جستجوی استاد راهنمای دانشگاهی

Professor Chris Holmes is a Professor of Biostatistics at the University of Oxford, where he moved from Imperial College London in February 2004. He is a Fellow at St Anne's College and works in the Department of Statistics. His research focuses on applications and statistical methods development in genomic sciences and genetic epidemiology, holding a prestigious Programme Leaders Grant in Statistical Genomics from the Medical Research Council. Prior to his position at Oxford, Professor Holmes completed his doctorate in Bayesian statistics at Imperial College London, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Before his academic career, he worked in industry for several years in scientific computing, developing techniques for real-time pattern recognition models in defense and SCADA systems. Professor Holmes has a broad interest in the theory, methods and applications of statistics and statistical modeling, with a particular foundation in Bayesian statistics which he views as providing a unified framework for stochastic modeling and information processing. His specific research interests include: Bayesian statistics and stochastic simulation Markov chain Monte Carlo methods Pattern recognition and nonlinear, nonparametric methods Spatial statistics Statistical genetics and genomics Genetic epidemiology His recent publications (2023-2025) demonstrate a strong focus on the intersection of biostatistics, artificial intelligence, and healthcare applications. His work spans multiple domains including AI-driven disease classification in neurology, genomic data analysis for health equity, machine learning tools for healthcare prediction, and addressing bias in medical AI systems. A notable trend across his research is the application of advanced statistical methods to solve pressing problems in genomics, epidemiology, and medical diagnostics, with an increasing emphasis on health equity and the ethical implications of AI in healthcare. Professor Holmes currently supervises PhD students Oscar Clivio, Sahra Ghalebikesabi, and Natalia Garcia Martin. His research is supported by multiple grants, including the MRC Programme Leaders Grant in Statistical Genomics which funds his work in statistical genomics. He is actively involved in three research groups at Oxford that reflect the breadth of his scholarly interests: Computational Statistics and Machine Learning Statistical Genetics and Epidemiology Statistical Theory and Methodology

• Bayesian statistics
• Stochastic simulation
• Markov chain Monte Carlo
• +۵ مورد دیگر

Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .

• Genomics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Kathryn Roeder is the UPMC University Professor of Statistics and Life Sciences at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences and the Departments of Statistics & Data Science and Computational Biology. Her research focuses on developing statistical methods for genetic and genomic data, particularly in identifying autism risk genes and analyzing single-cell multi-omic data. She earned her Ph.D. in Statistics from Penn State University and has been at CMU since 1994, previously serving as Vice Provost for Faculty (2015–2019). Education: Ph.D. in Statistics, Penn State University (1988) B.S. in Wildlife Resources, University of Idaho (1982) Research Interests: Her work integrates modern statistical techniques (high-dimensional statistics, machine learning, networks) to study complex diseases like autism and schizophrenia. Recent efforts include tools for analyzing single-cell RNA-seq and proteomic data, such as UNICORN, DAWN, and SCEPTRE. Key Awards: COPSS Distinguished Achievement Award (2020) National Academy of Sciences Member (2019) COPSS Presidents’ Award (1997) AAAS Fellow (2020) Advising & Grants: She has advised over 20 Ph.D. students, many contributing to landmark studies in autism genetics. Her grants include NIH funding for projects like the Autism Sequencing Consortium. Current research teams focus on computational biology and statistical genetics. Labs & Collaborations: Her lab develops software tools (e.g., TADA, MIND) and collaborates with the Autism Sequencing Consortium and iPSYCH-BROAD Consortium on large-scale genomic studies.

