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K.C. ChangView profile

Professor

• Data Fusion
• Bayesian Networks
• Probabilistic Reasoning
• +9 more

K.C. Chang is a Professor in the Department of Systems Engineering and Operations Research at George Mason University's Volgenau School of Engineering. He is the Director of the Sensor Fusion Lab and the coordinator of the Financial Engineering Program at GMU, reflecting his interdisciplinary research focus on data fusion and probabilistic reasoning. Education: PhD, Electrical Engineering, University of Connecticut MS, Electrical Engineering, University of Connecticut BS, Communication Engineering, National Chiao-Tung University Dr. Chang's research centers on distributed data fusion, Bayesian networks, and decision-making under uncertainty, with applications in sensor networks, wireless communications, satellite systems, and financial engineering. His work integrates statistical modeling, machine learning, and systems engineering principles to solve complex, real-world problems involving uncertainty and large-scale information processing. The recent publications reflect a strong trend in applying probabilistic reasoning to dynamic spectrum access, satellite communication, and electronic warfare. His research leverages copula functions and information genealogy to enhance fusion accuracy in non-Gaussian and dependent environments. Applications span defense, aerospace, and finance, demonstrating the versatility of his methodological contributions. Scientific Awards: IEEE Fellow (2010) for contributions to sensor data fusion and Bayesian probabilistic inference Dr. Chang has advised numerous graduate students and led major research initiatives funded by the Office of Naval Research (ONR), National Science Foundation (NSF), Army Research Office (ARO), Air Force Office of Scientific Research (AFOSR), and industry partners like Intelligent Fusion Technology Inc. and Impact Technologies, LLC. His editorial roles include serving as Editor for Tracking/Navigation Systems and Large Scale Systems in IEEE Transactions on Aerospace and Electronic Systems, and as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics. He was also Technical Program Co-Chair of the 2009 International Conference on Information Fusion. He leads the Sensor Fusion Lab at George Mason University, a research group focused on advancing the theory and application of multi-sensor, multi-source information fusion. The lab works on foundational algorithms as well as real-world implementations in defense, communication, and financial systems. The lab collaborates with government agencies and private sector organizations to translate theoretical advances into operational technologies.

Yuanchang XieView profile

Professor

• Transportation Engineering
• Traffic safety
• Traffic operations and simulation
• +5 more

Yuanchang Xie serves as Professor in Civil and Environmental Engineering at UMass Lowell's Francis College of Engineering, where he leads research in the Center for Smart Cyber-Physical Systems. His work integrates computational methods with transportation infrastructure analysis, focusing on safety-critical applications through federal partnerships. Dr. Xie earned his Ph.D. in Civil Engineering from Texas A&M University (2007), preceded by M.S. and B.S. degrees in Transportation Engineering from Southeast University, China (2003, 2000). His academic foundation supports interdisciplinary research bridging civil engineering and cyber-physical systems. Research centers on traffic safety, intelligent transportation systems, and logistics optimization. He pioneers AI-driven approaches for crash prediction, connected vehicle operations, and infrastructure monitoring, emphasizing real-world implementation through partnerships with USDOT and state agencies. Current work explores multimodal data fusion for safety analytics in mixed-autonomy environments. Recent publications (2024-2025) reveal accelerating focus on deep learning applications: crosswalk detection via drone imagery, trajectory prediction in mixed traffic, and real-time work zone safety monitoring. This evolution demonstrates strategic alignment with emerging transportation technologies while maintaining core safety objectives. No scientific awards were explicitly documented in source materials. Dr. Xie has secured continuous funding as Principal Investigator through NSF, USDOT, DOE, and USDA programs. Key projects include Connected Vehicles: Toward the Understanding of "Firm Science" (NSF), Center of Multi-Scale Sensing Technologies (USDOT), and nuclear evacuation modeling for rural communities (USDA). His grants consistently address infrastructure resilience through cyber-physical integration. He directs research activities within UMass Lowell's Center for Smart Cyber-Physical Systems, which develops sensor networks and computational models for transportation infrastructure monitoring. The center's work on drone-based inspection systems and emergency response logistics demonstrates practical applications of his theoretical frameworks.

