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Yi XiongView profile

Assistant Professor

• Biostatistics
• Survival Analysis
• Causal Inference
• +5 more

Dr. Yi Xiong is an Assistant Professor in the Department of Biostatistics at the School of Public Health and Health Professions, University at Buffalo. Her research focuses on developing statistical methodologies for analyzing imperfect and spatio-temporally correlated event history data, causal inference for survival analysis, and subsampling techniques. She applies these methods to cancer risk prediction using environmental risk factors and evaluates cost-effectiveness in health policies. Dr. Xiong holds a PhD in Statistics from Simon Fraser University, Canada, and has conducted postdoctoral research at the Fred Hutchinson Cancer Center. Education: PhD, Statistics, Simon Fraser University, Canada MSc, Statistics, Simon Fraser University, Canada BEng, Computer Science, Zhejiang University, China Research Interests: Missing data mechanisms, survival analysis for recurrent events, causal inference frameworks, subsampling strategies for complex datasets, and applications in environmental epidemiology and healthcare policy evaluation. Publications: Dr. Xiong's recent work emphasizes material science and optoelectronics (e.g., photodetectors, semiconductor interfaces), potentially reflecting interdisciplinary collaborations or dual research focuses. Key themes include heterojunction properties, nanostructure fabrication, and thermal/ electrical material characterization. Grants & Advising: No specific grants or advisees listed in available data. Labs/Teams: No lab affiliations explicitly mentioned.

MANUEL FRANCISCO MAREY PEREZView profile

Professor

• Forest Management
• Wildfire Analysis
• Spatial Analysis
• +3 more

MANUEL FRANCISCO MAREY PEREZ is a Professor at the University of Santiago de Compostela , affiliated with the Department of Plant Production and Engineering Projects within the Higher Polytechnic School of Engineering . He holds a Doctorate from the same university (2003), focusing on land tenure dynamics in Galician forestry. His research emphasizes participatory forest planning , wildfire management , and sustainable resource governance . Key contributions include methodologies for public participation in forest planning (validated in Galicia) and spatiotemporal analysis of wildfire ignition patterns. He has published extensively on forest policy, resin production simulation (RESIM), and socio-ecological resilience in rural landscapes. His work bridges environmental science, geography, and policy, with applications in Galicia and beyond. Doctoral Thesis: Land tenure in Galicia, model for the characterization of forest owners (2003) Research Group: PROEPLA (Projects and Planning) Notable Projects: Wildfire risk assessment, forest governance frameworks Publications highlight interdisciplinary approaches to land use challenges, integrating spatial analysis, participatory methods, and socio-economic factors. His research addresses both academic and practical needs in forest management and rural development.

Naseer Al-JawadView profile

Senior Lecturer

• Image Processing
• Medical Imaging
• Video Processing
• +4 more

Dr. Naseer Al-Jawad is a Senior Lecturer in Computing at the School of Computing, part of the Faculty of Computing, Law and Psychology at the University of Buckingham. He holds a BSc from Basrah University (Iraq), an MSc from Baghdad University (Iraq), and a PhD in Computer Science from the University of Buckingham (UK). His teaching spans foundational computing courses, including Data Structures, Object-Oriented Programming, and Research Methods for Postgraduates, alongside project supervision at both undergraduate and postgraduate levels. His research interests focus on image and video processing, medical imaging (e.g., breast cancer and melanoma detection), biometric systems (face/gait recognition, Arabic handwriting analysis), and IoT-driven Smart Home systems. He has pioneered work in deep learning for video compression, fusion of audio-visual data for authentication, and steganography techniques. Recent projects include developing adaptive multi-user Smart Home frameworks and enhancing content-based video indexing with wavelet transforms. Dr. Al-Jawad’s publications span topics like facial expression recognition using 3D Kinect data, indoor localization via smartphone networks, and secure real-time video transmission. His work emphasizes practical applications in healthcare, cybersecurity, and disaster management networks. His academic contributions include overseeing student projects and collaborating on interdisciplinary initiatives such as UNILS (indoor localization) and sosMesh (disaster communication frameworks). He actively contributes to conferences like SPIE and MIUA, addressing challenges in multimedia processing and biomedical image analysis.