• Statistical Genetics
• Genomics
• Biostatistics
• +۵ مورد دیگر

Troy Duster is the Chancellor’s Professor at the University of California, Berkeley, and a Senior Fellow at the Warren Institute on Law and Social Policy. He holds a Ph.D. in Sociology from Northwestern University (1962), an M.A. from UCLA (1959), and a B.S. in Journalism from Northwestern (1957). His career includes roles as Silver Professor at New York University (1999–2012) and Professor at UC Berkeley (1979–99), alongside leadership positions such as Director of the Institute for the Study of Social Change (1979–1997) and Chair of the Sociology Department (1985–88). Research Interests focus on the intersection of race, genetics, criminal justice, and science policy. Key areas include the social consequences of genetic research, the ethics of DNA databases, and the historical and cultural dimensions of race. His work critiques how scientific advancements like genetic testing and forensic databases perpetuate racial disparities. Publications span over five decades, with notable works such as Backdoor to Eugenics (2003) and Whitewashing Race (2003). His articles address topics like the misuse of genetic data, the implications of the Human Genome Project, and the sociological challenges posed by behavioral genetics. Awards include the DuBois-Johnson-Frazier Award (2001) and a Guggenheim Fellowship (1971–72). He has advised institutions like the National Institutes of Health on ethical issues in genetics and contributed to public debates through media appearances and museum collaborations. Professional Contributions include over 20 years on advisory boards for organizations like the National Advisory Council for the Human Genome Research and the American Sociological Association. His work bridges academia and public policy, advocating for equitable science practices and social justice.

• Genetics and Society
• Race and Ethnicity
• Criminal Justice
• +۳ مورد دیگر

Xihong Lin is a Professor of Statistics at Harvard University and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a distinguished academic, holding membership in both the National Academy of Sciences and the National Academy of Medicine. Her research focuses on scalable statistical inference for big data, statistical machine learning, causal inference, and integrative data analysis, with applications in genomics, public health, and precision medicine. Lin’s work addresses challenges in analyzing large-scale genomic and multi-ancestry data, including methods for rare variant association testing, ancestry-adjusted sample analysis, and scalable computing frameworks. Her contributions span biobank studies (e.g., UK Biobank, TOPMed) and clinical applications in lung cancer, cardiovascular health, and smoking cessation. Her scientific awards reflect her leadership in statistical genetics and public health. Key research trends include leveraging single-cell sequencing for functional genomics, developing ensemble machine learning methods for health subtyping, and enhancing polygenic risk prediction across diverse populations. Lin’s methodologies prioritize interpretability and scalability, enabling impactful analyses of complex observational and genomic datasets. Awards: Member, National Academy of Sciences; Member, National Academy of Medicine Her grants and advising efforts focus on interdisciplinary collaborations, bridging statistics, AI, and domain sciences. Lin leads initiatives to improve genomic data management and ethical use of federated data (e.g., FADI framework). She is affiliated with labs advancing statistical genetics and cloud-based workflows (e.g., STAAR workflow).

• Statistical Machine Learning and AI
• Statistical Genetics and Genomics
• Causal Inference and Mediation Analysis
• +۴ مورد دیگر

Aliya Saperstein is a Professor of Sociology and the Benjamin Scott Crocker Professor in Human Biology at Stanford University. She holds a Ph.D. in Sociology and Demography from the University of California, Berkeley (2008) and a B.A. in Sociology from the University of Washington. Her research focuses on the social processes through which racial and gender categories are constructed, how these classifications create and sustain inequality, and how methodological choices in survey research affect findings on stratification and health disparities. Stanford University faculty since 2011 Visiting Scholar, Sciences Po and Russell Sage Foundation Expert committee member for National Academies of Sciences consensus studies on race/ethnicity (2024) and gender measurement (2022) Her work has been recognized with multiple awards including the Population Association of America Early Achievement Award (2016) , Oliver Cromwell Cox Best Article Award (2014) , and IPUMS Research Award (2013) . She leads the Lab for Social Research at Stanford, which trains undergraduate researchers and collaborates with interdisciplinary teams. Research trends in her publications reveal a progression from foundational work on racial fluidity (2012-2014) to contemporary analyses of genetic ancestry testing's sociological implications. Her recent projects examine how direct-to-consumer genetic testing affects racial self-identification patterns and challenges traditional survey methodologies, particularly in multiracial populations and gender categorization. Scientific contributions include: Transforming sex/gender questions in the 2018 General Social Survey Key findings on how genetic ancestry testing increases multiracial identification Experimental evidence showing ancestry question order affects racial reporting Theoretical advancements in racial formation theory

• Sociology of Race and Ethnicity
• Demography
• Gender and Sexuality Studies
• +۴ مورد دیگر

