber restoration, and physics-informed models for secure grid operations. His students have received notable awards, including the Rackham Predoctoral Fellowship and IEEE Best Paper Session. Senior Member, IEEE (2022–present) Associate Editor, IEEE ACCESS (2022–present) 13 US patents in energy cybersecurity He leads the Cyber-Physical System Lab for Energy Delivery Systems, which focuses on grid resilience, renewable integration, and advanced diagnostics.

• Machine Learning
• Natural Language Processing
• Climate Modeling
• +۶ مورد دیگر

Professor Andreas Hotho leads the Data Science Chair at the University of Würzburg's Faculty of Mathematics and Computer Science, while serving as founding spokesman of the Center for Artificial Intelligence and Data Science (CAIDAS). His academic journey includes senior researcher roles at the University of Kassel and research positions at the AIFB Institute (University of Karlsruhe) and L3S Research Center (Hannover). Research Focus: Machine Learning, Large Language Models, Climate Modeling, Semantic Web, and Knowledge Graphs Methodologies: ConvMOS architecture, DenseLoss approach, LanZ-DML, and BibSonomy system Applications: Environmental monitoring, Digital Humanities, Smart Beehive Analysis, and Social Media Analytics With over 15 recent publications, his work spans climate model output statistics, zero-shot metric learning, imbalanced regression techniques, and German language model development. He serves as editor-in-chief for the Transactions on Graph Data and Knowledge and maintains active roles in academic governance through PC memberships at ECML-PKDD, WWW, and other major conferences. Scientific awards include: NAACL 2024 Best Paper Honorable Mention ICDM 2023 Best Paper Award NeurIPS 2020 Best ML Innovation Award ISWC 2018 SWSA Ten-Year Award WWW 2015 Best Paper Award FAIML 2020 Best Student Paper His research group includes doctoral researchers like Jan Pfister, Julia Wunderle, and Albin Zehe. Key projects include LitBERT for literary analysis, BigData@Geo series for climate modeling, and BeeConnected for ecosystem monitoring.

Daniel Kriegerمشاهده پروفایل

پژوهشگر

• Climate Variability
• Storm Surges
• Machine Learning in Climate Prediction
• +۳ مورد دیگر

Daniel Krieger is a Postdoctoral Researcher at the Max Planck Institute for Meteorology since 2025, focusing on Earth System Modeling and Predictions within the Department of Climate Variability. Previously, he was a Postdoc at the University of Hamburg (2024-2025), contributing to coastal extreme event forecasting systems. PhD in Earth System Sciences (2020-2023), Helmholtz-Zentrum Hereon M.Sc. in Meteorology (2017-2020), University of Hamburg B.Sc. in Meteorology (2013-2017), Leibniz University Hannover Krieger’s research centers on climate variability and storm surge predictability , leveraging machine learning to enhance decadal-to-seasonal climate forecasts for coastal regions. His work addresses German Bight storm activity and European summer heatwaves , integrating initialized/uninitialized model ensembles and impact-based forecasting . Publications highlight his expertise in AI-driven coastal protection and historical storm analysis , with recent articles (2025) demonstrating sub-decadal ocean heat accumulation and machine learning skill for storm surges. Collaborations span institutions like the University of Hamburg and Helmholtz-Zentrum Hereon. He works within the Earth System Modeling and Predictions group, contributing to projects such as nextGEMS and WarmWorld . His methodological innovations bridge climate dynamics and computational extremes , aligning with the Institute’s focus on climate and weather extremes .

• Statistics
• Machine Learning
• Geoinformatics
• +۴ مورد دیگر

Matthias Schlögl is a Researcher at the Institute for Alpine Natural Hazards (Department of Landscape, Water and Infrastructure) within the University of Natural Resources and Life Sciences, Vienna (BOKU) . With a doctoral degree in technical sciences (Dr. nat. techn.) from BOKU (2015), he specializes in statistical analysis and geospatial modeling of natural hazards. Research Focus : Statistical modeling of extreme weather events Machine learning applications for hazard assessment Climate change impact on Alpine geohazards Exposure analysis for infrastructure Multi-hazard spatiotemporal risk assessment Recent publications (2023-2025) span landslide susceptibility modeling, wildfire vulnerability indices, debris flow analysis, and climate change impacts on Alpine hazards. He contributes to projects funded by the European Union , Austrian Research Promotion Agency (FFG) , and national ministries. Community engagement includes peer-review activities for journals like Natural Hazards and Earth System Sciences , Journal of Hydrology , and Remote Sensing .

