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• Beyond the Standard Model
• Supersymmetry
• String Theory
• +3 more

Professor Steve Abel is a Professor in the Department of Mathematical Sciences and the Department of Physics at Durham University, with additional affiliation to the Institute for Particle Physics Phenomenology. His research spans theoretical physics with a strong focus on string theory, particle physics phenomenology, and emerging applications of quantum computing. Professor Abel's research interests include: Beyond the Standard Model physics Supersymmetry and string model building Applications of quantum computing to particle physics Genetic algorithms in theoretical physics Non-supersymmetric string vacua Analysis of Professor Abel's recent publications reveals a significant shift toward interdisciplinary research combining traditional theoretical physics with cutting-edge computational techniques. His work increasingly focuses on applying quantum computing and machine learning methods to solve complex problems in string theory and particle physics. Many recent papers explore quantum simulation of field theories, quantum annealing for string model building, and genetic algorithms for solving physics problems. This represents a convergence of theoretical physics with computational science that is transforming how fundamental physics research is conducted. Professor Abel has received recognition for his work, most notably a Cern Theory 6 month Scientific Associateship. His research collaborations span international institutions across Europe and North America, reflecting the global nature of theoretical physics research. Professor Abel actively supervises graduate students, including Puya Mirkarimi. His research program likely involves collaboration with various research groups at Durham University working on theoretical particle physics, quantum computing applications, and computational methods for theoretical physics problems.

• Statistics
• Sampling Theory
• Quantitative Methods
• +3 more

Prof. Dr. Karlheinz Fleischer is a full professor at Philipps University of Marburg, where he holds the Chair of Statistics within the Department of Business Administration. He leads the Statistics research group (AG Fleischer) and maintains office hours by appointment during lecture periods. His primary research focuses on: Statistical sampling theory and survey methodology Data fusion techniques and multivariate analysis Quantitative methods in economics and finance Statistical estimation techniques and distribution theory Computational statistics and simulation methods An analysis of his 15 most recent publications (1993-2000) reveals consistent focus on statistical theory and applications: 73% concern sampling/estimation methods, 20% focus on financial/econometric applications, and 7% address computational statistics. Common themes include ratio estimation, survey methodology, and distribution theory with applications ranging from stock market analysis to industrial optimization. He leads a research team including Dr. Karl-Heinz Schild (retired 2024), Vladlena Prysyazhna (research associate), Robert Scherf (scientific staff), and Ute Bendix (secretary). The group offers courses in descriptive statistics, econometrics, and statistical programming using R at both bachelor's and master's levels.

Oguz DurumericView profile

Associate Professor

• Differential Geometry
• Medical Image Analysis
• Geometric Topology
• +5 more

Oguz Durumeric is an Associate Professor in the Department of Mathematics at the University of Iowa, part of the College of Liberal Arts and Sciences. His research focuses on differential geometry, medical image analysis, and geometric topology. He earned his PhD from SUNY Stony Brook and has contributed to interdisciplinary applications of geometry in medical imaging, particularly in lung biomechanics and radiation therapy planning. Education: PhD in Mathematics from SUNY Stony Brook. Research Interests: Dr. Durumeric’s work bridges pure and applied mathematics. In differential geometry, he explores knot energies and curvature properties. In medical imaging, he develops advanced registration techniques for 4DCT and MRI data to study lung ventilation patterns and improve cancer treatment accuracy. His geometric topology research includes ideal knot structures and conformal transformation analysis. Recent Research Trends: His articles highlight innovations in medical image registration (e.g., lung motion artifact correction, out-of-phase ventilation detection) and geometric models for biomedical applications. He also addresses foundational challenges like shape collapse in large-deformation registration. Grants & Collaborations: While specific grants are not listed, his work implies collaboration with medical imaging labs and oncology teams. No formal advisees are documented here. Labs/Teams: Affiliated with the University of Iowa Mathematics Department’s research groups in geometry and applied mathematics. His website provides further details on ongoing projects.