• Agricultural Climate Risk Modeling
• Environmental Finance
• Geospatial Intelligence
• +7 more

Dr. Thong Nguyen-Huy serves as a Senior Research Fellow at the Centre for Applied Climate Sciences within the University of Southern Queensland. His work bridges agricultural climate risk management, geospatial intelligence, and environmental finance through advanced statistical and AI methodologies. His educational background includes a BEng from VNUHCM (2009), MSpatSciTech (2013), and PhD (2019) from USQ, reflecting deep technical expertise in spatial sciences and climate modeling. Nguyen-Huy's research focuses on agricultural climate risk modeling , digital farming , and disaster risk management , utilizing copula-based models, GIS, remote sensing, and machine learning to develop decision-support systems for climate resilience. His work spans sustainable agriculture, renewable energy forecasting, and decarbonization strategies with significant real-world applications in Vietnam, Australia, and Pacific Island nations. His recent publications reveal a strong emphasis on hybrid AI models for flood forecasting , crop yield prediction , and climate-risk insurance mechanisms , demonstrating interdisciplinary integration of environmental science, data analytics, and financial instruments. Climate Risk Research Award (Top 10 Globally) - Allianz, 2021 Award for Excellence in Doctoral Research - University of Southern Queensland, 2019 Unit Specific Publication Excellence Award - University of Southern Queensland, 2019 Postgraduate Research Scholarship - University of Southern Queensland, 2015 Graduate Coursework Prize - University of Southern Queensland & QLD SSSI, 2012 Nguyen-Huy actively supervises graduate research in environmental monitoring and climate risk modeling while securing competitive grants including the InsuResilience Solutions Fund (2025) and APN Collaborative Regional Research Programme (2023). He collaborates extensively with universities, research institutions, and industry partners across Southeast Asia and the Pacific.

Claus HaslauerView profile

Research Professor

• Hydrogeology
• Geostatistics
• Spatial Statistics
• +2 more

Claus Haslauer is a Research Professor and Scientific Director at vegasIWS, leading the Stochastic Hydrogeology Group in the Department of Hydrogeology at the University of Tübingen. His DFG-funded research focuses on spatial dependence structures in hydrogeology, notably developing copula-based geostatistical models adopted by Baden-Württemberg's environmental agency (LUBW) for groundwater assessment. He collaborates with the Kansas Geological Survey and is affiliated with the International Research Training Group 'Integrated Hydrosystem Modelling'. His expertise spans: Multivariate copula models for aquifer parameter analysis Spatial interpolation techniques using secondary data (e.g., land cover) Impact assessment of spatial heterogeneity on solute transport Integration of categorical and uncertain measurements in environmental models He actively contributes to conferences like AGU Fall Meetings and GeoEnv, emphasizing data-model fusion in hydrogeology. As a doctoral advisor, he recruits researchers for projects on spatial dependence structures. His group develops computational tools including geostatistical code for operational use and Python-based scientific workflows.

