جست‌وجوی استاد و پژوهشگر

Jason Abrevayaمشاهده پروفایل

استاد

• Econometrics
• Applied Microeconomics
• Demography
• +۳ مورد دیگر

Jason Abrevaya serves as Associate Dean for Graduate Education and holds the Murray S. Johnson Chair in Economics at the University of Texas at Austin within the College of Liberal Arts and Department of Economics. His research spans econometric methodology, applied microeconomics, and demography, with significant contributions using U.S. birth databases to study birthweight inequality, smoking effects, and gender selection practices among ethnic groups. Dr. Abrevaya's work centers on econometrics (panel data analysis, treatment effects, missing data), health economics (birth outcomes, vaccination), labor economics, and sports economics. His methodological innovations address data challenges in causal inference and microeconomic applications, characterized by rigorous statistical approaches to real-world problems. Recent publications demonstrate continued emphasis on theoretical econometrics with empirical applications in public health and sports analytics. His article trends reveal sustained focus on treatment effects under heteroskedasticity, partial effects in nonlinear models, and applications to vaccine uptake and sports (e.g., penalty calls in hockey). Methodological contributions frequently bridge theoretical econometrics with empirical practice in health and social sciences. Dr. Abrevaya has secured research funding from the National Science Foundation and Robert Wood Johnson Foundation. He is author of the textbook Probability and Statistics for Economics and Business: An Introduction Using R . No scientific awards are explicitly mentioned in available sources. As Associate Dean for Graduate Education, he oversees graduate programs while maintaining active research and mentorship. His warning about scam impersonation attempts (via Gmail addresses posing as research opportunities) underscores the importance of verifying communications through his official university email.

Daniela Gospodinova Nedevaمشاهده پروفایل

مدرس ارشد

• Environmental Monitoring
• Nuclear Physics Applications
• Laser Technologies
• +۴ مورد دیگر

Daniela Gospodinova Nedeva is a Senior Lecturer at the Technical University of Gabrovo, Bulgaria, working in the Faculty of Economics within the Department of Mathematics, Computer Science and Natural Sciences. She holds a Doctorate in Technical Sciences and has established herself as a prominent researcher in environmental physics and applied engineering technologies. Dr. Nedeva's research primarily focuses on environmental radiation monitoring, with extensive work measuring natural radioactivity in medicinal plants, soil, and sand samples from various Bulgarian regions including the Shumen Plateau, Rhodope Mountains, and the Black Sea coast. Her research portfolio also includes significant contributions to laser technologies for engineering applications, particularly in laser welding, cutting, and marking of materials for electric motor components. Additionally, she has developed numerous educational materials and methodologies for physics instruction. Analysis of her publication record reveals a strong interdisciplinary approach combining physics, environmental science, and engineering. Over the past decade, she has published extensively on radionuclide analysis in biological and environmental samples, electron beam surface modification of materials, and innovative physics teaching methods. Her recent work demonstrates increasing focus on ecological and energy-saving technologies, as evidenced by her leadership in projects like "Intelligent, ecological and energy-saving technologies in modern industry and practice." Her research shows consistent application of nuclear physics methods to address environmental challenges. Lead researcher on "Intelligent, ecological and energy-saving technologies in modern industry and practice - stage 1" (2025) Lead researcher on "Modern engineering solutions for environmental management and protection - Stage III" (2024) Researcher on "MountResilience - HORIZON-MISS-2022-CLIMA-01" focusing on climate adaptation in European mountain regions Researcher on "Integrative approach to solving environmental problems - phase 2" (2024) Dr. Nedeva has supervised or participated in 35 research projects, demonstrating strong leadership in environmental monitoring through nuclear physics methods. Her work shows consistent commitment to applying physics principles to solve practical environmental challenges while contributing significantly to physics education through textbooks and laboratory materials.

• Industrial Engineering
• Operations Research
• Artificial Intelligence
• +۵ مورد دیگر

