جست‌وجوی استاد و پژوهشگر

Andréia G. Arrudaمشاهده پروفایل

دانشیار

• Infectious disease epidemiology
• Application of novel epidemiological tools for disease control and prevention in food production animals
• Emerging and re-emerging swine diseases
• +۳ مورد دیگر

Dr. Andréia G. Arruda, DVM, MSc, PhD, is an Associate Professor at Ohio State University's Department of Veterinary Preventive Medicine. She focuses on infectious disease epidemiology in food production animals, particularly swine, and has developed innovative epidemiological tools for disease control. DVM, São Paulo State University MSc, University of Minnesota PhD, University of Guelph Her research emphasizes emerging/re-emerging swine diseases, with a strong focus on Porcine Reproductive and Respiratory Syndrome (PRRSV), depopulation methods, and personnel movement-based disease transmission. She utilizes geofencing platforms for contact tracing and studies stress responses during animal procedures. Recent publications highlight trends in swine disease management, including foam depopulation methods, PRRSV lineage tracking, and biosecurity optimization. Her work bridges veterinary practice with data science to improve farm productivity and animal welfare. Contact: arruda.13@osu.edu , Sisson Hall 1233, 1920 Coffey Road Columbus, OH 43210.

Shixiang (Woody) Zhuمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Optimization
• +۹ مورد دیگر

Shixiang (Woody) Zhu is an Assistant Professor at the Heinz College of Information Systems and Public Policy, Carnegie Mellon University, with office location at Hamburg Hall 2118H in Pittsburgh, PA. His research bridges machine learning, statistics, and operations research to address high-impact societal challenges through methodological innovation. Education: Ph.D. in Machine Learning, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology Dr. Zhu's research focuses on sequential modeling and uncertainty quantification, human-AI collaboration in decision making, and operations management in energy systems. His work develops novel methodologies for social good across critical domains including security and privacy, climate resilience, public policy, and infrastructure management. Key applications span police operations, power systems, transportation networks, healthcare, and epidemiology, with emphasis on spatio-temporal data analysis and robust decision frameworks under uncertainty. Scientific Awards: 2024 Gen-AI Fellows Finalist, 2021 INFORMS Wagner Prize Second Place, 2019 INFORMS Doing Good with Good OR Best Paper Award, 2025 IEEE Power & Energy Society General Meeting 3rd Place, 2025 PG&E Energy Analytics Challenge Dr. Zhu actively mentors undergraduate researchers including Wendy, Michelle, Zekai, Aoran, and Wenbin, who have received Summer Undergraduate Research Fellowships (SURF) and won competitions such as the YinzOR Student Conference Best Poster Competition. His secured funding includes the 2024-2025 Block Center Seed Grant and NSF collaborative research with Dr. Zhigang Peng focused on energy systems resilience. He leads collaborative research teams in energy analytics and security applications, organizing the 19th INFORMS Workshop on Data Mining and Decision Analytics and participating in initiatives like the IMSI Long Program on Green Energy Architecture.

Alexandros Stergiouمشاهده پروفایل

استادیار

• Video Understanding
• Action Recognition
• Model Interpretability
• +۵ مورد دیگر

Alexandros Stergiou is an Assistant Professor in the Data Management & Biometrics (DMB) group at the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) of the University of Twente. His research focuses on video understanding, model interpretability, and spatio-temporal analysis. PhD from Utrecht University (2021) Postdoctoral work at VUB and University of Bristol Key research areas include: Action Recognition and Interaction Modeling Temporal and Motion Analysis Audio-Visual Recognition Deep Learning for Video Tasks Recent publications span large-scale benchmarks (LAVIB), temporal attention mechanisms, and motion consistency modeling. Awards include Outstanding Reviewer at ECCV 2022. Teaching roles: Course administrator for REDI and Deep Learning Guest Lecturer for INFOMCV

• Computer Networks
• Software Defined Networking
• Edge Computing
• +۱۲ مورد دیگر

