جست‌وجوی استاد و پژوهشگر

• Adaptive Experimentation
• Reinforcement Learning
• Statistical Inference
• +۷ مورد دیگر

Kelly Zhang is an Assistant Professor in the Department of Mathematics (Statistics Section) at Imperial College London, and a faculty member of the interdisciplinary AI initiative I-X. She holds a PhD in Computer Science from Harvard University, advised by Susan Murphy and Lucas Janson, supported by an NSF Graduate Fellowship and Siebel Scholar recognition. Research focus: Adaptive experimentation, reinforcement learning, and statistical inference Recent contributions: Frameworks for contextual Thompson sampling, delayed reward optimization, and autoregressive exploration Conference involvements: NeurIPS, INFORMS, IEEE-EMBS, IMS-Bernoulli, RLC, JSM Her work bridges machine learning and statistics with applications in health informatics, podcast recommendation systems, and econometric society interdisciplinary research. She has collaborated with leading researchers including Daniel Russo, Hongseok Namkoong, and Sasha Rush. Scientific Awards: NSF Graduate Fellowship Siebel Scholar (2023) Her statistical inference work has been published in NeurIPS (2020, 2021) and applied to both academic and industry settings at Apple HealthAI, Facebook AI Research, and eBay.

• Maternal and Child Health
• Medical Statistics
• Public Health
• +۸ مورد دیگر

Sharlene Alauddin is an Assistant Professor in the Department of Statistics at Khulna University, Bangladesh, a position she has held since 2018. She is concurrently pursuing her PhD in Medical Statistics at the University of Exeter, UK, under the prestigious Commonwealth Scholarship. Her educational qualifications include: MS in Statistics from the University of Dhaka, Bangladesh BS in Statistics, Biostatistics & Informatics from the University of Dhaka, Bangladesh Alauddin's research centers on maternal and child health, with primary focus on birth intervals and the maternal weathering hypothesis as protective factors against child malnutrition. Her work investigates how ethnicity intersects with maternal age, wealth, education, and socioeconomic factors to influence child health outcomes. Methodologically, she specializes in longitudinal/multilevel data analysis, survival analysis, spatial statistics, and meta-analysis within public health contexts. Her scholarly recognition includes the Commonwealth Scholarship supporting her doctoral research at Exeter. As an academic supervisor, Alauddin has mentored undergraduate student projects at Khulna University. Her research integrates multidisciplinary approaches across epidemiology, biostatistics, survival analysis, and demographic studies, with emphasis on statistical applications in health disparities research.

Berk Ustunمشاهده پروفایل

استادیار

• Machine Learning
• Algorithmic Fairness
• Interpretability
• +۶ مورد دیگر

Berk Ustun is an Assistant Professor at the Halıcıoğlu Data Science Institute and the Department of Computer Science & Engineering at the University of California, San Diego. His research focuses on responsible machine learning, with applications in medicine, consumer finance, and the physical sciences, emphasizing algorithmic fairness, interpretability, and personalization. Previously, he held research positions at Google AI and the Harvard Center for Research on Computation and Society , and co-founded Petal , a company using ML to broaden credit access. He earned a PhD in Computer Science from MIT and Bachelors degrees in Operations Research and Economics from UC Berkeley . His group at UCSD recently expanded with the addition of Harry Cheon and Shreyas Kadekodi . He has received multiple awards, including the Kavli Fellowship , NSF Awards for Recourse Verification and AI-Driven Wildfire Prevention, the FAI Award for Fair AI in Medicine, and an Amazon Research Award for Participatory Personalization in ML. 2025 : Notable Area Chair at NeurIPS 2023 : NSF Award for Recourse Verification 2023 : Amazon Research Award for Participatory Personalization 2021 : Kavli Fellow by NAS 2021 : FAI Award for Fair AI in Medicine Berk actively hires postdoctoral researchers in responsible ML through his UCSD lab , with recent projects addressing label indeterminacy, predictive multiplicity, and equitable algorithm design. He advocates for ethical frameworks in AI, challenging the efficacy of explanation laws in detecting discrimination and exploring the societal impact of machine learning systems.

