Find Academic Supervisors

Nadia KamalView profile

Assistant Professor

• Plant Genomics
• Comparative Genomics
• Crop Improvement
• +3 more

Prof. Nadia Kamal is an Assistant Professor in Computational Plant Biology at the Technical University of Munich (TUM), affiliated with the TUM School of Life Sciences. Her research focuses on plant genome analysis, particularly using computational tools to study genetic diversity in crops like barley, oat, and lablab. She aims to enhance climate resilience in crops through understanding drought tolerance and genomic variations. Education: She earned her biology degree from the Free University of Berlin and a doctorate in molecular cell biology from Bielefeld University under Prof. Bernd Weisshaar. Postdoctoral research (2018–2023) at Helmholtz Zentrum München under Prof. Klaus Mayer preceded her TUM appointment. Research Interests: Prof. Kamal’s work spans comparative genomics, pangenome analysis, and orphan crop improvement. Her ERC Starting Grant (2023) supports projects like genome assembly for lablab and oat, advancing sustainable agriculture. Awards: Recipient of the highly competitive ERC Starting Grant in 2023. Grants & Labs: Leads the Computational Plant Biology lab at TUM, actively involved in pan-genome projects and crop resilience initiatives. Her research bridges computational methods with agricultural challenges, emphasizing open-access genomic resources for global crop improvement.

Sharon L.R. KardiaView profile

Professor

• Genetic Epidemiology
• Cardiovascular Disease
• Gene-Environment Interactions
• +3 more

Sharon L.R. Kardia is the Millicent W. Higgins Collegiate Professor of Epidemiology and Associate Dean for Education at the University of Michigan School of Public Health (SPH). She holds a PhD in Human Genetics (1991) and MA in Statistics (1990), both from the University of Michigan. Her research focuses on genetic epidemiology of chronic diseases, particularly cardiovascular conditions, emphasizing gene-environment and gene-gene interactions. She directs the Public Health Genetics Program and co-directs the Life Sciences and Society Program and the Michigan Center for Genomics and Public Health. Education: PhD in Human Genetics (University of Michigan, 1991); MA in Statistics (University of Michigan, 1990). Postdoctoral training in systems biology at the Department of Microbiology and Immunology. Research Interests: Genetic epidemiology of cardiovascular diseases, chronic disease prevention, genomic/epigenomic analyses in large cohorts, and translational public health genetics. Notable Projects: Co-leads initiatives integrating genomics with public health practice, including the Michigan Center for Genomics and Public Health. Her work spans population studies, analytical method development, and interdisciplinary program leadership. Labs/Teams: Public Health Genetics Program, Life Sciences and Society Program. Teaches courses on AI in public health (PUBHLTH555) and integrative learning experiences for MPH students (PUBHLTH680).

Wendy RobinsonView profile

Professor

• Medical Genetics
• Epigenetics
• Fetal Development
• +4 more

Wendy P. Robinson is a Professor in the Department of Medical Genetics at the University of British Columbia Faculty of Medicine , and a Senior Scientist at the BC Children’s Hospital Research Institute . She holds the CIHR Sex and Gender Science Chair . Research Interests: Genetics and epigenetics of early human development, placental function in pregnancy complications (fetal growth restriction, preterm birth), DNA methylation, non-coding RNA, sex differences, and polymorphisms. Her lab employs genomic and bioinformatic tools to study placental health and its impact on newborn outcomes. Recent Publications (2025-2024) focus on X-chromosome inactivation patterns in placenta, cell-type specific DNA methylation, maternal socioeconomic effects on placental epigenetics, and modeling placental development with organoids. Key themes include sex-specific epigenetic regulation , maternal-fetal interactions , and human placental methylome . Awards: UBC Faculty of Medicine Distinguished Achievement Award (2018), with trainees receiving the James Miller Memorial Prize and Mary-Jane Carroll Trainee Award. Students & Collaborations: Supervised PhD/MSc students include Li Qing Wang, Icíar Fernández Boyano, Giulia Del Gobbo, Victor Yuan, and Magda Price. Collaborators span the Alex Beristain Lab and University of Toronto institutions. Laboratory Activities: Regular team-building events like mountain hikes, climbing outings, and kayaking trips, alongside providing open access to epigenetic tools (e.g., Bisearch, SeqDoc) for the research community.

