جست‌وجوی استاد و پژوهشگر

Jun Wangمشاهده پروفایل

استاد

• Big Data
• Big Learning
• Data Intensive Computing
• +۴ مورد دیگر

Jun Wang serves as a Professor in the Department of Computer Science and Engineering at the University of Central Florida (UCF). He holds a Ph.D. from the University of Cincinnati (2002) and is actively involved in academic leadership roles, including associate editor positions for IEEE Transactions journals and organizing major conferences like the IEEE Conference on High Performance Computing and Communications. His research focuses on big data analytics, traffic safety modeling, and high-performance computing systems. Wang has received multiple prestigious awards, including the NSF Early Career Award (2009), DOE Early Career PI Award (2005), and UCF Research Incentive Awards. His work emphasizes developing efficient data storage solutions, optimizing energy use in data centers, and creating crash modification factor tools for transportation safety analysis. Recent research trends include long-term safety evaluations of traffic policies, pavement maintenance impacts, and legislative effects on motorcyclist fatalities. His publications highlight methodological advancements in empirical Bayes techniques and cross-state safety performance function transferability.

David Isaacsonمشاهده پروفایل

استاد

• Mathematical Physics
• Medical Imaging
• Electrical Impedance Tomography
• +۳ مورد دیگر

David Isaacson holds the position of Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI), where he conducts cutting-edge research at the intersection of theoretical mathematics and clinical medicine. His primary institutional affiliation is with RPI, and he is also associated with the Center for Biotechnology and Interdisciplinary Studies, reflecting the multidisciplinary nature of his work. Professor Isaacson's long-term research goals center on improving diagnostic and therapeutic approaches for heart disease and cancer through innovative mathematical and engineering solutions. Professor Isaacson's research program is distinctly bifurcated into two major domains: mathematical physics and medical imaging. In mathematical physics, he develops advanced computational methods for approximating energy levels, particle masses, and critical exponents in quantum mechanics, quantum field theory, and statistical mechanics. His medical imaging research focuses on electrical impedance tomography (EIT), where he pioneers algorithms and hardware for non-invasively measuring and visualizing the electrical state of the body's interior from surface measurements. Specific applications include monitoring cardiac and pulmonary functions, diagnosing breast cancer through molecular impedance signatures, and optimizing chemotherapeutic interventions. His work in this area has significant clinical implications for conditions such as congenital heart defects, cystic fibrosis, and bronchopulmonary dysplasia. A review of Professor Isaacson's publication record from 2016 to 2025 demonstrates a sustained and evolving research trajectory in EIT technology. Early work concentrated on foundational reconstruction algorithms (e.g., D-bar and Calderón methods) and hardware development (ACT series systems), while recent publications emphasize clinical translation, particularly in pediatric cardiology and pulmonology. His team has produced innovative solutions for simultaneous EIT/ECG acquisition, 3D absolute imaging, and dynamic monitoring of ventilation-perfusion relationships. The consistent theme across these studies is the rigorous application of mathematical theory to solve practical biomedical imaging challenges, resulting in tools that are increasingly adopted in clinical settings for real-time physiological monitoring. No scientific awards or major honors were explicitly documented in the available sources. While detailed information about academic advisees, grant funding, or dedicated laboratory facilities was not provided in the current materials, Professor Isaacson's extensive collaborative network is evident from his numerous co-authored publications with clinicians and engineers. His work with the Center for Biotechnology and Interdisciplinary Studies suggests active participation in multidisciplinary research teams focused on translating engineering innovations into medical practice. Future research directions likely include further refinement of EIT for point-of-care diagnostics and expansion into new disease areas through continued cross-disciplinary partnerships.