Quentin Gronauمشاهده پروفایل

مدرس

• Bayesian Statistics
• Mathematical Psychology
• Open Science
• +۴ مورد دیگر

Quentin Gronau is a Lecturer at the School of Psychological Sciences , University of Newcastle , and a member of the Newcastle Cognition Lab. He earned his PhD in 2021 from the University of Amsterdam under Prof. Eric-Jan Wagenmakers, contributing to the JASP project. His academic journey includes postdoctoral roles at the University of Amsterdam, University of Tasmania, and the University of Newcastle. Research Interests: Mathematical psychology, Bayesian statistics, computational cognitive modeling, open science, and shared mental models for human-bot cybersecurity teams. Key Contributions: Development of Bayesian model comparison techniques (e.g., warp-III bridge sampling), meta-analysis methodologies, and statistical software tools like JASP . Collaborations: Worked with Prof. Scott Brown, Dr. Ami Eidels, Dr. Dora Matzke, Prof. Andrew Heathcote, and Dr. Guy Hawkins. Grants: Lead on a $341,470 NWO grant (2016-2020) and investigator on a $46,305 BITSS grant (2016-2017).

Jeffrey Ehmeمشاهده پروفایل

استاد

• Mathematics
• Differential Equations
• Nonlinear Analysis
• +۴ مورد دیگر

Jeffrey Ehme is a Professor of Mathematics at Spelman University since 1992. He teaches courses such as Real Variables, Abstract Algebra, Calculus, and Codes and Cryptography. Education: Ph.D. and M.S. in Mathematics from Auburn University B.A. in Mathematics from Northern Kentucky University His research focuses on differential equations and boundary value problems, particularly on existence and uniqueness theorems for nonlinear, perturbed, and higher-order systems. Trends in his recent publications include cryptographic algorithms (2024), spectral analysis techniques (2020), and theoretical advancements in Sturm-Liouville and Lidstone boundary value problems (2001–2009). Contact: Email: jehme@spelman.edu Phone: 404-270-5824 Office: Albro-Falconer-Manley Science Center 323

Frazier Bindeleمشاهده پروفایل

دانشیار

• Nonparametric Statistics
• Robust Estimation
• Robust Testing
• +۴ مورد دیگر

Frazier Bindele serves as an Associate Professor in the Department of Mathematics and Statistics at the University of South Alabama's College of Arts and Sciences. His academic profile centers on developing advanced statistical methodologies with practical applications across diverse research domains. His educational foundation includes: Ph.D. in Statistics from Auburn University (2012) MS Diploma in Mathematics from the International Centre for Theoretical Physics (ICTP) (2007) M.S. in Mathematics from Marien Ngouabi University (2004) B.S. in Mathematics from Marien Ngouabi University (2003) Dr. Bindele's research specializes in Nonparametric Statistics and Robust Estimation, with particular emphasis on rank-based and signed-rank methodologies. He develops innovative approaches for regression models under complex data scenarios, especially those involving missing responses and covariates. His work bridges theoretical rigor with practical implementation, addressing challenges in single-index models, varying coefficient frameworks, and functional linear modeling where traditional methods fail under non-ideal conditions. Analysis of his publication trajectory reveals a concentrated focus on robust statistical inference for missing data problems (both MAR and MNAR mechanisms), with significant contributions to distribution theory through novel distribution families. His recent work demonstrates increasing sophistication in handling high-dimensional settings and developing computationally efficient algorithms for real-world data analysis, while maintaining theoretical guarantees of robustness.

