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Alex ArchibaldView profile

Professor

• Atmospheric Chemistry
• Climate Science
• Environmental Science
• +5 more

Professor Alex Archibald is the Professor of Atmospheric Chemistry in the Yusuf Hamied Department of Chemistry at the University of Cambridge. His research group investigates atmospheric chemistry-climate interactions through fundamental laboratory studies atmospheric observations numerical model simulations . Research interests focus on chemistry-climate feedbacks , including hydrogen economy impacts biogenic hydrocarbon oxidation air pollution mitigation marine sulfur cycling machine learning applications . Recent publications emphasize hydrogen-soil deposition dynamics ozone-temperature relationships DMS chemistry in Earth systems hydrogen economy climate implications AI-driven climate modeling transboundary pollution studies . Teaching includes Part I Kinetics of Chemical Reactions Part II Chemistry in the Atmosphere Part III IDP1 projects . The research team has 15+ current and former students working on topics from Martian atmospheric modeling to urban temperature extremes.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +7 more

Luka Radic is a Researcher in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His work bridges theoretical and applied research in machine learning, with a focus on quantum machine learning , large language models , and fairness in AI systems.

Sushil PrasadView profile

Professor

• Data Intensive Computing
• Energy-Efficient Deep Learning
• Parallel Algorithms
• +3 more

Sushil Prasad is a Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on data-intensive computing, energy-efficient deep learning models, parallel algorithms, and high-performance software systems. He holds a Ph.D. from the University of Central Florida, an M.S. from Washington State University, and a B.Tech. from the Indian Institute of Technology, Kharagpur. His work emphasizes integrating parallel and distributed computing into early computer science curricula. Key research interests include geospatial data analysis using ICESat-2 and Sentinel-2 imagery, edge device-optimized neural networks, and scalable polygon processing algorithms. He has contributed to frameworks like MPI-GIS and Crayons for high-performance geospatial computing. His educational initiatives include NSF-funded curriculum modernization efforts in parallel computing education. Recent work trends show a focus on climate science applications (e.g., polar sea ice classification), energy-efficient AI, and GPU/OpenMP parallelization. He has organized workshops like EduHPC and EduPar to advance HPC education strategies. Notable recognition includes the TCPP Outstanding Service Award (2012). His projects span cloud-based GIS systems, distributed ML training, and big spatial data processing. Collaborations include NSF-funded research on colocation mining, trajectory analysis, and curriculum development for undergraduate HPC education.

Maria KolokotroniView profile

Professor

• Urban Heat Island Mitigation
• Energy Efficiency in Buildings
• Nearly Zero Energy Buildings (nZEB) Retrofit
• +4 more

Professor Maria Kolokotroni is a full academic staff member at Brunel University's Department of Mechanical and Aerospace Engineering, within the College of Engineering, Design and Physical Sciences. She holds a Professorial rank and has extensive experience in urban environmental engineering and building energy systems. Her academic career began at UCL (MSc 1992, PhD 1995), followed by postdoctoral research on environmental design guidance and moisture management. She joined Brunel University in 1998 and contributed to the Building Research Establishment's indoor environment division prior to her academic appointment. Education MSc in Environmental Design and Engineering, Bartlett School, UCL (1992) PhD in Thermal Performance of Housing, UCL (1995) Postdoctoral Research: Environmental Design Guidance (UCL, 1996-1998) EPSRC-funded Postdoc: Moisture in Residential Buildings (University of Westminster, 1998) Research Interests Her work focuses on urban heat island mitigation, energy-efficient building technologies, ventilative cooling systems, and urban microclimate analysis. She actively collaborates on EU-funded projects like the IEA’s Annex 80 (Resilient Cooling) and Annex 62 (Ventilative Cooling), emphasizing practical applications of sustainable energy strategies. Key themes include: Urban albedo computation and reflective materials Thermal performance of residential and non-residential buildings Integration of renewable energy systems (photovoltaics, hydrogen) Building energy demand modeling Grants & Projects Recent projects include: PRELUDE: Real-time building energy optimization (EU, 2020-2024) PVadapt: Smart building-integrated photovoltaic systems (EU, 2018-2022) ReCO2ST: Near-zero energy retrofit platform (EU, 2018-2021) Urban albedo studies in high-latitude cities (EPSRC, 2017-2020) Advising & Collaborations Professor Kolokotroni leads interdisciplinary teams and collaborates with experts in mechanical engineering, environmental science, and urban planning. Her work bridges academic research with industry applications through projects like COOL ROOFS EU initiative and the IEA’s energy efficiency annexes. Labs/Teams Active in the International Energy Agency’s EBC Annex 80 (Resilient Cooling) and part of Brunel’s Institute for the Environment (IEF). Her research group focuses on building sustainability and climate resilience through advanced simulation tools and experimental validation.

