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David Serre is a Professor in the Department of Microbiology and Immunology at the University of Maryland School of Medicine, with an additional appointment at the Institute for Genome Sciences. His research focuses on developing genomic approaches to study eukaryotic pathogens, particularly Plasmodium vivax, the leading cause of malaria outside Africa. His laboratory investigates parasite responses to antimalarial drugs, host immune responses, and mosquito vector biology using genomic and transcriptomic techniques. Education 1997–2000: Engineering degree in Chemistry, École Nationale Supérieure de Chimie, Montpellier, France 2000–2004: PhD in Biology, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany 2004–2007: Postdoctoral fellowship, McGill University and Genome Quebec Innovation Centre, Montreal, Canada Research Focus Dr. Serre’s work integrates genomics to study Plasmodium vivax’s drug resistance, relapse mechanisms, and interactions with hosts and vectors. Key areas include: Genomic assays to characterize parasite drug responses Transcriptomic analysis of host immune responses Genomic studies of Anopheles mosquitoes as malaria vectors Recent Trends in Publications Recent work highlights genomic and transcriptomic approaches to dissect Plasmodium vivax biology, including: Single-cell RNA sequencing to resolve transcript isoforms and stage-specific expression Analysis of relapse dynamics and drug resistance mechanisms Microbiome studies in mosquitoes and environmental contexts Grants & Advising No explicit grants or advisee names are listed in the provided text. Collaborators include institutions like the Max Planck Institute, McGill University, and the Institute for Genome Sciences. Labs & Teams His lab is affiliated with the University of Maryland School of Medicine and the Institute for Genome Sciences, focusing on genomic and molecular approaches to infectious diseases.

• Genomics
• Malaria Research
• Parasitology
• +3 more

Dr. Fengzhu Sun is a Professor of Quantitative and Computational Biology and Mathematics at the University of Southern California. His research spans computational biology, bioinformatics, statistical genetics, and mathematical modeling, with a focus on metagenomics, protein interaction networks, and genome sequence analysis. Dr. Sun earned his Bachelors in Mathematics from Shandong University, Masters in Probability and Statistics from Peking University, and PhD in Applied Mathematics from USC. He returned to USC in 2000 as an associate professor after serving at Emory University (1995-2000), becoming a full professor in 2006. His research interests encompass protein interaction networks, gene expression, SNPs, linkage disequilibrium, and their applications in predicting protein functions, gene regulation networks, and disease gene identification. He pioneered alignment-free methods for genome and metagenome sequence comparison, with recent work focusing on virus-host interactions in metagenomic data. His publication record shows consistent innovation, with recent work (2023-2025) emphasizing deep learning approaches (DeepMicroClass, DeepDecon, DeepLINK) and novel statistical methods. His research demonstrates strong interdisciplinary integration of computational methods with biological applications. Fellow of American Association for the Advancement of Sciences (AAAS, 2012) Fellow of American Statistical Association (ASA, 2015) Fellow of Institute of Mathematical Statistics (IMS, 2023) Fellow of International Society for Computational Biology (ISCB, 2024) Fellow of Asia-Pacific Artificial Intelligence Association (AAIA, 2025) Member of International Statistical Institute (ISI, 2012) USC Mellon Mentoring award for faculty mentoring (2012) USC Dornsife College senior Raubenheimer Outstanding Faculty Award (2017) Dr. Sun has mentored numerous successful students and postdocs, many now in academic positions or at leading tech and biotech companies. His research group develops computational methods for complex biological data analysis, with current focus on advanced deep learning for metagenomic classification, cancer cell fraction estimation, and virus-host interaction analysis. He has created influential software tools including DeepMicroClass, ImputeCC, DeepDecon, and ViralCC that have become standard resources in computational biology.

• Computational Biology
• Bioinformatics
• Statistical Genetics
• +6 more

Prof. Valentina Boeva is an Assistant Professor at the Department of Computer Science, ETH Zürich, specializing in biomedical informatics. Her research focuses on integrating machine learning and computational methods to address challenges in genomics, oncology, and precision medicine. She holds a position in the Professur für Biomedizininformatik (Biomedical Informatics) and is based at CAB G32.2, Universitätstrasse 6, Zürich, Switzerland. Her work emphasizes applications such as cancer biomarker discovery, tumor heterogeneity analysis, and epigenetic profiling. She teaches courses including Machine Learning Seminar, Data Science Lab, and Machine Learning for Genomics. Her research group develops computational tools like CDState and UniversalEPI to decode complex biological systems. She actively publishes in top-tier journals, with recent work on exosome-driven diagnostics and chromatin interaction modeling. Her scientific contributions span methodologies for single-cell data analysis, survival modeling, and drug response prediction. She collaborates across disciplines to bridge computational science with clinical applications in cancer research.