Leanna L. HouseView profile

Associate Professor

• Bayesian Statistical Modeling
• Uncertainty Analysis
• Data Mining
• +5 more

Leanna L. House is an Associate Professor in the Department of Statistics at Virginia Tech's College of Science. She specializes in Bayesian statistical modeling, with a focus on model averaging, kernel regression, and uncertainty analysis of computer models. Her research spans applications in proteomics, bioinformatics, cosmology, climatology, and hydrology. She teaches courses such as Applied Bayesian Statistics and Hierarchical Models, emphasizing data visualization and interactive analytics. Education: Ph.D. in Statistics, Duke University (2006) M.S. in Statistics, Duke University (2003) M.A.T. in Curriculum Development, Cornell University (1999) B.S. in Biometry and Statistics, Cornell University (1998) Her work integrates data mining and visualization to enhance human-data interaction, with notable contributions to environmental modeling and proteomic analysis. She collaborates on interdisciplinary projects, including climate change studies and route choice models in transportation systems.

Daniel J. StilwellView profile

Professor

• Autonomous Underwater Vehicles (AUVs)
• Marine Robotics
• Control Systems
• +7 more

Daniel J. Stilwell is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University (Virginia Tech), and Co-Director of the Center for Marine Autonomy and Robotics. He holds affiliations including the Seale Coastal Observatory Faculty Fellow role. His research focuses on autonomous underwater vehicles (AUVs), marine robotics, control systems, and sensor networks. He earned his Ph.D. in Electrical Engineering from Johns Hopkins University (1999), M.S. from Virginia Tech (1993), and B.S. in Computer Engineering from the University of Massachusetts (1991). His notable contributions include advancements in AUV control, underwater acoustic communication, multi-agent systems, and sensor network optimization. Key projects include the "Unconventional Marine Platforms" funded by the Office of Naval Research and collaborative subsea mapping initiatives. His work bridges theoretical control systems with practical robotic applications in marine environments. Dr. Stilwell has received prestigious awards such as the NSF CAREER Award and ONR Young Investigator Program Award. His research emphasizes robust control strategies, adaptive systems, and decentralized learning algorithms. He leads efforts in experimental validation of AUV control systems and underwater sensor networks, contributing to both academic and military applications.

Andrea IchinoView profile

Professor

• Labour Economics
• Economics of Education
• Economics of the Family
• +7 more

Andrea Ichino is a Full-time Professor in the Department of Economics at the European University Institute (EUI) in Florence, Italy. He previously held positions at the University of Bologna, Bocconi University, and the EUI, where he was a Jean Monnet Fellow. His research focuses on Labour Economics, Education Economics, Family Economics, and Microeconometrics. His work addresses topics like intergenerational mobility, gender norms, judicial efficiency, and policy impacts on education and labour markets. Affiliations: EUI, University of Bologna (on leave), Research Fellowships at CEPR, IZA, and CESifo. Education: PhD in Economics from MIT (1990), Laurea in Economics from Bocconi University (1985). Research Interests: Ichino examines socio-economic policies, including childcare effects, taxation, and education systems. His recent work analyzes cognitive impacts of daycare, regional wage disparities, and pandemic economic strategies. He emphasizes causal inference and experimental methods in his analyses. Publications: Over 40 peer-reviewed articles in top journals like the Journal of Political Economy, American Economic Review, and Journal of Labor Economics. Recent work includes studies on multi-cutoff RD designs and pandemic policy implications. Awards: EALE Fellowship, CEPR Research Fellowship, and grants from the Unicredit Foundation for education research. Advising & Grants: Supervised over 40 PhD students across institutions. Received grants for projects on education policy, judicial productivity, and intergenerational mobility. Active in policy discussions on tax systems, wage equality, and public sector reforms. Labs/Teams: Collaborates with research clusters at EUI on topics like civic engagement and judicial efficiency. Leads projects on educational tracking systems and labour market dynamics.

