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Dan SheldonView profile

Professor

• Computational Biology
• Bioinformatics
• Machine Learning
• +5 more

Dan Sheldon is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, holding a Five College joint faculty position with Mount Holyoke College. His research focuses on developing algorithms to address environmental challenges using large datasets, emphasizing computational sustainability. Key areas include spatial optimization for endangered species conservation, continent-scale bird migration modeling, and interpreting weather radar data for ecological insights. Methodologically, his work leverages probabilistic inference, network modeling, and machine learning. Sheldon earned a PhD in Computer Science from Cornell University and an AB in Mathematics from Dartmouth College. His postdoctoral training at Oregon State University was supported by an NSF Bioinformatics Fellowship. He co-leads the BirdCast project, an NSF-funded initiative applying novel machine learning to avian migration studies. His research affiliations include the Center for Data Science and the Computational Social Science Institute. Research interests span computational biology, machine learning, and data privacy. Notable contributions include algorithms for ecological decision-making, differentially private synthetic data techniques, and Gaussian process applications in environmental forecasting. Awards include an NSF Fellowship in Bioinformatics. Current projects integrate radar data analysis, biodiversity tracking, and privacy-preserving statistical methods. Grants include the BirdCast NSF grant and collaborations in computational sustainability. His work bridges theoretical computer science with applied ecological challenges, emphasizing interdisciplinary approaches to global-scale environmental problems.

Hui ZhangView profile

Professor

• Computer Science
• Networking
• Data-Driven Systems
• +3 more

Hui Zhang is a Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on data-driven networking systems, video streaming optimization, and network control frameworks. He has contributed to innovations in adaptive resource allocation, real-time analytics, and sustainable strategies for resource utilization. Key research themes include time-state analytics, network anomaly detection, and integrating machine learning for enhanced performance. His work addresses challenges in content delivery networks (CDNs), peer-to-peer systems, and environmental applications like waste management. Recent publications (2021–2024) highlight advancements in neural network-based prediction, timeline frameworks, and sustainable material science innovations. No scientific awards are mentioned in the provided text. His research emphasizes practical solutions for improving video quality of experience (QoE), network efficiency, and cross-disciplinary applications.

• Solid Mechanics
• Micromechanics
• Dislocation Plasticity
• +3 more

Professor Dan Balint is the Head of the Mechanics of Materials Division in the Department of Mechanical Engineering at Imperial College London. He holds a Ph.D. in Engineering Sciences from Harvard University (2003), an S.M. in Applied Mathematics from Harvard (2001), and a B.S. in Engineering Mechanics from Michigan State University (1998). Prior to joining Imperial in 2006, he was a Research Associate at the Cambridge Centre for Micromechanics. His research spans theoretical and computational solid mechanics, with focus areas including: Micromechanics of crystalline materials (metals/ceramics) Dislocation-defect interactions and failure mechanisms Discrete dislocation plasticity methods Nuclear cladding materials and zirconium hydrides Thin film failure and metal forming processes Fracture mechanics and material size effects Recent publications (2022-2025) predominantly explore dislocation dynamics, zirconium alloy behavior under nuclear conditions, computational modeling of microstructural stresses, and machine learning applications in materials science. Common themes include thermomechanical degradation, crack initiation mechanisms, and multi-scale modeling approaches. Professor Balint serves as Associate Editor of the European Journal of Mechanics - A/Solids and consults for industrial partners including Rolls Royce, BP, and the US Air Force.

• Catalysis Engineering
• Green Chemistry
• Biofuels
• +3 more

Professor Jun Huang is a faculty member in the School of Chemical and Biomolecular Engineering at the University of Sydney, where he holds the rank of Professor and is Director of the Laboratory for Catalysis Engineering. He is also a Domain Leader for Materials at the nanoscale at Sydney Nano Institute and a member of several interdisciplinary institutes, including the China Studies Centre and Sydney Institute of Agriculture. His research focuses on catalysis engineering, with an emphasis on developing sustainable processes for renewable fuels, pollutant treatment, and greenhouse gas mitigation. Huang has held prestigious awards such as the Australia Research Council Future Fellowship (2022) and the Sydney Accelerator Fellowship (2018). Education: Huang earned his PhD from the University of Stuttgart (2008) and completed postdoctoral research at Georgia Institute of Technology and ETH Zurich. He joined the University of Sydney in 2010 as a Lecturer, advancing to Senior Lecturer, Associate Professor, and Professor. Research Interests: Huang's work centers on catalyst design for green chemical processes, including biomass conversion to biofuels, wastewater treatment, and CO2 utilization. He emphasizes sustainable manufacturing and environmental impact reduction through innovative catalytic systems. Current Projects: These include catalytic transformation of hydrocarbons/CO2/biomass, nano-catalysts for renewable energy, and advanced NMR spectroscopy for catalysis analysis. Collaborative projects involve anti-cancer therapies and drug pharmacology studies. Awards: Over 15 awards, including the 2021 ACS Sustainable Chemistry & Engineering Lectureship and 2017 Vice-Chancellor’s Research Excellence Award. Teaching: Huang instructs courses such as CHNG2801 (Conservation Processes), CHNG3802 (Industrial Systems), and advanced chemical engineering topics. He supervises PhD/Master students in catalysis and sustainable engineering. Labs/Teams: Leads the Catalysis Engineering Lab and collaborates with Sydney Nano Institute on nanomaterials research.

