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Jill CrossmanView profile

Researcher

• Environmental Science
• Ecotoxicology
• Water Quality Management
• +4 more

Jill Crossman is a researcher at the University of Windsor's Great Lakes Institute for Environmental Research (GLIER), focusing on microplastics pollution, water quality dynamics, and catchment management. Her work integrates hydrology, ecotoxicology, and environmental engineering to address global challenges such as microplastic contamination and climate change impacts on aquatic ecosystems. She collaborates on multidisciplinary projects like the Lake Erie HABs Grab and the IMPASSE initiative, which investigate microplastic transport in agrosystems and harmful algal blooms. Key research areas include: Development of novel methods for microplastic analysis in environmental samples Assessment of microplastic effects on plant-soil interactions and agricultural systems Modelling phosphorus cycles and dissolved oxygen dynamics in lakes and rivers Evaluation of long-term trends in water quality under climate and land-use changes Her projects often involve partnerships with international institutions, such as Yunnan University in China, and government agencies. Crossman has contributed to binational initiatives to characterize cyanobacterial blooms in Lake Erie and improve water management strategies for lakes like Simcoe. Her work emphasizes bridging scientific research with practical policy solutions for sustainable freshwater resources. Notable contributions include advancements in integrated water quality models (e.g., INCA-PEco) and evaluations of biofouling effects on environmental monitoring instruments. Her research highlights the interconnectedness of terrestrial, riverine, and limnological systems, providing critical insights for addressing anthropogenic and climatic pressures on freshwater ecosystems.

Tim VadasView profile

Associate Professor

• Stormwater treatment and management
• Metal biogeochemistry
• Microbial controls on metal fate and transport
• +6 more

Tim Vadas is an Associate Professor at the University of Connecticut's Department of Civil and Environmental Engineering, within the College of Engineering. He holds a Ph.D. from Cornell University (2008) and leads the Vadas Lab, focusing on contaminant transformations at the intersection of natural and built environments. His research emphasizes biological interactions, metal biogeochemistry, and stormwater management. Key research areas include microbial controls on metal fate, organic carbon-metal interactions, and the design of ecological treatment systems. Current graduate advisees include Julia Czarnecki, Nafis Fuad, and Randi Mendes. He is affiliated with the Connecticut Transportation Institute and Connecticut Manufacturing Simulation Center, and maintains a research website at http://vadas.engr.uconn.edu . Recent work spans topics like copper toxicity in aquatic systems, disinfection byproduct formation in recycled water, and real-time soil monitoring technologies. His lab employs field-scale studies in streams, wetlands, and stormwater systems, alongside controlled microcosm experiments. No specific scientific awards are listed, though his contributions are evident through active grant-funded research. Collaborations include interdisciplinary projects on urbanization impacts and sustainable water management.

Geoffrey MainlandView profile

Associate Professor

• Programming Languages
• Systems
• High-level Programming Languages
• +12 more

Geoffrey Mainland is an Associate Professor in the Department of Computer Science at the College of Computing and Informatics, Drexel University. His research bridges programming languages and systems, with a focus on enabling high-level, efficient programming of specialized hardware such as GPUs, FPGAs, and software-defined radios. He has developed domain-specific languages and runtime systems to simplify and optimize non-general-purpose computation. PhD in Computer Science, Harvard University, 2011 AB in Physics, Harvard University, Magna Cum Laude, 2000 His research interests include high-level programming languages, runtime support for specialized devices, functional programming (particularly in Haskell), stream fusion, SIMD vectorization, and domain-specific languages for systems programming. He investigates how to make it easier to exploit the performance of GPUs, sensor networks, and FPGAs without sacrificing abstraction or safety. The recent publications highlight a consistent theme: enhancing performance in functional languages through language design, compiler optimizations, and runtime systems. Key areas include SIMD support in Haskell, GPU programming via Nikola and MetaHaskell, staged programming for sensor networks (Flask), and domain-specific languages like Ziria for software-defined radio. These works demonstrate a strong focus on bridging the gap between high-level abstractions and low-level efficiency. Geoffrey Mainland has not been mentioned as receiving any scientific awards in the provided material. He advises current PhD student Dresden Feitzinger and has previously advised Mahshid Shahmohammadian, now at Intel. His research has been supported through academic and possibly federal funding, though specific grants are not detailed. His work is implemented in open-source projects hosted on GitHub, including contributions to the Haskell vector library and the development of Ziria and DragonRadio. He leads a research group focused on programming systems, with active projects in software-defined radio (Ziria, DragonRadio), high-performance numerics in Haskell (HSpiral), and language support for heterogeneous computing. His team develops tools that enable researchers and developers to write efficient, correct code for specialized hardware using high-level functional abstractions.