• Animal Health Surveillance
• Livestock Disease Management
• Veterinary Parasitology
• +۳ مورد دیگر

Dr Rob Woodgate is an active Research Fellow within the Faculty of Agricultural, Environmental and Veterinary Sciences at Charles Sturt University, specializing in animal health surveillance systems and livestock disease management. His work focuses on strengthening Australia's capacity to detect and respond to emergency animal diseases through collaborative research with veterinarians, producers, and government agencies. His research interests center on Animal Health Surveillance , Livestock Disease Vulnerability , and Sustainable Parasite Management . Key projects include evaluating predictive models for foot-and-mouth disease vulnerability in sheep and beef producers, analyzing veterinary workforce contributions to surveillance systems, and developing co-creation frameworks for knowledge sharing between stakeholders. His work bridges epidemiological modeling with practical on-farm applications, emphasizing socio-ecological factors in disease prevention. Recent publications demonstrate consistent output in high-impact veterinary journals including Preventive Veterinary Medicine and Frontiers in Veterinary Science , with a clear trend toward interdisciplinary approaches combining epidemiology, social science, and risk communication. His 2022-2024 work shows particular emphasis on transforming producer-veterinarian relationships during biosecurity threats. Woodgate actively supervises higher degree research, serving as Principal Supervisor for projects on nematode population genetics. His media engagements include ministerial visits to Charles Sturt University and coverage of innovative animal disease surveillance approaches in national media outlets.

Alexandre Sousaمشاهده پروفایل

دانشیار

• High-Energy Physics
• Neutrino Oscillations
• Particle Physics
• +۳ مورد دیگر

Dr. Alexandre Sousa is an Associate Professor in the Department of Physics at the University of Cincinnati, affiliated with the College of Arts & Sciences. His primary research focuses on experimental high-energy physics, particularly neutrino oscillations and their implications for understanding fundamental particle interactions and cosmological evolution. He has held positions at Harvard University and the University of Oxford before joining UC in 2012. Education: Ph.D. in Physics (Tufts University, 2006), M.Sc. in Physics (Tufts University, 2000), B.Sc. in Physics Engineering (Instituto Superior Técnico, 1998). Research Interests: Neutrino properties, CP violation, neutrino mass hierarchy, sterile neutrino searches, and long-baseline experiments (NOvA, DUNE, MINOS+). His work addresses questions like whether neutrinos violate CP symmetry and the structure of neutrino mass ordering. He leads the NOvA Test Beam effort and coordinates detector simulations for NOvA. Grants & Awards: ORAU Ralph E. Powe Junior Faculty Enhancement Award (2014), UC Rising Star Award (2017), multiple Fermilab Intensity Frontier Fellowships (2013–2019), and NSF grants for education and detector development. He has led over $2.5M in federal and private grants, focusing on HEP data analytics and neutrino experiments. Teaching & Service: Championed active learning methods in physics education. Served as Teaching & Learning Liaison (2016–2018) and active member of the Diversity and Inclusion Committee. Recognized with the Darwin T. Turner Breakfast of Champions Award (2018). Labs & Collaborations: Co-leader of NOvA Test Beam, NOvA Detector Simulations Group, and MINOS/MINOS+ Analysis Coordinator. Collaborates on DUNE, LBNE, and CHIPS projects, advancing neutrino detection technologies and computational methods.

Paula Buchananمشاهده پروفایل

دانشیار

• Development and Treatment of Chronic Illnesses
• Epidemiological Modeling
• Statistical Methods
• +۴ مورد دیگر

Overview Paula Buchanan, Ph.D., M.P.H., is an Associate Professor and Associate Director of Academic Affairs in the Department of Health and Clinical Outcomes Research at Saint Louis University School of Medicine. She also directs AHEAD Consulting & Training Services. Her work focuses on epidemiological and statistical modeling of chronic and acute illnesses, leveraging large population datasets from sources like the VA and Medicare. She teaches courses in biostatistics, multivariate analysis, and outcomes research methodologies. Education Ph.D. in Health Services Research, Saint Louis University M.P.H. in Biostatistics and Epidemiology, Saint Louis University B.A. and B.S. in Mathematics, Truman State University Research Interests Buchanan’s research spans statistical methods for healthcare outcomes, pediatric medicine, cost analysis, and transplant outcomes. She examines disease progression, database management, and translational research applying statistical models to clinical data. Her work often addresses gaps in understanding chronic illness trajectories and optimizing healthcare delivery systems. Advising & Grants No specific advisees or grant awards are listed in the provided materials. Her research is supported through institutional affiliations and collaborative projects. Labs/Teams Director of AHEAD Consulting & Training Services, which focuses on applying health outcomes research to clinical practice and policy.