Xi YuView profile

Lecturer

• Low Carbon Fuels
• Granulation and Tableting
• Computational Fluid Dynamics
• +3 more

Dr. Xi Yu is a Lecturer in Chemical Engineering at the University of Southampton, affiliated with the Faculty of Engineering and the Environment. He holds a Bachelor's from Tianjin University and a Ph.D. from the University of Sheffield. His research focuses on low carbon fuels, granulation techniques, and computational fluid dynamics. He has supervised PhD students such as Jerin Jacob and is currently accepting new PhD applicants in these areas. Dr. Yu's educational background includes degrees in Chemical Engineering and prior academic roles at Aston University and the Energy and Bioproducts Research Institute (EBRI). His work spans bioenergy systems, particle technology, and multi-physics modeling. Key research projects include advancements in biomass gasification, biofuel production, and sustainable energy systems. His publications emphasize computational modeling, fluid dynamics, and biomass utilization. Recent articles explore topics like absorption chiller systems, fluidization validation, and bio-oil aging strategies. He contributes to teaching modules such as CHEG3000 and CHEG3004, reflecting his commitment to both research and education.

Trym Vegard HaavardsholmView profile

Lecturer

• Computer Vision
• Machine Learning
• Robotics
• +3 more

Trym Vegard Haavardsholm is a 20% part-time Lecturer at the University of Oslo (UiO) within the Section for Autonomous Systems and Sensor Technologies. He also serves as Principal Scientist at the Norwegian Defence Research Establishment (FFI) and is a PhD candidate at the Department of Engineering Cybernetics, NTNU. His research focuses on computer vision, machine learning, robotics, and image analysis, with a particular emphasis on multispectral imaging systems and unmanned aerial vehicles (UAVs). Haavardsholm has contributed to advancements in sensor technologies for tactical reconnaissance, autonomous navigation, and real-time data processing. His work spans applications in defense, environmental monitoring, and emergency response systems. Research Interests Haavardsholm’s research integrates interdisciplinary approaches to develop innovative sensor systems and algorithms. Key areas include compact multispectral imaging for small UAVs, in-operation camera calibration, and anomaly detection in hyperspectral data. His contributions emphasize practical applications such as urban feature classification, collaborative indoor navigation, and bioaerosol detection. His work bridges theoretical computer science with applied engineering, addressing challenges in autonomous systems and sensor fusion. Publications His publications highlight trends in multispectral sensor design, UAV imaging, and real-time georeferencing. Recent work includes compact sensor systems for tactical use and advancements in pushbroom image rectification. Earlier research explored band selection algorithms for target detection and GPU-accelerated anomaly detection. Professional Roles As a lecturer, Haavardsholm contributes to academic supervision and teaching in autonomous systems. His dual role at FFI and UiO reflects his commitment to translating academic research into practical defense and civilian applications. His PhD candidacy at NTNU underscores his ongoing academic engagement in engineering cybernetics.

Yanhua LiView profile

Associate Professor

• Artificial Intelligence
• Spatial-Temporal Data Science
• Smart Cities
• +5 more