José André Rocha Sá Moura is an Assistant Professor at ISCTE - Instituto Universitário de Lisboa, affiliated with the Department of Information Science and Technology (ISTA). He is also an Integrated Researcher at the Institute of Telecommunications – IUL, where he contributes to the Radio Systems Group in Lisbon. His academic and research work is centered on advanced networking technologies and systems. PhD in Computer Science, Lancaster University, 2011 Master's in Electrical and Computer Engineering, University of Porto, 2001 Bachelor's in Electrical Engineering and Telecommunications, University of Aveiro, 1989 His research interests include software-defined networking (SDN), edge and fog computing, network management, game theory, optimization, resource virtualization, and the resilience of networked systems. He explores intelligent access to heterogeneous wireless networks and the integration of cloud and big data technologies in modern network infrastructures. His work bridges theoretical models with practical implementations in next-generation networks. The most recent publications highlight a strong trend in decentralized network control, autonomous management using AI/ML, resilience in edge systems, and efficient resource provisioning for IoT and multimedia services. His work frequently applies SDN and game theory to solve complex networking challenges in dynamic and heterogeneous environments. Scientific Awards and Recognitions: No specific awards mentioned in the provided text. Academic Supervision and Research Contributions: José Moura has supervised 1 PhD (as co-supervisor) and 10 MSc theses, covering topics such as SDN-based intrusion detection, multi-access management, fog computing, and IoT architectures. He has been involved in research projects including ImpAr, NEUF, and SAAS. He serves as a reviewer for journals like IEEE Transactions on Vehicular Technology and conferences such as ConfTele. His research aligns with UN Sustainable Development Goals related to innovation and infrastructure. Labs and Research Teams: He is an active member of the Radio Systems Group at IT – Lisboa, focusing on wireless technologies and networked systems. His work is embedded in the broader ecosystem of the Institute of Telecommunications, a leading R&D unit in Portugal.

Shu-Ching Chenمشاهده پروفایل

استاد

• Data Science
• Multimedia Big Data
• Disaster Information Management
• +۸ مورد دیگر

Shu-Ching Chen is a Professor and Executive Director of the Data Science and Analytics Innovation Center (dSAIC) at the University of Missouri-Kansas City (UMKC), School of Science and Engineering. He holds a Ph.D. in Electrical and Computer Engineering from Purdue University and has extensive experience in data science, multimedia big data, disaster information management, AI/ML, and AR/VR. He leads a multi-university research center focused on data analytics, AI, and societal impact. Research Interests: His research spans data science , multimedia big data , disaster informatics , AI/ML , and spatial computing . He has pioneered work in multimodal data fusion, multimedia retrieval, and intelligent systems for disaster response and climate modeling. His recent work emphasizes AI-driven solutions for public health, transportation, and environmental resilience. The 15 most recent publications reflect a strong trend in AI for societal challenges , including pandemic response, disaster management, climate modeling, and transportation analytics. His work integrates deep learning , graph neural networks , remote sensing , and multimodal data fusion , often applied to real-world problems in public safety, healthcare, and infrastructure. There is a clear emphasis on practical software systems (e.g., hurricane loss modeling) and interdisciplinary collaboration . Scientific Honors: IEEE Fellow (2016), AAAS Fellow (2016), AIMBE Fellow (2025), AAIA Fellow (2021), SIRI Fellow (2009) ACM Distinguished Scientist (2011) Best Paper Awards (2006, 2016, 2022) IEEE TCMC Impact Award (2024), Service Award (2019) FIU Top Scholar (2011, 2012), Eminent Scholar (2014–2019) Research Leadership and Grants: Dr. Chen has secured major funding from NSF, NIH, DHS, DOE, NOAA, DoD, and industry partners (Microsoft, IBM). He is PI of a U.S. Department of Education Center of Excellence in AI-Empowered Spatial Computing and leads the dSAIC. His grants focus on AI for disaster management, pandemic response, transportation, and secure computing. He actively mentors students and collaborates across institutions. Labs and Centers: He leads the Data Science and Analytics Innovation Center (dSAIC) , a University of Missouri System-wide center. He is also PI of the AI-Empowered Spatial Computing Center of Excellence funded by the U.S. Department of Education.

Kuno Kurzhalsمشاهده پروفایل

پژوهشگر ارشد

• Data Visualization
• Eye Tracking
• Visual Analytics
• +۳ مورد دیگر

Kuno Kurzhals is a Research Fellow at the Institute for Visualization and Interactive Systems (VISUS) of the University of Stuttgart , affiliated with the Cluster of Excellence IntCDC . His work focuses on video visualization , eye tracking evaluation , and visual analytics for dynamic data. Research Interests : Video visualization, eye tracking, human-computer interaction, spatio-temporal analysis, and immersive environments. Recent Trends : Publications emphasize collaborative gaze analysis in AR/VR, mobile eye-tracking data processing, and gaze-adaptive interfaces. Key subfields include non-negative matrix factorization , user behavior in immersive systems , and evaluation of visualization literacy . Collaborative Work : Co-author in studies on projection displays for group gaze , collaborative molecular learning , and manufacturing process monitoring . Technical Focus : Innovations in fixation-image charts , gaze spiral visualization , and AOI transition trees for analyzing eye movement patterns. Applications : Tools developed for dance choreography planning , music practice reflection , and public transport map analysis . Labs & Teams : Affiliated with the Weiskopf Group at VISUS, contributing to the Cluster of Excellence IntCDC . Collaborates with researchers like Daniel Weiskopf , Maurice Koch , and Nelusa Pathmanathan on interdisciplinary projects.