• Survival Analysis
• Nonparametric Regression
• Semiparametric Regression
• +4 more

Ingrid Van Keilegom is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. She is a member of the LISS – KU Leuven Institute of Sports Science and serves as a program director for the POC Master in Statistics. Senior academic staff member of FEB Council and Campus Council Observer in Faculty of Science Council Member of LStat General Assembly Her research focuses on Survival Analysis and Nonparametric Regression , with expertise in: Dependent Censoring Cure Models Measurement Error Correction Quantile Regression Multivariate Statistical Inference Causal Surrogacy Metrics Recent publications emphasize copula-based methods, quantile regression for censored data, and applications to biomedical and economic forecasting. Key projects include instrumental variable approaches for endogeneity in cure models, bankruptcy prediction via machine learning, and measurement error modeling in multivariate settings. Notable affiliations: Centre for Efficiency and Productivity Analysis Collaborations in pharmaceutical statistics and actuarial science

Yinqiu HeView profile

Assistant Professor

• High-dimensional statistical inference
• Large-scale data analysis
• Mediation pathway analysis
• +5 more

Yinqiu He is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison. They hold affiliations with the School of Computer, Data & Information Sciences and the Data Science Institute at Columbia University (2021-2022 postdoc). Their research focuses on developing statistical methodologies for high-dimensional and complex data, with applications in genomics, metabolomics, and network analysis. Key areas include mediation pathway analysis, asymptotic theory for U-statistics, and functional connectivity modeling. Education includes a B.S. in Statistics from the University of Science and Technology of China (2016) and a Ph.D. in Statistics from the University of Michigan-Ann Arbor (2021), advised by Professors Gongjun Xu and Xuming He. They were awarded the ProQuest Distinguished Dissertation Award (2022) and received multiple travel grants from the Institute of Mathematical Statistics and ASA. Teaching includes core Ph.D. courses like STAT 849 (Regression Analysis) and applied courses like STAT 456 (Multivariate Statistics). Current mentoring includes MS student Yuhan Zheng (now pursuing UW-Madison Ph.D.) and Xiangyi Liao (Ph.D. in Educational Psychology). Research outputs include foundational work on adaptive U-statistics testing frameworks and scalable methods for large-scale genomic data analysis. Active in methodological contributions to biostatistics, their work bridges statistical theory and computational efficiency. Recent projects involve dynamic functional connectivity estimation from fMRI data and latent space modeling in heterogeneous networks. GitHub repository 'Adaptive-U-stats' hosts open-source implementations of high-dimensional testing algorithms developed in their research.

Sarah S. LamView profile

Professor

• System modeling
• Simulation
• Business analytics
• +9 more

Sarah S. Lam is a Professor of Industrial and Systems Engineering and serves as Graduate Director at the School of Systems Science and Industrial Engineering, Binghamton University. She also holds the role of Assistant Director for Systems Analysis and Modeling at the Watson Institute for Systems Excellence. Her expertise spans system modeling, simulation, and optimization using computational intelligence techniques such as neural networks, meta-heuristics, fuzzy logic, and stochastic simulation. Education: MS from University of Delaware PhD from University of Pittsburgh Her research focuses on neural networks, evolutionary optimization, machine learning, and system modeling and optimization. She actively contributes to fields like business analytics, medical informatics, and health systems. Awards: Chancellor's Award for Excellence in Teaching (2012) Dr. Lam’s work integrates probability and statistical methods (design of experiments, regression, ANOVA, PCA) with innovative computational approaches to solve complex engineering and healthcare challenges.

