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Amila AkagicView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +15 more

Prof. Amila Akagic is an Associate Professor at the Department of Computer Science and Informatics, Faculty of Electrical Engineering, University of Sarajevo. Her expertise spans Computer Architecture , Artificial Intelligence , Computer Vision , and High-Performance Computing . She holds a Ph.D. from Keio University (Amano Lab) and has conducted research at UC Riverside and the University of Ljubljana under prestigious scholarships including the Fulbright and MEXT awards. Her work focuses on FPGA-accelerated algorithms, reconfigurable architectures, and deep learning applications in medical imaging and environmental monitoring. Education: Bachelor's/Master's (Electrical Engineering) – University of Sarajevo (2006/2009) Ph.D. (Computer Science) – Keio University, Japan (2013) Research Interests: Combines image segmentation , embedded systems , and energy-efficient computing . Recent work includes semantic tumor segmentation (MRI), blockchain-based UXO tracking, and FPGA-based CRC acceleration. She explores interdisciplinary topics like AI-driven plant phenotyping and wildfire detection using computer vision. Recent Trends in Publications: Over 40% of her 2023-2025 articles address medical imaging and fire detection , with growing emphasis on explainable AI and humanitarian technology . Her work frequently uses frameworks like OpenVINO and TensorFlow to optimize computational efficiency. Awards: Fulbright Visiting Student Award (2007/2008) MEXT Scholarship (Japan, 2010) Grants & Labs: Active in the HiPEAC project (EU Horizon Europe), focusing on edge computing and smart grid resilience. Collaborates with the Embedded Systems and Architectures Lab (UC Riverside) and Amano Lab (Keio University). Labs/Teams: Leads research groups in FPGA-accelerated architectures and AI-driven environmental sensing. Engages in humanitarian demining initiatives via data observatories and blockchain systems.

• Computer Science
• Electrical Engineering
• Robotics
• +7 more

Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.

Pedro HenriquesView profile

Professor

• Programming Languages
• Compiler Design
• Software Analysis
• +2 more

Pedro Henriques is Professor of Computer Science at University of Minho, where he coordinates the Language Processing group at Algoritmi Research Center. With a PhD in Formal Languages and Attribute Grammars, he teaches compiler design and programming language engineering. His research develops: Formal methods for software analysis Educational tools for programming pedagogy Ontology-driven computational thinking frameworks AI applications in agriculture and healthcare Recent work includes neuroeducation-informed frameworks (OntoCnE) and Programming Cocktails methodology for optimizing cognitive load in code instruction. He has supervised 14 PhD dissertations and authored the foundational text "XML & XSL: da teoria a prática". Current EU projects explore VR cognitive rehabilitation and olive cultivar identification using deep learning.

Susanne WenzelView profile

Research Fellow

• Artificial Intelligence
• Remote Sensing
• Machine Learning
• +3 more

Dr. Susanne Wenzel is a Research Fellow at the Structural and Functional Organisation of the Brain (INM-1) within the Forschungszentrum Jülich GmbH. She serves as the Scientific Coordinator for the Helmholtz AI Local Unit at INM-1 and the Jülich Supercomputing Centre (JSC), fostering collaboration with the Helmholtz-wide AI platform. Additionally, she leads the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), a partnership with McGill University’s Montreal Neurological Institute and the Mila Quebec AI Institute. Her work focuses on advancing AI and high-performance computing for neuroimaging, particularly in developing 3D brain models through interdisciplinary research with CIFAR and MILA. Her research expertise spans artificial intelligence, remote sensing, and image analysis, with a strong emphasis on machine learning applications in environmental science, neuroscience, and urban scene understanding. She coordinates projects that integrate cutting-edge technologies to address complex challenges in climate modeling, neuroimaging, and spatial data analysis. Dr. Wenzel’s contributions include pioneering work in neural networks for ocean eddy tracking, sea level anomaly prediction, and facade image interpretation. Her projects emphasize interdisciplinary collaboration, combining computational methods with domain-specific knowledge across multiple institutions globally. Her current initiatives reinforce the utilization of AI and supercomputing resources to enhance scientific discovery, particularly in neuroscience through HIBALL. This collaboration aims to leverage advanced analytics for detailed brain model development, reflecting her commitment to bridging computational innovation with real-world applications.