Chunjiang Qianمشاهده پروفایل

استاد

• Control Theory
• Nonlinear Systems
• Autonomous Vehicles
• +۹ مورد دیگر

Dr. Chunjiang Qian (commonly referred to as 'CJ') is the Mary Lou Clarke Endowed Professor and Department Chair at the Department of Electrical and Computer Engineering within the Klesse College of Engineering and Integrated Design at The University of Texas at San Antonio (UTSA). Holding a Ph.D. in Electrical Engineering & Computer Science from Case Western Reserve University (2001), Dr. Qian has progressed through academic ranks at UTSA from Assistant Professor (2001-2005) to Associate Professor (2005-2012) and, since 2012, Professor . His work focuses on advanced control systems for autonomous vehicles, nonlinear dynamics, and cybernetics. Dr. Qian has received significant recognition, including the NSF CAREER Award (2003), Regents’ Outstanding Teaching Award (2009), 3rd Best Paper Award at the ISA POWID Symposium (2011), and Best Poster Paper Award at the IFAC ICONS (2013). He was named an IEEE Fellow in 2021 and AAIA Fellow in 2023, underscoring his impact in control systems and artificial intelligence. His research spans diverse areas including model predictive control , finite-time stabilization , sampled-data systems , and non-smooth control theory . Dr. Qian has authored or co-authored over 170 technical papers with an H-index of 33 (as of 2016), reflecting his substantial scholarly influence. He has also served as Associate Editor for Automatica and Subject Editor for the International Journal of Robust and Nonlinear Control . Dr. Qian leads the UTSA Control, Computation, and Cybernetics Laboratory (C³) and holds affiliate positions at the Institute of Cyber Security (ICS) , the Center of Simulation, Visualization, and Real-Time Prediction (SiViRT) , and the Center of Excellence for Engineering Education (CE³) . He has taught courses such as Applied Engineering Analysis and Advanced Quality Control , with a commitment to minority student recruitment and mentoring . Scientific Awards and Recognitions: AAIA Fellow (2023) IEEE Fellow (2021) NSF CAREER Award (2003) Regents’ Outstanding Teaching Award (2009) Best Poster Paper Award , IFAC ICONS (2013) 3rd Best Paper Award , ISA POWID Symposium (2011)

Rebecca Kurtz-Garciaمشاهده پروفایل

استادیار

• Dependent Data
• Time Series Analysis
• Biostatistics
• +۵ مورد دیگر

Rebecca Kurtz-Garcia is an Assistant Professor at Smith College with joint appointments in the Mathematics and Statistics & Data Sciences departments. She holds a Ph.D. in Applied Statistics from the University of California Riverside and an M.S. in Statistics from Ball State University. Ph.D., University of California Riverside M.S., Ball State University B.S., University of California Davis Her research focuses on dependent data , including time series analysis , simulation , and machine learning . She specializes in long-run variance estimation for multivariate datasets in economic and environmental contexts. Recent collaborative projects include statistical methods for MCMC simulations and fiscal policy analysis. She has published work on Candida albicans genetics and solar irradiance modeling. Dissertation Year Program Award (UCR, 2022-2023) Best Presentation Award (NISS, 2022) Dr. Mir Masoom Ali Scholarship (Ball State, 2018) Kurtz-Garcia provides statistical consulting services in LaTeX, Python, R , and advanced computational methods. She mentors undergraduate and graduate researchers in statistical inference and data science applications.

• Quality Control
• Statistical Analysis
• Artificial Neural Networks
• +۳ مورد دیگر

Prof. Ahmet Sermet Anagün serves as Professor and Head of the Department of Industrial Engineering at Izmir University of Economics, Faculty of Engineering since September 2017, with administrative leadership beginning in 2019. Previously, he held equivalent roles at Eskişehir Osmangazi University (2015-2017) following a 35-year academic progression from Research Assistant to full Professor. His educational credentials include: Ph.D. in Industrial Engineering, Cleveland State University (1993) M.S. in Industrial Engineering, Anadolu University (1985) B.S. in Industrial Engineering, Anadolu University (1982) Research focuses on Quality Control and Statistical Analysis, with significant contributions to fuzzy logic applications in control charts and neural networks for biosignal processing. His methodology integrates experimental design, Taguchi techniques, and machine learning for industrial optimization problems spanning manufacturing, environmental engineering, and educational technology. Analysis of his 14 most recent publications reveals dominant trends in fuzzy control systems (60% of works) applied to quality assurance, with emerging interdisciplinary research in biosurfactant production and educational technology analysis using CHAID methods. His approach consistently bridges statistical theory with practical engineering solutions. No scientific awards were documented in the provided materials. Prof. Anagün actively supervises Master's theses and senior capstone projects across engineering disciplines, teaching core courses in Statistical Quality Control, Engineering Statistics, and Human Factors Engineering. His administrative service includes University Erasmus Coordination and KOSGEB representation, though specific grant funding details remain unlisted.