Yuè LiView profile

Professor

• Computational Biology
• Machine Learning
• Population Genetics
• +4 more

Yuè Li is a Professor in the Department of Computer Science at McGill University, where he leads the Li Lab focused on machine learning applications in genomics and healthcare. His research develops computational methods for analyzing electronic health records (EHR), single-cell multi-omics data, and population genetics. Dr. Li teaches core courses including Applied Machine Learning (COMP 551), Machine Learning in Genomics and Healthcare (COMP 565), and Computer Programming for Life Sciences (COMP 204). His research interests span: AI methods for computational biology and translational healthcare Multi-modal EHR integration and clinical topic modeling Time-series health forecasting and trajectory analysis Single-cell transcriptomics and epigenomics Polygenic risk modeling and causal variant inference Regulatory genomics and functional annotation integration Publications demonstrate strong focus on transformer architectures for healthcare forecasting, Bayesian methods for genomic inference, and neural topic models for clinical phenotyping. Recent work emphasizes foundation models for single-cell data and federated learning for EHR analysis. Scientific Awards: KDD HealthDay2022 Best Paper Award for seed-guided topic modeling Dr. Li mentors graduate students and postdoctoral researchers working on machine learning applications in biomedical domains. Current lab members include Master's students Bo-Hong Wang, Claris Gu, Neda Esfehani, and Ruilin Wang, along with postdoctoral researcher Dr. Jun Bai. The Li Lab operates within McGill's School of Computer Science, developing computational frameworks to integrate heterogeneous biomedical data for improved disease understanding and clinical decision support.

Grant MontgomeryView profile

Professor

• Genetics
• Genomics
• Reproductive Disorders
• +4 more

Professor Grant Montgomery is a leading academic at the University of Queensland (UQ), serving as Director of the UQ Genome Innovation Hub and Group Leader of the Genomics of Reproductive Disorders at the Institute for Molecular Bioscience (IMB). His primary focus is on understanding the genetic and epigenetic basis of reproductive disorders, particularly endometriosis, through large-scale genomic studies and functional analyses. His work integrates multi-omics data to identify disease mechanisms and develop translational solutions. Research Interests: Genetic and epigenetic regulation of endometrial function Systems genetics approaches to study complex diseases Functional validation of genomic risk variants Development of clinical tools like eQTL/mQTL browsers Multi-omics integration for disease biomarker discovery Recent Articles: Highlighting endometriosis genetics, comorbidity mechanisms, and novel diagnostic approaches. Key themes include genetic risk stratification, epigenetic dysregulation, and translational applications of genomic data. Collaborations: Active partnerships with institutions globally, including University of California, San Francisco, Oxford Endometriosis CaRe Centre, and Monash University. Major consortia include International Endometriosis Genetics Consortium and Reprogen Consortium. Advising & Training: Supervises PhD students focusing on endometrial cell biology, epigenetic regulation, and computational genomics. Provides training opportunities in functional genomics and systems biology. Labs/Teams: Leads a multidisciplinary team with expertise in molecular biology, bioinformatics, and clinical research. Maintains cell culture facilities and single-cell sequencing capabilities.

Eduardo EyrasView profile

Professor

• RNA Biology
• Cancer Research
• Long-read Sequencing Technologies
• +4 more

Eduardo Eyras is a Professor at the Australian National University (ANU) and EMBL Australia Group Leader, leading research in computational RNA biology and cancer genomics. He directs the Centre for Computational Biomedical Sciences and is part of the Shine-Dalgarno Centre for RNA Innovation. His work focuses on transcriptome and epitranscriptome analysis using long-read sequencing, machine learning, and computational methods to study cancer mechanisms. Eyras holds a PhD in Mathematics from the University of Groningen (1999) and previously led research at the Sanger Institute and Pompeu Fabra University. Affiliations: Director, Centre for Computational Biomedical Sciences Researcher, Shine-Dalgarno Centre for RNA Innovation Member, Division of Genome Sciences and Cancer Leader, The Eyras Group - Computational RNA Biology Research Interests: Development of algorithms for long-read sequencing Machine learning applications in RNA biology Epitranscriptomic modifications and cancer Therapeutic mRNA platform steering Key Projects: Novel algorithms for transcriptome variation analysis Predictive models of RNA modifications in disease Ribosomal DNA variation analysis Advisees & Grants: Supervises PhD students (e.g., Favour Oyelami, Stefan Prodic) and leads ARC-funded projects on mRNA diagnostics and epitranscriptomic therapies. Collaborates with global teams on forensic genomics, cancer drug resistance, and AI-driven translational research. Labs/Teams: Leads the Eyras Group, collaborating with the Hannan Group (Cancer Therapeutics) and Shirokikh Group (Protein Biosynthesis).