Yang YangView profile

Assistant Professor

• Data Mining
• Machine Learning
• Computational Social Science
• +4 more

Yang Yang is an Assistant Professor at the Mendoza College of Business, University of Notre Dame. He holds a PhD in Computer Science from the same institution and previously served as Assistant Professor at Syracuse University and Research Assistant Professor at Northwestern Institute on Complex Systems (NICO) and Kellogg School of Management. His research focuses on data mining, machine learning, computational social science, and science of science, particularly exploring how social networks influence leadership attainment and innovation dynamics in scientific contexts. Yang's work bridges computer science and social sciences, examining topics like team composition effects on innovation, media's role in science communication, and gender disparities in leadership. His research has been published in top-tier venues such as PNAS, KDD, ICDM, and featured in media outlets like Forbes, The Washington Post, and WIRED. His recent technical contributions include advancements in multimodal AI, remote sensing interpretation, and video synthesis techniques. Notable projects include geo-localization systems using satellite imagery, transformative video generation models, and human-centric animation technologies leveraging diffusion models and transformers.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +3 more

João Paulo Papa is a Professor at the Department of Computing within the Institute of Biosciences, Humanities and Exact Sciences at São Paulo State University (UNESP), Brazil. His research spans machine learning, computer vision, and quantum computing with significant applications in medical diagnostics and environmental monitoring. Over the past three years, he has published extensively in top-tier journals including IEEE Access, ACM Computing Surveys, and Neural Computing and Applications. His research interests focus on developing innovative machine learning approaches for healthcare applications, particularly in Parkinson's disease detection through speech and facial analysis, medical image processing for cancer detection, and quantum-classical hybrid models. His work demonstrates strong integration of theoretical machine learning advancements with practical medical and environmental applications. Papa has established a productive research group that has produced numerous publications in computer vision conferences and medical informatics venues. Analysis of his recent publications reveals a strong emphasis on medical applications of AI, with approximately 60% of his work focused on healthcare diagnostics, 25% on fundamental machine learning advancements, and 15% on environmental and remote sensing applications. His research shows increasing collaboration with international partners while maintaining strong roots in Brazilian academic networks. Among his notable contributions is the development of specialized machine learning architectures for medical image analysis, including TransConv for esophageal cancer detection and quantum-classical hybrid models for breast cancer diagnosis. He has also made significant contributions to the Portuguese language processing community through adaptations of large language models for Brazilian Portuguese medical applications. Prof. Papa actively supervises graduate students and collaborates with medical professionals across Brazil, translating AI research into practical clinical tools. His research group maintains strong connections with hospitals and medical research centers to ensure clinical relevance of their technical developments.

Ioana PopescuView profile

Research Professor

• Hydrology
• Flood Modelling
• River Systems Modelling
• +7 more

Ioana Popescu is a prominent hydroinformatics researcher affiliated with the IHE Delft Institute for Water Education , where she has worked since 2001. Previously, she served as an Associate Professor at the Faculty of Hydrotechnics, Timisoara, Romania (1990-1999), and a postdoc researcher at the National Research Council of Canada (2000).

• Computer Vision
• Panoramic and Spherical Image Processing
• Light Field Imaging
• +7 more

Yike Ma is an active Associate Professor in the Department of Computer Science at the University of Science and Technology of China (USTC), School of Computer Science and Technology. With a prolific publication record spanning from 2011 to 2025, Ma has established expertise in computer vision with specializations in panoramic/spherical image processing, light field imaging, and applications in agricultural robotics and autonomous driving systems. Ma maintains a strong collaborative network, primarily with Feng Dai, Qiang Zhao, Yongdong Zhang, and Yucheng Zhang, producing numerous high-impact publications in top venues including CVPR, IJCAI, IEEE Transactions, and NeurIPS. University: University of Science and Technology of China (USTC) School: School of Computer Science and Technology Department: Department of Computer Science Research Focus: Computer Vision, Panoramic Imaging, Agricultural Robotics Ma's research interests center on advanced computer vision techniques, particularly for spherical and panoramic imagery, with significant contributions to oriented object detection, semantic segmentation, and light field processing. Recent work demonstrates increasing focus on agricultural robotics applications and autonomous driving systems, showing interdisciplinary expansion of core computer vision expertise. The research employs sophisticated deep learning approaches while addressing practical challenges in real-world applications. Analysis of Ma's 15 most recent publications reveals a clear progression toward practical applications of computer vision research, with growing emphasis on agricultural robotics (4 publications) and autonomous driving systems (3 publications), while maintaining strong foundational work in spherical/panoramic image processing (5 publications). The research demonstrates technical sophistication in handling boundary discontinuity problems, topology reasoning, and space-time perceptive clues, with increasing integration of large language models and generative AI techniques in recent work. While no specific awards are documented in the available publication records, Ma's consistent presence in top-tier conferences and journals including CVPR, IJCAI, and IEEE Transactions indicates recognition within the computer vision and AI research communities. The publication record shows steady output with increasing impact, particularly in the last five years. Ma's collaborative network is extensive, with primary collaborations through the University of Science and Technology of China. The research appears well-supported through projects in agricultural robotics and autonomous systems, though specific grant details aren't visible in the publication metadata. Ma has advised numerous graduate students as evidenced by authorship patterns where Ma appears as senior author on papers with junior researchers as first authors.