John JacksonView profile

Adjunct Professor

• Ecology
• Freshwater Ecology
• Entomology
• +4 more

John Jackson, Ph.D., is an Adjunct Professor of Biology at the University of Pennsylvania and an Adjunct Professor of Entomology and Wildlife Ecology at the University of Delaware. He serves as Senior Research Scientist and Principal Investigator of the Entomology Group at the Stroud Water Research Center. His research focuses on stream ecosystems, including population and evolutionary ecology of aquatic insects, biogeochemical processes, and water quality monitoring. He holds a Ph.D. in Entomology from UC Berkeley and has held roles such as Fulbright Senior Scholar and Postdoctoral Research Associate. His work bridges basic and applied science, addressing ecological management and public health implications of freshwater systems. Education: Ph.D., Entomology, University of California, Berkeley M.S., Zoology, Arizona State University B.S., Biology (Honors), University of Notre Dame Research Interests: Dr. Jackson explores stream insect ecology , ecosystem dynamics , and water quality assessment . His studies address genetic structure of stream organisms, disturbance impacts, and organic matter dynamics, with projects in temperate and tropical streams. He emphasizes practical applications, such as informing management strategies for clean water policies and mitigating pollution risks. Professional Contributions: As Principal Investigator and scientist at Stroud, he leads initiatives in freshwater research and restoration. His collaborations with institutions like the University of Delaware and community scientists advance sensor-based ecological monitoring. His work highlights the need to prioritize upstream ecosystems for Clean Water Act compliance and sustainable watershed management. Labs/Teams: He directs the Entomology Group at Stroud, focusing on freshwater invertebrate ecology and their role in ecosystem health.

Søren Kejser JensenView profile

Assistant Professor

• Time Series Management
• Lossy Compression
• Renewable Energy Analytics
• +5 more

Søren Kejser Jensen is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University. He is affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on time series management systems, lossy compression techniques, and their applications in renewable energy analytics, IoT data management, and edge computing. He has contributed to the development of ModelarDB, a scalable model-based time series management system. Key research areas include: Optimizing lossy compression for sensor data and wind turbine analytics Designing scalable database solutions for high-frequency data streams Integrating machine learning with compressed IoT data Exploring freshness metrics in communication systems His publications highlight advancements in edge-cloud architectures, model-based data management frameworks, and holistic analytics for renewable energy systems. Collaborations extend to global projects like 6G wireless systems and AI-driven multi-network deployments.

• Smart Manufacturing
• Data Science
• Advanced Manufacturing Technologies
• +6 more