Marissa A. EhringerView profile

Professor

• Behavioral Genetics
• Substance Use Disorders
• Genomics
• +3 more

Marissa A. Ehringer is Professor and Chair of the Department of Integrative Physiology at the University of Colorado Boulder, with a joint appointment at the Institute for Behavioral Genetics. Her research focuses on identifying genetic mechanisms underlying substance use disorders using genomics and bioinformatics approaches. She directs the Genetics of Substance Use Disorders Laboratory and mentors graduate trainees. Education: B.S. Biology & B.A. French, Indiana University (1995) Ph.D. Human Medical Genetics, University of Colorado Health Sciences Center (2001) Research Interests: Her work integrates behavioral genetics with genomic technologies to study alcohol, tobacco, and opioid use disorders. Key areas include: genetic epidemiology of addiction, neuropharmacology of substance responses, microbiome-gene interactions in opioid use disorder, and translational models of anxiety-related behaviors. Publications: Recent articles demonstrate strong focus on opioid genetics (33%), anxiety mechanisms (25%), diversity/inclusion in genomics (17%), and translational rodent models (25%). Methodological emphasis includes GWAS, transcriptomics, and microbiome analyses. Honors: NIH Training Fellowships (NIMH, NIDA) Leadership positions: RSA Board Member, IBNGS President Faculty Leadership Institute Fellow UROP Outstanding Mentor Award Training & Grants: Currently advises 6 graduate students. Active NIH grants include: U01 DA051937 (genetic networks in opioid disorder), R21 DA05781 (glial role in nicotine behaviors), and AB Nexus award (GCKR variant effects).

Jinchuan XingView profile

Professor

• Mobile Elements
• Human Genomic Variation
• Evolutionary Genetics
• +14 more

Jinchuan Xing is a Professor at Rutgers, The State University of New Jersey, where he leads the Xing Lab of Genomics. His research focuses on human genomic variation, mobile DNA elements, evolutionary and population genetics, and their implications for human disease. He integrates computational and experimental methods to study genome-wide variation, with applications in disease gene identification and genomic technology development. His research interests include: Mobile DNA element biology Human demographic history and population diversity Disease gene identification using whole-exome and whole-genome sequencing High-altitude adaptation genetics Transposable element regulation and expression piRNA and small RNA pathways His recent publications highlight a strong trend in reproductive genomics, particularly in identifying genetic risk factors for embryo aneuploidy in IVF patients, using advanced sequencing technologies. Other work spans evolutionary genomics in diverse species (bats, moths, Drosophila), structural variation in neurodevelopmental disorders, and proteogenomic discovery. His lab actively develops and applies bioinformatic tools for variant analysis and gene prioritization. Scientific awards and honors are not explicitly mentioned in the provided text. Jinchuan Xing advises several graduate students, including Nan Wang, Siqi, Ellie Lu, and Tongji Xing. His lab has received significant funding, including an R01 grant from NICHD on aneuploidy risk, a grant from the Center for Human Evolutionary Studies, and a Life Sciences Alliance Pilot Seed Funding grant in collaboration with the Department of Statistics. These grants support research in fertility genomics, disease gene discovery, and genomic tool development. The Xing Lab of Genomics is an active research group conducting interdisciplinary studies combining genomics, bioinformatics, and molecular biology. The lab welcomes new members regularly, including master’s and PhD students, and collaborates across departments. Current projects include understanding the genetic basis of meiosis, fertility, and high-altitude adaptation, as well as developing Markovian gene networks for disease gene discovery.

Jean-Marc GhigoView profile

Professor

• Biofilm Biology
• Bacterial Genetics
• Antibiotic Resistance
• +7 more

Prof. Jean-Marc Ghigo is a leading researcher at the Institut Pasteur , Paris, affiliated with the Department of Microbiology and head of the Genetics of Biofilms Laboratory . He leads multiple research projects focused on biofilm formation, physiology, and resistance mechanisms in pathogenic and commensal bacteria. Institution: Institut Pasteur School: Department of Microbiology Department: Genetics of Biofilms Laboratory Academic Rank: Professor Email: jmghigo@pasteur.fr His research focuses on biofilm biology , bacterial genetics , antibiotic resistance , and microbial physiology . He investigates how bacteria form biofilms on surfaces, adapt metabolically, and resist antimicrobials, with a particular emphasis on Escherichia coli and Bacteroides thetaiotaomicron . His work includes studying anaerobic biofilms, volatile compounds, colonization resistance, and the development of genetic tools for biofilm research. The recent publications reveal a strong trend in understanding molecular mechanisms of biofilm formation and resistance , including the role of efflux pumps, extracellular matrices, adhesins, and interspecies interactions. His studies span microbial physiology , structural biology , environmental microbiology , and host-microbe interactions , often using advanced techniques like liquid-cell electron microscopy and transcriptomic analysis. He has not been explicitly associated with any scientific awards in the provided text. Prof. Ghigo actively mentors students and postdoctoral researchers, including Yankel Chekli , Anne-Aurélie Lopes , Julie Le Bris , and others. His lab collaborates extensively within the Institut Pasteur and with external partners. He has developed widely used genetic tools and in vivo models for studying biofilms. His research is supported by institutional funding and collaborative grants, contributing to the development of novel strategies to combat biofilm-related infections. His laboratory includes specialized spaces such as a biofilm room , zebrafish room , cell culture room , and microscopy facilities , enabling multidisciplinary research on bacterial communities and host interactions.

