Find Academic Supervisors

• Hepatology
• Experimental Medicine
• Applied Immunology
• +13 more

Professor Ellie (Eleanor) Barnes is a leading academic in hepatology and experimental medicine at the University of Oxford, affiliated with the Nuffield Department of Medicine, the Translational Gastroenterology Unit, the Ludwig Institute for Cancer Research, the Jenner Institute, and the Oxford NIHR Biomedical Research Centre. She leads a multidisciplinary research group focused on applied immunology in liver disease, including cancer and viral hepatitis. Her research spans vaccine development for hepatitis C and B, early detection of hepatocellular carcinoma (HCC), pathogenesis of IgG4-related disease, and stratified medicine approaches in chronic liver conditions. She leads major national and international consortia such as STOP-HCV and DeLIVER, integrating viral sequencing, host genetics, immune profiling, and biomarker discovery. The recent publications highlight a strong focus on HCV persistence post-treatment, metabolic liver disease in HBV, and innovative clinical trials for HCV elimination. Her work emphasizes translational impact, combining cutting-edge genomics, immunology, and clinical experimental medicine. Fellow of the Academy of Medical Sciences (FMedSci) Fellow of the Royal College of Physicians (FRCP) Prof. Barnes oversees a portfolio of clinical studies as NIHR CLRN lead for hepatology in the Thames Valley and collaborates with industry partners like Vaccitech. Her team also develops non-invasive liver imaging and novel blood-based DNA detection methods (e.g., TAPS) for early cancer diagnosis. She mentors a research group and contributes to science communication and gender equity in STEM.

Sara BallouzView profile

Senior Lecturer

• Transcriptomics (single-cell and bulk)
• Co-expression
• X-linked disorders
• +3 more

Dr. Sara Ballouz is a Senior Lecturer at the University of New South Wales (UNSW) , affiliated with the Garvan Institute of Medical Research and the Garvan-Weizmann Centre for Cellular Genomics . Her research integrates computational and experimental approaches to understand the genetic architecture of disease , with a focus on X-linked disorders , sex differences in disease , and personalized medicine . 2013 - PhD, Victor Chang Cardiac Research Institute and UNSW 2008 - BE Bioinformatics (First Class Honours), UNSW 2008 - BSc Genetics, UNSW Her work addresses challenges in analyzing multi-omics data (genomes, transcriptomes, epigenomes, proteomes) to develop robust tools for personalized medicine. Key contributions include improving RNA-seq accuracy through pan-human consensus genomes and creating computational methods like AuPairWise and CoCoCoNet for co-expression analysis. She explores developmental stochasticity and its transcriptional legacy, alongside cross-tissue X-chromosome inactivation patterns. Recent publications emphasize single-cell transcriptomics applications in autoimmune diseases (e.g., X-linked chronic granulomatous disease ), long COVID immune dysregulation , and cardiometabolic disease treatment. Her group at Garvan-Weizmann Centre leverages computational genomics and meta-analysis to address reproducibility in biomedical research. Dr. Ballouz supervises students including Lachlan Gray , who investigates X chromosome roles in female autoimmune disease . She collaborates with institutions like Cold Spring Harbor Laboratory and Victor Chang Cardiac Research Institute . Email: s.ballouz@unsw.edu.au

Maureen A. SartorView profile

Professor

• Bioinformatics
• Epigenomics
• Cancer Research
• +4 more

Maureen A. Sartor is a Professor in the Department of Computational Medicine and Bioinformatics at the University of Michigan Medical School, with a joint appointment in Biostatistics at the School of Public Health. She serves as Co-Director of the Bioinformatics Graduate Program and leads an active research laboratory focused on computational biology. Education: PhD in Biostatistics, University of Cincinnati (2007) MS in Biomathematics, North Carolina State University (2000) BS in Mathematics with minors in Biology and Computer Science, Xavier University (1998) Research Focus: Dr. Sartor develops bioinformatics methods for analyzing high-throughput regulatory and epigenomic data. Her primary research examines cancer epigenomics and biomarker discovery in oral squamous cell carcinomas, with additional projects on ALS pathogenesis and computational methods for predicting chemical exposure-gene interactions. Her lab specializes in multi-omics analyses and tool development for genomic data interpretation. Publication Trends: Her recent work (2016-2022) demonstrates strong emphasis on HPV-related head/neck cancer biology, epigenetic regulation mechanisms (DNA methylation/hydroxymethylation), and development of bioinformatics tools for genomic region annotation and enrichment analysis. Publications consistently integrate computational method development with translational cancer research applications. Student Advising: Actively mentors graduate students across bioinformatics, biostatistics, and cancer biology programs. Current advisees include 8 PhD candidates and 1 MS student. Previously advised 6 PhD graduates now working in academia and industry. Laboratory Leadership: Directs the Sartor Lab emphasizing collaborative science and Michigan Medicine's core values (Caring, Integrity, Teamwork, Innovation). Research activities focus on cancer epigenomics, HPV oncology, and bioinformatics tool development for the research community.