• Climate Compatible Growth
• Geospatial Modelling
• Machine Learning
• +7 more

Stephanie Hirmer is an Associate Professor in Climate Compatible Growth at the University of Oxford’s Department of Engineering Science and a Visiting Research Fellow at the University of Cambridge’s Centre for Sustainable Development. She leads the Climate Compatible Growth (CCG) Programme as Deputy-Research Director and oversees Advanced Geospatial Modelling. Her work integrates community perspectives into energy decision-making, focusing on vulnerable groups and SDGs such as gender equality and clean energy. Education: DPhil (PhD) in Sustainable Development (University of Cambridge), MPhil in Development Studies, and BEng in Engineering. Professional experience includes roles with GIZ, KfW, and Arup, specializing in energy infrastructure in Africa, the Middle East, and Europe. Co-founder of Rural Senses, recognized for AI-driven data collection methods addressing energy access gaps. Research interests span geospatial modeling, machine learning (NLP), and value-based energy solutions. Current projects include Kenya’s energy planning, lithium-ion battery systems for rural schools, and green hydrogen’s role in development. Awards include Google and Microsoft social impact awards for Rural Senses. Key contributions include frameworks for climate resilience, energy policy analysis, and gender-inclusive infrastructure design. Active in global initiatives like the UKPACT project and FCDO-funded CCG Programme.

Heng CaiView profile

Assistant Professor

• Geographic Information Science
• Disaster Resilience
• Socio-Environmental Sustainability
• +3 more

Dr. Heng Cai is an Assistant Professor at Texas A&M University, leading the GIScience for Resilience (GIResilience) Research Group. Her work focuses on geographic information science, disaster resilience, socio-environmental sustainability, and environmental health. She develops geospatial data mining and decision-support tools to model human-environment interactions under climate change and natural hazards. Education: Ph.D. in Environmental Science (Louisiana State University), M.S. in Geographic Information Science (Chinese Academy of Sciences), B.E. in Surveying and Mapping (China University of Geosciences). Research interests include geospatial data science, hazard modeling, and resilience assessment. Her group has pioneered studies on land loss prediction in coastal Louisiana, social media analysis for disaster response, and cyberGIS platforms like PRIME. Recent work emphasizes climate adaptation strategies and urban resilience frameworks. Publications span interdisciplinary journals like Remote Sensing , Annals of the American Association of Geographers , and International Journal of Digital Earth . She actively collaborates with NASA and disaster management agencies, securing grants for projects like HeatSMART and U-CARE. Labs/Teams: GIResilience Group (www.gis-resilience.info) focuses on innovative geospatial solutions for societal resilience. She mentors undergraduate and graduate students in GIS programming, webGIS, and geodatabase applications.

• Marine Robotics
• Autonomous Systems
• AI for Environmental Monitoring
• +3 more

Blair Thornton is a Professor of Marine Autonomy at the University of Southampton's Faculty of Engineering and Physical Sciences. He leads research in autonomous marine robotics, sensing, and AI for marine science, and co-directs the FEPS In situ and Remote Intelligent Sensing (IRIS) Centre of Excellence. His work focuses on developing low-cost robotic platforms, 3D seafloor mapping systems, and AI-driven data interpretation methods. Education: PhD in underwater robotics (University of Southampton, 2006), postdoctoral research at University of Tokyo (2006-2016), and over 55 ocean expeditions (30 as PI). Teaching includes Maritime Robotics and Intelligent Mobile Robotics modules with Python-based practicals. Key Projects: BioCam (NERC), TechOceanS (EU), DriftCam (EPSRC), and Smarty200 (EPSRC). Collaborations include Sonardyne International, National Oceanography Centre, and JAMSTEC. Awards include the Okamura Kenji Prize (2014), Shell Ocean Discovery Xprize (2019), and IEEE Mid-Career Rising Star Award (2022). Active in journal editing (IEEE Oceanic Engineering, Robotics and Automation Letters) and external speaking roles.