• Software Engineering
• Agile Methods
• Embedded Systems
• +۳ مورد دیگر

Adilson Marques da Cunha is a Professor at the Brazilian Aeronautical Institute of Technology (ITA), with a focus on software engineering and computational systems. His career spans academic research and development in embedded systems, real-time applications, and machine learning methodologies. Affiliation: Brazilian Aeronautical Institute of Technology Research Interests: Dr. da Cunha's work centers on agile software development , embedded system optimization , and numerical integrator frameworks . He has contributed to deep reinforcement learning for robotics and recommender systems for news, while also exploring climate prediction models using big data. Publication Trends : 2025 papers focus on universal numerical integrators and Python testing frameworks; 2024 work involves autonomous air navigation; 2022 studies include humanoid robotics and climate modeling

Florian Schierhornمشاهده پروفایل

پژوهشگر

• Land Change Research
• Plant Growth Modeling
• Environmental Impacts of International Food Trade
• +۶ مورد دیگر

Dr. Florian Schierhorn is a researcher in the Structural Change Department at the Leibniz Institute of Agricultural Development in the Global South (IAMO) , where he has worked since 2009. He also teaches at the Martin Luther University Halle-Wittenberg , focusing on agriculture-climate change linkages. His research spans environmental impacts of food trade, climate change effects on agriculture, organic farming, and land use dynamics in post-Soviet regions. He contributes to policy briefs and public discourse through media outlets like Nature News , Harvard Business Review , and ZEIT ONLINE . Research Projects : BALTRAK – Balancing trade-offs between agriculture and biodiversity conservation in Kazakhstan EPIKUR – Agricultural production and carbon trade-offs in post-Soviet states GERUKA – Global food security and grain markets in the "Eurasian breadbaskets" RIMARA – Risk management for small farmers in Caucasus Risk-Contingent Credit for Resilience – Financial solutions for African agriculture Expertise includes machine learning applications in yield prediction, carbon sequestration potential on abandoned lands, and socio-economic drivers of agricultural transitions. He collaborates on interdisciplinary teams and advises the German Federal Ministry of Food and Agriculture (BMEL) on climate policy. Publications appear in Environmental Research Letters , Global Environmental Change , and Regional Environmental Change .

Jiarong Hongمشاهده پروفایل

استاد

• Fluid Mechanics
• Turbulence
• Atmospheric Flows
• +۱۱ مورد دیگر

Jiarong Hong is a Professor in the Department of Mechanical Engineering within the College of Science and Engineering at the University of Minnesota. He serves as Principal Investigator at the Flow Field Imaging Laboratory and leads research with the cavitation and bubbly flow group at the Saint Anthony Falls Laboratory. His work spans multiple interdisciplinary institutes including the Minnesota Robotics Institute and the Institute on the Environment. Dr. Hong's research focuses on fundamental fluid mechanics related to turbulence, atmospheric and multiphase flows, wind turbine aerodynamics, cavitation and bubbly flows, and microfluidic systems. His work integrates optical imaging and instrumentation with applications in flow diagnostics, biophysics, geophysics, medical and material sciences. His innovative approach to field flow imaging using natural snowfall has significantly advanced the study of utility-scale wind turbine flows. His recent publications demonstrate a strong trend toward practical applications of fluid mechanics in renewable energy, environmental protection, and public health. During the pandemic, his research expanded to address airborne transmission of diseases, showing his ability to pivot research toward pressing societal needs. His work consistently bridges fundamental fluid mechanics with real-world engineering applications. Robert T. Knapp Award for scale-dependent energy fluxes research Featured Article in Theoretical and Applied Mechanics Letters Editor's suggestions in Physical Review Fluids Cover Article in Acta Mechanica Sinica Cover of Journal of Fluid Mechanics Selected as highlight of Nature Communication As Principal Investigator of the Flow Field Imaging Laboratory, Dr. Hong leads a research group that develops advanced imaging techniques for fluid flow measurement. His work on super-large-scale particle image velocimetry using natural snowfall represents a significant methodological innovation in the field. His research has been supported by various funding mechanisms that enable both fundamental investigations and practical applications in wind energy and environmental fluid mechanics. Dr. Hong maintains strong collaborative relationships with researchers across multiple disciplines, as evidenced by his diverse publication record spanning mechanical engineering, environmental science, medical applications, and robotics. His laboratory serves as an interdisciplinary hub connecting fluid mechanics with practical engineering challenges.