Dr. James Curry serves as Department Chair and Associate Professor in the Department of Industrial and Systems Engineering at Lamar University's College of Engineering, while also directing Online Programs and Outreach for the College. His leadership drives the development of innovative engineering education pathways and industry-focused research initiatives. Dr. Curry earned his Ph.D. in Industrial Engineering from Texas A&M University, complemented by an M.S. in Operations Research and Industrial Engineering and a B.S. in Mechanical Engineering from the University of Texas. His research integrates Logistics and Scheduling, Software Development, and Natural Language Generation with practical applications in Supply Chain Management and Optimization. Current work focuses on marine safety culture assessment, where he develops computational frameworks for processing safety data and generating natural language reports. This bridges theoretical optimization with real-world industrial engineering challenges, particularly in offshore energy systems. Analysis of Dr. Curry's 15 most recent publications reveals a strategic shift from pure algorithm development toward interdisciplinary applications. Recent work emphasizes safety culture assessment toolkits for offshore industries and machine learning applications in environmental modeling, demonstrating growing collaboration between engineering education, marine safety, and data science domains. Dr. Curry's scientific recognition includes: Anita Riddle Excellence in Engineering Teaching Fellowship As an active mentor, he has guided 14 graduate students through doctoral and master's research while securing $1.5+ million in funding across 10 projects. His grant portfolio spans NSF education initiatives, National Academies safety research, and industry partnerships with American Bureau of Shipping, focusing on practical solutions for marine safety and engineering education innovation. The Mariner Safety Research Initiative processes over 100,000 safety events from international marine organizations, developing assessment toolboxes that translate complex data into actionable safety improvements. This lab serves as a nexus for students to gain hands-on experience with industry-standard safety systems and international collaboration networks.

Joran Jongerlingمشاهده پروفایل

استادیار

• Bayesian Statistics
• Multilevel Analysis
• Dynamic Modeling
• +۷ مورد دیگر

Dr. Joran Jongerling is an Assistant Professor in the Department of Methodology and Statistics at Tilburg University's Tilburg School of Social and Behavioral Sciences. His academic work focuses on developing and applying advanced statistical methods for analyzing longitudinal and intensive longitudinal data, with particular expertise in multilevel AR(1) models and Bayesian statistics. Dr. Jongerling's research interests span several interconnected areas within quantitative methodology. He specializes in Bayesian Statistics , Multilevel Analysis , Dynamic Modeling , and the analysis of Longitudinal Data collected through Experience Sampling and diary methods. His methodological work addresses critical challenges in modeling individual differences in dynamic processes, measurement invariance across time, and appropriate statistical techniques for intensive longitudinal datasets. Analysis of his publication trends shows a significant increase in research output in recent years, with 11 publications in 2025 alone. His work spans both methodological development and substantive applications across psychology, education, and health sciences. His methodological contributions often focus on refining techniques for analyzing intensive longitudinal data, while his applied work demonstrates these methods in contexts ranging from multicultural personality assessment to motor development in children with Down syndrome. Dr. Jongerling actively contributes to advancing methodological standards in psychological research through his work on measurement invariance, simulation studies evaluating statistical techniques, and development of models that appropriately handle the complexities of real-world longitudinal data. His research has practical implications for researchers designing and analyzing intensive longitudinal studies across multiple domains of psychological science.

• Algorithms
• Data Structures
• Computational Complexity
• +۳ مورد دیگر

Jingsen Chen is an Associate Professor in the Department of Computer Science within the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology. His primary research focus is on Dependable Communication and Computation Systems. Professor Chen's research interests center around algorithm design and analysis, with specific expertise in: Efficient heap construction algorithms Maximum-scoring segment problems k maximum sum subsequences Optimization of computational resources Theoretical computer science foundations His publication record demonstrates consistent contributions to the field of algorithm design, particularly his influential 2012 work on heap construction that optimized comparisons, moves, and cache misses. Professor Chen's research shows a strong theoretical foundation with practical considerations for real-world computational constraints. Professor Chen maintains an active research profile with publications spanning from 2006 to recent years. His work primarily appears in computer science venues including conference proceedings and journals focused on theoretical computer science and algorithms. As a faculty member, Professor Chen contributes to the academic community through his research and likely participates in teaching and supervision activities within the Computer Science department at Luleå University of Technology.