Stefan Endlerمشاهده پروفایل

پژوهشگر

• Sports Informatics
• Modeling and Simulations
• Software Engineering
• +۳ مورد دیگر

Dr. Stefan Endler is an Academic Councillor at the Institute of Computer Science, Johannes Gutenberg University Mainz, where he has held various roles including Research Associate and Academic Council member since 2006. His work bridges computer science and sports science, focusing on sports informatics, modeling/simulations for training impact analysis, and software engineering (design patterns). Educational Background: Bachelor of Science (2006): Model-based training planning with GPS data at Johannes Gutenberg University Mainz Bachelor of Science (2008): Attacker models and attack detection in mobile ad-hoc networks at TU Darmstadt Doctorate (2013): Adaptation of the PerPot metamodel for endurance-oriented running optimization at Mainz University Research Interests: Development of simulation models for athletic performance optimization Application of design patterns in software engineering for sports technology Integration of sensor data (e.g., smartwatches) for real-time training analysis Teaching Contributions: Extensive teaching experience across 15+ years, including courses on software development, design patterns, and computer engineering. Notable roles include lecture leadership for Design Patterns (2014–2021) and programming fundamentals. Awards: 2018 Teaching Award from Johannes Gutenberg University recognizing excellence in academic instruction. Advisory Work: Supervised 45+ theses (2014–2020) across computer science and sports science disciplines. Maintained active involvement in thesis supervision and academic council governance. Collaborative Efforts: Cross-disciplinary work with the Institute of Sports Science, contributing to biomechanical studies and training methodology research.

Xiaojing Wangمشاهده پروفایل

دانشیار

• Bayesian modeling
• Latent variable models
• Item response models
• +۵ مورد دیگر

Xiaojing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. She holds multiple affiliations including the Institute for Collaboration on Health, Intervention, and Policy (InCHIP), the Connecticut Institute for the Brain and Cognitive Sciences (IBACS), and the Center for Environmental Sciences and Engineering. Education: Ph.D. in Statistics and M.A. in Economics from Duke University (2012) M.S. in Probability and Mathematical Statistics from Chinese Academy of Sciences (2008) B.S. in Information and Computational Science from Hunan University (2005) Her research focuses on Bayesian methodologies with applications spanning neural decoding, environmental science, psychometrics, and computational biology. She develops novel statistical models for high-dimensional data including latent variable frameworks for neural spiking activity and dynamic item response theory for educational testing. Her work integrates machine learning with traditional statistical inference to address complex problems in interdisciplinary domains. Wang's publications demonstrate a consistent focus on Bayesian computation and model development, with significant contributions to Gaussian process methodologies, subgroup analysis in clinical applications, and ecological forecasting. Recent work shows increasing emphasis on neural data science and computationally intensive methods for high-dimensional inference. Awards and Honors: NSF Faculty Early Career Development (CAREER) Award (2019) Elected Member of International Statistical Institute (ISI) (2017) Jeffrey’s Excellence Prize for Best Methodological Poster (2013) As a statistical consultant, she advises through UConn's Statistical Consulting Services. Her methodological innovations support applications across health interventions, cognitive science, and environmental research through interdisciplinary collaborations.

Panagiotis Stamatopoulosمشاهده پروفایل

استادیار

• Artificial Intelligence
• Constraint Programming
• Logic Programming
• +۹ مورد دیگر

Panagiotis Stamatopoulos is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, where he has been employed since 1993. He holds a PhD in Computer Science (1988) and a Diploma in Physics (1982) from the University of Athens. His research spans artificial intelligence, constraint programming, natural language processing, machine learning, and optimization. Specific interests include: Hybrid approaches combining constraint programming with operations research Natural language understanding for database access Parallel processing and distributed constraint solving Multi-agent systems and web intelligence applications His publications show consistent focus on constraint satisfaction algorithms, text summarization techniques, educational timetabling systems, and AI applications in diverse domains like sports analytics and robotics. Recent works demonstrate increased attention to NLP evaluation metrics and multimodal learning. He has supervised numerous diploma theses and led projects funded by the European Union (EDS, APPLAUSE, PARACHUTE, PARROT), University of Athens, and Olympic Airways. Stamatopoulos teaches undergraduate courses in Introduction to Programming and Logic Programming, plus postgraduate courses in Advanced Artificial Intelligence. He previously taught Artificial Intelligence, System Programming, and Expert Systems.

