جست‌وجوی استاد و پژوهشگر

Dingfan Xingمشاهده پروفایل

پژوهشگر

• Applied Statistics
• Area Estimation
• Environmental Sampling
• +۳ مورد دیگر

Dingfan Xing is a Postdoctoral Research Associate in the Department of Geography & Sustainability at the University of Tennessee, Knoxville. Her research focuses on the application and development of statistical methods for area estimation of forest characteristics and land cover, with an emphasis on uncertainty evaluation. Education Ph.D. in Environmental Science, State University of New York College of Environmental Science and Forestry, 2023 Research Interests : Dr. Xing's research is directed towards two main topics: (1) Small area estimation of forest characteristics using model-assisted estimators to improve precision; and (2) Uncertainty evaluation of sample-based area estimates, particularly for land cover and land cover change. She develops advanced methods for estimating confidence intervals and total variance, and incorporates spatial heterogeneity and interpreter variability into estimation frameworks. Her work has applications in sustainable forest management, carbon accounting, and environmental monitoring. Publication Trends : Dr. Xing's publications from 2019 to 2024 reveal a strong methodological focus in environmental statistics. Her recent work (2023-2024) centers on improving forest inventory estimation through multi-metric approaches, incorporating spatial heterogeneity, and addressing under-sampled mangrove forests. She has also made significant contributions to land cover monitoring by developing methods to account for interpreter variability and to estimate confidence intervals under complex sampling designs. Her research bridges statistics, forestry, and remote sensing, and increasingly addresses climate-relevant topics such as carbon estimation. Advising and Grants : No information is available regarding students advised or research grants. Labs and Teams : Dr. Xing is affiliated with the GIST (Geographic Information Science and Technology) research group within the Department of Geography & Sustainability.

Mo Liمشاهده پروفایل

استادیار

• Statistics
• Categorical Time Series
• Changepoint Detection
• +۴ مورد دیگر

Mo Li is an Assistant Professor in the Department of Mathematics at the University of Louisiana at Lafayette since August 2023. He holds a Ph.D. in Systems Modeling and Analysis (2021) from Virginia Commonwealth University, an M.S. in Operations Research (2018) from the same institution, and dual B.S. degrees in Biology and Statistics (2015) from the University of New Mexico and Biology from Northwest University, China. His postdoctoral research at Johns Hopkins Bloomberg School of Public Health focused on statistical applications in microbiome and T-cell receptor sequencing data analysis. His research interests span categorical time series, changepoint detection, and statistical methodologies in environmental and biological sciences. He actively collaborates with interdisciplinary teams and mentors graduate/undergraduate students in research projects. Key areas of expertise include microbiome studies, TCR sequencing, and developing statistical tools for complex data analysis.

Emanuele Della Valleمشاهده پروفایل

دانشیار

• Big Data
• Stream Processing
• Semantic Technologies
• +۳ مورد دیگر

Emanuele Della Valle is an Associate Professor at the Department of Electronics, Information and Bioengineering of Politecnico di Milano. He holds a PhD in Computer Science from Vrije Universiteit Amsterdam and a Master's in Computer Science and Engineering from Politecnico di Milano. His research focuses on Stream Reasoning, Big Data, Semantic Technologies, and their industrial applications. He has led initiatives to establish Stream Reasoning as a recognized field, including organizing over 15 workshops/tutorials and developing the C-SPARQL Engine (downloaded 9,000+ times). He is a member of the editorial board of the Journal of Web Semantics and has authored multiple books and over 70 publications. Education: PhD in Computer Science, Vrije Universiteit Amsterdam Master in Computer Science and Engineering, Politecnico di Milano Research Interests: Big Data and Stream Processing Semantic Web and Knowledge Representation Data Science and Real-Time Analytics Applications in IoT, Telecom, and Social Media Awards include the CAiSE’19 Best Paper Award, IBM Faculty Awards (2006, 2013), and multiple best paper recognitions. His work has been applied with industry partners like Telecom Italia, IBM, and Siemens. Teaching focuses on Big Data, Data Science, and AI, with courses such as 'Streaming Data Analytics' and 'Artificial Intelligence and Big Data Science'. He has coordinated EU projects (FP7 Service Finder, EIT Digital activities) and founded Quantia Consulting and motus ml.