Alfonso Carlos Martínez Estudilloمشاهده پروفایل

استاد

• Machine Learning
• Evolutionary Computation
• Neural Networks
• +۷ مورد دیگر

Alfonso Carlos Martínez Estudillo is a Full Professor in the Department of Quantitative Methods at Loyola University's School of Business and Economics. He serves as Director of the Master's Program in Research Methods Applied to the Social Sciences and is a member of the Quantitative Research Methods and Applications (MICA) research group. With a PhD in Computer Science and Artificial Intelligence (2008) and a Computer Engineering degree (1995), both from the University of Granada, his academic journey includes previous roles as Network and Systems Administrator, Database Programmer, and IT Service Coordinator at ETEA-Faculty of Economics and Business. His research focuses primarily on evolutionary computation and neural networks for solving machine learning problems, with specialization in product-unit neural networks. His doctoral thesis opened an important research line within the AYRNA Research Group, where he has been a member since 2001. This work has led to publications in leading journals such as IEEE Transactions and Systems, Man and Cybernetics, and Neural Networks, with applications spanning predictive microbiology, chemical kinetics, pollen prediction, remote sensing, and financial risk assessment. Currently, he is working on multi-objective optimization, ordinal classification, and data mining with large databases using Big Data techniques. Professor Martínez Estudillo has participated in numerous research projects including R&D initiatives funded by the Ministry of Science and Technology, excellence projects from the Regional Government of Andalusia, and international cooperation projects analyzing social aspects of indigenous populations in Honduras. His recent publications demonstrate continued application of artificial intelligence techniques to economic problems, with his most recent work (2022) examining sovereign debt ratings in Europe. His teaching portfolio includes Databases, Fundamentals of Computer Science I, Computing Infrastructures and Databases, Applied Mathematics for Business Management, and Programming I, reflecting his expertise in both theoretical and applied computational methods for business and economics.

Graziano Vernizziمشاهده پروفایل

استاد

• Computational Physics
• Theoretical Physics
• Random Matrix Theory
• +۴ مورد دیگر

Graziano Vernizzi serves as Professor in the Department of Physics and Astronomy at Siena University (formerly Siena College) since 2016, having previously held Associate Professor (2013-2016) and Assistant Professor (2010-2013) positions at the same institution. His academic journey includes a Research Professor role at Northwestern University (2006-2010) and prestigious postdoctoral appointments at the Niels Bohr Institute, University of Oxford, CEA/Saclay, Spinoza Institute, and Northwestern University. His educational background includes: B.S. in Physics, University of Parma, Italy (1995) Ph.D. in Physics, University of Parma, Italy (2000) Vernizzi's research integrates computational and theoretical physics to address nanoscale phenomena, with core interests in Random Matrix Theory, biophysics (RNA/ssDNA structures and membrane theory), soft condensed matter physics, and nanoscale science. He develops computational models to analyze complex systems where theoretical frameworks are essential for interpreting experimental data, driving technological innovation and expanding career opportunities in emerging scientific fields. His methodology frequently bridges mathematical rigor with physical applications across disciplinary boundaries. Analysis of his publication trends reveals consistent interdisciplinary work connecting mathematics, physics, and engineering. Key recurring themes include topological constraints in biological and electronic systems, statistical mechanics approaches to soft matter, and algorithmic innovations for verification problems. His research demonstrates particular strength in applying abstract mathematical concepts like random matrix theory to concrete biophysical and engineering challenges, resulting in publications spanning Physical Review journals, Soft Matter, and specialized engineering conferences. His scientific recognition includes: Trust-Co Excellence Honor for advancing the mission of Siena College (Trustco Bank, 2015) No information regarding student advising, research grants, or dedicated laboratory facilities was provided in the source material. His professional activities focus on computational research, course development in nanoscale physics, and interdisciplinary collaboration without mention of specific research teams or experimental facilities.

Bobbie-Jo Webb-Robertsonمشاهده پروفایل

استاد بالینی

• Statistics
• Machine Learning
• Bioinformatics
• +۵ مورد دیگر

Bobbie-Jo Webb-Robertson serves as Division Director and Chief Scientist of computational biology in the Biological Sciences Division at Pacific Northwest National Laboratory (PNNL). She holds multiple academic appointments including Clinical Volunteer Professor at the University of Colorado Anschutz Medical Campus, Courtesy Assistant Professor at the University of Florida, and Clinical Associate Professor at Oregon Health and Sciences University. Dr. Webb-Robertson earned her PhD in Engineering Systems from Rensselaer Polytechnic Institute in 2002, an ME in Statistics from the same institution in 2000, and a BA in Mathematics from Oregon State University in 1997. With over 20 years of experience in statistics and data science, she has established herself as a leader in computational biology with an h-index of 30 and more than 100 publications. Her research focuses on developing and applying advanced statistical and machine learning methods to address challenges associated with large and complex omics data. She specializes in mass spectrometry-based omics data analysis, statistical data integration, predictive modeling, and biomarker discovery. Her work has significant applications in biomedical research, particularly in understanding type 1 diabetes through multi-omics approaches. Dr. Webb-Robertson's recent publications demonstrate a strong trajectory in type 1 diabetes biomarker discovery, multi-omics data integration, and the application of machine learning to biological problems. Her work spans proteomics, metabolomics, and transcriptomics with a focus on developing computational tools that can integrate multiple data types for improved biological insights. Chauncey and Doris Starr Graduate Fellowship, 1997 GE Future Faculty Scholarship, 1999-2000 National Science Foundation Program in Mathematics and Molecular Biology Fellowship, 1999-2001 HPC Analytics challenge in biology first place team member, 2007 Spirit of nPOD award from the Network for Pancreatic Organ Donors with Diabetes Dr. Webb-Robertson serves in various editorial and advisory roles including Executive Editor for the Journal of Proteomics and Genomics Research, Review Editor for Frontiers in Artificial Intelligence, Scientific Advisory Board Member for the Juvenile Diabetes Research Fund IBM Machine Learning Project, and member of the American Statistical Association. She actively contributes to the scientific community through these service roles while maintaining a productive research program focused on computational methods for biological data analysis. Her laboratory develops computational tools for multi-omics data analysis with particular emphasis on applications in type 1 diabetes research. She leads a team that integrates expertise in statistics, machine learning, and biological domain knowledge to tackle complex biomedical problems, with a focus on biomarker discovery and early disease prediction.