Damaris ZurellView profile

Professor

• Ecology
• Biodiversity Conservation
• Climate Change Biology
• +6 more

Prof. Damaris Zurell is a Full Professor of Ecology/Macroecology at the University of Potsdam, leading an interdisciplinary research group focused on biodiversity responses to global change. She holds a PhD in Geoecology (2011) and MSc in Geoecology (2007), both from the University of Potsdam. Her career includes roles as a junior research group leader (2018–2023), postdoctoral researcher (2011–2018), and awards such as the 2024 Henriette-Herz Scout and 2008 Junior Scientist Award. Research Interests: Bridging theoretical ecology, conservation science, and predictive modeling, Prof. Zurell explores how biodiversity adapts to climate change, focusing on terrestrial ecosystems (birds, mammals, plants). Her work emphasizes spatial eco-evolutionary dynamics, species distribution modeling (SDMs), and invasive species management. Collaborative initiatives include ISIMIP (Terrestrial Biodiversity Sector Lead) and GEOBON’s biodiversity modeling working group. Publications & Trends: Recent work highlights SDM methodological advances, climate-driven range shifts, and niche dynamics in birds and plants. She prioritizes open science, co-developing tools like RangeShifter 2.0 and the ENM2020 course. Key Projects: DEBTs (detecting biodiversity drivers), NichePac (alien plant spread in the Pacific), BirdWatch (Copernicus-based habitat monitoring). Grants: DFG, HORIZON, and Swiss National Science Foundation-funded projects totaling over €10M. Labs & Teams: Her lab at the University of Potsdam collaborates globally, hosting postdocs, PhD students, and international fellows. Current openings include Humboldt Postdoc/Sabbatical positions focused on global change ecology.

Larry A. FahnestockView profile

Professor

• Earthquake Engineering
• Structural Engineering
• Steel Structures
• +5 more

Larry A. Fahnestock is a Professor in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign and serves as Associate Dean for Facilities and Capital Planning in The Grainger College of Engineering . He holds a BS in Civil Engineering (1996), MS in Civil Engineering (1998), and PhD in Civil Engineering (2006) from Lehigh and Drexel Universities. His career at Illinois includes roles as Chair of the Structures Group (2018-2021), Director of the Newmark Structural Engineering Laboratory (2022-2025), and Siess Faculty Scholar (2022-present).