• Biostatistics
• Environmental Dose-Response Modeling
• Quantitative Risk Assessment
• +7 more

Paul J. Catalano is a Senior Lecturer in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. He is affiliated with the Dana-Farber Cancer Institute, where he serves as a statistician for clinical trials and collaborates on cancer research. His research focuses on biostatistical methods for analyzing multiple outcomes and repeated measures in environmental dose-response modeling, quantitative risk assessment, and neurotoxicity studies. He also develops software for implementing modeling algorithms and investigates experimental design in low-dose risk estimation. Recent publications highlight collaborations in immunotherapy for urothelial carcinoma radiation therapy for brain metastases pediatric neuroblastoma treatment AI-driven MRI segmentation . Dr. Catalano's work extends to clinical trials in colorectal, genito-urinary, and breast cancer, with emphasis on prognostic factor identification and translational research.

Hongzhe LiView profile

Professor

• bioinformatics
• biostatistics
• computational biology
• +3 more

Hongzhe Li is a Professor of Biostatistics and Director of the Laboratory of Statistical Genetics and Genomics. His research spans methodological and collaborative work in bioinformatics, biostatistics, computational biology, statistical genetics, genomics, and metagenomics, with applications to complex human diseases. His primary research focuses on developing statistical methodologies for multi-omics data integration, particularly in microbiome-host interactions. Key areas include robust high-dimensional modeling, transfer learning, mediation analysis with complex mediators, and phylogenetic methods. His work addresses critical challenges in analyzing compositional data, longitudinal microbiome dynamics, and multi-tissue genetic studies, with significant applications in inflammatory bowel disease, kidney disease progression, and rare disorders like Castleman disease. Recent publications (2023-2025) demonstrate a cohesive trajectory in advancing statistical frameworks for microbiome and multi-omics research. Dominant themes include transfer learning for high-dimensional models, mediation analysis with distributional mediators, and robust covariance estimation. These methods are consistently applied to translational problems in gastroenterology, nephrology, and rare hematologic diseases, emphasizing clinical utility through prospective cohort studies and multi-omics integration. Scientific Awards: No awards mentioned in provided text. Advising and Grants: Student advising details and grant information absent from source material. Research appears driven by methodological innovation with strong clinical collaborations, particularly in IBD and kidney disease consortia. Laboratory: Leads the Laboratory of Statistical Genetics and Genomics, which develops computational frameworks for genetic and microbiome data analysis. The lab specializes in creating open-source statistical tools for high-dimensional biological data, with emphasis on reproducibility and clinical translation through partnerships with medical researchers.

• Microbial Biotechnology
• Oral Microbiome Engineering
• Biofilm Dynamics
• +3 more

Kristel Bernaerts is a Professor at KU Leuven's Faculty of Engineering Sciences within the Department of Chemical Engineering, leading research in Chemical and Biochemical Reactor Technology and Safety (CREaS). She serves as Program Director for POC Safety Engineering and holds multiple leadership roles including membership in the Faculty Council of Engineering Sciences and the Committee for Prevention and Protection at Work. Faculty of Engineering Sciences Chemical and Biochemical Reactor Technology and Safety (CREaS) iSi Health - KU Leuven Institute for Physics-based Modeling Her research focuses on microbial biotechnology with emphasis on oral microbiome engineering, biofilm dynamics, and chemical process safety. Key areas include probiotic delivery systems for periodontal treatment, microbial reactor optimization, and safety engineering for biological products. She develops innovative technologies like slow-release devices for oral probiotics and electrospinning-based encapsulation methods. Recent publications reveal strong trends in oral biofilm modulation (65% of 2023-2025 works), microbial community engineering (25%), and safety/risk analysis (10%). Her work bridges fundamental microbial ecology with clinical applications, particularly in periodontitis treatment and probiotic delivery. As an active supervisor, she mentors PhD candidates including Loverdou, N. and Bäcker, L.E. across interdisciplinary projects spanning chemical engineering, dentistry, and synthetic biology. Her current €15M+ research portfolio includes: EU-funded MICROTUNe platform for bioprocess optimization (2024-2030) Probiotic dry toothpaste development (2024-2027) Wood-eating beetle microbiome valorization (2023-2027) AI-driven antivirulent protein design (2024-2028) She leads the POC Safety Engineering program and contributes to chemical engineering education through courses on process safety, reactor engineering, and analytical chemistry.

