جست‌وجوی استاد و پژوهشگر

Eugene Katsevichمشاهده پروفایل

استادیار

• Genetics
• Genomics
• High-dimensional Statistics
• +۳ مورد دیگر

Eugene Katsevich is an Assistant Professor at the University of Pennsylvania's Wharton Department of Statistics and Data Science. His research spans statistical theory, methodology, and applications in genomics and computational biology. Education: Ph.D. in Statistics (Stanford, 2019), A.B. in Mathematics (Princeton, 2014) His work focuses on: High-dimensional variable selection and multiple testing Conditional independence testing and computational efficiency Applications to genetics and genomics Development of statistical software for biological discovery Recent publications address: Robust differential expression analysis in single-cell CRISPR screens Theoretical advances in false discovery rate control Multi-resolution causal variant localization CRISPRi-based enhancer-gene mapping Scientific awards include: Wharton Undergraduate Teaching Excellence Award (2021) He teaches courses in modern data mining (STAT4710/5710) and statistical methodology (STAT9610), and leads the Katsevich Lab at UPenn, funded by NSF and Analytics at Wharton. His research develops methods like KnockoffZoom and SCEPTRE for genome-wide analysis and single-cell CRISPR data.

Trevor Bedfordمشاهده پروفایل

استاد

• Computational Molecular Evolution
• Phylogenetics
• Infectious Disease Epidemiology
• +۵ مورد دیگر

Trevor Bedford, PhD , is a Professor at the Fred Hutchinson Cancer Research Center (Fred Hutch) across multiple divisions including Vaccine and Infectious Disease, Public Health Sciences, and Human Biology. He is also an Affiliate Professor at the University of Washington in the Department of Genome Sciences and Department of Epidemiology, and a Howard Hughes Medical Institute Investigator. His work bridges computational biology, genomics, and public health to combat infectious disease outbreaks. Education: PhD in Biology, Harvard University (2008) BA in Biological Sciences, University of Chicago (2002) Dr. Bedford specializes in computational molecular evolution , phylogenetics , and infectious disease epidemiology , with a focus on antigenic evolution and Bayesian statistics . His research develops tools to translate viral genomic data into actionable public health strategies, exemplified by the Nextstrain platform for real-time surveillance. Recent projects include analyzing SARS-CoV-2 campus transmission, refining influenza vaccine selection, and mapping mpox dynamics through genomic data. His scientific contributions have been recognized by his appointment as a Howard Hughes Medical Institute Investigator . He mentors students such as Sidney Bell (PhD candidate) and Allison Black (postdoc), fostering innovation in genomic epidemiology. At Fred Hutch, he leads the Bedford Lab , which integrates open science with computational methods to tackle global health threats.

Burcu F. Darstمشاهده پروفایل

استادیار

• Genetic Epidemiology
• Metabolomic Epidemiology
• Prostate Cancer
• +۷ مورد دیگر

Burcu F. Darst, PhD is an Assistant Professor at the Public Health Sciences Division of Fred Hutchinson Cancer Center , where she leads research on the genetic and multi-omic basis of prostate cancer risk in diverse populations. As a member of the Translational Data Science Integrated Research Center , her work bridges genetic epidemiology, metabolomics, and health disparities research. Research Interests : Identifying genetic risk factors for prostate cancer using whole-exome sequencing and polygenic risk scores Investigating health disparities in prostate cancer and Alzheimer's disease Developing methods for multi-omics data integration in complex trait analysis Advancing precision medicine through genomic-clinical risk modeling Scientific Contributions include: Prostate Cancer : Discovery of ancestry-specific risk variants and development of clinical risk prediction models Diabetes & Alzheimer's : Identification of cross-ancestry protein biomarkers and metabolomic associations Method Development : Creation of admix-kit for admixed population analysis and LUCID multi-omics framework Scientific Awards : Prostate Cancer Foundation Young Investigator Award AACR NextGen Star Award