• Bayesian nonparametrics
• Mixture models
• Point processes
• +۵ مورد دیگر

Athanasios (Thanasis) Kottas is a Professor in the Department of Statistics at the University of California, Santa Cruz (UCSC), part of the Baskin School of Engineering. He holds a Ph.D. in Statistics from the University of Connecticut (2000) and a M.Sc./B.Sc. in Mathematics from the University of Ioannina, Greece. His research focuses on Bayesian nonparametric methods, mixture models, point processes, and their applications in biometrics, ecology, and environmental science. He has served as an editor for Bayesian Analysis and has authored numerous influential papers in top statistical journals. Education : Ph.D. in Statistics, University of Connecticut, 2000 M.Sc. in Statistics and Operations Research, University of Ioannina, 1996 B.Sc. in Mathematics, University of Ioannina, 1994 Research Interests : Kottas specializes in Bayesian nonparametric modeling, including mixture models, ordinal regression, and applications in environmental and ecological statistics. His work emphasizes flexible statistical frameworks for complex data structures, such as point processes, survival analysis, and spatial-temporal dynamics. Recent projects include developing methods for risk assessment in developmental toxicity and quantile regression. Publications : His 60+ publications span journals like Bayesian Analysis , Journal of the American Statistical Association , and Environmetrics , reflecting contributions to both methodology and interdisciplinary applications. Key themes include nonparametric density regression, spatio-temporal modeling, and Bayesian quantile analysis. Awards & Honors : ISBA Savage Award (via student Maria DeYoreo, 2015) Honorable Mention for Savage Award (via student Matthew Taddy, 2009) Editor of Bayesian Analysis (2018–2024) Students & Advising : Kottas has mentored over 20 Ph.D. and M.Sc. students, many of whom have won prestigious awards. His advising emphasizes methodological innovation and interdisciplinary impact. Labs & Collaborations : He collaborates actively with researchers in ecology, environmental science, and astrophysics, leveraging Bayesian nonparametric tools to address real-world challenges.

John O'Haraمشاهده پروفایل

مدرس ارشد

• Financial Mathematics
• Machine Learning in Finance
• Quantitative Finance
• +۳ مورد دیگر

Dr. John O'Hara is an Honorary Senior Lecturer at the University of Essex's School of Mathematics, Statistics and Actuarial Science. He holds additional affiliations as a Research Fellow at Stellenbosch University and an Honorary Associate Professor at the University of Cape Town. His expertise spans financial mathematics, machine learning in finance, stochastic processes, and quantitative finance. Education: BSc in Pure Mathematics, University of Ulster (1976) PGCE in Education, Queen’s University Belfast (1977) MSc in Probability Theory (Hilbert Spaces), Queen’s University Belfast (1981) PhD in Differential Equations, University of the Witwatersrand (1991) His research focuses on financial mathematics and machine learning applications in finance, including volatility modeling, option pricing, and algorithmic trading strategies. O'Hara is a Fellow of the Institute of Mathematics and its Applications and a member of the London Mathematical Society. He previously directed the Centre for Computational Finance and Economic Agents (CCFEA) at Essex and has held academic leadership roles in Southern African institutions. Awards and Memberships: Fellow of the Institute of Mathematics and its Applications London Mathematical Society Membership Professional Activities: O'Hara has contributed to interdisciplinary research at the intersection of finance and computational methods. His work emphasizes symmetry analysis in financial PDE models and innovative applications of wavelet transforms to option pricing. He has supervised multiple research projects in computational finance and maintains active collaborations across institutions. Labs and Centers: Director, CCFEA (2019–2021) Research Fellow, School for Data Science and Computational Thinking, Stellenbosch University