Emanuela VolpiView profile

Professor

• Medical Molecular Genetics
• Genome Functional Organisation
• Chromosomal Fragility and Instability
• +3 more

Emanuela Volpi is a Professor in the School of Life Sciences at the University of Westminster, affiliated with the College of Liberal Arts and Sciences. She holds a PhD in Evolutionary Biology from the University of Rome ‘La Sapienza’ and has extensive postdoctoral experience at Cancer Research UK and the Wellcome Trust Centre for Human Genetics. Her expertise spans medical molecular genetics, genome functional organization, nuclear architecture, and cancer biomarker research. She leads research on genome-environment interactions and their health impacts, with high-impact publications in journals like Science and Nature Communications . Teaching roles include Module Leader for L5 Genetics in Medicine and L7 Post-Graduate Project , and co-Course Leader for the Master’s in Biomedical Sciences. Her research focuses on genomic instability in obesity, CD180 signaling in cancers, and translational biomarkers. She is Editor-in-Chief of Molecular Cytogenetics and chairs the European Cytogeneticists Association’s ‘Chromosome Stability, Integrity and Dynamics’ working group. Key awards include Fellowships from the Royal Society of Biology and the Higher Education Academy. Her advisory roles span national and international committees, emphasizing her leadership in medical genetics and education. Current projects explore precision medicine, AI regulation in healthcare, and pediatric obesity biomarkers, with a focus on early intervention strategies.

Thomas OommenView profile

Professor

• Geohazard Characterization
• Remote Sensing
• Machine Learning
• +2 more

Thomas Oommen is Professor and Chair of the Department of Geology and Geological Engineering at the University of Mississippi. His research utilizes remote sensing, machine learning, and geospatial analysis for geohazard characterization and infrastructure monitoring. Education includes a PhD from the University of Michigan (2013) and positions at Michigan Technological University (2010-2023) where he directed the Computational Science & Engineering PhD program. Research focuses on landslide susceptibility modeling, flood risk assessment, and infrastructure monitoring using multi-sensor remote sensing data. Recent work employs UAVs, InSAR, and machine learning for terrain characterization and disaster resilience. Publications (2023-2025) demonstrate advancements in landslide prediction algorithms, flood mapping techniques using Google Earth Engine, and machine learning applications for soil characterization. Key methodological innovations include synthetic data augmentation for tailings impoundment classification and HAND model improvements for flood risk assessment. Grant funding includes NSF awards for community resilience projects in Mississippi. Laboratory facilities include geophysical equipment for field investigations and computing resources for spatial analysis. Dr. Oommen leads field investigations of significant geohazards including the 2024 Wayanad landslide and Edenville Dam failure. Research teams collaborate with USGS on earthquake hazard projects in the Eastern Tennessee Seismic Zone.

• Sustainability Transitions
• Digital Technologies
• Energy Consumption
• +4 more

Sanneke Kloppenburg is a Researcher in the Department of Environmental Policy at Wageningen University. She holds a PhD in Sociology from the University of Amsterdam (2013) and has conducted postdoctoral research at Wageningen University (2015–2018) and Maastricht University UNU-MERIT (2014–2015). Her research focuses on digital technologies’ implications for sustainability transitions in energy, mobility, and food systems. She examines platformization, blockchain, and datafication’s societal impacts, emphasizing equity and environmental governance. Key projects include the ShaRepair initiative for circular economy practices, StoRE (energy storage in households), and JustRES (social justice in renewable energy strategies). She co-leads the DigiMetis network, exploring digitalization’s societal implications in agrifood, natural resources, and circular economies. Kloppenburg teaches courses on environmental sociology, smart environments, and advanced social theory, integrating practice theory with digital sociology. Her work bridges academic and applied contexts, addressing ethical and governance challenges in emerging technologies. Recent publications explore topics like circular consumption rebound effects, digital twins in environmental governance, and blockchain applications in food systems.

Andreas MøgelmoseView profile

Associate Professor

• Computer Vision
• Artificial Intelligence
• Autonomous Systems
• +3 more

Andreas Møgelmose is an Associate Professor at Aalborg University's Department of Architecture, Design and Media Technology under the Technical Faculty of IT and Design. His research focuses on computer vision, artificial intelligence, and their applications in autonomous systems, driver assistance, and industrial vision. He leads projects like AI Color Fashion and Real-world adaption of generative AI for architecture. Møgelmose teaches introductory programming, computer vision, and advanced master's courses, emphasizing practical project-based learning. His work includes developing datasets such as the Multi-view Traffic Intersection Dataset (MTID) and exploring vision-language models for autonomous vehicle safety. He actively engages in media discussions on AI ethics and societal impacts. Research interests span dynamic gesture interpretation for cooperative autonomous vehicles, surgical skill assessment via automated metrics, and multimodal classification of environmental data. He has contributed to over 50 publications, including work on 3D object detection frameworks and generative AI education. Møgelmose collaborates on projects funded by industry partners like COWI and Danish government initiatives. His teaching philosophy centers on blended learning and practical application, fostering innovation in AI and computer vision education. Notable projects include AI:Xpertise Lab (2025–present), which explores AI-driven expertise systems, and collaborations on forest biodiversity analysis using LiDAR and orthophotos. His recent media engagements highlight societal AI challenges, emphasizing responsible implementation in public sectors.