Blaž ZupanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +4 more

Blaž Zupan is a Full Professor at the University of Ljubljana, also affiliated with Baylor College of Medicine in Houston, teaching artificial intelligence and machine learning. He leads a fifteen-member bioinformatics laboratory and has authored over one hundred publications with more than ten thousand citations, reflecting significant impact in computational sciences. His research centers on explainable AI, machine learning, and data visualization, with deep applications in bioinformatics. He pioneered the Orange data mining suite—a visual programming tool for accessible analytics—and bioinformatics platforms like dictyExpress for gene expression analysis and GenePath for genetic network discovery, emphasizing user-friendly interfaces and real-world usability. Analysis of his recent publications reveals a trajectory from foundational AI (e.g., concept hierarchies in 1999) to contemporary bioinformatics, with current work focusing on single-cell genomics, batch-effect correction, and democratizing AI through visual programming. His tools bridge computational methods and biological discovery, particularly in systems biology and genomics. Zupan’s accolades include the Zois Award (2010), two University of Ljubljana Golden Plaques (2011, 2019), a Fulbright Scholarship (2013), and six student-voted Best Teacher Awards (2008–2017). He was named among the Top 100 Most Influential Innovators of Central and Eastern Europe (2016) by Res Publica, Financial Times, and Google. He secures substantial research funding, currently leading projects like KATARINA (computer science education for all), DALI4US (data literacy for primary schools), DANIO-ReCODE (genomics of regeneration), and L2-60154 (explainable AI for gene expression). Past projects span AI systems (2009–2020), drug discovery, and systems biology, demonstrating sustained interdisciplinary impact. As head of the Bioinformatics Laboratory, he fosters collaboration between computer science and biology, developing open-source tools that empower researchers globally. His consulting for industry and public organizations extends his influence into data-driven decision support and AI training.

Kei HiroseView profile

Professor

• Sparse Estimation
• Multivariate Analysis
• Statistical Modeling
• +2 more

Kei Hirose serves as Professor and Division Leader of the Division of Industrial and Mathematical Statistics, with concurrent appointments in the Division of Strategic Liaison and Division of Fujitsu Mathematical Modeling for Decision Making. Division Leader: Industrial and Mathematical Statistics Concurrent Roles: Strategic Liaison, Fujitsu Mathematical Modeling for Decision Making His research pioneers sparse estimation techniques for high-dimensional data analysis, focusing on multivariate methods including factor analysis and Gaussian graphical modeling. He develops computationally efficient algorithms for parameter estimation while investigating theoretical properties of sparse models, with implementations distributed via R packages. Applications prominently include genomic data analysis such as gene expression datasets, bridging statistical theory with practical computational biology challenges. Scientific recognition: No awards or fellowships specified in source material Academic mentorship and funding details were not documented in the provided text. Similarly, laboratory infrastructure, research teams, and future research trajectories remain unspecified in the available information.

Evelina AkimovaView profile

Assistant Professor

• Social Science Genomics
• Chronotype & Mental Health
• Career Trajectories
• +3 more

Dr. Evelina Akimova is an Assistant Professor of Sociology at Purdue University. Her research integrates social science genomics , chronotype analysis , and quantitative causal inference to explore health inequalities and career trajectories.