Per Sigvald Bakke is a Professor at the University of Bergen's Faculty of Medicine, Department of Clinical Science, with extensive expertise in respiratory medicine. His research primarily focuses on Chronic Obstructive Pulmonary Disease (COPD), asthma, and related pulmonary conditions, with significant contributions to understanding disease mechanisms, clinical phenotyping, and epidemiology. Dr. Bakke's research interests span COPD phenotyping, asthma heterogeneity, genomics of respiratory diseases, pulmonary function testing, and clinical epidemiology. His work frequently involves large-scale cohort studies and international collaborations, particularly through the U-BIOPRED consortium. His research has significantly advanced understanding of COPD progression, exacerbation risk factors, and the relationship between respiratory diseases and systemic conditions like metabolic syndrome. His publication record demonstrates consistent contributions to respiratory medicine, with recent work focusing on multi-omics approaches to disease phenotyping, genetic determinants of lung function, and clinical management of COPD. His research often bridges basic science with clinical application, addressing critical questions in respiratory disease management and patient outcomes. Dr. Bakke has been instrumental in numerous international collaborative studies including the ECLIPSE cohort, U-BIOPRED, and various genome-wide association studies examining COPD and asthma. His work has contributed to clinical guidelines and improved understanding of respiratory disease mechanisms across diverse populations.

• Chronic Obstructive Pulmonary Disease (COPD)
• Respiratory Medicine
• Asthma
• +۴ مورد دیگر

Geir Selbæk is a Professor II at the University of Oslo in the Department of Geriatric Medicine . His research focuses on dementia , neurodegenerative diseases , and aging-related mental health , with extensive work on the HUNT study —a large Norwegian population-based cohort. He investigates risk factors like depression , loneliness , metabolic health , and social determinants to understand dementia progression. Key research areas: Dementia epidemiology, geriatric mental health, neurodegenerative disease, aging, cognitive impairment Recent publications analyze AI in diagnostics, genetic risk scores, social media's role in pandemic isolation, and metabolic markers Collaborations Selbæk collaborates with international teams across Alzheimer's & Dementia , Nature Genetics , PLOS ONE , and other high-impact journals. His work integrates epidemiological data , neuropsychological testing , and molecular biology to address dementia prevention and care.

• Dementia Research
• Geriatric Psychiatry
• Neurodegenerative Diseases
• +۴ مورد دیگر

David S. Jones is the A. Bernard Ackerman Professor of the Culture of Medicine at Harvard University, holding joint appointments in the Faculty of Arts and Sciences, Harvard Medical School, and the Harvard T.H. Chan School of Public Health. He is also an affiliate professor in the Department of History. His academic career includes prior roles as an assistant professor at MIT (2005–2011), where he directed the Center for the Study of Diversity in Science, Technology, and Medicine (2004–2008). He has been honored with MIT’s MacVicar Faculty Fellowship (2009), Harvard’s Everett Mendelsohn Excellence in Mentoring Award (2018), and appointment as a Harvard College Professor (2020). Jones earned his A.B. in History and Science from Harvard College (1993), followed by concurrent Ph.D. (History of Science) and M.D. (2001) degrees from Harvard. He trained in pediatrics, psychiatry, and emergency psychiatry. His research spans medical history, science & race, and global health, with notable works like Broken Hearts: The Tangled History of Cardiac Care (2013) and What's the Use of Race? (2010). Current projects include histories of Indian cardiac care and air pollution’s health impacts. His teaching focuses on medical ethics, social medicine, and history of medicine. Research grants include support from the Robert Wood Johnson Foundation, NIH, and NEH. Jones directs Harvard Medical School’s Arts and Humanities Initiative and collaborates across disciplines on topics like climate justice and healthcare equity.

