جست‌وجوی استاد و پژوهشگر

Diana Coleمشاهده پروفایل

مدرس ارشد

• Ecological statistics
• Integrated Population Modelling
• Identifiability
• +۲ مورد دیگر

Diana Cole is a Senior Lecturer in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. She earned her PhD in Statistics from the same university in 2003, focusing on stochastic branching processes in biology. Her research interests include ecological statistics, integrated population modeling, identifiability, parameter redundancy, and generalized linear mixed models. She is a core member of the SE@K (Statistical Ecology at Kent) research group and the National Centre for Statistical Ecology (NCSE). Dr. Cole teaches courses such as Statistical Consultancy and Data Presentation (MAST6012) and Data Modelling and Consultancy (MAST7200). She actively supervises PhD students in statistical ecology, emphasizing applications in ecological and environmental data analysis. Her publications span ecological modeling, statistical methodology, and interdisciplinary applications, with a focus on parameter redundancy and identifiability in complex models. Notable contributions include her 2020 book Parameter Redundancy and Identifiability , which provides theoretical and practical insights into model identifiability. Her work bridges statistical theory and ecological applications, addressing challenges in population dynamics, conservation biology, and environmental monitoring. Recent studies explore (e)DNA concentration modeling, state-space approaches for ecological time series, and methodological advancements in capture-recapture and removal models.

Joshua Landonمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Stochastic Processes
• Markov Chain Monte Carlo Methods
• +۲ مورد دیگر

Joshua Landon is an Associate Professor of Statistics and Undergraduate Program Director at The George Washington University. His expertise includes Bayesian Statistics, Stochastic Processes, Markov Chain Monte Carlo Methods, Decision Analysis, and Reliability and Risk Analysis. He teaches courses such as Statistical Analysis for Managers, Foundations of Risk Analysis, and Introduction to Bayesian Statistics. Education: Ph.D. in Statistics from The George Washington University (2007); B.A. in Mathematics from the University of Oxford (2000). His research focuses on advancing Bayesian methodologies for complex systems, with applications in reliability engineering, software failure analysis, and decision-making under uncertainty. Notable contributions include the development of Bayesian models for Markov processes and latent-factor analysis in operational research contexts. Publications span high-impact journals like Applied Stochastic Models in Business and Industry and Technometrics . His work emphasizes interdisciplinary applications, blending statistical theory with practical problem-solving in engineering, economics, and physics. No scientific awards are explicitly listed in the provided texts. His advising and grants activities are not detailed here, though his extensive teaching portfolio indicates active mentorship roles.

Samiran Sinhaمشاهده پروفایل

استاد

• Epidemiology
• Biostatistics
• Bayesian Methods
• +۳ مورد دیگر

Samiran Sinha is a Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on statistical methodologies for epidemiological studies, measurement error correction, Bayesian computation, and missing data techniques. He has held academic positions since 2004, including roles as Associate Head for Operations and Associate Graduate Adviser. Education: PhD in Statistics (University of Florida, 2004), MSc (Calcutta University, 1999), BSc (Kalyani University, 1997). Research Interests: Bayesian methods, epidemiological modeling, statistical computation, and applications in health sciences. His work bridges statistical theory and practical applications, including collaborations in astrophysics and genomics. Notable grants include NIH-funded projects on breast cancer data analysis and epigenetic biomarkers. He has advised over 30 graduate and undergraduate students, fostering interdisciplinary research in biostatistics and data science.