• Sports Biomechanics
• Exercise Physiology
• Rowing Performance
• +4 more

Dr. Mathijs Hofmijster is an Assistant Professor affiliated with the Faculty of Behavioural and Movement Sciences, Physiology Department at Vrije Universiteit Amsterdam, as well as the Amsterdam Movement Sciences (AMS) and the Institute for Brain and Behavior Amsterdam (IBBA). His research focuses on biomechanics and physiology in sports performance, particularly in rowing and cycling. Key areas include power output measurement, neuromuscular adaptations, and training optimization. He teaches courses such as 'Moving Matters in Health' and supervises graduate research in elite athlete performance. Research Interests: Rowing biomechanics and power output analysis Neuromuscular fatigue and training adaptations Para-cycling performance and adapted physical activity Instrumentation for sports performance monitoring Recent work highlights include a 2024 scoping review on elite para-cycling performance and advancements in blade force measurement techniques (2019). His articles explore topics ranging from muscle morphology in Olympic rowers (2018) to real-time feedback systems improving training compliance (2017). He has supervised one PhD thesis and maintains active collaborations in sports science, contributing to UN Sustainable Development Goals related to health and well-being.

• Econometrics
• Agricultural Economics
• Resource Economics
• +3 more

Zheng Li is an Assistant Professor in the Department of Agricultural and Resource Economics at North Carolina State University. His research focuses on econometric methodologies with applications in agricultural economics, resource management, and policy analysis. He holds expertise in nonparametric estimation, quantile regression, and structural econometric modeling. Key research interests include analyzing agricultural production risks, evaluating policy impacts on housing markets, and developing advanced statistical techniques for mixed data types. His work bridges econometric theory with practical applications in environmental, urban, and transportation sectors. Recent publications explore topics such as lung cancer detection via biomedical sensing technologies, ridesharing platform incentives, and pandemic effects on real estate markets. Methodologically, his contributions span kernel-based specification tests, bootstrap methods for heavy-tailed data, and monotonicity-constrained estimation techniques. No scientific awards or formal advisees are listed. His research often intersects with interdisciplinary challenges, reflecting a commitment to innovative solutions in applied economics and data science.

Luan OliveiraView profile

Assistant Professor

• Precision Agriculture
• Agricultural Engineering
• Crop Production
• +3 more

Dr. Luan Oliveira is an Assistant Professor and Precision Agriculture Extension Specialist at the University of Georgia's College of Agricultural & Environmental Sciences (CAES), Department of Horticulture. His roles are distributed as 75% Extension, 20% Research, and 5% Service. He holds a Ph.D. in Agronomy (Crop Production) from São Paulo State University and a B.Sc. in Agronomic Engineering from Federal University of Paraíba, Brazil. His research focuses on precision agriculture tools, agricultural machinery optimization for vegetables and specialty crops, and mechanized operations like planting, spraying, and harvesting. He leads the Precision Horticulture Team, aiming to improve crop quality through innovative technologies. Dr. Oliveira has received the 2021 Gerald O. Mott Award for Meritorious Graduate Student in Science. He has authored/co-authored 9 refereed articles, 7 Extension Publications, 40 conference papers, and 13 book chapters, securing ~$220,000 in grants as PI/Co-PI. His work emphasizes practical applications in precision agriculture, including drone spraying, robotic systems, and mechanized sugarcane planting. Key grants and awards highlight his contributions to agricultural technology and crop management. His research spans diverse crops like peanuts, corn, cotton, and sugarcane, addressing challenges in seeding depth, soil compaction, and equipment wear. He collaborates with the Institute for Integrative Precision Agriculture and UGA's Precision Agriculture initiatives.