Elizabeth Rebecca HauserView profile

Professor

• Statistical Genetics
• Biostatistics
• Genetic Epidemiology
• +10 more

Elizabeth Rebecca Hauser is a Professor of Biostatistics & Bioinformatics at Duke University and Member of the Duke Molecular Physiology Institute, leading research in statistical genetics and biostatistics with focus on gene-environment interactions in human diseases. Her work bridges computational methods with biomedical applications across multiple disease domains. Her educational qualifications include: Ph.D. in Biostatistics, University of Michigan, Ann Arbor (1998) M.S. in Biostatistics, University of Michigan, Ann Arbor (1992) M.H.S., Johns Hopkins University (1985) Dr. Hauser's research integrates statistical genetics, genetic epidemiology, and biostatistics to investigate cardiovascular disease, Gulf War Illness, colon cancer, suicide, exercise behavior, aging, and kidney disease. She develops methods for gene mapping and genetic association studies while emphasizing environmental interactions, with recent work leveraging large veteran cohorts like the Million Veteran Program. Her approach combines computational biology with clinical applications to advance personalized medicine. Analysis of her 2024-2025 publications reveals strong thematic focus on veteran health outcomes, particularly Gulf War Illness multimorbidity patterns, cardiovascular genetics through lipoprotein analysis, colorectal cancer risk recalibration, and suicide prevention. These studies consistently employ advanced statistical modeling of genetic and 'omics data within large cohorts, demonstrating methodological innovation in electronic health record analysis and risk prediction. Dr. Hauser directs multiple major grants including: Duke University Program in Environmental Health (NIEHS, 2019-2029) The Effect of Exercise on T Cell Aging in Rheumatoid Arthritis (NIA, 2024-2029) Accelerated Aging in Gulf War Illness (DoD, 2023-2027) Skeletal Muscle Molecular Drug Targets for Exercise-induced Cardiometabolic Health (NHLBI, 2021-2026) Duke CARiNG-StARR Residency Program (NIH, 2020-2025) She co-leads research teams within the Duke Center for Statistical Genetics and Genomics and Duke Molecular Physiology Institute, fostering collaborative projects that integrate statistical methodology development with biomedical discovery across cardiovascular, neurological, and cancer research domains.

Zhicheng JiView profile

Assistant Professor

• Biostatistics
• Bioinformatics
• Single-cell RNA sequencing
• +5 more

Zhicheng Ji is an Assistant Professor of Biostatistics & Bioinformatics at Duke University, affiliated with the Division of Integrative Genomics. His research focuses on developing computational methods for single-cell RNA-seq , spatial transcriptomics , and genomic data analysis . He teaches courses like BIOSTAT 824: Case Studies in Biomedical Data Science . Research Highlights : Statistical modeling of microbiome data Machine learning for cell segmentation T cell immunology in cancer Multi-omics data integration Spatially variable gene detection Foundation models for epigenetics Scientific Awards : NIGMS Grant (2024-2029) for spatial transcriptomics NIEHS Grant (2023-2028) on PFAS exposure NSF ERC PreMiEr (2022-2027) NCI Grant (2022-2027) on ferroptosis

Seung Yeoun LeeView profile

Professor

• Survival Analysis
• Gene-Gene Interactions
• Clinical Trials
• +3 more

Seung Yeoun Lee is a Professor in the Department of Mathematics and Statistics at Sejong University, where he has been faculty since 1993. His research focuses on applying advanced statistical methodologies to biomedical problems, particularly in cancer research and survival analysis. He serves as Vice President of the Korean Statistical Society and previously served as President of the International Biometric Society Korean Region from 2016-2017. Education: Ph.D. in Statistics, University of Michigan (1990) M.S. in Statistics, Seoul National University (1986) B.S. in Statistics, Seoul National University (1984) Professor Lee's research primarily centers on survival analysis methodologies and their applications in biomedical research, with a particular emphasis on gene-gene interactions and clinical trial statistics. He has pioneered innovative approaches including the Cox model based unified MDR method for gene-gene interaction analysis for survival phenotypes. His work bridges mathematical statistics with practical medical applications, particularly in pancreatic cancer diagnostics and prognostics. The fingerprint analysis of his work shows strong connections to Dimensionality Reduction (94%), Pancreas Cancer research (89%), and Gene Interaction studies (64%). His recent publications demonstrate a clear trend toward interdisciplinary research combining traditional biostatistical methods with modern machine learning approaches. Many papers focus on survival prediction models, dimensionality reduction techniques, and applications to pancreatic cancer research. His work consistently involves international collaborations across statistics, oncology, and computational biology fields, reflecting the highly interdisciplinary nature of modern biomedical research. Professor Lee has made substantial contributions to biostatistical methodology with an h-index of 22, reflecting the impact of his 89 research publications. His work contributes to UN Sustainable Development Goals related to good health and well-being through advanced statistical approaches to medical research. His research group focuses on developing and applying advanced statistical methods to solve complex biomedical problems, with particular emphasis on cancer research and survival analysis. The interdisciplinary nature of his work suggests extensive collaborations with medical researchers, oncologists, and computational biologists across multiple institutions.