• Machine Learning
• Bioinformatics
• Genomics
• +3 more

Emily Bayer is appointed as Assistant Professor of Neural Science and Biology at New York University, commencing January 2026. Her research program investigates neural mechanisms of visceral sensation and behavioral outputs, with emphasis on fertility status and sex differences using C. elegans and D. cerebrum (zebrafish) models. Her educational background includes: Ph.D. in Biological Sciences, Columbia University (2019) B.S. in Biology/German, Muhlenberg College (2014) Dr. Bayer's research explores how internal bodily sensations (visceral sensation) modulate behavior and environmental interaction, focusing on molecular and anatomical sex differences in neural circuits. Her lab examines how fertility status influences behavior through genetic and neural mechanisms, revealing conserved principles of neural circuit organization. Key approaches include C. elegans genetics, zebrafish neuroimaging, and behavioral analysis to dissect how reproductive state and sex converge in brain function. Analysis of her recent publications (2025-2016) demonstrates a cohesive focus on sexual dimorphism in neural development and behavior, with increasing integration of genomic and connectomic methodologies. Her work spans molecular genetics, neural circuit mapping, and behavioral phenotyping, consistently highlighting sex-specific differences in neural wiring and function across model organisms. Her scientific recognition includes: Research Fund for Excellent Junior Researchers (University of Basel, 2025) Jane Coffin Childs Memorial Fund for Medical Research Fellowship (2021-2023) Dr. Bayer has secured competitive fellowships supporting her independent research program. Her lab establishes innovative frameworks for studying visceral sensation-behavior relationships, with current work emphasizing monoaminergic signaling in experience-dependent neural plasticity and the role of conserved transcription factors in sexually dimorphic development. Future directions include expanding to vertebrate models to investigate evolutionary conservation of these mechanisms. She leads a research team utilizing C. elegans and zebrafish to dissect neural circuits underlying internal sensation, with particular focus on how fertility signals modulate social and mating behaviors through sex-specific neural pathways.

• Visceral Sensation
• Sexual Dimorphism
• Behavioral Genetics
• +4 more

Dr. Jing Zhang is an Assistant Professor in the Department of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. She holds a Ph.D. in Electrical Engineering and Molecular/Computational Biology from the University of Southern California (2012) and completed postdoctoral training in Computational Biology at Yale University. Her research focuses on developing computational methods to unravel gene regulation mechanisms and link genetic variations to diseases, particularly in noncoding regions of the genome. She has contributed extensively to the ENCODE project, co-authoring pivotal studies in Nature and producing over 5,900 experimental datasets. Dr. Zhang’s work bridges engineering, mathematics, and biology, with applications in precision medicine for cancers and psychiatric disorders. She emphasizes the importance of noncoding DNA in disease causation and has pioneered tools like EN-TEx and scENCORE to analyze epigenomes and regulatory elements. Her lab actively seeks to recruit Ph.D. students, postdocs, and interns to advance genomic technologies. Key research areas include single-cell and spatial transcriptomics, gene regulatory networks, and computational methods for multi-omics data integration. Despite pandemic-related challenges, she maintains strong collaborations and teaches courses in bioinformatics. Her future goals include expanding lab interactions and applying computational models to predict disease susceptibility and treatment responses.

• Genomics
• Computational Biology
• Biomedical Informatics
• +6 more

Guo-Cheng Yuan is a Senior Faculty member in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His research focuses on genomics, epigenetics, and computational biology , with particular emphasis on single-cell analysis, chromatin regulation, and cancer immunology. Institution: Icahn School of Medicine at Mount Sinai School: Graduate School of Biomedical Sciences Department: Genetics and Genomic Sciences Research Interests Dr. Yuan's work spans multiple domains including: Single-cell RNA sequencing applications in brain development and cancer Chromatin structure analysis in differentiation processes Spatial transcriptomics for tissue microenvironment characterization Computational methods for multiomic data integration Enhancer-promoter interaction dynamics Development of robust bioinformatics pipelines Scientific Trends Analysis His recent publications (2021-2025) demonstrate a focus on single-cell and spatial omics methodologies applied to diverse biological contexts ranging from cancer immunology to neurodevelopment . Notable trends include: Development of trajectory analysis algorithms Multiomic integration for regulatory network mapping Epigenetic mechanisms in cell differentiation Computational approaches for data robustness Three-dimensional genome organization studies Applications in both developmental biology and oncology