• Political Science
• Methodology
• Public Opinion Dynamics
• +۳ مورد دیگر

Suzanna Linn is a Distinguished Professor of Political Science at Penn State University's College of the Liberal Arts, affiliated with the Department of Political Science and the Program in Social Data Analytics. She serves as Associate Editor for Political Analysis, the discipline's leading methodological journal. Her research focuses on time series analysis innovations, public opinion dynamics, and computational studies of digital discourse. Her work integrates advanced statistical methods with substantive political questions, particularly examining how economic indicators shape public sentiment through traditional surveys and social media analysis. Notable contributions include a forthcoming textbook on time series analysis and co-authored work on the decline of the death penalty, awarded the Gladys M. Kammerer Award (APSA, 2008). Linn's research spans over 4,900 citations (h-index 25), with publications in top journals like the American Political Science Review and Political Analysis. Recent projects analyze millions of tweets about the U.S. economy (2012–2023), comparing digital sentiment with survey data to understand modern opinion formation. She also develops bounds approaches for handling model uncertainty in dynamic systems. Linn's work bridges methodological rigor with real-world political questions, emphasizing the evolving role of digital media in shaping public perception. Linn's awards and editorial roles underscore her methodological leadership. Her research agenda includes advancing computational methods to study societal attitudes and refining techniques for analyzing temporal data in political science. While no specific advising or grant details are provided, her academic roles suggest significant contributions to graduate education and research mentorship.

• Algorithms
• Cryptography
• Online Scheduling
• +۳ مورد دیگر

Stanley Fung is a Lecturer in the School of Computing and Mathematical Sciences at the University of Leicester. He holds B.Eng., M.Phil., and Ph.D. degrees from the University of Hong Kong. His research focuses on algorithms, with teaching responsibilities in third-year/MSc modules on Algorithms, C++, and Cryptography. He currently serves as the School's Deputy Director of Education and Examination Officer for UG degrees, overseeing Panels and Boards of Examiners. His research interests emphasize algorithm design and analysis, particularly in online scheduling, thermal-aware resource allocation, and optimization problems. Notable work includes studies on sorting algorithms, energy-harvesting sensor networks, and haplotype inference in genetics. Selected publications (2003-2021) highlight contributions to scheduling theory, algorithmic efficiency, and interdisciplinary applications like bioinformatics. His work often explores trade-offs between computational complexity and practical constraints such as temperature management and energy efficiency. Administratively, he manages educational governance processes, reflecting his dual role as both a researcher and academic leader in computer science education.

• High-Intensity Interval Training (HIIT)
• Adolescent Health
• Exercise Physiology
• +۵ مورد دیگر

Kathryn Weston is a Senior Lecturer in the Department of Physical Activity And Health at the University of Strathclyde. Her research focuses on high-intensity interval training (HIIT), adolescent health, and the application of exercise interventions in clinical and community settings. She leads projects funded by organizations like the North of England Commissioning Support Unit and the Royal Society of Edinburgh. Her work contributes to UN Sustainable Development Goals related to good health and well-being. Key projects include investigating HIIT's impact on physical fitness and mental well-being in adolescents, and systematic reviews on intervention effects in children's health. She has collaborated with global experts to develop standardized youth fitness testing frameworks. Dr. Weston's research spans diverse populations, including cancer survivors, older adults, and spaceflight personnel. She emphasizes translational research, translating evidence into scalable interventions like 'exercise snacks'—brief, high-intensity bursts of activity. Her findings have informed school-based programs and workplace initiatives to enhance physical activity and health outcomes. Notable projects include: Feasibility Study (2023-2025): Evaluates HIIT's role in improving adolescent fitness and mental health. Systematic Review (2024): Analyzes intervention effects on children's physical fitness. She accepts PhD students and has advised on studies involving HIIT protocols, pandemic health impacts, and exercise in clinical populations.