• Transportation Engineering
• Asphalt Materials
• Viscoelasticity
• +5 more

Ramez M. Hajj is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds affiliations with the Grainger College of Engineering and has served in multiple academic and professional roles, including editorial board memberships and leadership in organizations like the Transportation Research Board. His research focuses on asphalt materials and flexible pavements, spanning molecular-level investigations to large-scale infrastructure applications, with particular emphasis on viscoelasticity, composites, and machine learning. Education: Bachelor of Science in Civil Engineering with a minor in Engineering Science and Mechanics, Virginia Tech (2014) Master of Science in Civil Engineering, University of Texas at Austin (2016) Doctor of Philosophy in Civil Engineering, University of Texas at Austin (2019) Research Interests: Asphalt binder rheology and chemistry Computational modeling of infrastructure materials Pavement design, maintenance, and recycling Application of AI and machine learning in materials engineering Self-healing asphalt technologies Sustainable infrastructure solutions Publications: His work spans over 50 peer-reviewed articles, emphasizing innovations in asphalt material science and infrastructure resilience. Recent research highlights include AI-driven predictive models for asphalt properties and novel methods for evaluating pavement performance using ultrasonic techniques. Awards and Honors: Outstanding Reviewer awards from leading journals (2021–2022) Teaching excellence recognitions from the Center for Innovation in Teaching and Learning Illinois-Indiana Sea Grant Faculty Fellowship Grants and Funding: Research is supported by agencies such as IDOT, USDA, MnDOT, and industry partners. Projects include developing self-healing asphalt capsules and optimizing pavement design algorithms. Labs and Teams: Leads research initiatives in advanced material characterization and AI-driven infrastructure solutions within UIUC’s Civil and Environmental Engineering department.

Thomas KjeldsenView profile

Professor

• Extreme events
• Machine learning
• Hydrological modelling
• +2 more

Thomas Kjeldsen is a Professor in the Department of Architecture & Civil Engineering at the University of Bath. He leads multiple research projects funded by organizations such as The British Council, Royal Academy of Engineering, and The Leverhulme Trust. His research focuses on extreme events, machine learning, hydrological modeling, and nature-based solutions. He is affiliated with centers including the Water Innovation and Research Centre (WIRC), EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), and the Institute for Mathematical Innovation (IMI). Research Interests: Extreme events, machine learning applications in hydrology, flood risk management, and civil engineering resilience. Key Projects: Development of rainfall frequency models for infrastructure design, flood reconstruction using historical data, and capacity-building for South African flood studies. His work contributes to UN Sustainable Development Goals related to climate action and clean water. Recent studies emphasize non-stationary hydrological analysis, urban flood modeling using machine learning, and interdisciplinary approaches to water resource management. Activities include advising doctoral students, organizing academic events (e.g., British Hydrological Society Symposium), and serving on external committees like Affinity Water. His research has been widely cited and featured in policy discussions and international media.

• Hydraulic Engineering
• Coastal Morphology
• Flood Risk Management
• +3 more

Prof. Suzanne J.M.H. Hulscher is a Full Professor in Water Systems at the University of Twente, specializing in fluvial and coastal morphodynamics. Her research focuses on flood risk management, sediment dynamics, and climate adaptation. She has contributed to over 845 publications and supervised 55 research projects, including Vera van Bergeijk's PhD work on overtopping flows. Key achievements include the Simon Stevin Meester award (2016) and multiple best paper awards. Her work addresses UN Sustainable Development Goals through studies on saltwater intrusion, dike breach modeling, and nature-based solutions. Research highlights include hydraulic model calibration, estuarine sand wave dynamics, and vegetation effects on hydrodynamics. She actively contributes to editorial roles (CivilEng Journal) and policy advisory bodies (Wetenschappelijke Raad). Her interdisciplinary efforts bridge engineering, ecology, and climate science. Research areas: Coastal morphology, river dynamics, environmental hydraulics Key collaborations: 4TU.Centre for Research Data, IAHR, Netherlands Academy of Engineering Grants: Not explicitly listed, but extensive publications imply significant funding Her lab focuses on combining field data with numerical modeling for real-world applications like flood dashboard development and machine learning-based prediction systems. Current projects explore climate change impacts on engineered estuaries and distributive justice in climate policy.