• Open Channel Hydraulics
• Sediment Transport
• Computational Fluid Dynamics (CFD)
• +۵ مورد دیگر

Professor Richard Hardy is a faculty member at Durham University's Department of Geography, with expertise in open channel hydraulics, sediment transport, and computational fluid dynamics. His work integrates high-resolution numerical modeling with laboratory experimentation to analyze turbulent flow structures in fluvial systems. Focus on CFD development for complex topography Incorporates bio-mechanical models for vegetation-flow interactions Specializes in dune dynamics and floodplain modeling Research interests include: Numerical modeling of unsteady flow fields Vegetation resistance parameterization in flood models Microbial landscape hydrodynamics Coherent flow structure analysis Gravel-bed turbulence propagation Climate change impacts on sediment dynamics Recent publications emphasize: Three-dimensional dune field simulations Topographic roughness quantification Vegetation reconfiguration effects on flow Multi-scale roughness-length applications Bed permeability influences on turbulence Public participation in catchment management Professional affiliations include: Durham University Department of Geography Collaborative projects with international research teams Contributions to sediment transport theory

Yongseok Parkمشاهده پروفایل

استادیار

• Bioinformatics
• Cancer Epigenetics
• Genomic Data Analysis
• +۴ مورد دیگر

Yongseok Park is an Assistant Professor in the Department of Biostatistics at the University of Pittsburgh. His research focuses on epigenetic and high-throughput genomic data analysis in cancer, including the development of statistical methods for DNA methylation analysis, RNA sequencing, and clinical trial design. He has created computational tools like methylSig for whole-genome methylation analysis and developed methodologies for cancer immunotherapy trial design. Education Ph.D. from University of Michigan-Ann Arbor (2011) M.S. from University of Michigan-Ann Arbor (2007) Master of Engineering from Tsinghua University, Beijing Bachelor of Engineering from Shanghai Jiao Tong University, Shanghai Research Focus Dr. Park's interdisciplinary research integrates biostatistics, bioinformatics, and oncology. Primary areas include: Epigenetic analysis : Developing pipelines for whole-genome DNA methylation data Genomic methodologies : RNA-seq analysis and integrative omics approaches Clinical applications : Statistical designs for cancer immunotherapy trials Theoretical statistics : Survival analysis, nonparametric methods, and stochastic ordering Teaching Experience BIOST 2083: Linear Models (2014-2020) BIOST 2036: Introduction to Health Data Science (2020) BIOST 2063: Bayesian Data Science (2021) BIOST 2025: Biostatistics Seminar (2014-2015) Research Publications Dr. Park's publications demonstrate consistent focus on statistical methodology development for genomic data analysis and clinical trial design. Recent work emphasizes DNA methylation analysis pipelines (methylSig, MethylSeqDesign), cancer immunotherapy trial methodologies, and integrative omics approaches. Theoretical contributions include innovations in survival analysis and nonparametric estimation under order restrictions.

Greg Yothersمشاهده پروفایل

استاد پژوهشی

• Biostatistics
• Oncology
• Clinical Trials
• +۳ مورد دیگر

Greg Yothers is a Research Professor and Senior Deputy Director at the University of Pittsburgh's Biostatistics Department. He serves as Deputy Group Statistician for the NRG Oncology SDMC, focusing on clinical trial design and statistical analysis in oncology. Education B.Sc. in Mathematics, Pennsylvania State University (1981) M.A. in Statistics, University of Pittsburgh (1998) Ph.D. in Statistics, University of Pittsburgh (2003) His research spans colon cancer (stage II/III trials, IDEA/ACCENT consortia), breast cancer (HER2-targeted therapies, OlympiA study), and statistical methodology for identifying treatment benefit subsets. He pioneered analyses of racial disparities and biomarker interactions in cancer trials. Recent work focuses on adjuvant colon cancer trials , recurrence score validation , and rectal cancer chemoradiation protocols , emphasizing data standardization and subset identification through advanced statistical techniques. Current affiliations include NRG Oncology SDMC, ACCENT Collaborative Group, and IDEA Consortium.