• Genomics
• Epigenetics
• Gene Expression
• +4 more

Dr. Daniel Shu is an Assistant Professor in the Department of Medicine at the University of Maryland School of Medicine and a medical oncologist at the University of Maryland Greenebaum Comprehensive Cancer Center. His clinical expertise encompasses gastrointestinal cancers, including liver, bile duct, gallbladder, pancreas, and colon malignancies. Education: MD from Temple University School of Medicine (2017) Internal Medicine Residency at Johns Hopkins Hospital (2020) Medical Oncology Fellowship at Johns Hopkins Hospital (2024) Dr. Shu's research centers on cancer immunology, with emphasis on adaptive immune responses to malignancies. He employs multiomic techniques including bulk/single-cell RNA sequencing, TCR/BCR sequencing, and imaging mass cytometry to investigate tertiary lymphoid structures in liver cancer immunotherapy. His work bridges clinical oncology and mechanistic immunology to optimize therapeutic strategies. His 2024-2025 publications reveal a concentrated focus on tertiary lymphoid structures as biomarkers and therapeutic targets in hepatocellular carcinoma. Research spans clinical trial analysis, pathological response assessment, and morphological characterization of immune structures during neoadjuvant immunotherapy, establishing foundational insights for precision immuno-oncology. Scientific Awards: Conquer Cancer/ASCO Young Investigator Award (2024-2025) Supported by the ASCO Young Investigator Award, Dr. Shu leads research integrating clinical oncology with advanced immunological profiling. He actively collaborates within multidisciplinary teams at the Greenebaum Comprehensive Cancer Center, focusing on gastrointestinal malignancies and immune microenvironment dynamics. His ongoing projects include biomarker development for immunotherapy response prediction. As a member of ASCO, AACR, and SITC, Dr. Shu contributes to national cancer research initiatives while maintaining clinical practice in gastrointestinal oncology at the University of Maryland's NCI-designated cancer center.

• Cancer Immunology
• Liver Cancer
• Immuno-Oncology
• +3 more

Pablo G Cámara is an Associate Professor of Genetics at the University of Pennsylvania's Perelman School of Medicine. He serves as Senior Fellow at the Institute for Biomedical Informatics and is affiliated with the Center for AI and Data Science in Integrated Diagnostics, the Statistical Center for Single-Cell and Spatial Genomics, and the Department of Genetics. His research focuses on computational approaches to cellular heterogeneity in diseases, particularly brain tumors, integrating topology, geometry, statistics, physics, and computer science. BS, Theoretical Physics, Universidad Autonoma de Madrid (2002) PhD, Theoretical Physics, Universidad Autonoma de Madrid (2006) His work combines single-cell technologies with mathematical frameworks to decode tumor ecosystems and signaling networks. Key themes include metric geometry applications, tumor-genome interactions, and interdisciplinary collaborations in neurodevelopment and cancer research. Recent publications highlight his methodological innovations in single-cell data analysis, tumor biology, CAR T-cell therapy, and quantum computing. Collaborations span immunology, neuro-oncology, and developmental biology domains. The Camara Lab at Penn Medicine develops computational tools for integrating morphometric, transcriptomic, and physiological data, aiming to advance precision oncology and developmental neuroscience through geometric and topological approaches.

• Computational Biology
• Genetics
• Biomedical Informatics
• +6 more

Professor Yuanfang Guan is affiliated with the University of Michigan in the Department of Computational Medicine and Bioinformatics. Their research focuses on bioinformatics and computational biology applications in medical research. Key research areas include: Computational oncology with emphasis on tumor subclonal reconstruction AI applications in clinical pharmacology and drug response prediction Machine learning for neurogenetic disorders like SCA3 Development of digital health measures for neurological diseases Single-cell sequencing data analysis and quality control Epigenomic data imputation and genomic feature analysis Recent publications highlight collaborative projects on LSD1 inhibitors for sickle cell disease, long COVID prediction models, and optimization of genomic deep learning. Their work involves algorithm development for cancer evolution analysis, disease biomarker identification, and AI-driven biomedical applications.

