Find Academic Supervisors

• Microbiome Studies
• Computational Genomics
• Host-Microbiome Interactions
• +3 more

Jethro Johnson is an Innovation Track Principal Investigator at the Kennedy Institute of Rheumatology and Deputy Director of the Oxford Centre for Microbiome Studies (OCMS) at the University of Oxford. His work integrates computational genomics and microbiome research to explore host-microbiome interactions in health and disease. PhD in Nutritional Ecology (University of Auckland, 2012) MRC Career Development Fellowship Former postdoctoral researcher at Jackson Laboratory for Genomic Medicine Research focuses on: Mechanistic understanding of gut microbiome impacts on metabolic diseases Multi-omic data integration for host-microbiome studies Computational approaches to microbiome analysis Methodological developments in 16S rRNA gene profiling Publications emphasize microbiome-disease associations, methodological innovations, and computational genomics applications across human and mouse models. Key themes include metabolic dysfunction, immune interactions, and microbial diversity analysis. Scientific recognition includes: MRC Career Development Fellowship in Computational Genomics As OCMS Deputy Director, he contributes to advancing microbiome research infrastructure and collaborative projects while leading his own computational genomics group at the Kennedy Institute.

• Machine Learning
• Bioinformatics
• Deep Generative Models
• +3 more

Anders Krogh is a Professor at the Department of Computer Science, University of Copenhagen, and also holds a position at the Department of Public Health in the Section for Health Data Science and AI. He serves as the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. Previously, he was affiliated with the Department of Biology at the University of Copenhagen until 2020. Dr. Krogh earned his PhD in theoretical physics but transitioned into machine learning and bioinformatics during his doctoral studies. His research spans both theoretical foundations and practical applications in these fields. He is particularly renowned for his pioneering work on hidden Markov models for biological sequences, which has had significant impact in computational biology. In recent years, Krogh's research has focused on deep generative models applied to gene expression data and other biomedical applications. His work bridges computer science with healthcare, developing AI-driven approaches for precision medicine, cancer diagnostics, and analysis of complex biological systems. His current research integrates machine learning with quantum computing applications in biomolecular modeling. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence to healthcare challenges, particularly in rare diseases, cancer diagnostics, and personalized medicine. His work increasingly incorporates federated learning approaches to address privacy concerns while enabling collaborative research across institutions. There's also a growing emphasis on quantum computing applications in biomolecular modeling and drug discovery. As head of the Center for Health Data Science, Krogh leads interdisciplinary research efforts that bring together computer scientists, medical researchers, and clinicians. His team develops novel computational frameworks like MOSAIC for multimodal analysis of rare cancers and multiDGD for multi-omics data integration. These tools are designed to translate AI innovations into clinical practice while addressing the unique challenges of medical data.

Rong FanView profile

Professor

• Single-cell Omics
• Spatial Omics
• Immunology
• +5 more

Dr. Rong Fan is the Harold Hodgkinson Professor of Biomedical Engineering and Professor of Pathology at Yale University. His research focuses on developing and applying single-cell and spatial omics technologies to study immune systems, cancer, and aging. His lab has pioneered technologies like the IsoCode microchip for high-throughput protein profiling, and spatial multi-omics platforms (e.g., DBiT-seq, spatial-ATAC-seq) to analyze tissue complexity at cellular resolution. He co-founded IsoPlexis, Singleron Biotechnologies, and AtlasXomics to commercialize these innovations. Education: PhD in Chemistry from UC Berkeley (2006), B.S. in Applied Chemistry from University of Science and Technology of China (1999). Postdoctoral training at Caltech before joining Yale in 2010. Research interests include CAR-T cell therapy optimization, spatial epigenomics, and multi-omics integration. Key achievements include discovering biomarkers predictive of CAR-T efficacy and defining spatial genomic landscapes in cancer and neuroinflammation. Awards: NSF CAREER Award, Packard Fellowship, election to AIMBE, CASE, and NAI. Serves on advisory boards for Bio-Techne and Yale Ventures. Active in training future scientists via the Yale Biomedical Engineering and Yale School of Medicine programs.