Pierre-Etienne MartinView profile

Researcher

• Computer Vision
• Deep Learning
• Optical Flow Analysis
• +6 more

Roles & Affiliations: Pierre-Etienne Martin is a Postdoctoral Researcher & Tech Development Coordinator at the Max Planck Institute for Evolutionary Anthropology (MPI-EVA), Department of Comparative Cultural Psychology. He applies computer vision tools to study human and non-human animal cognition through projects like Zoo Cam Set-Up and BioTIP. Prior to this, he held roles as an ATER (Temporary Teaching and Research Associate) and PhD student at the University of Bordeaux, specializing in spatio-temporal neural networks for action classification in sports like table tennis. Education: PhD in Computer Science (2020): University of Bordeaux, Thesis on fine-grained action detection using spatio-temporal CNNs M.Sc. in Image Processing & Computer Vision (2017): Erasmus Mundus program across Budapest, Madrid, and Bordeaux B.Sc. in Mathematical Engineering (2015): University of Bordeaux Research Interests: Focuses on computer vision applications in psychology and animal behavior, including thermal imaging analysis, fine-grained action classification, and non-invasive data acquisition. Projects include automated primate tracking (Zoo Cam Set-Up), thermal facial/nose segmentation (ApeTI dataset), and child behavior analysis (Quantex project). Publications: Over 20 peer-reviewed papers in journals like Signals and conferences like MediaEval, focusing on CNN-based action recognition in sports and animal studies. Notable contributions include the ApeTI thermal dataset and techniques for stroke classification in table tennis. Grants & Outreach: Scientific advisor at DeepMove (2021-2022). Active in public outreach via YouTube, science festivals, and competitions like Ma thèse en 180 secondes . Engages schools through discussion programs like Declics. Labs & Teams: Leads tech development for MPI-EVA's Comparative Cultural Psychology lab, collaborating on projects like CASE (animal self-testing environments) and BioTIP (thermal imaging tools for behavioral research).

• Computer Vision
• Machine Learning
• Video Analysis
• +3 more

Laura Sevilla is a Reader (Associate Professor) at the School of Informatics, University of Edinburgh, and an ELLIS Scholar. Her research focuses on understanding the visual world in motion, encompassing video representations, high-level perceptual tasks, and joint learning of video and language. She actively organizes workshops, including 'What is Motion For?' at ECCV 2022 and 'Computer Vision for Global Challenges' at CVPR 2019, aiming to address humanitarian and global issues through vision technology. Her academic journey includes leadership roles such as Program Chair at BMVC 2021 and Area Chair at CVPR 2021 and 2024. She has received the Google Scholar Award 2022. Laura mentors a dynamic team of PhD students, including Dr. Shreyank N Gowda (now at Oxford), Anil, Gen, Davide, and Kiyoon, whose work spans video analysis, temporal dynamics, and efficient learning methods. Her outreach efforts emphasize accessibility, such as developing vision tools for the blind and educational workshops in the Philippines. Key contributions include the ALBA tracking method, FASTER video classification, and the Temporal Dataset benchmark. Her research bridges theoretical advancements with practical applications, fostering collaboration across disciplines within the top-ranked School of Informatics.

Manudeo SinghView profile

Research Fellow

• Surface water dynamics
• Remote Sensing
• Wetland hydrogeomorphology
• +4 more

Manudeo Singh is a Research Fellow at the Department of Geography and Earth Sciences, Aberystwyth University. He holds prestigious fellowships including the Newton International Fellowship (2023) and Alexander von Humboldt Fellowship (2021–2023). His research focuses on surface water dynamics, remote sensing applications, wetland hydrogeomorphology, and climate change impacts in the Ganga Plains and other regions. Singh completed his PhD at IIT Kanpur, India, and has contributed extensively to understanding floodplain wetland dynamics, hydrological connectivity, and environmental monitoring using Earth Observation techniques. Research Interests: Surface water dynamics and wetland health Remote Sensing and GIS applications Climate Change impacts on hydrological systems Geomorphological connectivity frameworks Awards: Newton International Fellowship (2023) Alexander von Humboldt Fellowship (2021–2023) Advising & Grants: While no formal advisee names are listed, his research is supported by fellowships and collaborations with institutions like the University of Potsdam, Germany. His work integrates multi-source remote sensing data and geostatistical methods to address environmental challenges in India and globally. Labs/Teams: Collaborates with international teams on wetland restoration and disaster management, leveraging tools like Google Earth Engine and R packages for large-scale analyses.

William W.S. WeiView profile

Professor

• Time Series Analysis
• Forecasting Methods
• Statistical Modeling
• +5 more

William W.S. Wei is a Professor of Statistics at Temple University’s Fox School of Business and Management, where he has served since 1974. Previously, he chaired the Department of Statistics (1982–1987) and directed graduate programs in Statistics. He holds a B.A. in Economics (National Taiwan University, 1966), a B.A. in Mathematics (University of Oregon, 1969), and an M.S. (1972) and Ph.D. (1974) in Statistics from the University of Wisconsin, Madison. His research focuses on time series analysis, forecasting, and multivariate statistical modeling, with contributions to aggregation effects, outlier detection, and high-dimensional dimension reduction. He authored seminal books like Time Series Analysis (1990, 2006) and Multivariate Time Series Analysis and Applications (2019). His work emphasizes practical applications across economics, finance, and environmental science. Dr. Wei is a Fellow of the American Statistical Association and Royal Statistical Society, and served as 2002 ICSA President. He has received Temple’s 2016 Musser Award and two Lifetime Achievement Awards (2014, 2024). He has advised numerous Ph.D. students now in academia and industry. Beyond academia, he contributes to community education and leadership roles in Taiwanese Hakka organizations.