• Clinical Biochemistry
• Protein Analysis
• Hormone Research
• +۴ مورد دیگر

Claus Gyrup Nielsen is a clinical biochemist at Aalborg University Hospital, affiliated with the Faculty of Medicine at Aalborg University. He works in the Department of Clinical Biochemistry where he conducts research at the intersection of clinical medicine and biochemical analysis. His research interests focus on clinical biochemistry with emphasis on protein analysis, hormone research, medical diagnostics, cancer genetics, and metabolic disorders. Nielsen's work demonstrates significant interdisciplinary connections between human clinical medicine and wildlife physiology, particularly in his studies of hibernation in brown bears. Analysis of his publications reveals strong trends in clinical laboratory medicine, with particular focus on reference intervals, biomarker analysis, and pharmacogenetics. His work spans both human clinical applications and comparative physiological studies, showing a unique approach to biochemical research. Nielsen has served as a Co-Investigator on the 'Big data' research project (2015-2022) alongside Christensen, P. A. (PI), Andersen, S. L., and Lykkeboe, S. His research output includes 23 publications spanning from 2004 to 2025, with significant activity in recent years.

Alessandro De Lucaمشاهده پروفایل

استادیار

• Combinatorics on Words
• Automata and Formal Languages
• Discrete Mathematics
• +۶ مورد دیگر

Alessandro De Luca is an Assistant Professor of Computer Science at the Department of Electrical Engineering and Information Technology (DIETI) of the University of Naples Federico II, Italy. He has held previous positions at the University of Turku (Finland) and UQAM (Canada). PhD in Mathematical and Computer Science (2008), University of Naples Federico II MSc in Mathematics (2003), University of Naples Federico II His research focuses on combinatorics on words , with emphasis on Sturmian words, Christoffel words, pseudopalindromes, rich words, and their connections to number theory, automata theory, and discrete mathematics. Key contributions include work on well-distributed occurrences (WDO) in infinite words, aperiodic pseudorandom number generation, and the study of open/closed prefixes in Sturmian sequences. Recent publications highlight his expertise in digital segments , semi-regular continuants , and structural properties of combinatorial words . Collaborative efforts include work with Gabriele Fici (University of Palermo), Golnaz Badkobeh (Goldsmiths University of London), and Luca Zamboni (Université Lyon 1). He has participated in international conferences such as WORDS , DLT , and FLA , and is affiliated with research groups including the Centre for Combinatorics on Words and Applications (CCWA) at Murdoch University and GNSAGA (INdAM).

• GNSS Technology
• Atomic Clocks
• Collaborative Positioning
• +۸ مورد دیگر

Steffen Schön is a Professor at the Institute of Geodesy, Leibniz University Hannover, affiliated with the Faculty of Civil Engineering and Geodetic Science. His roles include Dean of Studies for Geodesy and Geoinformatics, and Speaker of the DFG Research Training Group GRK2159 (i.c.sens). He is also a liaison professor for the Studienstiftung des deutschen Volkes. Schön’s research focuses on GNSS technology, atomic clocks, collaborative positioning, antenna calibration, quantum inertial navigation, and satellite gravity missions. He leads projects in the QuantumFrontiers and CRC 1464 TerraQ initiatives. His research explores antenna phase center corrections, multipath mitigation, and hybrid sensor systems combining classical and quantum accelerometers. Recent work includes evaluating quantum sensors for space navigation and improving GNSS integrity in urban environments. He has collaborated globally through programs like the IGS Antenna Ring Campaign and contributes to 5G NR positioning research. Publications span antenna calibration methodologies, inertial navigation improvements, and applications of GNSS in autonomous systems. His work addresses challenges in frequency transfer, tropospheric parameter retrieval, and resilient timekeeping for technical infrastructure. Schön’s research integrates geodetic, aerospace, and quantum physics domains, emphasizing practical applications for navigation and environmental monitoring.