Juan BotasView profile

Professor

• Neurodegenerative Disorders
• Genetic Research
• Molecular Neuroscience
• +3 more

Juan Botas is a Professor at Baylor College of Medicine with joint appointments in the Department of Molecular and Human Genetics and Molecular & Cellular Biology . His research focuses on neurodegenerative disorders , particularly Huntington's, Parkinson's, and Alzheimer's diseases, using Drosophila and mice models to dissect molecular mechanisms and identify therapeutic targets. Botas's lab specializes in high-throughput genetic screens , integrating robotic instrumentation with multi-omics datasets to uncover gene networks driving pathogenesis. Key themes include proteolysis impairment , neuronal compensatory mechanisms , and cross-species validation for drug discovery. Their work has identified critical modifiers like TRIM28 and NUAK1 for tau and huntingtin toxicity. Recent publications highlight studies on glial gene regulation in Huntington's disease, APOE allele interactions in Alzheimer's, and lipid signaling pathways as therapeutic targets. The lab's interdisciplinary approach bridges computational analysis with in vivo models , emphasizing genome-scale screens and neuroprotective strategies .

James SchnableView profile

Professor

• Plant Genomics
• Crop Phenomics
• Quantitative Genetics
• +3 more

James Schnable serves as the Charles O. Gardner Professor of Agronomy in the Department of Agronomy and Horticulture at the University of Nebraska-Lincoln. His research integrates genomic, phenomic, and environmental data to advance crop breeding methodologies for maize and sorghum, with particular emphasis on climate-resilient varieties. Dr. Schnable's research program spans plant genomics , high-throughput phenomics , and quantitative genetics , focusing on genetic dissection of nitrogen use efficiency, photosynthetic traits, and stress tolerance mechanisms. His laboratory pioneers UAV- and satellite-based phenotyping systems, machine learning applications for image analysis, and genomic prediction models that bridge genotype-phenotype gaps under variable environmental conditions. Key innovations include nighttime fluorescence phenotyping to reduce environmental noise and spectral feature extraction for accelerated trait assessment. Analysis of his 2021-2025 publications reveals consistent leadership in genotype-environment interaction studies and computational phenotyping , with dominant themes including transcription factor binding site variation explaining heritability, nonphotochemical quenching kinetics in stress responses, and scalable satellite-based yield prediction methods. His work frequently employs the Genomes to Fields Initiative infrastructure for multi-state field validation. Charles O. Gardner Professorship Dr. Schnable directs an active research program involving advanced sensor networks, genomic selection pipelines, and multi-institutional field trials. His team develops computational frameworks like PlantSegNet for 3D plant reconstruction and SPARC-LoRa for agricultural IoT applications. While specific grant details aren't provided in source materials, his extensive publication record in high-impact journals indicates sustained funding from major agricultural research programs. The laboratory maintains cutting-edge phenotyping infrastructure including UAV fleets, hyperspectral imaging systems, and gas sensor networks for early stress detection. Current projects focus on nitrogen-responsive growth trajectories, chilling tolerance mechanisms in panicoid grasses, and gut microbiome interactions with grain composition, reflecting an integrative approach from molecular mechanisms to field performance.