Aditya Groverمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Unsupervised Representation Learning
• Sequential Decision Making
• +۱ مورد دیگر

Aditya Grover is an Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), where he conducts research at the intersection of probabilistic machine learning and artificial intelligence. His research focuses on developing foundational methods for unsupervised representation learning and sequential decision making, with significant contributions to generative AI including multimodal model personalization and in-context learning frameworks. His work bridges theoretical machine learning with practical applications in climate forecasting and conversational AI systems. Dr. Grover's publications demonstrate expertise in geometric deep learning and efficient generative modeling, with his 2021 NeurIPS paper on manifold-based generative flows representing a key contribution to the field. His research has been featured in leading scientific publications including Nature and Science magazine, highlighting real-world impact in weather prediction and AI response optimization. His scientific achievements have been recognized through numerous prestigious awards: AI2050 Early Career Fellowship (2024) Forbes 30 Under 30 - Science (2023) Samsung AI Researcher of the Year Award (2022) NeurIPS Outstanding Paper Award (2021) ACM SIGKDD Doctoral Dissertation Award (2021) Gores Award for teaching excellence at Stanford (2020) Google-Simons Research Fellowship (2020) Centennial Award (2020) Lieberman Fellowship (2019-20) He currently holds the 2023 Amazon Research Award for his project "Personalizing Multimodal Generative Models via In-Context Preference Modeling," which advances the frontier of user-adaptive AI systems. While no student advising information is publicly available, his research collaborations with Amazon and media coverage through 2025 indicate active ongoing research programs.

Negin Nazarianمشاهده پروفایل

مدرس ارشد

• Urban Climate
• Climate Modeling
• Environmental Sensing
• +۳ مورد دیگر

Dr. Negin Nazarian is a Scientia Senior Lecturer at UNSW Sydney's School of Built Environment, with affiliations at the ARC Centre of Excellence for Climate Extremes and City Futures Research Centre. She leads the Climate-Resilient Cities (CRC) research lab, focusing on urban climate interactions through climate modeling, environmental sensing, and IoT technologies. Her work addresses urban heat exposure, ventilation, and multiscale urban canopy parameterizations.

Jiwen Fanمشاهده پروفایل

پژوهشگر

• Clouds
• Aerosols
• Aerosol-Cloud Interactions
• +۸ مورد دیگر

Dr. Jiwen Fan is Deputy Director of the Environmental Science Division at Argonne National Laboratory and holds a joint appointment as a senior research scientist at the University of Chicago. Her work focuses on critical challenges in atmospheric science, including cloud dynamics, aerosol impacts, and human-earth system interactions. She previously served as a Laboratory Fellow and Earth Scientist at Pacific Northwest National Laboratory before joining Argonne in June 2023. Education: Ph.D., Atmospheric Sciences, Texas A&M University, 2007 M.S., Environmental Engineering, University of Central Florida, 2002 M.S., Environmental Science, China University of Mining & Technology (Beijing Campus), 1999 B.S., Chemistry, Hunan University of Science & Technology, 1996 Dr. Fan’s research spans interdisciplinary domains, leveraging multiscale modeling (large-eddy simulations, regional cloud-resolving models, global earth system models) and observational data to address climate and weather extremes. Current projects include advancing cloud microphysics in DOE’s Energy and Exascale Earth System Model (E3SM), integrating machine learning for climate modeling, and co-leading initiatives like the AI for Climate (AICE) Initiative and the DOE’s Energy Earthshot Research Center ACE-FWICC, which studies floating wind energy in a changing climate. Scientific Awards: Argonne Distinguished Fellow (2025) AMS Fellow (2022) PNNL Laboratory Fellow (2021) DOE Early Career Research award (2017) AGU ASCENT award (2015) NASA Earth System Science Fellowship (2004-2007)