Ashif Iquebal is an Assistant Professor at Arizona State University's School of Computing, Informatics, and Decision Systems Engineering (CIDSE). His research focuses on smart manufacturing, data science, and advanced manufacturing technologies, particularly addressing challenges in materials-on-demand manufacturing. He holds a PhD in Industrial and Systems Engineering from Texas A&M University, along with an M.S. in Statistics and a B.Tech from IIT Kharagpur. His work emphasizes integration of IIoT, embedded sensors, and data science methods like streaming analytics and unsupervised learning. Key research interests include change point detection for streaming data, unsupervised image segmentation, and leveraging machine learning for manufacturing quality control. His contributions have been recognized through awards such as the IISE Pritsker Dissertation Award (3rd place, 2021) and multiple best student paper/poster awards at INFORMS and IISE conferences. He collaborates with institutions like Mayo Clinic on projects involving breast cancer diagnosis and segmentation using AI. Teaching responsibilities include courses on Advanced Quality Control and ASU Experience programs. His research has led to publications in top journals like IEEE Transactions on Signal Processing and Pattern Analysis and Machine Intelligence, focusing on topics spanning material characterization, process optimization, and AI-driven manufacturing solutions.

Sujay S. KaushalView profile

Professor

• Freshwater Salinization Syndrome
• Urban Watershed Continuum
• Biogeochemistry of Aquatic Ecosystems
• +3 more

Dr. Sujay S. Kaushal is a Professor in the Department of Geology at the University of Maryland (UMD). He holds a Ph.D. from the University of Colorado (2003). His research focuses on the biogeochemistry of watersheds and aquatic ecosystems, particularly addressing how human activities influence freshwater chemistry and restoration strategies. Key areas include Freshwater Salinization Syndrome, urban watershed dynamics, and climate-driven changes in water quality. His work integrates long-term monitoring, field experiments, and advanced analytical techniques such as stable isotope analysis. Current projects investigate salt pollution's global impacts, human-accelerated weathering, and urban evolution concepts. Supported by the NSF, NASA, and EPA, his research bridges environmental science with policy, emphasizing stakeholder-driven solutions. He leads the Biogeochemistry Laboratory and collaborates internationally to address water quality challenges in coastal and urban environments. Education: Ph.D. in Geology, University of Colorado, 2003 Labs/Teams: Biogeochemistry Laboratory Grants: NSF, NASA, EPA, Maryland Sea Grant Future Work: Expanding studies on salinization, freshwater warming, and restoration efficacy using novel sensor technologies Dr. Kaushal's contributions have shaped understanding of 'chemical cocktails' in urban streams and the socio-ecological systems underpinning water management. His findings on salt pollution's unregulated spread and infrastructure impacts highlight urgent global challenges requiring interdisciplinary solutions.

• Watershed Hydrology
• Surface Water Quality
• Hydroinformatics
• +5 more

Jeff Horsburgh is a Professor in the Civil and Environmental Engineering department at Utah State University , affiliated with the Utah Water Research Laboratory . He specializes in hydroinformatics , focusing on environmental sensor networks, data management systems, and watershed processes. His research integrates Geographic Information Systems (GIS) and cyberinfrastructure to advance hydrologic data analysis and modeling. He has led developments in platforms like HydroShare and ODM2 for collaborative water data sharing and semantic interoperability. His work emphasizes high-frequency water quality monitoring , open-source tools , and reproducible research practices. Recent publications highlight trends in environmental data science , sensor network integration , and hydrologic modeling frameworks. Notably, he explores web services , machine learning , and edge computing applications for water systems. Scientific awards include: 2024 Outstanding Researcher, USU 2023 Reproducibility Author Award 2019 Outstanding Teacher, USU 2014 Early Career Excellence Award 2008 AWRA Presentation Award As a mentor, he has guided numerous graduate students, including Riya Kadel , Sabin Panta , and Camilo Bastidas in topics spanning smart metering , stormwater analysis , and hydrologic cyberinfrastructure . Current projects involve collaborations with institutions like University of Utah and University of South Carolina under NOAA/USGS CIROH initiatives.