• Medical Humanities
• History of Medicine
• Science & Race
• +۵ مورد دیگر

Chiara Sabatti is a Professor of Biomedical Data Science and Statistics at Stanford University, with affiliations to the Stanford Center for Computational, Evolutionary and Human Genomics (CEHG), Bio-X, and the Stanford Cancer Institute. She serves as Associate Director for Stanford Data Science and has led the development of the Data Science Major curriculum since 2012. Research Focus: Statistical models for high-throughput genomics data, causal inference in genetic studies, false discovery rate control, and knockoff methods for variable selection. Key Leadership: Associate Chair for Education and Training (2020-present), Vice Chair of Biomedical Data Science (2018-2019). Her work bridges statistical genetics with data science education, emphasizing robustness and interpretability in scientific findings. Recent publications highlight innovations in genome-wide association studies (GWAS), causal variant localization, and cost-effective sequencing techniques for underrepresented populations. Current projects include developing knockoff-based methods to address population structure and multi-resolution hypothesis testing. Scientific Awards: Institute of Mathematical Statistics (IMS) Fellow (2022) NSF CAREER Award (2003-2008) She mentors doctoral and graduate students in Biomedical Data Science, collaborates with the Data Studio on interdisciplinary projects, and actively recruits curious researchers to her lab. Her outreach efforts focus on expanding data science education and increasing research participation from underrepresented groups.

• Statistical Genetics
• Computational Biology
• Bayesian Statistics
• +۲ مورد دیگر

Michael Nothnagel is a Professor at the University of Cologne, where he leads the Department of Statistical Genetics and Bioinformatics within the Cologne Center for Genomics (CCG). His work spans statistical genetics, genetic epidemiology, and forensic genetics, focusing on methodological development and large-scale genomic data analysis. His research interests encompass theoretical and applied statistical genetics, with emphasis on human genetic diversity, disease etiology, and forensic applications. Key areas include Y-chromosomal phylogeography, genome-wide association studies for complex diseases, development of statistical methods for variant interpretation, and forensic marker optimization. His group leverages next-generation sequencing data and specialized forensic markers to address questions in population history, disease mechanisms, and identification systems. Recent publications reveal a strong focus on computational approaches to genetic analysis, including spatial frequency interpolation for haplogroup mapping, polygenic risk score applications for behavioral traits, and advanced methods for variant classification. His work demonstrates consistent integration of statistical theory with practical applications in medical and forensic genetics, often through international collaborations like the VISAGE Consortium. Nothnagel maintains active involvement in the Cologne Center for Genomics, contributing to seminars and collaborative projects including the upcoming 34th International Genetic Epidemiology Society meeting. His research group operates at the intersection of computational biology and medicine, with particular strengths in handling complex genomic datasets and developing novel analytical frameworks for genetic epidemiology.

• Statistical Genetics
• Genetic Epidemiology
• Population Genetics
• +۵ مورد دیگر

Associate Professor Jeremy Austin is a leading evolutionary biologist at the University of Adelaide , affiliated with the School of Biological Sciences and serving as Deputy Director of the Australian Centre for Ancient DNA (ACAD) . His work bridges ancient DNA analysis, molecular ecology, and forensic biology to address critical questions in conservation genetics and human identification. PhD in Evolutionary Biology from University of Tasmania (1995) Jeremy's research focuses on ancient DNA techniques to study evolutionary histories of extinct and extant vertebrates, assess environmental change impacts, and develop genomic tools for forensic identification of degraded human remains. His work supports agencies like the Australian Defence Force and humanitarian organizations. Recent publications highlight applications of LAMP assays for invasive species detection, genome-wide hybridization capture in forensic profiling, and phylogeographic analysis of endangered species. His team's innovations in trace DNA recovery and environmental DNA (eDNA) have advanced biosecurity monitoring. Australian Research Council Future Fellowship (2011) As a Senior Lecturer , Jeremy supervises research students and collaborates across conservation, forensics, and evolutionary biology . He leads ACAD's efforts to integrate historical museum samples into modern genomic studies, with applications for wildlife management and human remains identification.

• Ancient DNA
• Molecular Ecology
• Conservation Genetics
• +۵ مورد دیگر

Matthew McGue is a **Regents Professor** in the Department of Psychology at the **University of Minnesota**, with a guest professor position at the **University of Southern Denmark's Department of Epidemiology**. He specializes in **behavioral genetics**, focusing on **substance use disorders** and **aging processes**. His research leverages long-term studies like the **Minnesota Twin Family Study (MTFS)** and **Danish Studies on Aging**. **Educational Background**: Ph.D. in Psychology from the University of Minnesota (1981). **Research Interests**: Behavioral genetics, individual differences, quantitative psychology, aging, and substance abuse. He investigates genetic contributions to substance use disorders, aging mechanisms, and the interplay between genes and environment in human traits. **Publications**: Over 150 articles on topics like cannabis use effects, personality genetics, and twin studies. Recent work examines recreational cannabis legalization impacts and genetic influences on educational achievement. **Awards/Grants**: Not explicitly listed in provided texts. **Labs/Teams**: Co-director of the **Minnesota Center for Twin and Family Research**. Collaborates internationally on aging studies in Denmark.