Isabel Cristina Maciel Natárioمشاهده پروفایل

دانشیار

• Spatio-Temporal Statistics
• Bayesian Analysis
• Hierarchical Modeling
• +۷ مورد دیگر

Isabel Cristina Maciel Natário is an Associate Professor in the Department of Mathematics at the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT-UNL). She is an integrated member of the Centro de Matemática e Aplicações (CMA), specifically within the Statistics and Risk Management research group. Her academic and research profile is deeply rooted in statistical theory and applied data analysis, with strong interdisciplinary applications. Her educational background includes a Doctorate in Statistics and Operational Research (2005), a Master’s in Probability and Statistics (1999), and a Bachelor’s in Applied Mathematics and Computation (1995), all from Universidade de Lisboa. Doctorate: Hierarchical Bayesian Models for Epidemiological Analysis of Rare Events, Universidade de Lisboa (2005) Master’s: Spatial, Temporal and Spatio-Temporal Distribution of Rare Diseases, Universidade de Lisboa (1999) Bachelor’s: Avaliação de Erros em Inquéritos: Entrevista/Reentrevista, Instituto Superior Técnico (1995) Her research interests center on Spatio-Temporal Statistics, Bayesian Analysis, Hierarchical Modeling, Stochastic Processes, and Big Data. She applies these methodologies to diverse domains such as environmental risk, fisheries, epidemiology, and climate modeling. Her recent work demonstrates a strong focus on geostatistical inference, stochastic partial differential equations, and risk assessment in maritime and ecological contexts. Her 15 most recent publications reflect a consistent trajectory in advanced statistical modeling, particularly in spatial and spatio-temporal frameworks. The research spans theoretical developments in stochastic processes and Bayesian methods, as well as applied studies in health, marine biology, and environmental science. Key themes include maritime surveillance, species distribution modeling, wind velocity analysis, and clinical outcome prediction using statistical models. She has received no explicitly mentioned scientific awards in the provided text. Isabel Cristina Maciel Natário has supervised or collaborated with several researchers, including Sílvia Isabel Belo Guerra and Paula Cristina Pires Simões. While no formal list of advisees is provided, her collaborative publications and leadership in research groups suggest active mentoring. She has been involved in interdisciplinary research projects, particularly those involving environmental and health data. There is no mention of specific grants, but her sustained publication record and research group affiliation indicate active research funding. She is affiliated with the CMA – Centro de Matemática e Aplicações, a recognized research center at FCT-UNL, where she contributes to the Statistics and Risk Management group. This center supports collaborative, interdisciplinary research in mathematical modeling and statistical applications.

• Graph Neural Networks
• Time Series Analysis
• Anomaly Detection
• +۵ مورد دیگر

Cesare Alippi is a Professor at Politecnico di Milano in the Department of Electronics, Information and Bioengineering within the School of Industrial and Information Engineering. With an extensive publication record spanning over three decades from 1991 to 2025, he has established himself as a leading researcher in machine learning, particularly in graph neural networks, time series analysis, and anomaly detection. His research interests focus on developing advanced machine learning methodologies for time series analysis, graph-based learning, and anomaly detection systems. Professor Alippi's work bridges theoretical foundations with practical applications across various domains including smart systems, healthcare, and cybersecurity. His recent publications demonstrate a strong emphasis on graph-based approaches to time series forecasting, structural anomaly detection, and spatiotemporal modeling. His publication trends from 2023-2025 reveal a concentration on graph neural networks for time series applications, with significant contributions to forecasting, anomaly detection, and representation learning. These works often address challenges in spatiotemporal data, missing values, and structural pattern recognition, demonstrating both theoretical depth and practical utility. Professor Alippi has mentored numerous researchers who have become prominent collaborators, including Andrea Cini, Daniele Zambon, and Lorenzo Livi, among others. His collaborative network extends across multiple institutions and research domains.