Rémi Monassonمشاهده پروفایل

پژوهشگر

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Rémi Monasson is a Researcher at the Laboratoire de physique de l’ENS (LPENS) within the Department of Physics at École normale supérieure (ENS-PSL) in Paris, France. His work bridges Statistical Physics , Machine Learning , Biophysics , and Computational Biology . Active in interdisciplinary research, he contributes to understanding complex systems through theoretical modeling and data-driven approaches. University: École normale supérieure (ENS-PSL) Department: Department of Physics Research Interests: Statistical Physics, Machine Learning, Biophysics, Computational Biology, Neural Networks, Protein Structure Prediction Email: remi.monasson@phys.ens.fr Rémi Monasson's recent publications focus on leveraging Restricted Boltzmann Machines for molecular design, protein sequence modeling , and neural network dynamics . His work spans applications in evolutionary biology , immunology , and biological sequence analysis , often integrating statistical mechanics with machine learning methodologies.

Ranadip Palمشاهده پروفایل

استاد

• Machine Learning
• Computational Biology
• Genomic Signal Processing
• +۵ مورد دیگر

Ranadip Pal is a Professor and Assistant Dean for Strategic Initiatives at Texas Tech University, associated with the Department of Electrical and Computer Engineering under the Whitacre College of Engineering. His interdisciplinary research bridges machine learning, computational biology, and biomedical engineering. He has earned recognition through prestigious awards including the NSF CAREER Award and the Ed and Linda Whitacre Faculty Fellowship (2018). PhD, Electrical Engineering, Texas A&M University (2007) MS, Electrical Engineering, Texas A&M University (2004) B Tech, Electronics & Electrical Communication Engineering, IIT Kharagpur (2002) Pal's research focuses on predictive modeling in precision medicine, particularly for anti-cancer drug sensitivity and Alzheimer's disease. He pioneered REFINED CNN for feature representation in deep learning, developed topological regression methods for QSAR modeling, and explored federated learning frameworks for biomedical data privacy. His work integrates multi-omics data and addresses tumor heterogeneity through stochastic modeling and control. Recent publications highlight his innovations in topological regression, conformal prediction for uncertainty quantification, and federated learning applications. His methodologies have been validated through collaborations with institutions like NIH and Argonne National Laboratory. NSF CAREER Award Whitacre Faculty Fellow (2018) Pal has mentored numerous PhD students, many of whom now hold positions at leading organizations including Merck, Mayo Clinic, Genentech, and Argonne National Laboratory. His lab actively investigates predictive oncology models and contributes to biomedical software tools like Probabilistic Target Inhibition Maps.

Elior Rahmaniمشاهده پروفایل

استادیار مدعو

• Computational Biology
• Statistical and Computational Genomics
• Machine Learning
• +۱ مورد دیگر

Elior Rahmani is an Adjunct Assistant Professor in the Department of Computational Medicine at the University of California, Los Angeles (UCLA) . He develops novel machine-learning methods and statistical models for analyzing high-dimensional genomic and clinical data, focusing on robustness against unknown confounding effects. Education: BSc in Computer Science and Biology, Tel Aviv University MSc in Computer Science, Tel Aviv University PhD in Computer Science, UCLA Rahmani’s research spans computational biology , machine learning , and AI/ML in healthcare . His work enables cell-type-specific analysis of genomic data without requiring reference panels, advancing applications in epigenetics and single-cell biology. His software tools include Keris (single-cell variation analysis), TCA (tensor decomposition for heterogeneous data), BayesCCE (Bayesian cell-type estimation), GLINT (DNA methylation analysis), and ReFACTor (reference-free correction methods). Scientific Awards: Travel fellowship, RECOMB 2022 Charles J. Epstein Trainee Award, ASHG 2021 LSRF Fellowship finalist, 2021 Google outstanding graduate research award, UCLA 2019 Best poster award, UCLA Human Genetics Retreat 2019 Northrop-Grumman award, UCLA 2018 Edmond J. Safra prizes, Tel Aviv University 2013-2017