Yanhua Li is an Associate Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI), where he has served since 2021 (previously as Assistant Professor from 2015-2021). He is also an affiliated researcher at UMass Transportation Center (UMTC). His educational background includes dual PhDs: Computer Science from University of Minnesota, Twin Cities (2013) and Electrical Engineering from Beijing University of Posts and Telecommunications (2009), along with an MS (2006) and BS (2003) in Electrical Engineering from Sichuan University. Dr. Li's research focuses on artificial intelligence and spatial-temporal data science with applications in smart cities and urban intelligence. His work particularly emphasizes imitation learning and meta learning in AI for understanding and influencing urban human agents' decision-making strategies, such as taxi drivers' passenger-seeking behaviors and urban travelers' transit choices. His laboratory develops advanced computational methods for urban transportation systems, traffic prediction, and spatial-temporal data analytics. His publication record shows a strong trajectory in top AI and data science venues, with recent work bridging foundation models with urban computing, enhancing robustness in spatial-temporal representation learning, and applying generative models to urban traffic estimation. His research spans computer vision, reinforcement learning, generative modeling, and spatio-temporal data analysis with applications in transportation, environmental monitoring, and urban planning. Best Applied Data Science Paper Award at SDM 2019 NSF CAREER Award (2020) Runner-up for the 10-Year Impact Award for SIGSPATIAL Conference (2024, for 2014 paper) Dr. Li has secured significant research funding including an NSF CAREER award ($529k), multiple NSF grants totaling over $2 million, and industry collaborations with DiDi Chuxing Research. He has advised numerous PhD students who have gone on to faculty positions at institutions like San Diego State University and SUNY Binghamton University. His research group maintains active collaborations with industry partners including DiDi Chuxing, Pitney Bowes Inc., and NVIDIA. He leads several research initiatives including the CityLines project for urban transportation systems and has contributed to foundational work in spatial-temporal imitation learning. His laboratory continues to expand into new areas including applying large language models to urban dynamics prediction and developing advanced methods for environmental monitoring.

Stefan WrobelView profile

Professor

• Machine Learning
• Data Mining
• Artificial Intelligence
• +3 more

Prof. Stefan Wrobel is a Professor of Computer Science at the University of Bonn and Director of the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). He holds leadership roles, including Co-Director of the Lamarr Institute for Machine Learning and Artificial Intelligence and Managing Director of the Bonn-Aachen International Center for Information Technology (b-it). His research focuses on AI, machine learning, and big data applications in industry and society. He earned his PhD from the University of Dortmund and has held academic positions at Magdeburg University and Berlin Technical University. Active in national/international AI initiatives, he chairs the Fraunhofer Strategic Research Field on Artificial Intelligence and co-leads the Machine Learning Rhine-Ruhr (ML2R) Competence Center. Education: Master's (Georgia Tech), PhD (University of Dortmund). Research emphasizes intelligent algorithms, data analysis, and AI ethics. Awarded GI-Fellow (2022) and honored by the German Computer Science Society for contributions to AI history. Key roles include Editorial Board member of Machine Learning journals and advisory roles in AI ethics and certification. Scientific contributions span over 100 publications in machine learning, data mining, and visual analytics. Advised numerous PhD students on topics like graph mining and trustworthy AI. Leadership in institutions like Fraunhofer Technology Hub for Machine Learning and the German Computer Science Society's Special Interest Group on Knowledge Discovery.

• Building Information Modeling (BIM)
• Civil Engineering Informatics
• Tunneling and Underground Construction
• +5 more

Markus König is a Professor of Informatics in Civil Engineering at Ruhr University Bochum, where he has been researching and teaching since October 2009. His work focuses on Building Information Modeling (BIM), digital construction technologies, and civil engineering informatics, with significant contributions to the development and implementation of digital methods in German construction industry. Dr. König earned his degree in civil engineering with a focus on applied computer science at Leibniz University Hannover, where he also completed his doctorate on cooperative building planning at the Institute for Building Informatics. He subsequently held a junior professorship for Theoretical Methods of Project Management at Bauhaus University Weimar before joining Ruhr University Bochum. His research spans multiple cutting-edge areas including Building Information Modeling (BIM), construction process simulation, tunneling informatics, infrastructure asset management, and the application of artificial intelligence and computer vision in civil engineering. As chair of the Building Informatics Working Group from 2012-2016, he played a key role in developing the first national BIM curriculum for German universities and serves as editor of the book 'Building Information Modeling: Technological Foundations and Industrial Practice.' Analysis of his recent publications reveals a strong trend toward semantic technologies, digital twins, automated compliance checking, and the integration of AI in construction processes. His work increasingly focuses on information containers, ontology development, and the application of large language models to infrastructure data, reflecting the evolving landscape of digital construction. Dr. König's significant contributions to digital construction have been recognized with prestigious awards: Lower Saxony-Bremen Construction Industry Award (2017) for 'services in the development and introduction of digital construction in Germany' Konrad Zuse Medal (2020) While specific details about his advising and grant activities aren't explicitly mentioned in the provided text, his extensive publication record with numerous co-authors suggests active supervision of doctoral students and research staff. His involvement in multiple collaborative research projects is evident from his publication history. At Ruhr University Bochum, Professor König leads a research group focused on civil engineering informatics, with particular emphasis on BIM, digital construction technologies, and their application across the building lifecycle. His team appears to work at the intersection of computer science and civil engineering, developing innovative solutions for construction process optimization, infrastructure management, and digital transformation of the AEC industry.