Vitaly KheyfetsView profile

Associate Professor

• Pulmonary Arterial Hypertension
• Critical Care Medicine
• Pediatric Cardiology
• +4 more

Vitaly Kheyfets, PhD, serves as Associate Professor in the Department of Pediatrics-Critical Care Medicine at the University of Colorado Anschutz Medical Campus School of Medicine, where he directs research at the intersection of pediatric critical care and cardiopulmonary pathophysiology with emphasis on pulmonary arterial hypertension (PAH). His primary research focuses on right ventricular adaptation to pulmonary hypertension, utilizing machine learning-driven multi-omics analysis to identify disease biomarkers and molecular networks. He pioneers computational fluid dynamics approaches for hemodynamic modeling in congenital heart conditions like Glenn physiology, while also investigating sleep oscillatory patterns as neurodegenerative biomarkers. His methodology integrates proteomics, spatial transcriptomics, and pressure waveform analysis to dissect vascular remodeling mechanisms. Publication trends reveal a strong emphasis on translating computational models into clinical applications for PAH prognostication, with recent work developing AI-cooperative diagnostic platforms and characterizing microvascular changes in the right ventricle. Cross-disciplinary collaborations span proteomics, imaging, and sleep neuroscience, demonstrating consistent innovation in both pulmonary hypertension and neurodegenerative disease biomarker discovery.

• Medical Genetics
• Nephrology
• Genomics
• +3 more

Dr. Ian Wilson is a researcher at Newcastle University with a focus on medical genetics, nephrology, and genomic analysis. His work spans genetic determinants of kidney diseases, mitochondrial disorders, and biomarker development. Notable contributions include studies on uromodulin genetics in African populations, copy-number variations in rare diseases, and kidney ciliopathies. He has collaborated extensively on projects involving genome sequencing, mitochondrial replacement therapy, and muscular dystrophy biomarkers. Wilson's research integrates computational tools like machine learning for predictive modeling in urolithiasis and employs advanced imaging techniques for disease progression monitoring. Key areas: Genetic epidemiology, renal genomics, mitochondrial DNA analysis Focus on translational applications: Biomarker development for kidney stones and muscular dystrophies Interdisciplinary collaborations in ophthalmology and orthopedics His publications reflect a commitment to advancing diagnostic accuracy and understanding complex genetic disorders through multi-omics approaches.

Bertram Müller-MyhsokView profile

Research Professor

• Psychiatric Genetics
• Statistical Genetics
• Transcriptomics
• +3 more

Bertram Müller-Myhsok is a Research Professor and Research Group Leader at the Max Planck Institute of Psychiatry in Munich, Germany. His research focuses on statistical genetics and transcriptomic data analysis in psychiatric disorders, particularly major depression, PTSD, schizophrenia, and their treatment responses. He integrates machine learning with genetic and clinical data to develop predictive models and stratified treatment approaches. Professional activities include leadership roles in the International Max Planck Research School for Translational Psychiatry and collaborations with institutions like the Institut du Cerveau (Paris) and Bernhard Nocht Institute (Hamburg). His work spans genetic epidemiology, psychiatric genomics, and precision medicine, with over 400 publications in high-impact journals. Key research areas include identifying genetic risk factors for mental disorders, developing polygenic scores, and leveraging omics data to uncover disease mechanisms. He leads projects like Psych-STRATA, a Horizon Europe-funded initiative advancing personalized psychiatry through pharmacogenomics.

• Statistical Genomics
• Computational Immunology
• Human Genetics
• +2 more

Yang Luo is a Kennedy Trust Senior Research Fellow in Data Science at the University of Oxford's Kennedy Institute of Rheumatology. His research bridges statistical genomics and computational immunology to unravel genetic contributions to immune-mediated traits, with a focus on the major histocompatibility complex (MHC) region. His work leverages large biobank datasets (UK Biobank, Biobank Japan), gene expression resources (GTEx), and proteomic data to decode molecular mechanisms linking genetic variation to disease risk. Specific interests include tuberculosis genetics, multi-ancestry polygenic risk scores, and single-cell eQTL modeling. Recent publications highlight expertise in HLA association studies, evolutionary immunogenetics, and disease-specific cell state dynamics. Key contributions include constructing a global HLA haplotype panel and developing novel statistical methods for admixed population genetics. Scientific Awards: Kennedy Trust Senior Research Fellow in Data Science His lab integrates computational and experimental approaches to translate genetic findings into clinical applications for immune disorders.