Anna GreenView profile

Assistant Professor

• Computational Biology
• Machine Learning
• Antibiotic Resistance
• +3 more

Anna Green is an Assistant Professor at the Manning College of Information & Computer Sciences (CICS) at the University of Massachusetts Amherst. She directs the Sequence Analysis and Genomics (SAGE) lab, which focuses on developing computational methods to understand genetic variation, with special emphasis on antibiotic-resistant bacteria. Her research integrates machine learning with genomic analysis to predict antibiotic resistance and understand its genetic basis. Education: Postdoctoral Fellow in Biomedical Informatics (2023) - Harvard Medical School PhD in Systems Biology (2019) - Harvard University BS in Molecular and Cell Biology (2013) - University of Connecticut Her research program at SAGE Lab focuses on: Applying machine learning to genomic sequences to predict antibiotic resistance Developing interpretable models of resistance mechanisms in bacteria Studying evolutionary patterns in Mycobacterium tuberculosis populations Discovering protein interactions through computational analysis of genomic sequences Building frameworks for coevolutionary sequence analysis Her recent publications demonstrate a consistent focus on antimicrobial resistance mechanisms in tuberculosis using cutting-edge computational approaches. Research spans machine learning applications, genomic dependency analysis, transcriptomic responses, and protein interaction mapping, consistently addressing public health challenges posed by evolving bacterial resistance. Dr. Green teaches several courses including: Introduction to Computational Biology and Bioinformatics (INFO 390C) Computational Biology and Bioinformatics (CS 690U) Machine learning on biological sequence data (CS 692X) She directs the SAGE Lab at UMass Amherst, where her team develops computational methods to combat antibiotic resistance through genomic analysis and predictive modeling.

Bru Cormand RifàView profile

Professor

• Genetics
• Psychiatric Disorders
• Neurogenetics
• +5 more

Research Focus: His work spans genetics of psychiatric disorders (ADHD, autism, addiction), neurogenetics, and episodic neurological disorders like migraine. Key projects include the CoCA consortium studying ADHD comorbidities and the Aggressotype project investigating aggression mechanisms. Awards: National Habilitation (2011), Advanced Research Accreditation (2011), multiple teaching/research awards. Grants & Projects: Led studies funded by EU (e.g., CoCA, Eat2beNICE) and Spanish Ministry of Science. Major contributions include identifying genes like NFAT5 in cocaine dependence and RBFOX1 in aggression. Collaborations: Member of IMpACT, Psychiatric Genomics Consortium, and CIBER-ER network. Active in international initiatives like the ADHD Working Group of the Psychiatric Genomics Consortium. Publications: Over 100 peer-reviewed articles, including landmark studies on autism genetics and ADHD genome-wide loci. Labs/Teams: Leads the Human Molecular Genetics Research Group, focusing on translational research in psychiatric and neurological disorders.

• Maize genetics
• Sorghum genetics
• Quantitative genetics
• +6 more

Addie M. Thompson is an Assistant Professor at Michigan State University, affiliated with the Plant Resilience Institute, Genetics & Genome Sciences Program, and Molecular Plant Sciences Program. Her research focuses on maize and sorghum genetics, phenomics, and environmental response, with agricultural applications in drought tolerance and climate resilience. Degrees: B.S. from Iowa State University, Ph.D. and postdoc from University of Minnesota, postdoc from Purdue University Research Focus: Thompson's lab investigates genotype-environment interactions in maize and sorghum, integrating quantitative genetics, phenomics, and modeling to address agriculturally relevant questions. Key areas include drought stress response, plant morphology, high-throughput phenotyping, and computational breeding tools. Publication Trends: Her recent work spans maize/sorghum comparative genomics, hyperspectral disease detection, climate-resilient breeding strategies, and computational tools for multi-objective breeding optimization. Grants & Projects: Thompson contributes to USDA-funded corn tar spot resistance research and a $2.7 million Department of Energy project on plant genetics. Laboratory: The Thompson Maize Lab develops digital phenotyping technologies and physiological models for crop improvement.

Bonnie BergerView profile

Professor

• Computational Biology
• Algorithms & Theory
• Molecular Biology
• +3 more

Bonnie Berger is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT), with a joint appointment in Computer Science in the Department of Electrical Engineering and Computer Science (EECS). She leads the Computation and Biology group and is part of the Theory of Computation group at MIT's CSAIL. Her research focuses on computational biology, algorithms, and their applications to molecular biology. She has pioneered work in computational molecular biology, influencing the field through her mentorship of students and collaborations. Her academic roles include Vice President of the International Society for Computational Biology (ISCB), Head of the RECOMB steering committee, and membership on the NIGMS Advisory Council. Berger has received numerous awards, including membership in the American Academy of Arts and Sciences, the NIH Margaret Pittman Award, and an Honorary Doctorate from EPFL. Her current projects include developing algorithms for metagenomic binning, context-aware functional genomics, and secure federated genomic analysis using frameworks like Sequre and SCA. She also leads research on protein structure prediction, privacy-preserving data analysis, and single-cell transcriptomics integration with tools like Scanorama and CryoDRGN. Berger’s research bridges computational methods with biological insights, addressing challenges in health care, disease genetics, and data privacy. Her labs and collaborative efforts emphasize interdisciplinary approaches to solving complex biological questions through advanced computational techniques.