Alison Galvani is the Burnett and Stender Families Professor of Epidemiology at Yale School of Public Health and Yale School of Medicine, where she serves as founding director of the Center for Infectious Disease Modeling and Analysis (CIDMA). Her interdisciplinary work bridges epidemiology, evolutionary ecology, and health economics to inform public health policies for diseases including HIV, Ebola, influenza, and COVID-19. Her research focuses on optimizing vaccination strategies and healthcare interventions through mathematical modeling. Recent studies examine SARS-CoV-2 transmission dynamics, RSV vaccine impact, and integration of social determinants into infectious disease models. She has pioneered frameworks for conflict-induced migration analysis and pharmaceutical policy evaluations. Notable scientific awards include the Bellman Prize, Blavatnik Award for Young Scientists, and Guggenheim Fellowship. Her publications span top journals like The Lancet , Nature Communications , and PNAS , with media coverage in major outlets and policy references.
Jian Peng is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on computational biology, machine learning, and their applications to protein structure prediction, drug design, and molecular modeling. He has contributed to advancements in antibody engineering, protein-ligand docking, and generative models for biological systems. Key research areas include: Machine Learning for Molecular Modeling Protein Structure Prediction Antibody and Peptide Design Genomics and Single-Cell Analysis Structure-Based Drug Discovery His work emphasizes integrating deep learning techniques with biological datasets to address challenges in precision medicine, drug development, and systems biology. Notable achievements include developing the FastFold system to accelerate AlphaFold training and pioneering flow-based methods for antibody design. Awards include the Overton Prize (2020), recognizing contributions to computational biology. His research has been published in top journals and conferences, spanning topics from protein mutation prediction to geodesic-based immune complex modeling.
Maria Chikina is an Assistant Professor at the University of Pittsburgh School of Medicine's Department of Computational and Systems Biology. She holds a PhD in Molecular Biology from Princeton University. Her research focuses on developing computational methods to analyze large-scale genomic datasets, bridging statistical rigor with biological insights to overcome experimental biases. Key research areas include latent variable modeling (e.g., PLIER, CellCODE), interpretable neural networks for sequence-to-function modeling, evolutionary rate analysis (RERconverge), and applications in tumor immunology, exercise genomics, and infectious disease (e.g., SARS-CoV-2). Her lab has developed tools like InstaPrism, NIFA, and L0 segmentation for data-driven biological discovery. Her work spans collaborations with institutions like UPMC (on tumor microenvironment) and the Molecular Transducers of Physical Activity Consortium (MoTraPAC). Notable projects include analyzing convergent evolution in marine mammals and subterranean species, and developing epigenetic biomarkers for disease states through the ECHO program. Lab members include PhD students (Rezwan Hosseini, Tugrul Balci) and postdocs (Tina Subic, Anish Sevekari). Past students Wynn Meyer now leads a group at Lehigh University. Her group emphasizes open-source tools (GitHub repository ChikinaLab) and interdisciplinary approaches to systems biology challenges.
Rachel Sippy is a Research Fellow at the University of Cambridge , specializing in epidemiology and infectious disease dynamics within the Department of Psychiatry . Her work bridges public health, climate science, and computational methods.
Gary M. Shaw is the Rosemarie Hess Professor and Professor (Research) at Stanford University , with courtesy appointments in the Department of Epidemiology and Population Health and Department of Obstetrics & Gynecology - Maternal Fetal Medicine . He serves as Co-PI of the March of Dimes Prematurity Research Center at Stanford and PI of the California Center for Finding Causes and Preventives of Birth Defects . His research focuses on the Epidemiology of birth defects Gene-environment interactions in perinatal outcomes Nutritional factors in reproductive health . He has developed machine learning approaches for precision parenteral nutrition and predictive models for preterm birth, while investigating persistent metabolomic signatures following hypertensive pregnancy disorders. Shaw's recent work explores Climate change impacts on reproductive health Maternal-fetal immune interactions Epigenetic mechanisms in perinatal disease with applications of multiomics to neonatal intensive care units. As a member of Bio-X and the Maternal & Child Health Research Institute , he contributes to translational research networks while serving as Associate Editor for Birth Defects Research and American Journal of Medical Genetics . He supervises Med Scholar Project student Richard Liang Doctoral co-advisor for Saskia Comess and Richard Liang Master's advisor for Lenae Joe while leading the Division of Neonatology as Associate Chair for Clinical Research (2012-2025). His laboratory work integrates Metabolomic profiling Proteomic analysis Computational modeling Machine learning for biomedical data to advance neonatal care through precision medicine approaches.