• Mathematical Modeling
• Infectious Disease Epidemiology
• Public Health
• +۳ مورد دیگر

Emma McBryde is a Professor of Infectious Diseases Epidemiology at the University of Queensland Centre for Clinical Research (UQCCR) , with additional affiliations to the Centre for the Business and Economics of Health . She holds dual credentials as an infectious diseases physician (FRACP) and mathematical modeler (PhD, QUT) , combining clinical expertise with advanced computational methods. Bayesian inference Compartmental disease modeling Social network analysis Forecasting for public health Emerging pathogens (tuberculosis, influenza, SARS, Ebola, COVID-19) Her research focuses on mathematical modeling of infectious diseases, particularly tuberculosis, influenza , and mosquito-borne diseases in small island nations . She leads international collaborations with institutions including Imperial College, LSHTM, Harvard , and Mahidol University , and has provided modeling guidance to WHO , Global Fund , and Australian governments .

• Stochastic Modeling
• Statistical Inference for Dynamic Processes
• Bayesian Statistics
• +۵ مورد دیگر

Prof. Dr. Christiane Fuchs is a full professor at the Faculty of Economics of Bielefeld University and heads the Data Science Group . She is also leading the Biostatistics Research Group and Core Facility Statistical Consulting at Helmholtz Munich . Her academic affiliations include the Bielefeld Graduate School of Economics and Management and the Bielefeld Center for Data Science (BiCDaS) . Education : MSc in Computational Modeling, Brunel University West London (2003) Diploma in Mathematics with Computer Science, University of Hanover (2005) PhD in Statistics, Ludwig Maximilian University of Munich (2010) Research interests span stochastic modeling , Bayesian inference , uncertainty quantification , and statistical applications in economics, medicine, and epidemiology. She specializes in diffusion processes , high-dimensional data analysis , and integrated statistical methods for cross-domain data (genomics, clinical, environmental). Recent publications focus on AI-driven clinical decision support systems , spatial epidemiology , fractional diffusion modeling , and statistical serology validation . Her work bridges methodological innovation in Bayesian statistics with real-world applications in infectious disease dynamics and hematological malignancies . Principal investigator in third-party funded projects from DFG , BMBF , NIH , and Helmholtz Association , including the UQ Consortium (2019-2024) and KoCo19 prospective COVID-19 cohort (2020-2024). She has developed statistical software packages like stochprofML and adaSC3 , and contributed to network-regularized regression methods. As Vice Rector for Research and Networking at Bielefeld University since 2023, she drives institutional research strategy while maintaining active roles in scientific societies including the International Society for Bayesian Analysis and Deutsche Statistische Gesellschaft .

Martin Bootsmaمشاهده پروفایل

استادیار

• Mathematical Epidemiology
• Antimicrobial Resistance
• Infectious Disease Dynamics
• +۳ مورد دیگر

Martin Bootsma is an Assistant Professor affiliated with the Mathematics Department of Utrecht University (40% appointment) and the Julius Center for Health Sciences and Primary Care at UMCU. His work bridges mathematical and statistical modeling with infectious disease dynamics, particularly antimicrobial resistance and public health interventions. Education: Master in Theoretical Physics and Mathematics (cum laude), PhD in Mathematical Dynamics of Antibiotic Resistance (2005). His research focuses on mathematical epidemiology , analyzing the spread of antimicrobial resistance, transmission dynamics of pathogens (e.g., SARS-CoV-2, E. coli, Pseudomonas aeruginosa), and evaluating interventions like contact tracing apps and vaccination strategies. He employs computational models, Bayesian inference, and phylodynamic assessments to study disease control. Recent publications highlight his expertise in SNP distance analysis , host population heterogeneity , and travel-associated resistance gene acquisition . His collaborations span institutions such as Imperial College London and Utrecht University, with applications in healthcare epidemiology, zoonotic diseases, and interdisciplinary education. No scientific awards or student advising information is currently available.