Vladan BabovicView profile

Professor

• Hydroinformatics
• Artificial Intelligence
• Machine Learning
• +5 more

Professor Vladan Babovic is a leading academic and scientist in hydroinformatics at the National University of Singapore's Department of Civil Engineering. His career spans over three decades, focusing on data-driven research, computational hydraulics, and decision-making under climate uncertainties. He holds adjunct roles at the University of Idaho and University of Iowa. Babovic pioneered research institutes like the Singapore Delft Water Alliance (SDWA) and NUSDeltares, recognized with the Albert Winsemius Award in 2014. Education includes a Double Ph.D. from Delft University of Technology, M.Sc. in Hydroinformatics, and a B.Sc. in Civil Engineering from the University of Sarajevo. His expertise spans artificial intelligence, machine learning, hydrological modeling, and real options analysis for infrastructure resilience. Key contributions include genetic programming applications in hydrology and AI-driven climate adaptation strategies. Research interests emphasize integrating AI with environmental systems, including deep learning for flood mechanisms, physics-informed rainfall-runoff models, and data assimilation techniques. He has advised multiple institutions and served on councils like the International Association for Hydro-Environmental Research and the Singapore Coastal Climate Adaptation Panel. Notable awards include the IWA Fellowship (2012), Arthur Thomas Ippen Award (1999), and recognition as a Chartered Engineer. His work bridges academia and industry, addressing global water challenges through innovative methodologies and adaptive infrastructure design.

Zhenyu YangView profile

Associate Professor

• Control Theory
• Mathematical Modelling
• Fault Detection and Diagnosis
• +7 more

Zhenyu Yang is an Associate Professor at Aalborg University's Department of Energy within The Faculty of Engineering and Science. He specializes in control theory, AI-driven automation, and industrial process control, with a focus on offshore energy and water treatment systems. He leads the Offshore Process Control and Cybernetics Research Group and chairs the Mission for Sustainable Fuels, Chemicals, and PtX. His work aligns with UN Sustainable Development Goals related to clean energy and environmental protection. Education: B.Sc., M.Sc., and Ph.D. in Control Theory from Shandong University and Beijing University of Aeronautics and Astronautics. Postdoctoral training at Delft University of Technology and Aalborg University. Completed pedagogic education for assistant professors and interdisciplinary leadership training. Research interests include fault-tolerant control systems, hybrid dynamical systems, AI for process automation, and water treatment technologies. He has supervised 6 PhD students, including M. Kashani. Notable awards include Teacher of the Year 2012 and ABB Best Application Award Finalist 2009. Key projects involve high-temperature heat pumps, offshore produced water treatment automation, and microplastics capture using hydrocyclones. He has authored 198 publications and actively participates in academic committees and international conferences. Collaborations span institutions globally, emphasizing sustainable energy solutions and advanced control systems.

Felix RempeView profile

Researcher

• Mobility Systems
• Machine Learning
• Data Exploration and Analyses Methods
• +1 more

Felix Rempe is a Rudolf Diesel Industry Fellow at the Technical University of Munich Institute for Advanced Study (TUM-IAS) , affiliated with the BMW Group in the field of Autonomous Driving . He holds a PhD in Computational Science and Engineering from TU Munich, awarded summa cum laude in 2018, and an M.Sc. in Mechatronics from DHBW Stuttgart in 2014 with Honours. His research focuses on data-driven approaches to traffic engineering and control , leveraging machine learning and sensor data from vehicles for real-time traffic state estimation and prediction. His work explores the intersection of mobility systems and artificial intelligence , particularly in the domains of traffic flow theory and digital twin development for transportation infrastructure. Felix’s publications highlight advancements in data fusion techniques , deep learning architectures , and statistical modeling applied to autonomous driving and urban mobility challenges. 2018: PhD thesis awarded summa cum laude 2014: M.Sc. with Honours award for completing the Honour’s track at TUM 2012: BayLat grant for study exchange program Latin America