• Behavioral Genetics
• Substance Use Disorders
• Aging
• +۲ مورد دیگر

Weerachai Jaratlerdsiri is a Research Fellow at the School of Medical Sciences , Faculty of Medicine and Health , University of Sydney . He is also a member of the University of Sydney Nano Institute and leads bioinformatics initiatives at the Charles Perkins Centre . His work focuses on genomics , prostate cancer , and health disparities in underrepresented populations. PhD in Comparative Genomics (University of Sydney, 2014) Postdoctoral Fellow at Australian Prostate Cancer Research Centre Co-Investigator for US Department of Defense Prostate Cancer Research Programs Developed frameworks for personalized genomics in cancer disparities His research interrogates "Big Data" genomics to uncover genetic and environmental factors influencing prostate cancer in African and other underrepresented populations. He has identified ancestral heritage connections to aggressive cancer and environmental effects on ethnic groups. His work spans computational biology , epigenetics , and translational research . His publications from 2014–2025 emphasize African-ancestral disparities , oncogenic drivers , mitochondrial genomics , and epigenetic machinery . Key journals include Nature , Science , and Cancer Discovery , often in collaboration with global institutions across Australia, South Africa, the UK, and the US. Scientific recognition includes: Best early-career presentation at the Australasian Genomic Technologies Association Conference FMH Showcase Travel Award 2023 (Faculty of Medicine and Health) Dr. Jaratlerdsiri supervises Ruotian HUANG and Jue JIANG , who investigate telomere length and genomic processes in prostate cancer. He collaborates with the Hayes Lab and the ICGC Pan-Prostate Cancer group to harmonize global genomic data from over 2,000 patients.

• Bioinformatics
• Prostate Cancer
• Genetics
• +۵ مورد دیگر

Marios Georgakis is a clinician-scientist and Junior Group Leader at the Institute for Stroke and Dementia Research (ISD) at LMU Munich. He also holds a Visiting Scientist position at the Broad Institute of MIT and Harvard. His research focuses on leveraging multi-omics data and causal inference methods (e.g., Mendelian randomization) to discover drug targets for atherosclerosis, develop personalized risk stratification tools for cerebrovascular disease, and identify in vivo biomarkers of disease activity. His work bridges human genetics, molecular biology, and clinical translation. Education : MD and PhD (Epidemiology) from the National and Kapodistrian University of Athens; doctoral studies in Systemic Neurosciences at LMU Munich. Honors : Emmy Noether Award (DFG), CHARGE Consortium Early Career Achievement Award, Hertie Network Fellowship, and multiple scholarships/fellowships. Research Themes : Drug target discovery for cardiovascular disease via multiomics integration Molecular phenotyping of atherosclerosis using single-cell RNA-seq and spatial transcriptomics Development of AI-driven tools for vascular imaging and aging Genetic studies of inflammation, cytokines, and stroke subtypes Causal inference in vascular risk prediction and post-stroke outcomes Recent Article Trends : His team's publications (2024-2020) emphasize: Proteogenomic and genetic studies of atherosclerosis Cytokine signaling pathways (e.g., IL-6, CCL2/CCR2) Polygenic and genomic risk scores for stroke Multi-omics biomarkers in cerebrovascular disease Clinical translation of Mendelian randomization findings Meta-analyses of population-based data Scientific Awards : Emmy Noether Group Leader Award (DFG, 2023) CHARGE Consortium Early Career Achievement (2023) Hertie Network Fellowship (2023) Walter-Benjamin Postdoctoral Fellowship (2021-2022) Team Leadership : Georgakis mentors multiple PhD students and postdocs in his lab. His group collaborates with vascular surgeons, neurologists, and computational biologists. Current projects include the AtherOMICS biobank and AI-driven vascular phenotyping tools.

• Cardiovascular Disease
• Stroke
• Atherosclerosis
• +۸ مورد دیگر