Yuyin ZhouView profile

Assistant Professor

• Biomedical Artificial Intelligence
• Medical Image Analysis
• Foundation Models for Healthcare
• +9 more

Yuyin Zhou is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, Santa Cruz (UCSC), within the Baskin School of Engineering. She previously held a postdoctoral fellowship at Stanford University, collaborating with Prof. Lei Xing and Prof. Matthew Lungren. She earned her Ph.D. in Computer Science from Johns Hopkins University under the supervision of Bloomberg Distinguished Professor Alan Yuille. Her research is centered on advancing biomedical artificial intelligence to match medical experts in decision-making. Key focuses include developing medical multimodal models, building fair and trustworthy real-time learning systems for clinicians and patients, enabling one-shot/few-shot adaptation of foundation models to diverse medical tasks, and generating synthetic data aligned with clinical knowledge. Dr. Zhou’s recent publications span top-tier venues such as Nature Medicine , Medical Image Analysis , ICLR, CVPR, NeurIPS, MICCAI, and ECCV, reflecting a strong trend in foundation models for medical imaging, trustworthy AI, and efficient deployment. Her work bridges computer vision, deep learning, and clinical applications, with notable projects including TransUNet, BioMedGPT, and MicroSegNet. She has been recognized with the Google Research Scholar Award and the Hellman Fellowship . Dr. Zhou actively contributes to the academic community as an Area Chair for CVPR, ICLR, MICCAI, and CHIL. She organizes workshops and tutorials, including the CVPR 2024 Workshop on Foundation Models for Medical Vision and MICCAI 2024’s FOMMIA tutorial. Google Research Scholar Award Hellman Fellowship Dr. Zhou is actively recruiting self-motivated PhD students and interns to work on machine learning, computer vision, and AI for healthcare. She leads a dynamic research group focused on pushing foundation models into real-world clinical settings. Her team has launched public datasets, such as a micro-ultrasound dataset for prostate segmentation, and open-sourced tools to foster community collaboration.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +7 more

Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.

• Biostatistics
• Data Science
• Neuroimaging Analysis
• +7 more

Yize Zhao is an Associate Professor in the Department of Biostatistics at Yale School of Public Health and an Associate Professor in the Department of Biomedical Informatics & Data Science at Yale University. She holds affiliations with multiple Yale research centers including the Yale Center for Analytical Sciences, Yale Alzheimer's Disease Research Center, Yale Wu Tsai Institute, Yale Center for Brain and Mind Health, and Yale Computational Biology and Bioinformatics. Dr. Zhao's research focuses on developing statistical and AI methods to analyze large-scale complex biomedical data including medical imaging, genomics, and electronic health records. Her methodological expertise spans Bayesian statistics, feature selection, predictive modeling, data integration, missing data analysis, and network analysis. Her research interests span multiple biomedical domains with a strong focus on mental health, psychiatry, neurodegenerative diseases, and aging. Her recent work includes brain-to-behavior modeling, multi-layer biomedical networks, imaging genetics and genomics, and the integration of multi-modal biomedical data with real-world data. Dr. Zhao's work has resulted in numerous high-impact publications, with recent research focusing on Alzheimer's disease, brain network analysis, and advanced statistical methods for neuroimaging. Her publications show a strong trend toward integrating multi-modal data sources and developing sophisticated statistical approaches to address complex biomedical questions. Thelma and Marvin Zelen Emerging Women Leaders in Data Science Award from the Institute of Mathematical Statistics (IMS) COPSS Emerging Leader Award from the Committee of Presidents of Statistical Societies (COPSS) YSPH Investigator Research Award Yale Alzheimer's Disease Research Center Research Scholar Award Elected member of the International Statistical Institute Dr. Zhao serves as an Associate Editor for Biometrics and is a standing member of the NIH Biodata Management and Analysis (BDMA) study section. Her research is supported by multiple NIH grants, highlighting the significance and impact of her work in biostatistics and biomedical data science.

• Statistical Genomics and Bioinformatics
• High-Dimensional Data Analysis
• Bayesian Statistics
• +4 more

Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.

• Bioinformatics
• Genomics
• Cancer Genomics
• +7 more

Dr. Andre Kahles is a Lecturer in the Department of Computer Science at ETH Zürich, specializing in biomedical informatics. His research focuses on computational methods for analyzing large-scale genomic and transcriptomic data, with applications in cancer genomics, metagenomics, and precision medicine. He has contributed to the development of tools such as SplAdder for alternative splicing analysis, MetaGraph for petascale genomic data exploration, and SECEDO for subclone detection in cancer genomes. His work bridges algorithmic innovation with biological insights, addressing challenges in single-cell analysis, genome graph alignment, and multi-omics integration. Key research themes include: Developing scalable algorithms for processing nanopore sequencing and metagenomic data Characterizing somatic mutations and non-coding drivers in cancer genomes Advancing genome graph-based alignment and annotation methods Integrating multi-omics data for clinical decision-making and tumor profiling His publications span topics like RNA-seq analysis, chromothripsis in cancers, and global urban microbiome tracking through the MetaSUB consortium. Kahles has collaborated on landmark projects including the Pan-Cancer Analysis of Whole Genomes (PCAWG) and the Tumor Profiler Study.