Doug Richardsonمشاهده پروفایل

پژوهشگر ارشد

• Climate Extremes
• Climate Change Science
• Climatology
• +۴ مورد دیگر

Doug Richardson is a Research Fellow at the University of New South Wales, affiliated with the ARC Centre of Excellence which he joined in January 2023. Previously, he worked as a Research Fellow at CSIRO, Hobart between 2019 and 2022, and collaborated with the UK Met Office in 2016 to design an operational forecast tool for the national Flood Forecasting Centre. His educational background includes a PhD in Climate Science from Newcastle University (2019) and a BSc (Hons) in Mathematics and Statistics from the same institution. Richardson specializes in extreme climate events with societal impacts, particularly compound climate events. His current research investigates Australia's energy grid exposure to renewable energy resource reductions and their synoptic and large-scale drivers. He has extensive expertise in climate hazards including drought and wildfire weather, examining how and why these hazards occur and co-occur across different global regions. His methodological approaches include climate prediction on subseasonal to decadal time scales, synoptic weather classification, and trend analysis of climate hazards. His recent publication record demonstrates significant contributions to understanding fire weather season overlaps, compound climate events affecting energy systems, synchronous climate hazards impacting global supply chains, and ENSO diversity. His work employs advanced techniques including archetypal analysis and machine learning applications to climate data. Australia-Germany Joint Research Cooperation Scheme (2023 - present) Richardson's research bridges fundamental climate science with practical applications for climate resilience. His work on early warning systems for extreme rainfall events demonstrates his commitment to translating research into operational tools that serve societal needs. His current focus on renewable energy vulnerabilities reflects the growing importance of climate-informed energy planning in the transition to sustainable power systems. As part of the ARC Centre of Excellence and his previous role with the Australian Climate Service, Richardson collaborates within national, multi-agency climate resilience programs, contributing to Australia's capacity to address climate challenges through coordinated scientific efforts.

David Coomesمشاهده پروفایل

استاد

• Biodiversity Conservation
• Remote Sensing
• AI and Machine Learning Applications
• +۶ مورد دیگر

Professor David Coomes leads the Forest Ecology and Conservation Research Group at the University of Cambridge's Department of Plant Sciences, where he serves as Director of the Conservation Research Institute. His work integrates cutting-edge remote sensing technologies with computational ecology to address global conservation challenges. His research focuses on biodiversity conservation through advanced applications of LiDAR, hyperspectral imaging, and machine learning algorithms. Key interests include mapping species distributions, quantifying carbon storage in forests, and evaluating climate change impacts on tropical ecosystems. The group develops nature-based solutions for landscape restoration, collaborating with organizations like RSPB, UK-CEH, and the world's largest pulp/paper company for native forest restoration in Brazil. Recent publications reveal strong emphasis on AI-driven conservation tools (notably TESSERA foundation model), REDD+ credit verification, and tropical forest resilience. His team's work spans from microclimate dynamics to planetary-scale carbon assessments, with significant contributions to the British Ecological Society's Nature-based Solutions report. Current projects include NERC CREATES Doctoral Landscape Award and large-scale restoration initiatives in the Atlantic Forest. Research funding and collaborations include: NERC-funded Centre for Landscape Regeneration TESSERA satellite imagery foundation model development Industrial partnership for Brazilian native forest restoration Global Wood Security Centre initiatives

• Climate Science
• Data-Driven Climate Modeling
• Machine Learning Applications in Climate
• +۴ مورد دیگر

Professor Emily Shuckburgh is a renowned climate scientist affiliated with the University of Cambridge , holding the OBE and CBE honors. Her work spans climate modeling, extreme weather prediction, and science-policy interfacing. Specializes in Southern Ocean dynamics and Arctic sea ice forecasting Utilizes AI and machine learning for climate model optimization Focuses on translating climate science into policy frameworks Contributes to global climate risk assessment across 68 countries Scientific Awards: Officer of the Order of the British Empire (OBE) Commander of the Order of the British Empire (CBE) Email: efs20@cam.ac.uk

FRANCESCA TITTARELLIمشاهده پروفایل

استاد

• Materials Science
• Sustainable Construction Materials
• Geopolymer Technology
• +۳ مورد دیگر

Prof. Francesca Tittarelli is a Full Professor at the Polytechnic University of Marche, affiliated with the Department of Materials, Environmental and Urban Science Engineering (SIMAU) within the School of Engineering. Her research focuses on sustainable construction materials, particularly alkali-activated systems, waste valorization in cementitious composites, and structural health monitoring. Recent publications highlight her work on: Development of sustainable alkali-activated mortars using copper mine tailings and metakaolin (2025) Creation of multifunctional finishes for indoor air quality improvement (2025) Comparative analysis of textile-reinforced mortars with different binder systems (2024) Implementation of AI-enabled monitoring platforms for concrete structures (2024) Characterization of construction and demolition waste (2024) Development of waste-valorized nanowebs for water purification (2024) The research demonstrates consistent focus on: Environmental sustainability in material science Waste-to-resource strategies Smart sensing materials Cultural heritage preservation Climate-resilient construction techniques Energy-efficient building solutions