• Control Systems
• Fractional-order Systems
• Robust Control
• +۲ مورد دیگر

Aleksei Tepljakov is a Senior Researcher at Tallinn University of Technology's Department of Computer Systems, specializing in fractional-order control systems and intelligent automation. His research develops novel methodologies for robust control design. Research focuses on fractional-order PID controllers, robust stabilization techniques, and data-driven control applications in power systems and industrial processes. Recent work integrates digital twin technology for virtual commissioning of control systems. Publications emphasize computational approaches to stability analysis in time-delay systems and practical implementations in energy systems. Article trends show consistent innovation in fractional calculus applications for industrial control challenges. Leads research in extended reality interfaces for control engineering and digital twin implementation frameworks.

• Control Systems
• Energy Systems
• Artificial Intelligence
• +۵ مورد دیگر

Eduard Petlenkov is a Tenured Full Professor at the Department of Computer Systems at Tallinn University of Technology (TalTech), leading the Centre for Intelligent Systems. His research focuses on advanced control systems, energy efficiency, robotics, and artificial intelligence applications. He holds the award of Recognized Lecturer 2023 . Key research areas include: Fractional-order control systems Data-driven optimization for energy systems Robotics and autonomous systems Model predictive control (MPC) Power grid stability and renewable integration Recent work emphasizes: Stabilization techniques for hybrid power systems Radar technologies for unmanned ground vehicles Energy harvesting in metamaterials Real-time control of unknown linear systems Smart grid digital twin frameworks His contributions span 20+ peer-reviewed articles (2022–2025), addressing topics like reinforcement learning in microgrid control and fractional-order PID tuning methods. Active in bridging industry 5.0 education through blended learning approaches.

Mohammad Abdulaziz Mansourمشاهده پروفایل

مدرس

• Formal Verification
• Artificial Intelligence
• AI Planning
• +۳ مورد دیگر

Mohammad Abdulaziz Mansour is a Lecturer in Artificial Intelligence at King's College London, affiliated with the Department of Informatics within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held a post-doctoral researcher position at the Chair for Logic and Verification at TU München and remains a visiting researcher there. His work focuses on formal methods, theorem proving, and their applications in AI, particularly in algorithm verification, planning, and graph theory. Research Interests: Formal verification of AI algorithms, formalization of mathematics, graph algorithms, Markov Decision Processes, and SAT-based planning. His contributions emphasize rigorous validation through proof assistants like Isabelle/HOL, ensuring correctness of algorithms in critical domains such as cybersecurity and automated reasoning. Publications highlight his expertise in formal analysis of algorithms (e.g., matroids, minimum cost flows) and verified solutions for MDPs. He collaborates with institutions like the King's Cybersecurity Centre, contributing to research themes in reasoning, planning, and software systems security. Recent work includes advancements in formally verified approximate policy iteration and formally validated SAT-based AI planning methods. His research bridges theoretical computer science with practical applications, ensuring foundational algorithms are both efficient and correct.

• Algorithms
• Data Structures
• Combinatorial Generation
• +۴ مورد دیگر

James F. Korsh is a Professor in the Department of Computer and Information Sciences at Temple University. His office is located in SERC 322, and he can be contacted via email at korsh@temple.edu or phone at (215) 204-8199. He teaches courses including CIS 2168 (Data Structures) and CIS 5511 (Programming Techniques), focusing on algorithms, data abstraction, and problem-solving methodologies. His research specializes in combinatorial algorithms and data structures, with emphasis on loopless generation techniques for trees, permutations, and Gray codes. Key areas include tree generation algorithms, multiset permutations, and efficient combinatorial object generation. Korsh has published extensively in journals including The Computer Journal, ACM Transactions on Algorithms, and Information Processing Letters, with research spanning tree structures, permutation algorithms, and combinatorial optimization. His publications demonstrate consistent focus on efficient algorithm design for discrete structures. He has co-authored two textbooks: Data Structures, Algorithms, and Program Style Using C (1988) and Data Structures, Algorithms, and Program Style (1986). No awards or student advisees are mentioned in available materials. No information is available regarding research grants, laboratories, or team collaborations.