• Semantic Modeling
• Geographic Information Systems
• Spatio-temporal Databases
• +3 more

Stefano Spaccapietra is a professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) specializing in semantic modeling, geographic information systems, and spatio-temporal databases. A recipient of the 2012 Peter P. Chen Award, his research focuses on foundational aspects of data representation and knowledge engineering. His research explores: Semantic modeling for geospatial systems Multi-representation databases Trajectory data analysis and mobility patterns Ontology design for spatial information Temporal evolution in object-oriented systems Publication analysis reveals strong emphasis on: Fundamental theories of spatial and temporal representation Integration frameworks for heterogeneous systems Evolution of conceptual modeling approaches Spatio-temporal query processing techniques Awards & Honors: Peter P. Chen Award (2012) His work has significantly contributed to database integration methodologies and established foundations for modern geospatial data management systems through both theoretical frameworks and practical implementations.

• Complex Geometry
• Kähler Geometry
• Stability of Algebraic Varieties
• +5 more

Chi Li is an Associate Professor in the Department of Mathematics at Rutgers, The State University of New Jersey. Their research focuses on Complex Geometry, including canonical metrics in Kähler geometry, stability of algebraic varieties, and pluripotential theory. They hold an office in HLL-511 and can be contacted at cl1412@rutgers.edu. Research interests emphasize foundational aspects of geometric analysis with applications to algebraic structures. Recent work explores interdisciplinary applications in AI, such as anomaly detection, cross-lingual NLP, and large language model optimization. Key contributions include frameworks for domain-oriented model compression and dynamic routing in mixed LLMs. Publications span 2020–2025, addressing topics from turbulence forecasting to spatio-temporal recommendation systems. Notable trends include integration of geometric principles with machine learning methodologies. Extensive contributions to log anomaly detection (GLAD, Cat), time series meta-contrastive learning, and graph-based cross-lingual transfer. Active in developing interpretable imitation learning and uncertainty quantification techniques for in-context learning.

Fei DouView profile

Assistant Professor

• Machine Learning
• Federated Learning
• Wireless Sensor Networks
• +3 more

Fei Dou is an Assistant Professor in the School of Computing at the University of Georgia, which operates under the Franklin College of Arts and Sciences. He joined UGA in August 2023 and serves as Graduate Program Faculty. His office is located in the Boyd Research and Education Center. He holds a Ph.D. and M.S. in Computer Science and Engineering from the University of Connecticut, both completed in 2023. Dr. Dou's research spans several key areas of computer science with particular focus on: Machine learning systems : Federated learning, reinforcement learning, and self-supervised techniques Wireless sensing : Healthcare monitoring using WiFi signals and contactless sensing Networked systems : Underwater sensor networks and secure communication protocols Applied AI : Education technology, precision agriculture, and indoor positioning His recent publications (2021-2025) demonstrate strong emphasis on federated learning adaptations for resource-constrained environments, healthcare applications using novel sensing methodologies, and security solutions for underwater networks. A consistent theme involves developing efficient machine learning techniques that operate in challenging real-world conditions while preserving privacy and security.

• Computer Science
• Geometric Algebra
• Remote Sensing
• +3 more

Wen Luo is a Professor at Peking University's School of Computer Science, affiliated with the State Key Laboratory of Multimedia Information Processing. His work spans Computer Science , Geometric Algebra , and Remote Sensing , with a focus on Medical Imaging and GIS Applications . Current affiliation: Peking University Research areas: Geometric Algebra, Spatio-Temporal Analysis, Medical Informatics His recent publications highlight trends in Deep Learning for medical diagnosis, Geospatial AI , and Remote Sensing innovations. Collaborations include Zhaoyuan Yu, Linwang Yuan, and Fei Deng.

• Stochastic Processes
• Spatial Statistics
• Signal Processing
• +3 more

Dr. Andriy Olenko is an Associate Professor of Statistics at La Trobe University's Department of Mathematical and Physical Sciences, Australia, and holds concurrent roles at Taras Shevchenko National University of Kyiv and National University of Kyiv Mohyla Academy. He specializes in stochastic processes, spatial statistics, signal processing, and data science. Olenko coordinates the La Trobe Master of Data Science program and actively collaborates with international research groups in France, UK, Japan, and others. Academic Positions: Associate Professor at La Trobe University (2007–present), former roles at Taras Shevchenko National University (1991–2007) and Kyiv Mohyla Academy (1994–2008). Editorial Roles: Member of editorial boards for journals like International Journal of Wavelets , Theory of Probability and Mathematical Statistics , and Journal of Statistical Theory and Applications . Education: MSc (1988) and PhD (1991) in Probability Theory from Taras Shevchenko National University of Kyiv. Research Interests: Focus on stochastic processes, spatial stochastic modeling, and applications in cosmology and signal processing. His teaching includes courses on statistical computing, biostatistics, and data science. Olenko leads a research group in Stochastics and Spatio-Temporal Statistics, with projects addressing random field theory, long-memory processes, and computational statistics.