• Functional Data Analysis
• Time Series Analysis
• Spatio-temporal Statistics
• +۴ مورد دیگر

Dr Israel Martinez Hernandez is a Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University. His research focuses on functional data analysis with applications in environmental, energy, health, and engineering domains. His research interests include: Functional data with temporal dependence and nonstationary Large-scale and complex time series data Spatio-temporal data modeling Nonstationary functional time series methods Methodologies for large spatio-temporal data Factor models for complex spatio-temporal data Methodologies for large environmental data Methodologies for Point Processes Dr Martinez Hernandez is affiliated with several research groups including the Centre of Excellence in Environmental Data Science, Changepoints and Time Series group, Energy Lancaster, Environmental and Ecological Statistics group, and the STOR-i Centre for Doctoral Training. His recent publications demonstrate expertise in functional time series analysis, changepoint detection, and environmental statistics, with applications ranging from particle size distribution modeling to home activity pattern analysis. Dr Martinez Hernandez is involved in the STORi: DSI project on "Evolution of statistical earthquake models to account for measurement errors and dependence" (2023-2026), supervising PhD student Wanchen Yue.

Saskia Schirmerمشاهده پروفایل

پژوهشگر

• Wildlife Conservation
• Population Ecology
• Capture-Recapture Modeling
• +۴ مورد دیگر

Dr. Saskia Schirmer is a researcher at the University of Greifswald in the Department of Applied Zoology and Nature Conservation. Her research focuses on statistical ecology with particular emphasis on wildlife conservation applications. She works at the intersection of ecological field data collection and advanced statistical modeling to address critical conservation challenges. Research interests: Using mark-recapture modeling for nature conservation, particularly with bat populations where she collaborates with volunteers to analyze long-term datasets Modeling spatio-temporal survival patterns of migrating species including both long-distance migrants (like ospreys) and short-distance migrants (like robins) Developing improved multi-species population indices that can handle rare, disappearing, or newly emerging species Her recent publications (2022) demonstrate expertise in spatial statistics and ecological modeling, with a focus on survival analysis, migratory connectivity, and population dynamics. She has developed innovative approaches to handle observational data from marked animals, particularly those found dead and reported, to better understand survival patterns across space and time. Scientific contributions: Advanced statistical frameworks for analyzing migratory species in continuous space Novel approaches to multi-species population indices that address limitations of existing methods Integration of volunteer-collected data with sophisticated statistical modeling for bat conservation Dr. Schirmer's work bridges the gap between theoretical statistical ecology and practical conservation applications, providing tools that can inform evidence-based conservation policy decisions.

Peter Fienerمشاهده پروفایل

استاد

• Soil Erosion
• Surface Runoff
• Water and Soil Resource Management
• +۷ مورد دیگر

Peter Fiener serves as Professor for Water and Soil Resource Research at the Department of Geography, Faculty of Applied Computer Sciences, University of Augsburg since 2013. He concurrently holds the position of Dean of Students in the Department of Geography (since 2016) and coordinates the Marie Skłodowska-Curie Innovative Training Network SOPLAS (since 2021). His academic journey includes prior appointments as Professor at the Indo-German Centre for Sustainability, Indian Institute of Technology Madras (2011-2012) and Senior Scientist at the University of Cologne (2004-2013). PhD from Technische Universität München (2003) Habilitation from University of Cologne (2010) Master's in Geography from Universität Augsburg and LMU München (1998) Professor Fiener's research program focuses on measuring and modeling surface runoff, soil erosion, and lateral matter fluxes in agricultural landscapes. He investigates the effects of spatio-temporal patterns and connectivity on water and matter movement, coupling these processes with biogeochemical cycles in terrestrial and aquatic ecosystems. A significant recent emphasis has been on macro and microplastics in arable soil systems, particularly their redistribution mechanisms. His geographical research areas of specialization include South India and Central Africa, where he has conducted extensive field studies on sustainable agricultural practices. Analysis of his 2024-2025 publications reveals a strong research trajectory toward microplastic detection methodologies, advanced soil erosion modeling using machine learning, and carbon dynamics in eroding landscapes. His work increasingly integrates remote sensing technologies with computational approaches to address large-scale soil conservation challenges, demonstrating particular innovation in plasticulture detection using satellite data and erosion nowcasting systems. Coordinator of Marie Skłodowska-Curie Innovative Training Network SOPLAS Professor Fiener leads the Water and Soil Resource Research Team comprising multiple doctoral researchers including Dr. Florian Wilken, Dr. Pedro Batista, and Dr. Lena Katharina Öttl, alongside numerous M.Sc. researchers. His M²ESH laboratory supports advanced measurement and modeling of soil hydrological processes. Current research directions include understanding tillage erosion's role in carbon dynamics, developing cost-effective microplastic detection protocols, and assessing long-term impacts of agricultural practices on landscape evolution across multiple temporal scales.