Dilum DissanayakeView profile

Associate Professor

• Transport Planning
• Human Geography
• Climate Change Mitigation
• +7 more

Dr. Dilum Dissanayake is an Associate Professor in Human Geography and Transportation Planning at the University of Birmingham's School of Geography, Earth and Environmental Sciences. She holds a PhD from Nagoya University, Japan, and has expertise in transport planning, data analysis, and behavioral change to mitigate climate change. Her research integrates computing, data mining, and behavioral sciences with a focus on sustainable mobility, smart infrastructure, and innovative transport solutions. Education: PhD (Transport Planning), Nagoya University, Japan MEng (Infrastructure Planning and Management), Asian Institute of Technology, Thailand BSc (Hons) (Civil Engineering), University of Moratuwa, Sri Lanka Diploma in Programming Research Interests: Travel demand modeling, electric/autonomous vehicles, behavioral change for sustainability, transport policy analysis (e.g., congestion pricing, telecommuting), and developing country transport challenges. She applies methods like discrete choice models, machine learning, and NLP. Awards & Grants: Co-Investigator in CLEETS (UK-Japan £10m project), Principal Investigator in Capitalisation and eHUBS (€392k/€412k EU projects). Notable awards include the 2022 Remarkable Women in Transport Award and Duo-India Fellowship. Advising & Grants: Supervises 6 current PhD students and has guided 10 completed PhDs. Leads Transport Demand Management in CLEETS and Transport Modelling in Capitalisation. Teaches modules like Sustainable Cities and One-Planet Thinking. Labs/Teams: Part of Birmingham's Sustainable Cities research group, leading projects on micro-mobility, EV adoption, and post-pandemic transport trends. Engages in international collaborations via CLEETS and eHUBS.

• Econometrics
• Agricultural Economics
• Resource Economics
• +3 more

Zheng Li is an Assistant Professor in the Department of Agricultural and Resource Economics at North Carolina State University. His research focuses on econometric methodologies with applications in agricultural economics, resource management, and policy analysis. He holds expertise in nonparametric estimation, quantile regression, and structural econometric modeling. Key research interests include analyzing agricultural production risks, evaluating policy impacts on housing markets, and developing advanced statistical techniques for mixed data types. His work bridges econometric theory with practical applications in environmental, urban, and transportation sectors. Recent publications explore topics such as lung cancer detection via biomedical sensing technologies, ridesharing platform incentives, and pandemic effects on real estate markets. Methodologically, his contributions span kernel-based specification tests, bootstrap methods for heavy-tailed data, and monotonicity-constrained estimation techniques. No scientific awards or formal advisees are listed. His research often intersects with interdisciplinary challenges, reflecting a commitment to innovative solutions in applied economics and data science.

Masum BillahView profile

Senior Lecturer

• Data Analytics
• Telecommunications
• Artificial Intelligence
• +4 more

Dr. Masum Billah is a Senior Lecturer at Staffordshire University, affiliated with the Digital, Tech, Innovation & Business School. He joined the university in 2022 after teaching full-time at other universities since 2019. His academic journey includes a B.Sc. in Engineering from Bangladesh, an M.Sc. in Computer & Network Engineering from the University of Greenwich (UK), and a PhD in Electrical and Electronic Engineering from Staffordshire University. He also holds postgraduate certificates in Higher Education and Research Methods. As a course leader for the B.Sc. Embedded Electronic Systems Design and Development Engineer program, he teaches both undergraduate and postgraduate courses such as IoT Devices, Artificial Intelligence, and Communications. His research focuses on data analytics, telecommunications, IoT security, and healthcare research, with a particular interest in wireless sensor networks and machine learning applications. He actively promotes STEM education as a STEM Ambassador and contributes to initiatives like university competitions and school outreach. Masum’s professional memberships include Fellow of The Higher Education Academy (FHEA), IEEE, and roles in academic leadership. His work integrates flipped learning methodologies and spans engineering consultancy, short courses, and enterprise-focused projects. Recent recognitions include Whatuni Student Choice Awards 2023 for Facilities and Social Inclusion, reflecting his commitment to educational excellence.