Jun Yuمشاهده پروفایل

استاد

• Statistical Learning
• Spatiotemporal Data Analysis
• Compressive Sensing
• +۷ مورد دیگر

Jun Yu is a Full Professor in Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden, where he also serves as Director of Doctoral Studies. His research focuses on Statistical Learning and Inference for Spatiotemporal Data, with applications in artificial intelligence and various scientific domains. Professor Yu leads a research group dedicated to tackling theoretical data science problems and developing statistical learning methods for solving real-world challenges across multiple disciplines. Professor Yu's primary research interests include statistical learning with sparsity, compressive sensing, mathematics of data science, hierarchical spatiotemporal modeling, nonparametric density/intensity estimation, statistical inference for hidden Markov models, and wavelet theory applied to signal and image analysis. His work spans numerous application areas including atmospheric icing, automobile industry, biomedical engineering, climate research, epidemiology, forestry, geochemistry, hydrology, radiation oncology, spatial ecology, sports science, and transportation. Professor Yu's recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of statistical methodology and environmental science, medical imaging, and transportation systems. His work on tree-ring isotope analysis for climate reconstruction, compressive sensing for medical imaging, and statistical models for train delay prediction shows his ability to develop sophisticated statistical methods that address complex real-world problems across diverse domains. As Director of Doctoral Studies, Professor Yu oversees doctoral education in Mathematical Statistics and has supervised numerous PhD students. His teaching spans mathematical statistics at all levels, from basic education to postgraduate courses, for students in mathematics, statistics, biology, engineering, and forestry, delivered in English, Swedish, or Chinese. Professor Yu leads the research group on statistical learning and inference for spatiotemporal data at Umeå University. This group develops innovative approaches for analyzing complex spatiotemporal datasets using tools such as intelligent data sampling, large-scale environmental data modeling, multimodal image processing, and tree growth models, with applications across multiple scientific fields.

Diana Parry-Cruwysمشاهده پروفایل

دانشیار

• Applied Behavior Analysis
• Autism Spectrum Disorder
• Early Intervention
• +۳ مورد دیگر

Dr. Diana Parry-Cruwys is an Associate Professor in the Master of Science in Applied Behavior Analysis program at Regis College, where she also serves as the fieldwork coordinator and co-director of the Regis Autism Center. She holds a PhD from Western New England University, a master’s from Northeastern University, and a Bachelor of Arts in English and Psychology from Furman University. Her research focuses on early intensive behavioral intervention (EBI), joint attention development, and inclusive practices for children with autism spectrum disorders. Dr. Parry-Cruwys has presented widely at national conferences and serves on the editorial board of Behavioral Interventions and the Berkshire Association of Behavior Analysis and Therapy events committee. Her academic roles include teaching courses such as ABA 604 (Treatment Evaluation), ABA 608 (Behavior Intervention), and fieldwork thesis series. Her work emphasizes practical applications of behavior analysis in educational and clinical settings, with recent publications exploring compassionate care in graduate programs, APA citation training methodologies, and environmental behavioral interventions for sustainability. Dr. Parry-Cruwys’ research trends reflect a commitment to bridging academic rigor with real-world impact. Her articles address topics ranging from autism intervention efficacy to systemic racism mitigation through behavior analytic frameworks. She has contributed to textbook chapters on behavioral treatment for autism and pioneered training programs for graduate students in data management and practicum adherence. Her work consistently integrates innovative instructional methods with ethical considerations in behavioral practice. Notable collaborative efforts include co-authoring studies on observational learning mechanisms and recycling behavior modification on college campuses. While no formal grants are highlighted in the provided text, her editorial and committee roles indicate active participation in professional community-building initiatives. Dr. Parry-Cruwys’ academic leadership extends to the Regis Autism Center, where she oversees applied research and clinical training programs. Her work exemplifies the intersection of empirical research, pedagogical innovation, and societal impact within the field of applied behavior analysis.