Ryan Admiraalمشاهده پروفایل

مدرس ارشد

• Social Network Analysis
• Exponential Random Graph Models
• Revealed Preference Models
• +۷ مورد دیگر

Ryan Admiraal is a Senior Lecturer in Statistics and Data Science at the School of Mathematics and Statistics, Victoria University of Wellington, New Zealand. He holds a PhD in Statistics from the University of Washington and a BA in Mathematics from Calvin College, United States. His academic career includes prior roles at Murdoch University, Australia, as Senior Lecturer (2017–2019) and Lecturer (2009–2017). Research Interests: Ryan’s research spans applied statistics and interdisciplinary applications. Key areas include social network analysis (e.g., exponential random graph models, Bayesian inference), computational problems in disease transmission modeling (sexually transmitted infections, forest pathogens), and impact assessment for water, sanitation, and hygiene (WASH) interventions. He also contributes to marine ecology (abalone ranching, microplastics) and plant virology (tobamoviruses, virion resilience). Teaching: He teaches courses such as DATA101: Introduction to Data Science STAT452: Bayesian Inference DATA473: Statistical Modelling for Data Science DATA581: Data Science Practicum at Victoria University of Wellington, alongside prior roles at Murdoch University and the University of Washington. Scientific Awards: Doroth M. Gilford Teaching Award (2008) Nominee for academic awards (2012, 2018) Publications: His work includes recent articles on Phytophthora-induced forest decline (2025) Bluetooth in contact tracing (2022) Marine microplastics and megafauna (2019) Network models for sexually transmitted diseases (2016) Wildlife tourism impacts (2017) Hydrological interpolation methods (2020)

Rebecca Deekمشاهده پروفایل

استادیار

• Biostatistics
• Computational Biology
• Metagenomics
• +۳ مورد دیگر

Rebecca Deek is an Assistant Professor at the University of Pittsburgh in the Department of Biostatistics and Health Data Science. Her research focuses on statistical and computational methods for analyzing omics data, particularly metagenomics and multiomics integration, to address challenges arising from advanced sequencing technologies in medical studies. PhD in Biostatistics (University of Pennsylvania, 2023) MS in Biostatistics (Columbia University, 2018) BS in Biological Sciences Teaching (New Jersey Institute of Technology and Rutgers University, 2016) Her work develops novel methodologies for analyzing microbial covariation networks using copula models with mixture margins, zero-inflated latent Dirichlet allocation, and deep learning applications to microbiome research. She collaborates with clinicians to study interactions between genetics, transcriptomics, proteomics, metabolomics, and metagenomics in human health. Recent publications highlight her contributions to multiomics data integration, microbiome-specific statistical models, and network analysis techniques. She maintains open-source R packages like CoMiCoN and zinLDA for these methods. Dr. Deek teaches courses including BIOST 2143: Applied Longitudinal and Clustered Data Analysis and BIOST2025: Biostatistics Seminar . Her research is supported by collaborations with clinicians and computational scientists, focusing on translating complex data into actionable health insights. She actively contributes to open-source software development and maintains a personal website showcasing her work.

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.

• Learning Analytics
• Computational Psychometrics
• Adaptive Learning Systems
• +۷ مورد دیگر

Dr. Matthieu Brinkhuis is an Associate Professor at the Faculty of Science , Utrecht University , with a focus on Software Technology for Learning and Teaching . He serves as Director of Education for the Department of Information and Computing Sciences and is a Senior Fellow at the Center for Academic Teaching and Learning. His work bridges Learning Analytics , Computational Psychometrics , and Human-Centered AI . Research Themes : AI Labs, Governing the Digital Society, Human-Centered Artificial Intelligence Technical Expertise : Data Science, Learning Analytics, Adaptive Learning Systems, Process Mining His scholarly output includes pioneering methods like the Urning Algorithm for dynamic ability tracking and Federated Learning Analytics balancing privacy and performance. He leads the Utrecht Platform for Applied Data Science and contributes to Higher Education Research through large-scale online learning systems. Notable collaborations include projects on Privacy in SMEs , E-Assessment Validation , and Cognitive Diagnostic Models for actionable educational feedback. His work has been published in venues such as the British Journal of Mathematical and Statistical Psychology and Frontiers in Education .