Marcin Jurekمشاهده پروفایل

استادیار

• Spatial Statistics
• Computational Methods
• Gaussian Processes
• +۳ مورد دیگر

Marcin Jurek is an Assistant Professor in the Department of Statistics and Data Science at Southern Methodist University (SMU). He previously held a postdoctoral position at the University of Texas at Austin, working under Dr. Catherine Calder and Dr. Corwin Zigler, and completed his PhD in Statistics at Texas A&M University in August 2020 under Dr. Matthias Katzfuss. Research Focus: Computational methods in spatial statistics, scalable Gaussian process approximations, spatio-temporal modeling Software Development: Maintains the GPvecchia package for R/Python Collaborations: National Center for Atmospheric Research (NCAR), Texas A&M University His work addresses challenges in analyzing massive spatio-temporal datasets through hierarchical sparse Cholesky decompositions and Vecchia approximations. Current research includes statistical inference for mobility trajectories and high-dimensional data analysis. He organized an SMU workshop on high-dimensional data in 2024 and has contributed to applications in environmental monitoring and agricultural data science. Recent publications demonstrate his expertise in developing computationally efficient algorithms for complex statistical problems, including applications to groundwater contamination datasets and maize breeding programs. He actively engages in interdisciplinary research across climate modeling, movement ecology, and Bayesian inference.

Rob Deardonمشاهده پروفایل

استاد

• Bayesian statistics
• computational statistics
• infectious disease epidemiology
• +۵ مورد دیگر

Rob Deardon is a Professor jointly appointed in the Faculty of Veterinary Medicine and the Department of Mathematics and Statistics at the University of Calgary. His research spans Bayesian statistics, infectious disease epidemiology, and spatial modeling, with applications in human and animal health. PhD in Applied Statistics, University of Reading (2001) MSc in Medical Statistics, University of Southampton (1997) BSc in Pure Mathematics & Mathematical Statistics, University of Exeter (1996) Rob Deardon's work focuses on computational statistics, infectious disease modeling (including foot-and-mouth disease and influenza), and spatio-temporal analysis. His methodological interests include Monte Carlo methods, approximate Bayesian computation, and statistical learning. Recent publications emphasize spatial epidemic models, behavioral change analysis, and computational methods for disease surveillance. He leads a research group of 10 graduate students. He teaches graduate courses in infectious disease modeling and maintains collaborations across biostatistics, veterinary medicine, and public health.

• Autonomous Driving
• Multi-Object Tracking
• Sensor Calibration
• +۳ مورد دیگر

Leah Strand is a Ph.D. candidate and research assistant at the Technical University of Munich (TUM) in the Department of Robotics, Artificial Intelligence and Real-time Systems under Prof. Knoll. She joined TUM in March 2020 and contributes to projects like Providentia++ , which focuses on creating real-time digital twins of traffic on the Autobahn A9. Education: M.Sc. in Mechatronics and Information Technology, B.Sc. in Engineering Science (both from TUM) Research interests include: Autonomous Driving Multi-Object Tracking Sensor Calibration and Fusion Her recent publications emphasize image enhancement , multi-camera calibration , and traffic surveillance datasets . She actively participates in teaching and project advising, with expertise in real-time systems and infrastructure-based perception for autonomous vehicles. Contact: leah.strand@tum.de

Shane Jensenمشاهده پروفایل

دانشیار

• bioinformatics
• biostatistics
• genetics
• +۱ مورد دیگر

Shane Jensen is an Associate Professor of Statistics in the Department of Statistics at the Wharton School of the University of Pennsylvania, where he applies advanced statistical methodologies to interdisciplinary challenges. His primary research domains include: Bioinformatics and genetic mutation analysis Biostatistics in healthcare and surgical workforce equity Sports analytics for baseball, hockey, and NFL performance evaluation Urban crime patterns and spatial statistics in Philadelphia Analysis of his recent publications reveals consistent methodological innovation in Bayesian clustering, hierarchical time series forecasting, and spatial modeling. His work bridges theoretical statistics with practical applications in urban policy (vacant lot remediation, school proximity effects), public health (Lyme disease forecasting, colorectal surgery outcomes), and sports management (Hall of Fame metrics, salary cap optimization). The interdisciplinary nature of his research demonstrates significant impact across criminology, epidemiology, and sports analytics.