Zhong Shengمشاهده پروفایل

پژوهشگر ارشد

• Sustainable Development
• Energy Efficiency
• Applied Econometrics
• +۳ مورد دیگر

Zhong Sheng is a Senior Research Fellow at the Energy Studies Institute, National University of Singapore. He holds a PhD in Economics from Maastricht University and specializes in quantitative analysis of energy efficiency, innovation systems, and sustainable development. Research Focus His work applies econometric methods to study energy efficiency determinants, global value chains, decarbonization pathways, and policy impacts. Current projects examine ASEAN energy transitions, cross-border electricity trade, and embodied carbon in trade flows. Honors UNIDO Ph.D. Fellowship (2012) UN University Ph.D. Scholarship (2012) Erasmus Mundus Scholarship (2010) Methodological Expertise Publications feature advanced quantitative techniques including input-output analysis, optimization modeling, and microdata analytics to investigate energy systems and emission patterns across global and regional scales.

Regina Deminaمشاهده پروفایل

استاد

• Experimental High-Energy Physics
• Cosmology
• Particle Astrophysics
• +۳ مورد دیگر

Regina Demina is a Professor of Physics at the University of Rochester since 2007, previously serving as Associate Professor (2003–2007) and Assistant Professor at Kansas State University (1999–2003). She holds a BA/MS in Physics from Novosibirsk State University (1985/1988) and a PhD in Physics from Northeastern University (1994). Her research focuses on experimental high-energy physics and cosmology, with major contributions to the CMS experiment at CERN's LHC, including silicon tracker construction and top quark studies. She also leads efforts in the Dark Energy Spectroscopic Instrument (DESI) project, analyzing galaxy redshift surveys and cosmic structure. Demina was honored as a DOE Outstanding Junior Investigator (2001). Her research integrates collider physics with astrophysical observations, addressing fundamental questions in particle physics and cosmology. Current projects include High Luminosity LHC detector upgrades, Higgs boson studies, and probing dark energy via baryon acoustic oscillations. Collaborations span CMS and DESI consortia, emphasizing interdisciplinary approaches to understanding quantum mechanics, galaxy evolution, and cosmic acceleration. Key Projects: CMS Top Quark Physics, DESI Dark Energy Surveys, Higgs Sector Exploration Techniques: Collider Data Analysis, Algorithm Development, Cosmic Tomography Future Work: High Luminosity LHC upgrades, next-gen galaxy surveys Her articles reflect cross-disciplinary engagement, combining precision measurements in particle physics with large-scale cosmological surveys. Advising emphasizes training in collaborative experimental frameworks and data-intensive astrophysics.

Suxing Huمشاهده پروفایل

استاد

• High-Energy-Density Physics
• Inertial Confinement Fusion
• Computational Physics
• +۵ مورد دیگر

Dr. Suxing Hu is a Distinguished Scientist and Group Leader of the High-Energy-Density Physics (HEDP) Theory Group at the Laboratory for Laser Energetics (LLE), University of Rochester. He holds joint professorships in Physics (Research) and Mechanical Engineering (Research) at the same institution. His research focuses on understanding matter under extreme conditions, including inertial confinement fusion (ICF), planetary science, and astrophysics. Dr. Hu earned his PhD in physics from the Chinese Academy of Sciences (Shanghai Institute of Optics and Fine Mechanics) and held postdoctoral positions at the University of Freiburg, Max-Born-Institute (Berlin), University of Nebraska-Lincoln, and Los Alamos National Laboratory. His career milestones include promotions to Senior Scientist (2013) and Distinguished Scientist (2019) at LLE. His research interests span High-Energy-Density Physics (HEDP), including equation-of-state modeling and quantum simulations, Inertial Confinement Fusion (ICF) target design and hydrodynamics, Ultrafast Attosecond Physics involving laser-matter interactions, Computational methods like time-dependent density functional theory (TD-DFT) and machine learning for materials modeling. Notable awards include the Hundred Outstanding Doctorate Thesis Prize (China), Alexander von Humboldt Fellowship, and APS Fellow (2013). His work has led to over 250 publications, ~10,000 citations, and an H-index of 54. Key projects involve developing physics-informed machine learning models, advancing radiation-hydrodynamics codes, and studying quantum effects in warm dense matter. His group collaborates on experiments at the National Ignition Facility, focusing on achieving fusion ignition and mitigating instabilities in ICF targets.