Scott L. Zegerمشاهده پروفایل

استاد

• Biostatistics
• Environmental Statistics
• Epidemiologic Statistics
• +۷ مورد دیگر

Scott L. Zeger is the John C. Malone Professor of Biostatistics and Medicine at Johns Hopkins Bloomberg School of Public Health and School of Medicine . He co-directs the Hopkins inHealth initiative and leads methodological research in biostatistics. PhD, Princeton University (1982) MS, Drexel University (1978) BA, University of Pennsylvania (1974) His research focuses on developing statistical models for longitudinal data analysis , Bayesian hierarchical models , and individualized health applications in global disease epidemiology and clinical outcomes. Recent publications emphasize global health (PSERENADE pneumococcal vaccine studies), diabetes monitoring (CGM metrics), and AML prognosis (measurable residual disease analysis). Scientific Awards Karl Pearson Award (2015) Samuel S. Wilks Award (2008) Marvin Zelen Leadership Award (2007) National Academy of Sciences Member (2006) Zeger serves as principal investigator for projects like PERCH pneumonia etiology research and Johns Hopkins Center for Global Health . He has mentored numerous doctoral students in biostatistics and epidemiology.

Aaron Schefflerمشاهده پروفایل

استادیار

• Biostatistics
• Functional Data Analysis
• Neuroimaging Statistics
• +۵ مورد دیگر

Aaron Scheffler is an Assistant Professor in the Department of Epidemiology & Biostatistics at the University of California, San Francisco (UCSF) School of Medicine. His research program addresses statistical challenges arising from highly structured biomedical data, particularly in neuroimaging and wearable technologies. As a core statistician for the UCSF Memory and Aging Center, including the Alzheimer's Disease Research Center and ALBA Language Neurobiology laboratory, he collaborates extensively with clinical researchers across multiple disciplines. Dr. Scheffler received his PhD and MS in Biostatistics from the University of California, Los Angeles and his BA in Biochemistry from Columbia University. His methodological expertise spans functional data analysis, disease progression modeling, and multi-modal brain imaging data integration. His research focuses on developing computationally efficient statistical methods for complex biomedical data that maintain information across multiple dimensions while providing interpretable components. His work addresses challenges in disease progression modeling for neurodegenerative disorders, joint analysis of multi-modal brain imaging data, and functional data analysis of electroencephalography (EEG) data. Current projects include Bayesian generalized nonlinear mixed effects models for disease progression, joint modeling of structural and network brain imaging data, and advanced methods for EEG analysis in developmental disorders. Analysis of his recent publications reveals a strong interdisciplinary focus spanning neurology, orthopedics, and neonatology. His work demonstrates expertise in analyzing complex data structures including longitudinal imaging, EEG, and biomechanical measurements. Key methodological themes include Bayesian modeling, functional data analysis, and machine learning applications to biomedical problems. His research consistently bridges advanced statistical methodology with clinically relevant questions. Dr. Scheffler's research is supported by extramural grants from the National Institutes of Health Institute of Neurological Disorders and Stroke and the National Science Foundation (Division of Mathematical Sciences). His recent NIH/NINDS R01 grant titled 'Bayesian Object-Oriented Modeling of Multi-Modal Imaging Data' and NSF DMS grant 'Use of Random Compression Matrices For Scalable Inference in High Dimensional Structured Regressions' demonstrate his leadership in methodological innovation for complex biomedical data. He maintains active collaborations with researchers at UCSF in neurology, orthopedics, and HIV/AIDS research, contributing statistical expertise to diverse clinical investigations. His work with the Memory and Aging Center and ALBA Language Neurobiology laboratory positions him at the forefront of statistical methods development for neurodegenerative disease research.