David UbilavaView profile

Associate Professor

• Commodity Markets
• Price Analysis
• Conflict and Political Violence
• +2 more

Dr. David Ubilava is an Associate Professor in the School of Economics at the University of Sydney, affiliated with the Faculty of Arts and Social Sciences. He holds a PhD in Agricultural Economics from Purdue University (2010) with research focusing on agricultural markets, commodity prices, and the socioeconomic impacts of climate variability and political conflict. His work examines how environmental factors and market dynamics influence food security and social stability in developing regions. Research interests center on: Price analysis and forecasting in commodity markets Climate shocks and agricultural adaptation strategies Political violence and conflict economics Economic development in vulnerable regions Publications demonstrate consistent focus on climate-economy interactions, with recent work examining El Niño impacts on global commodity markets and post-harvest conflict dynamics. Research consistently integrates econometric modeling with development policy applications. Awards & Honors: Quality of Research Discovery Award (2025) Research Collaboration Award, Faculty of Arts and Social Sciences (2024) Teaching Excellence Award, Faculty of Arts and Social Sciences (2017) Currently advises doctoral candidate Gilliane Angela on emerging Asian economies and serves as associate editor for the American Journal of Agricultural Economics (since 2022) and co-editor of Food Policy (since 2018). Research funding includes ARC Discovery Projects on political conflict and food security.

Sheheeda Mariam ManakkaduView profile

Associate Professor

• Robotics
• Data Analytics
• Machine Learning
• +4 more

Sheheeda Mariam Manakkadu is an Associate Professor in the Department of Computer Science at Southern Illinois University Carbondale. She teaches graduate courses in Data Structures, Object-Oriented Programming, Data Mining, Text Mining, and Cloud Architecture, along with undergraduate courses in Operating Systems and Data Analytics. Ph.D., Computer Engineering M.E., Biomedical Engineering Her research spans robotics, data analytics, and parallel computing. Key areas include adaptive control of robotic manipulators, big data processing via MapReduce, IoT resource allocation, and computational bioinformatics for protein networks. Recent publications focus on neuro-sliding mode control, cloud architecture, and scalable recommender systems. She actively participates in academic service as a committee member for graduate courses and the IEEE Erie Section. Her work integrates machine learning, optimization algorithms, and distributed systems across diverse domains.

• Construction Management
• International Construction Business
• Risk Management
• +5 more

Professor Roger Flanagan is a distinguished academic specializing in construction management with over two decades of research contributions. His work primarily focuses on international construction business, risk management, sustainable building practices, and whole life cost appraisal. Affiliated with the Chartered Institute of Building, London, he has authored numerous technical guides and publications that shape construction industry practices globally. Specializes in international construction business dynamics Expert in risk management and sustainable building practices Author of influential technical guides for the construction industry Research spans construction economics and supply chain management Flanagan's research interests center on the complexities of international construction projects, with particular emphasis on risk management, sustainable building practices, and whole life cost considerations. His work examines how contractors navigate international markets, manage project complexity, and implement sustainable practices. Recent publications address materials procurement in MSMEs, pre-construction planning complexity, and the integration of renewable energy in building projects, reflecting evolving industry challenges. Analysis of his 15 most recent publications reveals a consistent focus on construction management challenges with increasing emphasis on sustainability and international business dynamics. His work spans construction economics, risk management, sustainable building practices, and supply chain management, with particular attention to Korean and Middle Eastern construction markets. The research demonstrates methodological diversity, employing panel threshold regression, system dynamics, and empirical case studies to address complex construction industry problems. Professor Flanagan has made significant contributions through his authored guides for the Chartered Institute of Building, including the Guide to Quality Management in Construction (2021), Site Management and Production Guide (2020), and Code of Quality Management (2019). These practical resources translate academic research into industry applications, demonstrating his commitment to bridging theory and practice in construction management. His collaborative research extends across international boundaries, working with academics from institutions in Asia, Europe, and the Middle East. This global perspective enriches his understanding of diverse construction markets and practices, informing his research on international competitiveness, cross-cultural project management, and global supply chain dynamics. Professor Flanagan's work on construction sector futures, particularly his contributions to Vision 2020 initiatives, demonstrates leadership in anticipating industry trends and challenges. His research on information complexity, system dynamics applications, and decision support systems positions him at the forefront of addressing emerging challenges in digital construction and project management.