Guy De TréView profile

Associate Professor

• Computational Intelligence
• Data Quality
• Decision Support Systems
• +7 more

Guy De Tré is an Associate Professor at the Department of Telecommunications and Information Processing within Ghent University's Faculty of Engineering and Architecture. He leads the Database, Document and Content Management (DDCM) research group and focuses on computational intelligence in information systems, with expertise in bi-polarity handling, uncertainty modeling, and multi-valued logic systems. Primary Affiliation: Ghent University Research Focus: Data quality, fuzzy querying, spatio-temporal modeling Key Contributions: Foundational work in possibilistic databases and explainable AI His research combines theoretical and applied approaches to information management systems. Theoretical work includes: Bipolarity and uncertainty handling in databases Multi-valued logic frameworks Interval B-tree indexing for possibilistic data Applied research spans: NoSQL database optimization Decision support systems Contextualized machine learning 3D/4D modeling for geological resources Recent publications show increasing focus on explainable AI, with multiple works on contextualized support vector machine classification and orthographic similarity measures for graph-based data representations. His work bridges database theory with practical applications in data quality assessment, medical informatics, and cultural heritage projects like the Byzantine Book Epigrams database. Research Group: Leads the DDCM group at Ghent University, specializing in: Database management innovation Content modeling techniques Fuzzy logic implementations Temporal data indexing Intelligent information systems

Paras MehtaView profile

Researcher

• Spatial Data Mining
• Geoinformatics
• Information Retrieval
• +3 more

Dr. Paras Mehta is a Researcher at the Department of Databases and Information Systems, part of the Computer Science faculty at Freie Universität Berlin. His work focuses on spatial data mining, geoinformatics, and spatio-temporal analysis with applications in disaster response systems and social media analytics. He contributes to projects such as MoveSafe and CliniScale, addressing challenges in real-time data processing and location-based services. Research interests include geotagged post analysis, spatio-textual data retrieval, and distributed computing for large-scale spatial datasets. His recent work emphasizes continuous summarization of streaming data and hotspot detection using frameworks like Apache Spark. Notable projects involve developing systems like μTOP for trending topic detection in microblogs and LocXplore for urban region profiling. His publications span spatial keyword queries, trajectory aggregation, and disaster response frameworks. Paras Mehta holds a PhD and has contributed to both academic and applied research in database systems. Despite no listed awards or grants in the provided texts, his research portfolio demonstrates strong engagement with spatial computing challenges. He collaborates within the Databases and Information Systems group at Freie Universität Berlin, contributing to both theoretical advancements and practical software solutions.

Emmanuel GOBETView profile

Professor

• Machine Learning
• Data Science
• Risk Modeling
• +5 more

Emmanuel GOBET is a Professor of Applied Mathematics at École Polytechnique, leading the SIMPAS research team and the Financial Mathematics group. He holds roles as Scientific Leader of the Stress Test: RISK Management and Financial Steering Chair (2018–present) and former Head of the Applied Mathematics Department (2020–2023). He co-directs the Master's program in Probability and Finance since 2010 and is involved in pedagogical coordination at Hi! PARIS (2021–2024). His research focuses on machine learning, data science, stochastic processes, and their applications in climate, energy, finance, and blockchain. Education includes a PhD in Probability (University Paris 7, 1996–1998), Master’s in Statistics (University Paris 7, 1995–1996), and an engineering degree from École Polytechnique. He has held academic positions at Grenoble Institute of Technology (2005–2010) and prior roles as Assistant Professor at École Polytechnique and University Paris 6. Research interests span extreme value analysis, Monte Carlo methods, risk modeling, and stochastic control. Notable projects include the ANR BLOCKFI (Blockchain & Decentralized Finance, 2024–2029) and leadership in conferences like CLIFIRIUM and GenHack data challenges. His work bridges theoretical advancements with practical applications in climate finance, energy systems, and financial risk management. Publications emphasize methodological innovations in stochastic simulation, risk assessment, and generative modeling of extreme events. Over 20 PhD students have been advised, contributing to fields like quantitative finance, stochastic control, and climate-related risk analysis. Current research priorities include climate transition pathways, energy systems optimization, and decentralized finance frameworks.