Aaron Lynneمشاهده پروفایل

استاد

• Forensic Microbiology
• Microbiome Analysis
• Antimicrobial Resistance
• +۲ مورد دیگر

Dr. Aaron Lynne is Professor and Chair of the Department of Biological Sciences at Sam Houston State University. He holds a Ph.D. in Molecular Pathogenesis from North Dakota State University. His research program focuses on two primary areas: developing microbial clocks for postmortem interval estimation through decomposition microbiome analysis, and investigating antimicrobial resistance mechanisms in enteric pathogens like Salmonella and E. coli. Research interests include: Characterizing microbial succession patterns during human decomposition under varied environmental conditions Investigating plasmid-mediated antimicrobial resistance transmission Developing metagenomic approaches for forensic applications Analyzing virulence factors in foodborne pathogens Dr. Lynne's publications demonstrate consistent focus on microbial forensics with recent work emphasizing machine learning applications for decomposition analysis. Earlier research established foundational knowledge on Salmonella plasmid biology and resistance mechanisms. His laboratory participates in the Small World Initiative for antibiotic discovery. He directs an active research team at the Lynne Research Lab and collaborates nationally on decomposition ecology projects funded by the National Institute of Justice. Courses taught include General Microbiology, Virology, and Molecular Biology, along with specialized seminars linking science and society.

Brad Delineمشاهده پروفایل

استاد

• Paleontology
• Evolutionary Biology
• Geology
• +۶ مورد دیگر

Brad Deline, Ph.D., serves as Professor of Geology within the Department of Geology at the University of West Georgia's Dr. Perry College of Mathematics, Computing and Sciences. His academic appointments span both the School of Field Investigations and Experimental Sciences and the Dr. Perry College, reflecting interdisciplinary engagement across geoscience and computational disciplines. Teaching responsibilities include foundational courses such as Introductory Geology (GEOL-1121K), Paleontology (GEOL-4024), and Age of Dinosaurs (GEOL-2523) across multiple semesters through Fall 2025. Dr. Deline holds a B.S. in Geological Sciences from the University of Michigan (2003), followed by M.S. (2006) and Ph.D. (2009) in Geology from the University of Cincinnati. His research program centers on paleontological investigations of evolutionary transitions, with particular emphasis on echinoderm morphology, systematics, and disparity through deep time. Key methodological approaches include quantitative morphometrics, taphonomic analysis, and integration of phylogenetic frameworks to reconstruct evolutionary patterns during critical intervals like the Cambrian explosion and Great Ordovician Biodiversification Event. Analysis of Dr. Deline's publication record reveals consistent focus on echinoderm evolutionary dynamics, with recent work (2021-2025) increasingly incorporating genomic data integration, climate-biodiversity correlations, and advanced morphometric techniques. His scholarship demonstrates progression from descriptive systematics toward synthetic analyses connecting morphological disparity with ecological innovation and biogeographic patterns, particularly regarding crinoids and diploporan echinoderms across the Ordovician-Silurian boundary. Dr. Deline maintains active research collaborations with institutions including the University of Michigan, University of Cincinnati, and international partners, evidenced by co-authorship on publications spanning paleobiological methodology development, exceptional fossil preservation studies, and theoretical frameworks for disparity analysis. His laboratory work focuses on morphological quantification techniques applicable to both fossil and extant echinoderm specimens.

• Epidemiology
• Mathematical Biology
• Infectious Disease Modelling
• +۳ مورد دیگر

Michael Li serves as an Adjunct Assistant Professor in the Department of Mathematics and Statistics at McMaster University, a leading Canadian research institution. His academic role focuses on bridging advanced mathematical techniques with critical public health challenges, particularly in infectious disease dynamics. While the adjunct title typically indicates non-permanent status, the provided documentation does not explicitly specify part-time arrangements. His work demonstrates strong institutional ties to Canadian public health agencies and contributes significantly to national pandemic response strategies through quantitative modeling. Dr. Li's research program centers on mathematical epidemiology with emphases on pandemic preparedness, infectious disease modeling, and health policy evaluation. Key areas include SARS-CoV-2 transmission dynamics, vaccination program optimization, wastewater-based surveillance systems, and cross-disease analysis of outbreaks including mpox, Ebola, and influenza. His methodological expertise spans mechanistic epidemic modeling, statistical inference for disease transmission, cost-utility analysis, and genomic surveillance integration. Recent work increasingly addresses Canadian-specific contexts, particularly Ontario's public health landscape, while maintaining global relevance through collaborations with international research networks. Analysis of his 15 most recent publications (2018-2025) reveals consistent contributions to real-time pandemic response. Dominant themes include mathematical modeling for public health decision-making (73% of publications), Canadian outbreak investigations (60%), and methodological innovations in disease surveillance (47%). His work frequently appears in high-impact interdisciplinary journals including JAMA Network Open, PNAS, and BMC Public Health, demonstrating successful translation of mathematical theory into actionable public health guidance. Notable trends include increasing focus on endemic transition phases of diseases and integration of multiple data streams for outbreak forecasting. Scientific awards: No specific prizes, fellowships, or medals were documented in the provided materials. Regarding academic advising and research funding, the available documentation contains no information about graduate students supervised, postdoctoral mentees, or externally funded research grants. Similarly, details about teaching responsibilities beyond his adjunct appointment, laboratory facilities, or formal research teams are absent from the current profile. His scholarly impact is evidenced by substantial altmetric attention including policy document citations, news media coverage across 53 outlets, and academic engagement through 1,100+ Mendeley readers.