Rachit DubeyView profile

Assistant Professor

• Climate Change
• Computational Modeling
• Machine Learning
• +3 more

Rachit Dubey is an Assistant Professor in the Department of Communication at UCLA, affiliated with the College of Letters & Science. He holds a PhD in Computer Science from Princeton University, an M.S. in Education from UC Berkeley, and a BEng in Computer Science from Nanyang Technological University. His research bridges computational modeling, behavioral experiments, and policy design to address climate change challenges, focusing on human motivation and policy impact. Key research areas include using AI-generated visualizations to enhance climate communication, understanding cognitive underpinnings of climate inaction, and designing interventions to reduce polarization. His work has been featured in Nature Human Behavior , Nature Sustainability , and PNAS . Notable contributions include demonstrating that binary climate data visuals significantly amplify perceived urgency compared to gradual trends, and developing computational models of grief using reinforcement learning. He received the 2024 SfNC Dissertation Award for his doctoral work. Dr. Dubey collaborates closely with policymakers to bridge research and practice, and is recruiting PhD students in cognitive science, climate policy, and machine learning. His lab, the Computational Cognitive Policy Lab, emphasizes quantitative rigor and interdisciplinary collaboration.

• Biosensors
• Microfluidics
• Microfabrication
• +8 more

Renny Edwin Fernandez is an Associate Professor in the Department of Engineering at Norfolk State University's College of Science, Engineering and Technology. His multidisciplinary research focuses on microsensing platforms for healthcare, pollution control, and agriculture applications. Education: PhD in Electrical Engineering (2010) from Indian Institute of Technology Madras His research integrates microfabrication, microfluidics, and machine learning to develop wearable biosensors, disposable electrodes, and IoT-enabled soil monitoring systems. Key trends in his recent publications include: Real-time health monitoring via flexible nanosensors Machine learning integration in agricultural IoT Plasma-aided printing of conductive nanomaterials Smart PPE systems with NFC technology Scientific Awards: Research Initiation Award (2020) for cognitive monitoring systems in extreme environments Dr. Fernandez mentors graduate and undergraduate researchers at NSU, with prior teaching experience at University of Indianapolis and Florida International University. He holds a patent for biosensor technology and has developed innovative solutions for: Salivary cortisol detection Soil nutrient analysis Cell viability assessment Smart irrigation systems

• Deep Learning
• Data Mining
• Artificial Intelligence
• +7 more

Asad Abdi is a Lecturer in Computer Science at the College of Science and Engineering. His research focuses on deep learning, data mining, and artificial intelligence , with applications in traffic analysis, educational technology, and maritime logistics. He has published extensively on topics like social media-based traffic forecasting, fake news detection, and vessel arrival prediction. Abdi’s work bridges theoretical advancements in machine learning with practical challenges in domains such as transportation systems and education. He has explored hybrid approaches combining deep learning models with linguistic knowledge and knowledge graphs to address real-world problems. Notable contributions include frameworks for feedback analysis in hybrid classrooms and fusion-based prediction models for vessel arrival times. His recent articles highlight trends in leveraging large language models and multi-feature fusion techniques for tasks like opinion summarization and aspect extraction. Abdi’s research often emphasizes interdisciplinary collaboration, integrating insights from computer science, transportation engineering, and educational psychology. While no formal awards or grants are explicitly listed, his publication record demonstrates sustained contributions to applied AI and data-driven solutions across multiple sectors.