• Agricultural Water Management
• Soil Hydrology
• Irrigation Systems
• +3 more

Reinhard Nolz is an Associate Professor at the University of Natural Resources and Life Sciences, Vienna (BOKU) , affiliated with the Institute of Soil Physics and Rural Water Management . His work focuses on optimizing irrigation strategies, soil water monitoring, and hydrological modeling to ensure sustainable water use in agriculture. Research Highlights Developed climate-resilient irrigation frameworks Advanced lysimeter-based water balance studies Integrated sensor networks for real-time soil moisture Validated evapotranspiration models Conducted international agricultural hydrology collaborations Publications 20+ peer-reviewed articles (2009-2024) on irrigation technology Key contributions to Acta Hydrologica Slovaca and Die Bodenkultur Service Editorial Board Member: Acta Hydrologica Slovaca (2017-2020) Peer reviewer for Geoderma , Journal of Hydrology

• Fluid Dynamics
• Energy Systems
• Winter Road Maintenance
• +5 more

Jeanette Cleotilde Polanco Pinerez is an Associate Professor at the Department of Industrial Technology, UiT The Arctic University of Norway, based in Narvik. She specializes in fluid dynamics, energy systems, and industrial applications of machine learning, with significant contributions to winter road maintenance technologies and Arctic infrastructure solutions. Her research focuses on: Advanced fluid mechanics and multiphase systems in industrial contexts Machine learning applications for Arctic transportation challenges Energy efficiency and renewable energy systems analysis Sensor technologies for industrial safety and environmental monitoring Thermodynamic modeling and heat transfer optimization Publication analysis reveals strong emphasis on predictive maintenance systems, alternative energy solutions, and Arctic-specific engineering challenges, with recent work increasingly incorporating AI/ML methodologies. She is an active member of the Materials Science research group and contributes to developing practical engineering tools for extreme climate conditions.

• Computer Science
• Cybersecurity
• Artificial Intelligence
• +3 more

Nurhizam Safie is an academic at Universiti Kebangsaan Malaysia's Faculty of Information Science and Technology, focusing on cybersecurity, AI, and IoT research. His work addresses critical challenges in smart cities, healthcare systems, and public sector digitalization. Key Collaborations: Mohammad Kamrul Hasan (13 papers), Shayla Islam (8 papers), Taher M. Ghazal (4 papers) Research Themes: IoT security protocols, AI maturity models, cloud health systems, and 5G/6G network architecture His publications show strong focus on interdisciplinary applications of technology in healthcare and urban infrastructure. Notable work includes: 2025: 5G/6G network architecture for smart cities 2024: Hybrid encryption techniques against side-channel attacks 2023: Solar activity prediction using ML in urban environments While no formal awards are listed, his systematic literature reviews on AI maturity and BIM-IoT integration demonstrate methodological expertise. Current work emphasizes ethical technology implementation in both commercial and public sectors.

Ahmet SayarView profile

Associate Professor

• Distributed Systems
• Big Data
• Data-Intensive Computing
• +7 more

Assoc. Prof. Dr. Ahmet Sayar is an Associate Professor at the Department of Computer Engineering, Faculty of Engineering at Kocaeli University, Turkey. He holds an M.Sc. (2001) and Ph.D. (2009) in Computer Science from Syracuse and Indiana Universities, USA. His research focuses on Distributed Systems, Big Data, Data-Intensive Computing, Geographic Information Systems (GIS), and Exploratory Data Analysis (EDA). He has co-authored over 150 papers, 15 book chapters, and 1 book. Currently, he serves as Head of Student Affairs at Kocaeli University and as an associate editor for multiple journals. Education: M.Sc. in Computer Science, Syracuse University (2001) Ph.D. in Computer Science, Indiana University (2009) Research Interests: Parallel and Distributed Algorithms Big Data Analytics & Cloud Computing Machine Learning Applications (e.g., Image Processing, NLP) GIS and Remote Sensing Blockchain Technology for IoT and Logistics Recent Trends in Articles: His recent work emphasizes real-time data processing (e.g., video stream analytics), blockchain for supply chains, and AI-driven solutions for healthcare, transportation, and urban management. He explores ethical aspects of web scraping and low-code platforms for process automation. Advising & Grants: Advised 18 master’s/phd theses. Involved in TÜBİTAK projects and KOSGEB innovation programs. Member of editorial boards and national advisory committees. Labs/Teams: Leads university-level student affairs initiatives and collaborates on interdisciplinary projects involving distributed systems and big data frameworks.