• Bioinformatics
• Computational Biology
• Machine Learning in Medicine
• +5 more

Mark T. W. Ebbert, PhD, is an Associate Professor at the University of Kentucky's Sanders-Brown Center on Aging, where he leads the Ebbert Lab. His work focuses on developing biomarkers for neurodegenerative diseases, particularly Alzheimer's disease (AD), leveraging cutting-edge genomic and transcriptomic techniques. He co-leads efforts in the Alzheimer's Disease Research Center's Biomarker Core, aiming to integrate neuroimaging, fluid biomarkers, and genomic data for precision medicine applications. Key research interests include analyzing genetic variants, long-read sequencing for disease resilience, and understanding inflammation's role in neurodegeneration. He has presented at high-profile venues like the Markesbery Symposium, discussing topics such as using long-read sequencing to identify AD biomarkers. His lab has produced over 55 publications, with a focus on resolving genomic 'dark regions' and improving biomarker accuracy through innovative methods like RNApysoforms visualization tools. Collaborating with interdisciplinary teams, Dr. Ebbert explores the interplay between genetics, epigenetics, and environmental factors in neurodegenerative diseases. His work bridges basic science and clinical translation, emphasizing the importance of genomic data integration for advancing AD diagnosis and treatment strategies.

• Alzheimer's Disease
• Neurodegenerative Disorders
• Genetics
• +5 more

Joseph Ibrahim is an Alumni Distinguished Professor in the Department of Biostatistics at the Gillings School of Global Public Health, University of North Carolina at Chapel Hill, where he has served since 2002. He currently holds dual leadership roles as Director of Graduate Studies for the Department of Biostatistics and Director of the Biostatistics for Research in Genomics and Training Grant. His methodological innovations in Bayesian survival analysis and missing data methodologies have significantly advanced public health research, particularly in cancer genomics applications. Professor Ibrahim's research program centers on developing statistical frameworks for complex clinical and genomic data. His seminal contributions include Bayesian cure rate models, prior elicitation techniques, and diagnostic tools for high-dimensional survival analysis. Current work focuses on integrating multi-omics data with longitudinal tumor burden metrics and refining adaptive clinical trial designs for biomarker-driven populations. These methodologies directly address critical challenges in precision oncology and pharmacovigilance, enabling more robust inference from real-world evidence. His 2025 publications reveal three dominant trends: (1) Advancements in cure rate modeling for joint longitudinal-survival data with change points, (2) Computational innovations for high-dimensional penalized models using autoencoders and R packages like hdbayes, and (3) Methodological refinements for Bayesian trial design incorporating historical controls. These works consistently bridge theoretical statistics with cancer research applications, particularly in tumor phylogeny inference and signal detection for adverse events. Scientific awards include: Samuel S. Wilks Memorial Award (2024) from the American Statistical Association for distinguished contributions to biostatistics Professor Ibrahim has mentored 48 pre-doctoral students and 8 postdoctoral fellows, with exceptional thesis publication records in top statistical journals. As principal investigator of the T32 Cancer Genomics Training Grant since 2004, he has secured funding for 35 doctoral students. His curriculum leadership includes modernizing eight graduate courses and establishing new data science computing sequences since 2015. Current advising focuses on Bayesian methodology development for cancer genomics applications. He directs the department's Biostatistics for Research in Genomics initiative and leads the T32 Cancer Genomics Training Grant team, which integrates statistical methodology development with translational cancer research across UNC's Lineberger Comprehensive Cancer Center and clinical partners.

• Biostatistics
• Bayesian Survival Analysis
• Prior Elicitation
• +6 more

Bin Tian, Ph.D., is a Professor in the Department of Biochemistry and Biophysics at The Wistar Institute and serves as Program Co-Leader of the Genome Regulation and Cell Signaling Program at the Ellen and Ronald Caplan Cancer Center. He is also the Director of the Center for Systems & Computational Biology. His research integrates molecular biology, computational genomics, and functional studies to understand RNA processing, particularly alternative polyadenylation (APA), and its roles in cancer and immunity. His educational background includes a B.S. in Biochemistry from East China University of Science and Technology and a Ph.D. in Molecular Biology from Rutgers Biomedical and Health Sciences (formerly UMDNJ). He completed postdoctoral training in bioinformatics and genomics at Johnson & Johnson before establishing his independent lab at Rutgers New Jersey Medical School in 2003, where he became a tenured professor in 2014. He joined The Wistar Institute in 2020. His research focuses on molecular systems biology of RNA, including functional genomics of cleavage and polyadenylation, regulation of gene expression through early transcriptional termination, spatial and temporal control of mRNA metabolism via alternative 3’UTRs, and cleavage and polyadenylation inhibition (CPAi) as a cancer therapeutic strategy. His lab has developed key bioinformatics resources like PolyA_DB, APAlyzer, and MAAPER. His recent publications show a strong trend in dissecting APA mechanisms across physiological and pathological contexts, including cancer, neuronal function, and immune cell differentiation. The work combines high-throughput sequencing, single-cell analysis, and computational modeling to uncover how APA regulates mRNA stability, localization, and function. He mentors a team of postdoctoral fellows, predocotoral trainees, and research assistants, contributing to training the next generation of scientists. His lab is actively pursuing novel therapeutics targeting mRNA 3’ end processing in cancer and immune disorders.