• Biostatistics
• Neuroscience
• Cancer Research
• +3 more

Shengxian Ding is a Postdoctoral Associate at the Yale School of Public Health , focusing on Biostatistics, Neuroscience, and Public Health . Her research develops advanced statistical models for biomedical data analysis. Her work includes 2025: Subgroup Mediation Analysis , 2024: Shape Mediation in Alzheimer’s Disease , and 2023: Tumor Growth Quantification via MRI , reflecting expertise in Regression Models, Neuroimaging, and Computational Biology . Contact: naomi.ding@yale.edu

• Perinatal Epidemiology
• Climate Change and Pregnancy
• Reproductive Health
• +6 more

Tormod Rogne, MD, PhD, is an Assistant Professor Adjunct in Chronic Disease Epidemiology at the Yale School of Public Health. His research focuses on perinatal epidemiology, with particular interest in how climate change affects pregnancy, modifiable risk factors on reproductive health, and the long-term health consequences of being born preterm. He applies modern epidemiological methods including Mendelian randomization, negative controls, and genetic epidemiological approaches to address clinically relevant questions using high-quality population-based data. MD from Norwegian University of Science and Technology, NTNU (2015) PhD from Norwegian University of Science and Technology, NTNU (2016) Residency at Akershus University Hospital and Ski Municipality (2018) Fulbright Scholar at Yale School of Public Health (2019) Postdoctoral Scholar at Norwegian University of Science and Technology, NTNU (2021) Dr. Rogne's research spans several key areas of perinatal and environmental epidemiology. He investigates how environmental factors like climate change and temperature extremes affect pregnancy outcomes and child health. His work also explores the genetic and environmental determinants of reproductive health and adverse pregnancy outcomes. Of particular note is his research on how being born preterm affects long-term risk of cardiovascular and infectious diseases. Dr. Rogne emphasizes the use of high-quality data from population-based cohorts and national registries, applying sophisticated methods like negative controls, inverse-probability weighting, and genome-wide association analyses to ensure robust findings. His recent publications reveal a strong methodological focus on Mendelian randomization techniques to establish causal relationships in perinatal health. His work spans cardiovascular epidemiology, infectious disease epidemiology, and environmental health, with consistent themes including climate change impacts on pregnancy, socioeconomic determinants of health outcomes, and the developmental origins of disease. The geographic scope of his research includes both high-income settings like Norway and global health contexts in Africa. Fulbright Scholarship (2017) Tom Wilhelmsen Foundation's Research Stipend (2013) Dr. Rogne maintains active collaborations with multiple researchers including Andrew DeWan, Xiaomei Ma, Joshua Warren, Kai Chen, Rong Wang, and Zeyan Liew. His work is supported by various research initiatives including the Fulbright Program and the Tom Wilhelmsen Foundation. He is affiliated with the Yale Center for Perinatal, Pediatric and Environmental Epidemiology, where he contributes to advancing research on women's and children's health through epidemiologic studies investigating environmental, genetic, and clinical factors. As part of the Yale Center for Perinatal, Pediatric and Environmental Epidemiology, Dr. Rogne works within a collaborative team focused on promoting women's and children's health through rigorous epidemiologic research. The Center, originally founded in 1979 as the Yale Perinatal Epidemiology Unit, continues to be a leader in investigating how environmental, genetic, and clinical factors impact pregnancy, birth, and childhood development.

Christian BlouinView profile

Professor

• Bioinformatics
• Algorithms
• Phylogenetics
• +5 more

Christian Blouin is a Professor and Associate Dean, Academic in the Faculty of Computer Science at Dalhousie University. His interdisciplinary research bridges computer science and molecular biology, with a strong focus on bioinformatics and computational biophysics. Education: Ph.D. in Computer Science, Dalhousie University (2001) B.Sc. in Computer Science, Université Laval (1997) His research interests lie at the intersection of algorithms, phylogenetics, protein evolution, and molecular modeling. He develops computational methods to analyze protein structure evolution, multiple sequence alignments, and phylogenetic tree reconstruction. His work integrates high-performance computing and statistical mechanics to model biophysical properties of proteins, particularly in conformational dynamics and electrostatic interactions. The most recent publications reveal a consistent trend in developing algorithmic solutions for biological problems—especially in text mining for biological events, phylogenetic distance computation, and 3D mapping of evolutionary data. His work emphasizes automation, accuracy, and scalability in bioinformatics pipelines. Scientific Awards and Honors: TULA Fellow Dr. Blouin has secured significant research funding from NSERC, the TULA Foundation, and the CFI. His research group has contributed to tools like GenGIS for geospatial genomics and libcov for bioinformatics programming. He has advised students such as Haibin Liu and Vlado Keselj, who have co-authored key publications in text mining and phylogenetics. His lab integrates algorithm development with biological validation, aiming to bridge computational innovation with real-world biological insights.