Samuel DraycottView profile

Senior Lecturer

• Ocean Engineering
• Wave Energy
• Coastal Hydrodynamics
• +3 more

Samuel Draycott is a Senior Lecturer in Civil Engineering and Management at the University of Edinburgh. His research focuses on ocean engineering, wave energy systems, hydrodynamics, and numerical modeling. He leads projects on wave-energy converter (WEC) mooring systems, tidal turbine dynamics, and extreme wave phenomena. Draycott has contributed to the Supergen ORE Impact Hub and TIGER projects, exploring renewable energy integration and tidal stream technologies. His work aligns with UN SDG goals related to clean energy and climate action. Research interests include mooring system design for WECs, fluid-structure interactions in tidal environments, and the impact of bathymetry on wave behavior. He has conducted experimental studies on wave breaking mechanisms, mooring forces, and turbine performance under varying conditions. Draycott has published extensively in journals like Physics of Fluids and Coastal Engineering, with over 60 peer-reviewed articles and 5 datasets. His recent work explores data-driven approaches for condition monitoring of mooring systems and machine learning applications in turbulence analysis. Collaborations span institutions like the University of Manchester and involve experimental facilities such as FloWave.

Philip G ZhaoView profile

Senior Lecturer

• Artificial Intelligence
• Robotics
• Cyber-Physical Systems
• +5 more

Dr. Philip G Zhao is a Senior Lecturer in Robotics and AI at the University of Manchester's Department of Computer Science. Prior to this role (since 2024), he held positions at the University of Glasgow from 2018 to 2023. He leads the Machine Intelligence Alliance across multiple universities and is a Senior Member of IEEE and IET member. His research focuses on AI-driven cross-system design and optimization for robotics, Cyber-Physical Systems (CPS), IoT, communication networks, and computer vision. His work emphasizes practical applications such as edge intelligence, teleoperation systems, and medical robotics. Dr. Zhao has secured £3M in total research funding, including £800k as Principal Investigator, and holds two U.S. patents. Notable achievements include three Best Paper Awards and over 2,800 Google Scholar citations. His recent projects involve edge-enabled industrial CPS co-design, metaverse teleoperation frameworks, and dynamic human-robot interaction studies. He actively supervises PhD students and promotes collaborative robotics in healthcare and education. Labs/Teams: Machine Intelligence Alliance (MIA), leading interdisciplinary research across multiple universities.

Joseph LiftonView profile

Lecturer

• Advanced Manufacturing
• Materials Science
• Industrial X-ray Computed Tomography
• +3 more

Dr. Joseph Lifton is a Lecturer in Mechatronics at the University of Southampton. He holds a PhD from the same institution (2015) and has extensive experience in academic and industry roles. From 2015–2018, he lectured Engineering Design at the University of Southampton Malaysia Campus. Later, from 2018–2024, he worked at Singapore’s Agency for Science, Technology and Research (A*STAR), focusing on advanced metrology and industrial collaboration. His research emphasizes inspection of complex engineering components, intelligent tomographic techniques, 3D image processing, and high-energy X-ray CT systems for large/dense objects. He currently supervises PhD students and contributes to cutting-edge manufacturing research. Education: PhD in Engineering, University of Southampton (2015) Research Focus: Dr. Lifton’s work bridges academic innovation and industrial needs, particularly in advanced manufacturing and metrology. Key areas include: Development of X-ray CT systems for large/dense objects Algorithm optimization for tomographic imaging and defect detection Applications of deep learning in additive manufacturing quality control Professional Contributions: His ORCID -linked publications (e.g., in Journal of X-Ray Science and Technology , Surface Topography ) reflect his expertise in imaging and materials science. He collaborates with industries to solve real-world measurement challenges.