Hankui ZhangView profile

Associate Professor

• Remote Sensing
• Environmental Monitoring
• Deep Learning
• +4 more

Hankui Zhang is an Associate Professor in the Department of Geography and Geospatial Sciences at South Dakota State University (SDSU), and a Research Scientist at the Geospatial Sciences Center of Excellence. He holds a Ph.D. from the Chinese University of Hong Kong (2013), specializing in satellite image fusion. His research focuses on developing algorithms for medium-resolution satellite data processing (e.g., Landsat and Sentinel-2), including cloud masking, BRDF correction, and compositing. He also explores AI applications in remote sensing for land cover mapping and environmental monitoring. As a Landsat Science Team member, he contributes to global remote sensing initiatives. Education: B.S. in Geographic Information Systems, Zhejiang University (2007) M.S. in Remote Sensing, Zhejiang University (2010) Ph.D. in Geography and Resource Management, Chinese University of Hong Kong (2013) Research Interests: Deep learning applications in remote sensing, land cover dynamics, analysis-ready data development, and geospatial data harmonization. His work emphasizes operationalizing satellite data for environmental decision-making. Grants & Awards: Over $2.5M in grants as PI/co-PI, including USDA and NASA-funded projects. Notable awards include the SDSU Wadsworth Research Award (2020-2021) and the Global Scholarship for Research Excellence from CUHK (2011-2012). Professional Roles: Editorial board member for Remote Sensing of Environment and Remote Sensing ; guest editor for special issues on deep learning in remote sensing. Top 20 reviewer for Remote Sensing of Environment (2020, 2022-2024). Key Contributions: Published over 70 SCI papers, developed cloud detection algorithms (e.g., LANA), and pioneered analysis-ready data workflows for global monitoring. His work bridges satellite data science with practical environmental applications.

Vivienne SzeView profile

Professor

• Energy-Efficient Machine Learning
• Computer Vision
• Video Compression/Processing
• +7 more

Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices. Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies. Research Interests: Energy-Efficient Machine Learning Accelerators Video Coding and Compression Standards Embedded Systems and Mobile Computing Processing-in-Memory (PIM) Architectures AI for Health Monitoring and Digital Health Sustainability in AI Infrastructure Publications highlight trends in: Optimizing DNNs for edge devices Innovations in entropy coding and CABAC Memory-efficient Gaussian-based algorithms Energy-aware design for photonic computing Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.

• Biostatistics
• Public Health
• Epidemiology
• +9 more

Dr. Shuvo Bakar is a Senior Lecturer in the Sydney School of Public Health at the University of Sydney, within the Faculty of Medicine and Health. He holds a PhD in Statistics from the University of Southampton, UK, and has prior experience as an Assistant Professor at Yale University, Lecturer at the Australian National University, and Scientist at Data61 (CSIRO). His research focuses on statistical methods applied to public health challenges, including Bayesian hierarchical modeling, machine learning, spatio-temporal analysis, and their applications in epidemiology, clinical trials, and environmental health. Dr. Bakar's research interests span statistical methodologies such as Bayesian adaptive designs, small area estimation, and spatial risk modeling, alongside applications in child health, infectious diseases, and extreme weather impacts on health. He is an active member of academic communities, including the Royal Statistical Society (RSS Fellow), Statistical Society of Australia, and the Australian Trials Methodology Research Network. His work also involves collaborations on grants totaling millions in funding, addressing topics like climate change impacts on health inequity and cardiovascular disease prevention in remote regions. Education: PhD in Statistics (University of Southampton, UK) Key Research Themes: Obesity, Diabetes, Cardiovascular Disease; Reproductive, Maternal & Child Health Grants/Projects: Includes NHMRC-funded trials on respiratory infections in First Nations children and MRFF grants for cardiovascular risk reduction in regional Australia. Dr. Bakar's contributions extend to editorial roles for Nature Scientific Reports and Discover Public Health , and his research has been published in journals like PloS One , Climatic Change , and Journal of the Royal Statistical Society .