Mehdi H. AfsharView profile

Researcher

• Climate Change
• Soil Science
• Water Resources Engineering
• +3 more

Dr. Mehdi H. Afshar is a Researcher at the Technical University of Hamburg (TUHH) since August 2023, focusing on soil health and climate change impacts. Previously, he served as an Assistant Professor at Middle East Technical University (METU), Ankara, Turkey (2022–2023), and held postdoctoral positions at the John Innes Centre (UK) and the University of Manchester (UK). He holds a PhD in Civil Engineering from METU, an MSc in Water Resources Engineering, and a BSc in Water Engineering from Urmia University, Iran. His research integrates statistical, machine learning, and process-based models to enhance predictions in water-food security and soil health. Key projects include the Horizon Europe-funded AI4SoilHealth, which addresses soil degradation and global soil function prediction. His work emphasizes continental Europe, focusing on soil salinization, climate change scenarios, and drought analysis. Publications span drought prediction frameworks, soil moisture modeling, and agricultural insurance optimization. His recent work explores AI applications in soil health and groundwater-heatwave linkages, reflecting a blend of computational and environmental sciences. Dr. Afshar collaborates internationally on environmental monitoring and resilience, contributing to conferences like EGU General Assembly. His expertise bridges hydrology, remote sensing, and climate modeling to address global environmental challenges.

Baocheng GengView profile

Assistant Professor

• Human Decision Making
• Distributed Detection
• Information Fusion
• +7 more

Baocheng Geng is an Assistant Professor in the Department of Computer Science at The University of Alabama at Birmingham (UAB), within the College of Arts and Sciences. His research focuses on human decision making, distributed detection, and machine learning applications in IoT and autonomous systems. He holds a Ph.D. from Syracuse University and a B.S. from Shanghai Jiao Tong University. Research Interests: His work bridges artificial intelligence, behavioral psychology, and human-technology interactions. Key areas include decision fusion in human-machine inference networks (HMIN), cognitive biases in human decision making, and robust algorithms for distributed systems under adversarial conditions. Publications Trends: Recent work emphasizes adversarial machine learning, federated learning, and interpretable neural networks for remote sensing and cybersecurity. He has explored applications in mental health prediction, cognitive radar, and resilient sensor networks. Affiliations: Previously affiliated with Syracuse University's Sensor Fusion Lab under Prof. Pramod K. Varshney. Current projects involve interdisciplinary collaborations in AI ethics, autonomous systems, and data-driven healthcare solutions. Labs: Involved in advancing the Sensor Fusion Lab's legacy at UAB, focusing on human-in-the-loop decision systems and strategic defense mechanisms against Byzantine attacks.

• Machine Learning
• Time Series Analysis
• Generative Models
• +6 more

Srinjoy Das is an Assistant Professor in Data Science at the School of Mathematical and Data Sciences, part of the Eberly College of Arts and Sciences at West Virginia University (WVU). He holds a Ph.D. and M.S. in Electrical Engineering and Statistics from the University of California, San Diego (2018), followed by postdoctoral research at UCSD’s Department of Mathematics until 2021. His research focuses on algorithms for predictive inference on time series, generative models, and efficient implementation of deep learning on edge computing devices. Education: Ph.D. in Electrical Engineering, UCSD (2018) M.S. in Statistics, UCSD (2018) Postdoctoral Researcher, UCSD Mathematics (2018–2021) Research Interests: Algorithms for real-time inference on generative neural networks Efficient implementation of deep learning on FPGAs and edge devices Time series analysis and nonparametric prediction Data-efficient learning in additive manufacturing and healthcare Recent Research Trends: His recent work emphasizes machine learning applications in manufacturing defect detection (e.g., melt pool characterization in 3D printing), geospatial analysis, and healthcare prediction systems. He also explores hybrid optimization strategies and bandwidth-efficient video processing techniques. Mentoring & Collaboration: Advised students on projects including FPGA-based neural network design (Xinyu Zhang), generative model evaluation (Ojash Neopane), and statistical inference for random fields (Ivy Zhang). Collaborates with industry partners like Qualcomm and Microsoft Research, focusing on edge computing and hardware optimization. Labs/Teams: His research group focuses on interdisciplinary projects at the intersection of data science, signal processing, and applied mathematics, with active collaborations in both academia and industry.