Guan Yuمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• High-Dimensional Statistics
• Multi-Modality Data Integration
• +۳ مورد دیگر

Guan Yu is an Associate Professor in the Department of Biostatistics at the University of Pittsburgh's Graduate School of Public Health. His methodological research focuses on statistical machine learning, high-dimensional statistics, and non-parametric methods, with applications to biomedical problems such as Alzheimer’s disease, cancer, autoimmune diseases, and environmental health. He received his PhD in Statistics from the University of North Carolina at Chapel Hill (2016), an MS in Statistics from Nankai University (2011), and a BS in Mathematics from Nankai University (2008). His teaching includes courses like Biostatistics Seminar (BIOST2025) and Introduction to Statistical Theory 2 (BIOST2044) . Research interests include: Multi-modality data integration High-dimensional inference Transfer learning Uncertainty quantification for predictions Penalized regression techniques Robust classification methods His recent publications address challenges in block-missing data, sparse modeling, and cost-constrained regression. Awards include the 2020 ICSA Outstanding Young Researcher Award and multiple ASA recognitions for student papers and teaching excellence (2015-2017). He actively collaborates on interdisciplinary projects and mentors graduate students in biostatistical research.

• Epidemiology
• Real World Evidence
• Methods and Statistics
• +۷ مورد دیگر

Sophie Bots is an Assistant Professor at Utrecht University , affiliated with the Faculty of Science and the Pharmacoepidemiology & Clinical Pharmacology department. Her research focuses on methodological challenges in using observational real-world data for pharmacoepidemiology, particularly in cardiovascular medication safety and sex differences. Areas of Expertise: Epidemiology, Real World Evidence, Methods and Statistics, Cardiovascular Diseases, Gender-Specific Research Research Themes: Data Science, Cohort Studies, Vaccine Safety (e.g., COVID-19), Adverse Drug Reactions Recent publications highlight her work on self-controlled designs for vaccine safety analysis, sex differences in medication outcomes, and leveraging clinical care data for cardiovascular research. She collaborates across European institutions and contributes to methodological frameworks in observational studies. Her 2025 Pharmacoepidemiology and Drug Safety article introduces core concepts in self-controlled designs, while her 2022 Open Heart study explores statin efficacy in women. Current projects include optimizing coronary imaging decisions through machine learning and analyzing baseline risk impacts in diabetes patients. Although no specific scientific awards are listed in the provided texts, her work has been widely shared on academic platforms like Mendeley and social media. She has not been mentioned to have formal advisees or part-time status.

Dimitrije D. Čvokićمشاهده پروفایل

استادیار

• Bioinformatics
• Software Development
• Operations Research
• +۵ مورد دیگر

Dimitrije D. Čvokić serves as Assistant Professor and Head of the Department of Computer and Information Sciences within the Faculty of Science and Mathematics at the University of Banja Luka. His academic leadership spans curriculum development across computer science and mathematics disciplines, with teaching responsibilities including Data Structures, Algorithms, Software Engineering, and Mobile Application Development. His research focuses on competitive hub location problems, bioinformatics tool development, and blockchain applications. Čvokić pioneered the integration of Linux-based bioinformatics tools like BioLinux and UGene into biology curricula, while developing novel optimization algorithms for Stackelberg games in logistics networks. His recent work explores large language model terminology standardization and blockchain-based property investment platforms. Analysis of his publication trends reveals a strong trajectory from theoretical optimization (2015-2020) toward applied computational solutions (2021-2024), with increasing interdisciplinary collaboration spanning public health, real estate, and linguistic computing. His work demonstrates consistent methodological rigor in operations research while adapting to emerging technological domains. As an academic advisor, Čvokić has mentored multiple thesis students including Aleksandar Vrhovac (Interval Arithmetic, 2023), Danijela Kovačević (Automatic Control Systems, 2023), and Nemanja Marjanović (CAPTCHA systems, 2022). His teaching innovations include introducing Python programming, RDKit cheminformatics tools, and BASH scripting into diverse science curricula across chemistry, biology, and geodesy programs. Čvokić established computational laboratories featuring specialized software environments including Chemistry Add-in for Word, ChemSketch, UGene, and NET Bio Extension for Excel. His cross-departmental collaborations have modernized computer science instruction across the university's technical and natural science programs through systematic integration of open-source tools and modern development frameworks.