Gang Meiمشاهده پروفایل

دانشیار

• Numerical Simulation and Computational Modeling
• GPU Computing
• Machine Learning
• +۵ مورد دیگر

Dr. Gang Mei is an Associate Professor in Scientific Computing within the School of Engineering and Technology at China University of Geosciences (Beijing), where he has held academic positions since 2014. His career progression includes Postdoctoral Researcher (2014-2016), Lecturer (Oct-Dec 2016), and current Associate Professor (since Jan 2017). His research bridges computational science and engineering applications with significant editorial contributions to computer science literature. Education: Ph.D. in Computer Science, University of Freiburg, Germany (2014) Research Interests: Dr. Mei specializes in Numerical Simulation and Computational Modeling, GPU Computing, Machine Learning, and Data Mining, with strong applications in Network Science and Spatial Information Systems. His work integrates Distributed and Parallel Computing techniques for large-scale scientific simulations, particularly in geospatial modeling and network analysis. The research demonstrates consistent focus on computational efficiency through hardware acceleration and algorithmic optimization across diverse domains including satellite imagery processing, financial event detection, and medical image classification. Publication Trends: His editorial portfolio reveals strong interdisciplinary patterns connecting computer science fundamentals with domain-specific applications. Recent works emphasize GPU-accelerated methods for data-intensive problems (2020-2022), spatial-temporal modeling (2019-2020), and network science applications (2021). The publications consistently address computational scalability challenges while maintaining practical relevance across geospatial, financial, medical, and engineering contexts. Professional Recognition: As an IEEE Member, Dr. Mei serves on editorial boards for IEEE Access and PeerJ Computer Science, reflecting peer recognition in computational fields. His editorial contributions span 15+ publications demonstrating expertise in evaluating cutting-edge computer science research. Academic Service: Beyond editorial work, Dr. Mei's service includes advising on computational methodology across multiple disciplines. His role as Academic Editor demonstrates commitment to scholarly communication, particularly in bridging theoretical computer science with practical engineering applications. No grant funding details were specified in available materials.

• Computer Vision
• Big Data Analytics
• Generative AI in Education
• +۲ مورد دیگر

Dr. Kaveh Kiani is a faculty member at the University of Salford's School of Science, Engineering & Environment, with a primary affiliation to the Informatics Research Centre. His academic profile demonstrates expertise spanning both theoretical statistics and practical AI applications across multiple industries. Doctoral degree in Applied Statistics Master's degree in Data Science Postgraduate Certificate in Academic Practice (PGCAP) Fellow of the Higher Education Academy (FHEA) Dr. Kiani's research focuses on computer vision, big data analytics, generative AI applications in education, and survival analysis. His work bridges statistical theory with practical implementations, particularly evident in medical AI applications for respiratory disease detection, skin cancer diagnosis, and prostate cancer research. His industry experience informs his approach to solving real-world problems in healthcare, construction sustainability, and financial services. His recent publications (2023-2025) demonstrate strong activity in applying deep learning techniques to medical imaging and developing novel frameworks for industry-specific challenges. The research shows a clear trajectory toward increasingly sophisticated AI applications with practical impact. Fellow of the Higher Education Academy (FHEA) awarded by Advance HE Endorsed by The Royal Academy of Engineering as a Global Talent in Data Science Dr. Kiani actively supervises PhD students and teaches across multiple postgraduate programs including MSc Data Science, MSc Artificial Intelligence, and MSc Internet of Things with Data Science. His teaching modules cover Big Data Tools and Techniques and Applied Statistics and Data Visualisation, reflecting his dual expertise in both theoretical foundations and practical applications of data science.