Orkun FuratView profile

Lecturer

• Machine Learning
• Stochastic Modeling
• Spatial Statistics
• +5 more

Dr. Orkun Furat is a Lecturer at the Institute of Stochastics, University of Ulm, Germany, where he conducts research at the intersection of machine learning, stochastic modeling, and image analysis for materials science applications. His work focuses on developing advanced computational methods to characterize and reconstruct 3D microstructures from 2D image data, with significant contributions to battery materials and particle systems. His primary research interests include generative adversarial networks (GANs) and spatial stochastic models for tomographic image analysis of functional materials. He has pioneered techniques for super-resolving microscopy images, quantifying electrode degradation in batteries, and modeling particle morphology/separation processes in mineral processing. His interdisciplinary approach bridges statistics, computer science, and materials engineering through rigorous mathematical frameworks. Recent publications (2024-2025) reveal a concentrated focus on lithium-ion and all-solid-state battery technologies, particularly analyzing how operating conditions (charge rate, temperature, cycling) induce electrode degradation. Simultaneously, his particle systems research employs multidimensional stochastic models to optimize mineral beneficiation processes like flotation, using copula-based approaches for particle property distributions. Dr. Furat actively supervises seminar students in generative machine learning and spatial stochastic modeling while teaching core courses including Point Processes and Advanced Statistics. His research impact is evidenced by numerous invited talks at premier venues like the Dagstuhl Seminar (2025) and European Congress for Stereology (2025), where he presents as a plenary speaker on AI-driven microstructure reconstruction. Collaborating with interdisciplinary teams across materials science and engineering, his work on digital twins for battery electrodes and virtual materials testing has been featured in University of Ulm press reports (2024) highlighting applications in efficient battery recycling and sustainable material design. Current projects integrate generative AI with stochastic geometry to solve industrial-scale challenges in energy storage and mineral processing.

Pramod K. VarshneyView profile

Professor

• Distributed sensor networks
• Data fusion
• Statistical inference
• +4 more

Pramod K. Varshney is a Distinguished Professor at Syracuse University's Department of Electrical Engineering and Computer Science, within the College of Engineering & Computer Science. He also holds adjunct professorships at Upstate Medical University (Radiology) and Xidian University (China). His research focuses on distributed sensor networks, data fusion, machine learning, and signal processing, with applications spanning defense, healthcare, and IoT. Varshney has led over four decades of research funded by major agencies like DoD, NSF, and industry partners. Education: PhD (Electrical Engineering, University of Illinois, 1976), MS (1974), BS (1972). His research interests emphasize foundational aspects of data/information fusion, sensor networks, and AI-driven analytics. Notable contributions include pioneering work in distributed detection theory and secure inference in adversarial environments. Recent publications highlight advancements in federated learning, anomaly detection, and multi-target tracking. Varshney has been honored with prestigious awards such as the IEEE Fellow (1997), Yaakov Bar-Shalom Award (2018), and Syracuse University's Chancellor’s Lifetime Achievement Award (2023). He has advised numerous PhD and Master’s students, contributing to over 350 publications, including seminal books like Distributed Detection and Data Fusion (Springer, 1997). He has directed Syracuse's CASE (Center for Advanced Systems and Engineering) and actively collaborates with global institutions. His work bridges theoretical advancements with real-world applications in healthcare imaging, cybersecurity, and autonomous systems.

• Statistical Genetics
• Graphical Models
• Genomics
• +5 more

Pariya Behrouzi is an Assistant Professor in the Department of Mathematical and Statistical Methods - Biometris at Wageningen University & Research. She specializes in developing statistical methodologies with applications in genetics, genomics, and nutrition science. Her research focuses on graphical models, copula theory, and Bayesian approaches to analyze complex biological systems. Behrouzi leads or collaborates on projects such as SignAphy (plant signaling dynamics) and Sensing Age (epigenetic aging studies). She co-developed the netgwas R package for network-based genome-wide association studies. Her work bridges statistical theory with practical applications in agriculture, nutrition, and computational biology. Education: PhD in Statistics (as indicated by title) Key Research Tools: Copula graphical models, spatial statistics, and Bayesian networks Current Projects: Multi-omics network reconstruction, resilience in lettuce crops, and microbiota systems analysis Her recent publications explore topics like meat analogue texture optimization, telomere length dietary correlations, and plant disease resistance mechanisms. Behrouzi advises four PhD candidates and collaborates with international teams on interdisciplinary research.