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).
Betsy Foxman serves as the Hunein F. and Hilda Maassab Professor of Epidemiology at the University of Michigan School of Public Health. She directs three major initiatives: the Center for Molecular and Clinical Epidemiology of Infectious Diseases, the Integrated Training in Microbial Systems program, and the Certificate in Healthcare Infection Prevention & Control. Her academic leadership spans decades with continuous research contributions. Dr. Foxman earned her PhD and MSPH from UCLA (1983, 1980) and BS from UC Berkeley (1977). Her research centers on infectious disease transmission, microbiome ecology, antibiotic resistance, and wastewater surveillance . Key projects include analyzing the oral microbiome in dental caries using genomic methods, studying nose/throat microbiome associations with respiratory infections in nursing facilities, and developing wastewater monitoring for antibiotic-resistant pathogens. Her work integrates next-generation sequencing with epidemiological analysis to identify novel interventions. Publication trends reveal consistent focus on microbiome-pathogen interactions across multiple body sites (oral, vaginal, gut, respiratory). Recent articles demonstrate methodological innovation in wastewater epidemiology (2024 Norovirus GII monitoring) and clinical applications like predicting vancomycin-resistant enterococci contamination (2023 Lancet study). Her research bridges molecular microbiology with population health, emphasizing translational potential for diagnostics and public health interventions. Fellow of the Infectious Disease Society of America Fellow of the American College of Epidemiology Fellow of the American Academy of Microbiology Dr. Foxman's advising portfolio includes numerous NIH-funded projects on microbiome dynamics and infection control. Her leadership in the Center for Molecular and Clinical Epidemiology drives collaborative research across departments. Current initiatives focus on wastewater surveillance standardization and microbiome-based diagnostics for infection prevention. She maintains active laboratories for genomic analysis of microbial communities and clinical sample processing.
Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.
Miler T. Lee is an Associate Professor at the University of Pittsburgh , focusing on gene regulation during early embryonic development through high-throughput experimental and computational genomics. He earned his Ph.D. in Genomics and Computational Biology in 2009 from the University of Pennsylvania under Dr. Junhyong Kim, followed by postdoctoral work with Dr. Antonio Giraldez at Yale University. Joining the university in 2016, his research spans maternal-to-zygotic transition (MZT), RNA stability, pluripotency networks, and evolutionary developmental biology, utilizing model organisms like zebrafish, Xenopus, and Hydractinia symbiolongicarpus. Key Research Themes: Maternally inherited RNA dynamics during embryogenesis Mechanisms of RNA degradation and transcriptome remodeling Evolution of pluripotency networks in hybrid species Role of zinc signaling in fertilization barriers Computational tools for RNA regulation and sensing Scientific Awards: Pan-American Society for Evolutionary Developmental Biology Junior Faculty Award (2024) Outstanding New Investigator – International Xenopus Board (2023) Basil O'Connor Scholar – March of Dimes (2017-2019) Recent publications highlight his work on enhancer classification, RNA degradation mechanisms, and cross-species MZT comparisons. His lab develops innovative methods like RESA for regulatory sequence analysis and studies evolutionary divergence in RNA localization patterns. While the articles span computational and experimental approaches, they consistently address RNA's role in cellular identity, developmental timing, and evolutionary adaptation. Applications include understanding pluripotency, designing RNA biosensors, and elucidating fertilization barriers. Prospective Ph.D. students are encouraged to contact him for opportunities in gene regulation, development, evo-devo, and computational genomics.