Boris Jean Eudes Berangerمشاهده پروفایل

مدرس

• Extreme Value Theory
• Environmental Extremes
• Big Data
• +۳ مورد دیگر

Boris Jean Eudes Beranger is a Lecturer in Data Science at the School of Mathematics and Statistics, UNSW Sydney, and an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). He holds a PhD in Statistics from Université Pierre and Marie Curie (Paris 6) and UNSW Sydney (2016), and an MSc in Mathematics from Université Pierre and Marie Curie (2011). His research focuses on Extreme Value Theory applied to environmental extremes (e.g., floods, heatwaves), Symbolic Data Analysis for complex datasets, and Statistical Theory for big data challenges. He has developed novel methods for analyzing histogram-valued data and max-stable processes in high dimensions, with applications in climate science and telecommunications. Key publications include work on tail density estimation using kernel methods, composite likelihood approaches for spatial extremes, and likelihood-based inference for aggregated data. His work emphasizes computational efficiency and robustness in extreme event modeling. Awards: J.B. Douglas Award (2014), Outreach Participation Award (2019) Grants: ACEMS Research Support Scheme (2018–2021), multiple industry collaborations Teaching: Courses include Data Science fundamentals, regression analysis, and extreme value theory Labs/Teams: UNSW Data Science Hub (uDASH), leading the UNSW Statistics Seminar Series

Scott J. Richterمشاهده پروفایل

استاد

• Non-Parametric Methods
• Multivariate Analysis
• Statistical Consulting
• +۳ مورد دیگر

Scott J. Richter is a Professor and Department Head in the Department of Mathematics and Statistics at the University of North Carolina at Greensboro (UNCG), where he also directs the Statistical Consulting Center. He holds a Ph.D. in Statistics from Oklahoma State University (1997) and has been at UNCG since 2001. His research focuses on nonparametric methods, multivariate analysis, and statistical consulting, with applications in healthcare, ecology, and public health. Education: Ph.D. in Statistics, Oklahoma State University (1997). Research interests include nonparametric statistical methods, multivariate analysis, and interdisciplinary collaborations. His recent work spans topics such as adaptive statistical testing designs, nutrition training in medical education, and the impact of mask mandates on pandemic outcomes. He has advised numerous graduate students in statistics and informatics. Teaching includes courses like Statistical Methods for Data Analytics, SAS System for Statistical Analysis, and Statistical Consulting. His publications reflect expertise in statistical theory, biostatistics, and applied research across disciplines. He leads the Statistical Consulting Center, offering support for research design and data analysis across UNCG. No scientific awards are explicitly mentioned in the provided texts, but his extensive publication record and leadership roles highlight his contributions to the field.

• Economic History
• Agrarian Economics
• Institutional Economics
• +۷ مورد دیگر

Salvador Calatayud Giner is an Associate Professor in the Department of Economic Analysis at the Faculty of Economics, University of Valencia. He is affiliated with the Inter-university Institute López Piñero and actively participates in two research groups: HISCUCON (Cultural and Social History of Contemporaneity) and EHVALENCIA (Economic Development in Historical Perspective). His research focuses on economic history , particularly agrarian development, irrigation systems, institutional economics, and social inequality in Spain, with a regional emphasis on Valencia during the 19th and 20th centuries. He explores themes such as land use, market structures, water management, and the nutritional transition, often analyzing historical data to understand long-term socioeconomic trends. His recent publications (up to 2025) reflect a strong focus on Spanish and Mediterranean economic history, with recurring topics including sericulture decline, irrigation governance, bourgeois classes, and inequality during dietary transitions. While a few publications appear in neuroscience and microbiology, the overwhelming majority are in the social sciences, suggesting his primary scholarly identity lies in economic and social history. No scientific awards were mentioned in the provided text. Dr. Calatayud earned his PhD from the University of Valencia with a thesis on 19th-century Valencian agrarian development under the supervision of Dr. Joan Brines Blasco. There is no mention of students he has advised or research grants he has led. He is not noted as part-time, retired, or former staff, indicating active engagement in his academic role.