Lihua Leiمشاهده پروفایل

استادیار

• Econometrics
• Causal Inference
• Conformal Inference
• +۴ مورد دیگر

Lihua Lei is an Assistant Professor of Economics at the Stanford Graduate School of Business (GSB) with a courtesy appointment as Assistant Professor of Statistics. They are also a Faculty Fellow at the Institute for Economic Policy Research (SIEPR). Dr. Lei completed their Ph.D. in Statistics at UC Berkeley under the supervision of Professors Peter Bickel and Michael Jordan, followed by postdoctoral research in the Statistics Department at Stanford University with Professor Emmanuel Candès. Dr. Lei's research focuses on the intersection of statistics and economics, with particular expertise in: Econometrics Causal Inference Conformal Inference Multiple Hypothesis Testing Network Analysis High Dimensional Statistical Inference Optimization Their publication record shows a strong trajectory in developing statistically rigorous methods with practical applications. Recent work demonstrates increasing focus on conformal inference techniques, causal effect estimation, and high-dimensional statistical problems. Dr. Lei's research bridges theoretical statistics with real-world economic applications, particularly in developing methods with formal statistical guarantees for policy evaluation and decision making. Dr. Lei has received the 2015-2016 Outstanding Graduate Student Instructor Award and has taught theoretical statistics courses at UC Berkeley, including STAT 210A and STAT 210B with Professors Michael Jordan and Martin Wainwright. As an active methodological researcher, Dr. Lei has developed several widely used R packages including adaptMT, cfcausal, dbh, and transferUQ, which implement their methodological contributions for broader application by the research community. Their ongoing work continues to influence both statistical theory and economic applications through innovative methodological developments.

Alexander Strangمشاهده پروفایل

استادیار

• Bayesian Inference
• Stochastic Processes
• Random Graph Theory
• +۷ مورد دیگر

Alexander Strang is an Assistant Professor in the Department of Statistics at the University of California Berkeley. He earned his Ph.D. in applied mathematics from Case Western Reserve University in 2020 and served as a Kruskal Instructor in Statistics at the University of Chicago until 2023. His research spans interdisciplinary domains including statistical physics, spectral graph theory, optimization, and mathematical biology. Key research areas include: Stochastic processes and modeling in biological systems Structure-dynamics interplay in networks Bayesian inference and inverse problems Random graph theory and multi-agent training His recent publications focus on competitive systems, hierarchical Bayesian models, network analysis, and stochastic optimization. Scientific contributions include the 2022 Suzuki Postdoctoral Fellowship Award and foundational work on: Sparsity-promoting hierarchical Bayesian algorithms Spectral characterization of games Moment closure bounds via algebraic geometry Simplicial cohomology in stochastic processes

Russell Davidsonمشاهده پروفایل

استاد

• Econometrics
• Bootstrap Methods
• Poverty and Income Distribution
• +۲ مورد دیگر