Igor Rudan is Professor of International Health and Molecular Medicine at the University of Edinburgh, serving as Co-Head of the Centre for Global Health within the Usher Institute, part of the College of Medicine and Veterinary Medicine. His academic position is described as a 'Personal Chair of International Health & Molecular Medicine' at the Deanery of Molecular, Genetic and Population Health Sciences. His research interests include: Genome-Wide Association Studies Genetics and Genomics Genetic Divergence Meta-Analysis Single Nucleotide Polymorphism research Systematic Reviews in global health Epidemiology of respiratory diseases Health inequalities and social determinants of health Professor Rudan's recent publications reveal a strong focus on the intersection of molecular medicine and population health. His work spans genetic epidemiology, pandemic response research, and health equity studies, with particular emphasis on understanding how genetic, social, and environmental factors interact to influence health outcomes across diverse populations. His research frequently employs large-scale genomic analyses combined with population health approaches to address global health challenges. His scientific recognition includes: NIHR Impact Prize (2025) for respiratory disease research RSE Mary Somerville Medal (2023) Professor Rudan leads and collaborates on numerous international research projects including EQUI-RESP-AFRICA (improving respiratory health outcomes in Africa), the NIHR Global Health Research Unit on Respiratory Health (RESPIRE-2), and documentation of the EQUIST tool's global impact. His work often involves large-scale data analysis and international collaborations across multiple continents, positioning him at the forefront of global health research that bridges molecular medicine with population-level health interventions.
Dr. Vera Deneer is an Associate Professor of Clinical Pharmacology at the Utrecht Institute for Pharmaceutical Research (UIPS) within the Faculty of Science at Utrecht University, specializing in the Division of Pharmacoepidemiology and Clinical Pharmacology since 2019. She also serves as a hospital pharmacist and clinical pharmacologist at the University Medical Center Utrecht (UMC Utrecht) at the Department of Clinical Pharmacy since 2017. Dr. Deneer holds significant leadership positions including Vice-Chair of the Medicines Evaluation Board (MEB-CBG) since 2019 and Chair of the Dutch Pharmacogenetics Working Group (DPWG). Dr. Deneer received her PharmD from Utrecht University in 1991 and her PhD from Groningen University in 2003, with research focused on clinical pharmacology and pharmacokinetics of antiarrhythmic drugs in atrial fibrillation. She completed clinical training in hospital pharmacy in 1994 and clinical pharmacology training in 1998. Prior to her current positions, she served as Head of the Pharmacogenetics, Pharmaceutical and Toxicological Laboratory at St. Antonius Hospital, Nieuwegein/Utrecht from 1998 to 2017. Her research focuses on personalized medicine through the study of biomarkers including genetic variants, patient characteristics, and clinical parameters to optimize drug treatment efficacy and safety. Her work primarily targets cardiovascular disease, lung cancer, and immune-mediated inflammatory diseases. She also investigates clinical reasoning and decision-making by pharmacists to improve medication management in clinical practice. Dr. Deneer serves as principal investigator for multiple research projects funded by The Netherlands Organisation for Health Research and Development. Dr. Deneer's recent publications demonstrate her leadership in pharmacogenetics guidelines development, with numerous Dutch Pharmacogenetics Working Group (DPWG) guidelines published in 2023-2025 covering gene-drug interactions for various medication classes including antidepressants, antipsychotics, anti-epileptics, and cardiovascular medications. Her work spans both clinical implementation research and educational aspects of pharmacy practice. Among her significant appointments, Dr. Deneer serves as Vice-Chair of the Medicines Evaluation Board (MEB-CBG) and Chair of the Dutch Pharmacogenetics Working Group (DPWG). She previously chaired a medical research ethics committee from 2012-2017 and serves on multiple national and hospital committees related to pharmacotherapy and drug safety. Dr. Deneer has been actively involved in mentoring pharmacy students and professionals, with recent publications focusing on clinical decision-making education for pharmacists. Her work bridges the gap between pharmacogenetic research and clinical implementation, with particular emphasis on optimizing drug treatment strategies for individual patients while minimizing adverse drug reactions.
Luke O'Connor is an Assistant Professor of Biomedical Informatics at Harvard Medical School, affiliated with the Department of Biomedical Informatics. He leads the O'Connor Lab, which focuses on the genetic architecture of common diseases, statistical methods development, and translating genetic associations into biological insight. His work bridges computational and experimental approaches to understand the functional and phenotypic effects of genetic variation. Education: O'Connor earned his Ph.D. in Bioinformatics and Integrative Genomics (BIG) from Harvard Medical School in 2019. He was a Schmidt Fellow/Principal Investigator at the Broad Institute of MIT and Harvard before joining Harvard Medical School. Research Interests: His research emphasizes statistical genetics, functional genomics, and the integration of genetic data with phenotypic outcomes. Key areas include analyzing rare and common genetic variants, developing methods for polygenic risk prediction, and studying the impact of genetic perturbations on cellular and disease mechanisms. Grants: He currently leads an NIH-funded project (R35GM155278) investigating the functional and phenotypic effects of protein-coding genetic variation. This work aims to bridge gaps between genomic data and biological understanding. Labs/Teams: The O’Connor Lab collaborates with institutions like the Broad Institute and engages in interdisciplinary projects to advance precision medicine and genetic discovery.