• Medical Instrumentation
• Biomedical Signal Processing
• Cardiovascular Risk Analysis
• +۳ مورد دیگر

Vania Gomes de Almeida is a Senior Lecturer in Medical Instrumentation and Electronics at the School of Science and Technology, Middlesex University. Her research focuses on biomedical signal processing, cardiovascular diagnostics, and advanced forecasting techniques in energy systems. Specializations: Pulse wave analysis, Kalman filter applications, heart rate deterioration detection Key Collaborations: Research partnerships in Portugal, France, and South Korea Her work spans multiple domains including clinical monitoring, medical device validation, and smart grid applications. She has contributed to improving reproducibility in cardiovascular measurements and developed novel forecasting models for energy systems. Dr. Gomes de Almeida's publications demonstrate strong interdisciplinary focus, combining biomedical engineering with statistical modeling techniques. Her research outputs have been presented at international conferences like IEEE EMBC and published in journals such as Biomedical Signal Processing and Control. Contact: v.gomesdealmeida@mdx.ac.uk | ORCID: 0000-0002-2185-7850

Martin Vatshelleمشاهده پروفایل

دانشیار

• Graph Algorithms
• Parameterized Algorithms
• Algorithm Engineering
• +۹ مورد دیگر

Martin Vatshelle is an Associate Professor in the Department of Informatics at the University of Bergen (UiB) , Norway. He obtained his PhD from UiB in 2012 with a thesis titled New Width Parameters of Graphs . His research is centered on theoretical computer science, particularly in the design and analysis of graph algorithms. His main research interests include: Graph Algorithms Parameterized and Fixed-Parameter Tractable (FPT) Algorithms Algorithm Engineering Graph Theory, especially width parameters like boolean-width, clique-width, and branch decompositions Dynamic programming on structured graphs The recent publications of Martin Vatshelle reflect a strong focus on structural graph theory and algorithmic efficiency. His work frequently explores the computational complexity of graph problems, leveraging width parameters to design faster exact or parameterized algorithms. Themes such as boolean-width bounds, vertex partitioning, and satisfiability problems (#SAT, MaxSAT) are recurrent, indicating a deep engagement with both theoretical foundations and practical algorithmic improvements. There are no scientific awards mentioned in the provided texts. Martin Vatshelle has taught a variety of courses in algorithms, discrete mathematics, and complexity theory since 2004, including Algorithms, Data Structures and Programming , Complexity Theory , and Algorithm Engineering . There is no mention of research grants or supervised students in the provided materials. He collaborates extensively with researchers such as Jan Arne Telle and Sigve Hortemo Sæther. He is affiliated with the research group on Didactics within the Department of Informatics. No separate lab or research center is mentioned.

• Kidney Function Biomarkers
• Clinical Chemistry
• Mass Spectrometry Applications
• +۳ مورد دیگر

Joe El-Khoury is a Professor of Laboratory Medicine and Director of the Clinical Chemistry Laboratory at Yale School of Medicine. He holds dual roles as Director of Clinical Chemistry and the Clinical Chemistry Fellowship Program at Yale New Haven Health. His research focuses on improving kidney function biomarkers, clinical laboratory performance, and mass spectrometry applications. He completed his PhD at Cleveland State University and postdoctoral training at Cleveland Clinic. Education: PhD (Cleveland State University, 2012), BS (American University of Beirut, 2008), Postdoctoral Fellowship (Cleveland Clinic, 2014). Advanced certifications include Yale School of Management programs (2017-2018). Research interests include SDMA as a kidney function biomarker, mass spectrometry adoption in clinical labs, and reducing pre-analytical errors. He has developed novel assays and contributed to lipid testing guidelines through ADLM collaborations. Recent articles focus on hepatitis C serology assays, creatinine delta thresholds for AKI, and trimester-specific haptoglobin reference intervals. His work emphasizes diagnostic accuracy and laboratory automation advancements. Notable awards include 2024 SYCL Mentor of the Year and 2023 IFCC Young Investigator Award. He serves as a reviewer for Clinical Chemistry, The Journal of Applied Laboratory Medicine, and others. His team addresses fluid contamination detection using machine learning and evaluates new clinical assays for diagnostic reliability. Current projects include high-throughput SDMA assays and improving creatinine-based eGFR equations.