• Livestock Systems
• Animal Breeding
• Genetics
• +3 more

Matteo Bolner is a Post-Doctoral Research Fellow at the University of Bologna's Department of Agricultural and Food Sciences, specializing in livestock genomics and metabolomics. He holds a PhD in Agricultural and Food Sciences (defended March 2025) and an International Master in Bioinformatics from the University of Bologna. His research integrates genomic and metabolomic data to improve livestock sustainability, particularly in pig production systems. Key focuses include identifying metabolic pathways influencing production traits, analyzing pig viromes for disease outbreaks, and leveraging big data for One Health applications. His educational background includes a Biological Sciences degree (2018) and a bioinformatics master's (2021). He interned at CINECA's SCAI department, focusing on HPC software containerization. Current affiliations include membership in the Animal and Food Genomics group, where he explores genomic solutions for breed conservation and sustainable production. Research trends in his articles emphasize multi-omics integration to understand pig metabolism, stress responses, and breed-specific adaptations. He also applies genomics to authenticate food products and enhance conservation strategies for endangered livestock breeds like the Mora Romagnola pig. His work bridges animal science, computational biology, and agricultural sustainability. Notable contributions include developing genomic tools for honey bee population analysis and creating a catalog of mitochondrial insertions in pig genomes. Future directions involve advancing metabolomics-based precision livestock farming and applying big data analytics to livestock One Health challenges.

Igor JurisicaView profile

Professor

• Data Science and Computational Biology
• Machine Learning
• Artificial Intelligence
• +6 more

Igor Jurisica is a Professor at the University of Toronto and a Senior Scientist at the Krembil Research Institute’s Data Science Discovery Centre for Chronic Diseases. He also serves as Visiting Scientist at IBM CAS, Scientific Director of the World Community Grid, and Chief Scientist at the Creative Destruction Lab (Rotman School of Management). His research focuses on integrative computational biology, data mining, and AI-driven models for cancer mechanisms, drug discovery, and chronic disease management. Key affiliations include the Osteoarthritis Research Program, Schroeder Arthritis Institute, and leadership roles in open science initiatives like the World Community Grid, a global distributed computing platform with 810,000+ volunteers. Jurisica’s work bridges computational tools (e.g., NAViGaTOR visualization platform, MirDIP databases) and clinical applications, emphasizing explainable AI in healthcare. Research interests span proteomics, microRNA regulation, systems vaccinology, and multi-omics integration for disease stratification. Notable contributions include identifying prognostic signatures in cancer and osteoarthritis, machine learning models for drug repurposing, and sportomics analyses of athletic biomarkers. He has been recognized as a Thomson Reuters Highly Cited Researcher (2014-2016) and ranked among the Top 100 AI Leaders in Oncology (2023). His labs develop open-access tools like PathDIP, OsteoDIP, and miRAnno to advance translational research.

Mirko TrajkovskiView profile

Professor

• Metabolic Diseases
• Obesity
• Insulin Resistance
• +5 more

Professor Mirko Trajkovski leads the Laboratory of Metabolic Diseases at the Faculty of Medicine, University of Geneva. He completed his PhD at the International Max Planck School in Dresden (2005), followed by postdoctoral research at ETH Zurich, before establishing his lab at University College London (2012) and moving to Geneva (2013). His work focuses on adipose tissue plasticity , gut microbiota , and their roles in obesity , diabetes , and insulin resistance . Swiss National Science Foundation Professor (2014) ERC Starting Grant (2014) & Consolidator Grant (2019) Dr Walter Seipp Prize & Carl Gustav Carus Prize (2005) His lab investigates fat browning mechanisms , microbiota-host communication , and multi-tissue metabolic regulation using in vivo , in vitro , and human cohort approaches. Recent publications emphasize microbiome-based therapies , temperature effects on metabolism , and gut-bone-adipose crosstalk . Current advisees include PhD student Silas Kieser, with past members like Jing Xue, Salvatore Fabbiano, and Claire Chevalier contributing to immuno-metabolism and microbial engineering projects.