Prof. Dr. Heiko Paulheim is a Professor of Data Science and currently serves as University Vice President at the University of Mannheim. He leads the Data and Web Science Group (DWS), which focuses on Web Data Mining, Knowledge Graphs, and Semantic Web technologies. His research group contributes to open source knowledge graphs like DBpedia and develops new knowledge graphs such as WebIsALOD and DBkWik. As of October 1, 2024, he has limited teaching capacity due to his vice presidential duties. Prof. Paulheim's research interests span Knowledge Graphs, Semantic Web, Web Data Mining, Machine Learning, and Natural Language Processing. His work particularly focuses on knowledge graph refinement, embedding techniques (notably RDF2vec), and applications in various domains including news recommendation, biomedical informatics, and environmental monitoring. His group develops practical tools like the RapidMiner Linked Open Data Extension and RDF2vec for knowledge graph applications. His recent publications demonstrate a strong focus on knowledge graph embeddings, with particular attention to RDF2vec variants, applications in news recommendation systems, biomedical data integration, and spatio-temporal knowledge graphs for environmental monitoring. His work bridges theoretical advances in knowledge representation with practical applications across multiple domains. Among his notable achievements are a nomination for the Best Paper Award at CAiSE 2025 and securing an Open Science Grant for the SpatialBenchRAG project. His research has significant impact in both academic and industrial contexts, with multiple papers accepted at top conferences like ISWC and ESWC. Prof. Paulheim has supervised numerous PhD students including Alexander Brinkmann and Michael Schlechtinger, and has led several research projects including the DFG Project Mine@LOD, State of BW Project SyKoW², and BMBF Project DS4DM. His group maintains strong industry connections with partners like SAP AG, Daimler AG, and IDS.
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.
Aurélie Labbe is a Full Professor in the Department of Decision Sciences at HEC Montréal, holding the prestigious FRQ-IVADO Chair in Data Science. Appointed as Co-Scientific Director – Academic Partnerships at IVADO in October 2023, she plays a key leadership role in establishing connections between IVADO and partner universities. Her academic journey includes a PhD in Statistics from the University of Waterloo, a Master's degree in Statistics from the University of Montreal, and dual Bachelor's degrees in Applied Mathematics and Social Sciences from Paris-Dauphine University and Pure Mathematics from Versailles-St Quentin University. Her research spans multiple interdisciplinary domains with a focus on developing advanced statistical and machine learning methodologies for big data analysis. Labbe's work bridges theoretical statistics with practical applications across diverse fields including genomics, neuroscience, transportation systems, and health informatics. She has made significant contributions to kernel methods, matrix factorization techniques, random forest applications, and spatiotemporal data analysis, with publications appearing in top journals across multiple disciplines. Analyzing her recent publications reveals a clear trend toward methodological innovation applied to complex real-world problems. Her work demonstrates expertise in handling high-dimensional data from diverse sources including neuroimaging, transportation networks, and genomic studies. The interdisciplinary nature of her research connects statistical theory with applications in healthcare, transportation safety, and biological sciences, reflecting her ability to develop methods that address domain-specific challenges while advancing statistical methodology. Holder of the FRQ-IVADO Chair in Data Science Member of the Center for Mathematical Research Training Professor Labbe actively mentors the next generation of data scientists, supervising numerous doctoral and master's students. Her supervision portfolio includes 1 doctoral thesis (2023), 4 master's theses (2022-2024), and 32 supervised projects spanning 2019-2025. Her students' work covers diverse applications including transportation safety, healthcare analytics, financial modeling, and environmental analysis. Through her leadership of the FRQ-IVADO Chair in Data Science, she coordinates research activities that integrate mathematical, statistical, and computer science expertise with domain knowledge from various data-generating fields. As Co-Scientific Director at IVADO, Professor Labbe leads efforts to establish connections with faculties and departments across five partner universities, integrating them into IVADO's research and knowledge transfer activities. Her leadership role positions her at the forefront of advancing data science research and applications in Quebec's academic ecosystem.