Russell Davidson is a Professor and Canada Research Chair Tier 1 in the Department of Economics at McGill University's Faculty of Arts. He can be contacted at russell.davidson@mcgill.ca and his office is located at Leacock 321C, 855 Sherbrooke St. W., Montreal, Quebec, H3A 2T7. Dr. Davidson earned his PhD from the University of British Columbia and joined McGill University in August 2002 after many years at Queen's University in Kingston, Ontario. Since 1987, he has maintained a dual academic appointment, dividing his time between McGill (fall term) and GREQAM, a research laboratory in Marseille, France associated with the Université de la Méditerranée's Faculty of Economic and Management Sciences. His primary research focuses on econometric methodology, particularly developing and refining bootstrap techniques to enhance statistical reliability, and addressing methodological challenges in measuring poverty and income distribution through stochastic dominance analysis. Dr. Davidson emphasizes that 'there is no good empirical practice without a good mastery of the underlying theory' and cautions against treating econometrics as merely 'a set of cookbook recipes.' Analysis of his recent publications reveals a consistent pattern of advancing econometric theory with practical applications, particularly in hypothesis testing methodology, bootstrap techniques, and poverty measurement. His work bridges theoretical developments with real-world economic policy analysis. Canada Research Chair in Social Sciences and Humanities Dr. Davidson has supervised numerous graduate students and maintains active research collaborations, most notably with James MacKinnon. His teaching includes graduate Econometrics (Economics 662) and Honors Economic Statistics (Economics 257 D1), where he emphasizes the art of interpreting what data reveal beyond mere technical implementation.

• Maternal and Child Health
• Women's Health
• Health Inequalities
• +۴ مورد دیگر

Leigh Tooth is a Principal Research Fellow and Deputy Director of the Australian Longitudinal Study on Women's Health (ALSWH) at the University of Queensland's School of Public Health. She holds an affiliation with the Australian Women's and Torres Strait Islander Health Research Centre and has extensive expertise in maternal and child health, women's health, health inequalities, and socio-economic determinants of health. Her work includes leading the Mothers and their Children's Health (MatCH) study and contributing to the NHMRC-funded Centre for Research Excellence on Women and Non-Communicable Diseases (CRE WaND). Dr. Tooth completed her PhD and first-class honours in Occupational Therapy at the University of Queensland. Her research focuses on longitudinal health studies, including examining the relationship between maternal health and children's outcomes, and the impact of socio-economic factors on health disparities. She has been awarded a NHMRC Public Health Fellowship and has held roles such as NHMRC Fellow with the Longitudinal Studies Unit. Her research interests span maternal and child health, women's health, health inequalities, and comorbidity. She has authored over 140 journal articles, book chapters, and research reports, contributing to fields like epidemiology, public health policy, and clinical interventions. Dr. Tooth's work emphasizes translational research, advocating for evidence-based practices and interventions to improve health equity. She chairs the Data Access Committee of ALSWH and collaborates on projects addressing mental health and climate change, further demonstrating her commitment to comprehensive public health approaches.

Markus Barthمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging
• Neuroimaging
• +۳ مورد دیگر

Professor Markus Barth is a Full Professor in Biomedical Engineering at the University of Queensland (UQ), specializing in MRI physics and medical imaging. He holds roles as Deputy Head of School (Research) in the Faculty of Engineering, Architecture and Information Technology, Director of the National Imaging Facility – Queensland Node, and member of the ARC College of Experts. His academic journey includes a Doctorate in Technical Physics from Vienna University of Technology (1999) and prior appointments at the Medical University Vienna, Donders Institute, and Erwin L. Hahn Institute. His research focuses on advancing MRI techniques for high-resolution anatomical and functional imaging, including susceptibility-weighted imaging (SWI), quantitative susceptibility mapping (QSM), and ultra-high field MRI applications. He has pioneered methods to enhance MRI sensitivity for neurodegenerative diseases, cancer, and cognitive neuroscience studies. Barth’s work integrates cutting-edge technologies like deep learning and parallel transmission to improve image quality and clinical utility. His contributions include developing open-source tools (e.g., QSMxT) and large-scale datasets (e.g., HumanBrainAtlas) for reproducible neuroimaging research. Education: Bachelor of Technical Physics, Vienna University of Technology (1995) Doctorate in Technical Physics, Vienna University of Technology (1999) Research Interests: Optimizing MRI for ultra-high field applications, neuroimaging of brain function and connectivity, SWI/QSM for disease markers, and translational imaging techniques for clinical diagnostics. His work addresses topics like layer-specific fMRI, brain-computer interfaces, and decoding neural activity. Grants & Awards: ARC Future Fellowship (2014), leadership in national imaging initiatives, and contributions to MRI software frameworks. Labs/Teams: Leads the Ultra-high Field Human MR Research program at UQ’s Centre for Advanced Imaging and collaborates with interdisciplinary teams in biomedical engineering and neurology. His lab develops novel MRI protocols and open-source tools for global research impact.