• Molecular Systems Biology
• RNA Processing
• mRNA Metabolism
• +12 more

Jan Baumbach is a Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he leads cutting-edge research at the intersection of computer science, bioinformatics, and biomedical data science. His work integrates machine learning, network biology, and systems medicine to address complex challenges in health and disease. His research interests include Bioinformatics , Machine Learning , Gene Regulatory Networks , Drug Repositioning , Biomedical AI , and Computational Biology . He applies advanced computational methods to analyze large-scale biological data, with applications in cancer, metabolic diseases, dermatology, and infectious diseases. The 15 most recent publications highlight a strong trend toward biomedical artificial intelligence , network-based analysis , and translational bioinformatics . His work spans from foundational machine learning in healthcare to clinical applications in osteoarthritis, bone regeneration, and skin biology. A consistent theme is the integration of multi-omics data and the development of privacy-preserving federated learning frameworks for distributed healthcare systems. Jan Baumbach has been principal investigator on several major research projects, including: EU Horizon2020: Privacy-preserving federated machine learning in distributed healthcare Danish National Research Foundation: ATLAS Center for Functional Genomics of Tissue Plasticity Villum Foundation: Big Data Bioinformatics (Young Investigator Programme) Carlsberg Foundation: Computational profiling of bacterial volatile metabolomes (ProVol) He has supervised 13 PhD students and has an extensive publication record of 262 works. His research has been featured in high-profile media outlets, emphasizing the societal impact of drug repositioning and AI in medicine. He leads a research group focused on computational systems biology , with strong collaborations across Europe in the fields of genomics, metabolomics, and clinical data science.

• Bioinformatics
• Machine Learning
• Computational Biology
• +7 more

Dr. Yi Qiao is a researcher in the Department of Biomedical Informatics at the University of Utah School of Medicine. His work focuses on integrating computational methods with genomic data to study cancer evolution, tumor subclonal dynamics, and precision oncology. Key contributions include developing tools like SubcloneSeeker, quickBAM, and Gene.iobio for analyzing genomic variants and clinical data. Education : PhD in Biomedical Informatics from Boston College BS from Zhejiang University Research Interests : Dr. Qiao's research bridges computational biology and clinical oncology. He develops innovative methods to dissect tumor heterogeneity using single-cell and bulk sequencing, with applications in drug response prediction and therapy selection. His work emphasizes understanding how subclonal populations evolve in cancers like leukemia and lung cancer, and how this informs targeted treatments. Key Projects : Framework for combining bulk DNA and single-cell RNA sequencing to study tumor expression (Genome Res, 2024) quickBAM API for high-throughput sequence analysis (Bioinformatics, 2023) ScreenDL deep learning model for drug response prediction (2024) Grants & Collaborations : His research is supported by collaborations with oncology teams and computational biology groups, focusing on translational applications of genomic data. Tools like Taxonomer and bam.iobio have advanced clinical diagnostics through real-time genomic analysis. Labs & Teams : He leads the Computational Oncology Lab, specializing in developing AI-driven tools for precision medicine. Active in cross-disciplinary teams at the University of Utah Health's cancer research initiatives.

• Biomedical Informatics
• Cancer Genomics
• Computational Biology
• +3 more

Dr. Eran Halperin is a Professor at the University of California, Los Angeles, affiliated with the School of Engineering (Computer Science Department) and the School of Medicine (Human Genetics, Computational Medicine, Anesthesiology). His research bridges computational biology, genomics, and machine learning, with a focus on developing statistical methods to analyze big medical data for disease prediction and treatment. Developed open-source software tools like FEAST, ReFACTor, and Bisque Recipients of prestigious awards including Rothschild Fellowship and ISCB Fellow (2021) Research Interests: Computational Genomics: Applying machine learning to genomic data (methylation, RNA expression) for disease understanding. Machine Learning in Medicine: Creating deep learning architectures for ophthalmology, anesthesiology, and acute care applications. Medical Data Science: Integrating electronic health records with genomic datasets for predictive modeling. Scientific Recognition: Rothschild Fellowship Technion-Juludan Research Prize Krill Prize in Science Elected ISCB Fellow (2021) Dr. Halperin's lab collaborates across disciplines, utilizing software platforms such as GLINT (methylation analysis) and MTV-LMM (microbiome prediction). His work has received funding from NIH, NSF, and international foundations.

• Computational Biology
• Genomics
• Machine Learning in Medicine
• +4 more