Ahmet ÖZMENView profile

Professor

• Computer Vision
• Traffic Monitoring Systems
• Sensor Systems
• +6 more

Prof. Dr. Ahmet ÖZMEN is a Professor at Sakarya University's Faculty of Computer and Information Sciences, Department of Software Engineering. He has held various administrative positions including Head of the Software Engineering Department (2019-2028) and Director of the Computer Research and Application Center (2019-2022). With extensive experience in academia since 1991, he has made significant contributions to computer vision, traffic monitoring systems, and sensor technologies. Sakarya University: Professor (2019-present), Associate Professor (2011-2019) Dumlupınar University: Assistant Professor (2001-2011), Research Assistant (2000-2001, 1993-1998) Istanbul Technical University: Research Assistant (1991-1993) Prof. ÖZMEN's research spans computer vision applications for traffic monitoring, indoor air quality systems, parallel computing, and sensor technologies. His work bridges theoretical computer science with practical engineering applications, particularly in developing vision-based systems for nighttime vehicle detection, traffic flow monitoring, and environmental sensing. His interdisciplinary approach combines machine learning, image processing, and embedded systems to solve real-world problems in transportation and environmental monitoring. His publication record shows a clear evolution from parallel and distributed systems in his early career to computer vision and sensor applications in recent years. The majority of his recent work focuses on traffic monitoring systems using computer vision techniques, particularly for nighttime conditions, and indoor air quality monitoring systems using sensor networks. His research demonstrates strong industry and societal relevance, with applications in smart transportation, environmental protection, and educational technology. TÜBİTAK Publication Awards (2006, 2008, 2009, 2010) Physical implementation award from TÜBİDER (2008) Microsoft Certified Professional Certificate (2005) YÖK overseas study scholarships (1993, 1998) Elginkan graduate scholarships (1990, 1991) Prof. ÖZMEN has supervised numerous graduate students across multiple institutions, with a focus on practical engineering problems. His research has been supported by various projects including TÜBİTAK projects, institutional research grants, and industry collaborations. He has led significant research initiatives in traffic monitoring systems, indoor air quality monitoring, and educational technology platforms. His administrative leadership has included directing research centers and shaping curriculum development in software engineering. His work has involved establishing research teams focused on computer vision applications, sensor network development, and educational technology. These teams have produced numerous publications, developed practical systems, and trained the next generation of computer engineers. Current research directions include advanced traffic monitoring systems using deep learning and multi-camera setups for urban planning applications.

John RowanView profile

Professor

• Environmental Science
• Climate Change
• Water Resources Management
• +3 more

John Rowan is a Professor of Physical Geography and Director of the UNESCO Centre for Water Law, Policy & Science at the University of Dundee. His roles include advancing sustainable development, particularly SDG6 (water and sanitation), and leading interdisciplinary initiatives. Previously, he served as Vice Principal for Research and inaugural Dean of the School of Social Sciences. His research focuses on environmental change, climate impacts on water resources, and policy-driven solutions, with global collaborations in Bangladesh, India, and Kenya. He has held advisory roles in Scottish Government’s Centres of Expertise and chairs UNESCO’s UK International Hydrological Committee. Research Interests: Environmental change, climate-water-food nexus, river basin management, sediment dynamics, and policy integration. His work bridges academic and applied domains, emphasizing governance and sustainable practices. Key projects include enhancing water security under climate uncertainty, developing risk frameworks for drinking water, and addressing the water-food-energy nexus. Publications: Over 111 articles, focusing on climate adaptation, water resource vulnerabilities, and ecohydrological modeling. Recent highlights include studies on the Karakoram Anomaly’s hydrological tipping points and frameworks for climate-resilient water safety plans. Awards: Gold Engage Watermark Award (2020), United Nations Risk Award (2019). Projects span international collaborations and policy advisory roles. He has supervised 12 students and led over 33 research projects, including the Hydro Nation Scholars Programme. Labs/Teams: UNESCO Centre for Water Law, Policy & Science; Centre for Environmental Change and Human Resilience (CECHR), collaborating with the James Hutton Institute. Active in global forums like the UNFCCC Climate Assembly and World Water Forum.

Roberto PASTRESView profile

Associate Professor

• Marine Ecology
• Aquaculture Sustainability
• Environmental Modeling
• +3 more

Roberto Pastres is an Associate Professor at the Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice. He specializes in ecology, with a focus on coastal ecosystems, aquaculture sustainability, and environmental modeling. His research integrates interdisciplinary approaches to address challenges in marine resource management, including aquaculture impacts, water quality, and climate change adaptation. Teaching: He teaches Environmental Modelling (Master's level) and Ecology courses in Environmental Engineering and Cultural Heritage programs. Recent teaching roles include: Environmental Modelling (6 cfu) for M.Sc. in Environmental Sciences Ecology of Cultural Heritage (6 cfu) for M.Sc. in Conservation Science Research Interests: Pastres' work spans ecological modeling, sustainable aquaculture practices, and ecosystem services valuation. Key projects include: Leading the GAIN H2020 project for green aquaculture intensification Contributing to EU-funded initiatives like BeBlue (aquaponics) and FORCE (Egyptian fisheries) Modeling coastal zone management strategies for the Venice Lagoon Grants & Projects: He coordinates or participates in multiple EU and national grants, including: H2020 GAIN (2018-2022): €6M for sustainable aquaculture innovation Interreg BeBlue (2023-2025): Promoting sustainable aquaponics Life12 NAT/IT/000331: Restoring Venice Lagoon seagrass beds Labs/Teams: Active in the Research Institute for Green and Blue Growth and the Interconnected Nord-Est Innovation Ecosystem. Collaborates with international teams on digital twin systems for aquaculture and precision farming tools.