• Vegetation Ecology
• Ecological Modelling
• Grassland Biodiversity
• +3 more

Dr. Franziska Taubert leads the Grassland Modelling working group at the Department of Ecological Modelling, Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. Her research integrates vegetation ecology, ecological modeling, and Digital Twin technologies, focusing on grassland biodiversity dynamics and ecosystem functions. She developed the GRASSMIND model for simulating plant growth in managed grasslands. Research Interests: Her work investigates biodiversity emergence in grasslands, plant trait-environment relationships, and vegetation pattern analysis using field data and remote sensing. Methodologically, she specializes in process-based modeling and Digital Twin applications for environmental systems. Key Projects: EU-Horizon BioDT: Developing a Digital Twin for grassland biodiversity (WP6 Leader) LandTrans: Leading vegetation modeling team (GRASSMIND/FORMIND) PhD College LISA: Modeling socio-ecological impacts of grasslands iDiv Flexpool: Remote sensing of grassland diversity (Co-PI) Her recent publications demonstrate strong emphasis on ecological modeling (43%), biodiversity (29%), and Digital Twin applications (21%), with grassland ecosystems being the dominant focus (86%).

Wei ChenView profile

Professor

• Statistical Genetics
• Computational Biology
• Genomics
• +4 more

Wei Chen serves as Professor of Pediatrics (primary appointment) with secondary appointments in Biostatistics and Human Genetics at the University of Pittsburgh, where he leads the Chen Lab focused on statistical genomics and computational biology. His research develops innovative methods for analyzing high-throughput genomic data to study complex diseases including childhood asthma, age-related macular degeneration, and COPD. Dr. Chen earned his PhD in Biostatistics from the University of Michigan, MS in Mathematics from Michigan State University, and BS in Mathematics from Nanjing University. His expertise bridges mathematical theory with biomedical applications through rigorous statistical modeling. His research program centers on creating computational frameworks for single-cell and spatial multi-omics data integration, with particular emphasis on developing novel algorithms for doublet detection, spatial transcriptomics super-resolution, and longitudinal disease progression modeling. Current projects leverage bulk and single-cell multi-omics (DNA-seq, RNA-seq, methylation, ATAC-seq, Spatial Transcriptomics) to uncover disease mechanisms in pediatric asthma and macular degeneration. Recent publications demonstrate consistent leadership in statistical genomics, with 2024-2025 work advancing spatial transcriptomics transformer models and pediatric asthma endotype characterization published in Nature Communications and JAMA. His research shows increasing focus on multi-modal data integration and clinical translation of genomic findings. Pediatric Discovery Award from Department of Pediatrics (2025) Secretary of Chinese-American Lung Association (2024) Chapter Representative, Pittsburgh Chapter American Association Society (2024) Dr. Chen actively mentors 6 current PhD students and has guided over 20 trainees to successful careers in academia and industry. His lab secures substantial external funding including NIH R01 grants from NEI, NHGRI, NHLBI, NIAMS, and NIDDK, plus support from NSF, Scleroderma Research Foundation, Helmsley Charitable Trust, and UPMC. Recent grants include an NIAMS R01 for scleroderma subgroup identification and DoD funding for pediatric scleroderma multi-omics studies. The Chen Lab operates within the Rangos Research Building at Children's Hospital of Pittsburgh, maintaining a collaborative environment with biostatisticians, geneticists, and clinicians. Current projects integrate spatial transcriptomics with histology imaging and develop AI-driven models for disease progression prediction, supported by active partnerships with NIH institutes and disease-specific foundations.