Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.
Prof. Dr. Jörg Hackermüller is a computational biologist with expertise in Omics data integration Toxicology Environmental risk assessment Non-coding RNA biology . He serves as Head of the Department of Computational Biology and Chemistry at the Helmholtz Centre for Environmental Research (UFZ) since 2024 and holds a Professorship at the Faculty of Mathematics and Computer Science at Leipzig University since 2021. His research focuses on Developing AI methods for chemical toxicity prediction Multi-omics integration for mechanistic toxicology Data standardization in environmental monitoring Non-coding RNAs as biomarkers in disease and toxicity and has produced 15+ recent publications spanning tools like multiGSEA and deepFPlearn+ . He collaborates with teams across UFZ Leipzig University Novartis Fraunhofer Institute and leads projects like InCeTo and SafePol , integrating exposome research with systems biology.
Xin Lu is the John M. and Mary Jo Boler Collegiate Associate Professor in the Department of Biological Sciences at the University of Notre Dame. She is a full member of the Harper Cancer Research Institute (HCRI), the Boler-Parseghian Center for Rare and Neglected Diseases (CRND), and the Tumor Microenvironment and Metastasis Program at the Indiana University Simon Comprehensive Cancer Center. Her research spans tumor immunology, immunotherapy, metastasis, and multi-omics, with a focus on prostate, breast, and rare cancers. Ph.D. in Molecular Biology, Princeton University (2004–2010) B.S. in Biological Sciences, Tsinghua University, China (2000–2004) Postdoctoral Fellow, Dana-Farber Cancer Institute and M.D. Anderson Cancer Center (2010–2016) Assistant to Associate Professor, University of Notre Dame (2017–Present) Dr. Lu's research investigates the molecular and cellular mechanisms of tumor-immune crosstalk, particularly the role of myeloid-derived suppressor cells (MDSCs) and neutrophils in promoting immunotherapy resistance. Her lab uses genetically engineered mouse models, functional genomics, single-cell and spatial transcriptomics, and high-throughput screening to uncover novel therapeutic targets. A major focus is on how cancer-cell-intrinsic oncogenic signaling shapes the immunosuppressive tumor microenvironment. Her recent publications reveal mechanisms such as Acod1-mediated ferroptosis resistance in neutrophils, Pygo2-driven immunosuppression in prostate cancer, and the efficacy of ketogenic diets in overcoming checkpoint blockade resistance. These studies span multiple disciplines including cancer biology, immunology, metabolism, epigenetics, and bioengineering, reflecting a highly integrative approach to immuno-oncology. Jane Coffin Childs Postdoctoral Fellow John M. and Mary Jo Boler Faculty Appointment Cluster Chair, Cellular & Molecular Biology, IBMS PhD Program Junior Chair, Boler-Parseghian Center for Rare and Neglected Diseases Dr. Lu mentors a diverse team of graduate students, postdoctoral fellows, and undergraduates. Her lab is actively recruiting and has secured funding from federal agencies and private foundations. She collaborates with chemists, bioengineers, and bioinformaticians to develop novel therapeutics, including antibody-drug conjugates, CAR-NK cells, and small-molecule inhibitors. Her lab also develops innovative platforms like mini-tumor chips for immunotherapy evaluation. Her lab maintains affiliations with multiple interdisciplinary centers including the Warren Family Center for Drug Discovery, Eck Institute for Global Health, and Berthiaume Institute for Precision Health, underscoring her collaborative and translational research vision.
Professor Carlos Caldas is a leading academic in cancer medicine, affiliated with the University of Cambridge as a Professor of Cancer Medicine in the Department of Oncology . His research focuses on functional genomics of breast cancer, redefining its molecular taxonomy, studying clonal heterogeneity, and pioneering ctDNA as a liquid biopsy biomarker. MD (Lisbon), PhD (Porto, Honoris Causa) Member of the School of Clinical Medicine His laboratory has developed patient-derived tumor explants and advanced computational models for biomarker discovery. Recent work integrates AI with spatial transcriptomics and histopathology for precision oncology. Scientific Awards : Fellow of the Academy of Medical Sciences (FMedSci)
Dr. Emre Sefer is an Associate Professor at the Faculty of Engineering, Özyeğin University, specializing in machine learning and bioinformatics. He holds a Ph.D. in Computational Biology from Carnegie Mellon University (2015), an M.S. in Computer Science from University of Maryland College Park (2011), and a B.S. in Computer Engineering from Boğaziçi University (2008). His research bridges graph machine learning with financial networks, bioinformatics, and data engineering. Ph.D.: Computational Biology, Carnegie Mellon University M.S.: Computer Science, University of Maryland College Park B.S.: Computer Engineering, Boğaziçi University Research focuses on applying machine learning to financial and biological networks: Bioinformatics : 3D genome modeling, protein modifications, transcriptomic analysis Graph Machine Learning : GNNs for fraud detection, drug response prediction, and network evolution Financial Networks : Cryptocurrency investment strategies, asset price prediction His lab (OzU Machine Learning in Finance and Bioinformatics Lab) develops graph-based deep learning methods for cross-domain applications, including NFT market analysis and chromatin structure prediction. He received the Best research paper award at Recomb 2016 for work on 3D genome architecture. Former postdoc at CMU Machine Learning Department Industry experience as Quantitative Strategist at Goldman Sachs and JPMorgan
Julian Knight is a Professor of Genomic Medicine at the University of Oxford, with affiliations including the Centre for Human Genetics , Merton College , and leadership roles in the NIHR Oxford Biomedical Research Centre and Central and South NHS Genomic Medicine Service . His work bridges clinical practice and research, focusing on translational genomics. Principal Investigator Deputy Director, Centre for Human Genetics Honorary Consultant Physician Tutor and Fellow, Merton College Director, Medical Sciences Division Graduate School Genomic Medicine Theme Lead, NIHR Oxford BRC Research interests include mechanisms of dysregulated immune responses in sepsis , autoimmune disease , and infection . Key contributions involve RNA signature stratification for sepsis outcomes and HLA allele associations in COVID-19 immunogenicity. Current work explores genetic/epigenetic modulators of innate immunity and causal relationships in multi-omic datasets. Recent publications highlight diverse applications of his group’s work: from pleural infection endotyping (2025) to TLR7 variants in severe COVID-19 (2024), with methodological advancements in single-cell demultiplexing (2024) and pathway analysis (2025). Keywords span genomic medicine , immunology , and multi-omic integration . Knight’s leadership extends to clinical implementation of genomics, education (DPhil/MSc programs), and public engagement. Collaborations span institutions including Imperial College , Wellcome Sanger Institute , and Queen Mary University of London .
Mikail Rubinov serves as Assistant Professor of Biomedical Engineering (primary appointment), Computer Science, Psychiatry, and Psychology at Vanderbilt University's School of Engineering. His interdisciplinary work bridges computational neuroscience, network science, and clinical applications. His research focuses on integrative statistical models of large-scale neural data , exploring brain network organization across species and scales. Key interests include evolutionary principles of brain networks, transcriptomic basis of neural individuality, information transfer in neural systems, and neuropsychiatric connectivity phenotypes. The Rubinov Lab develops computational frameworks for analyzing complex neural systems and integrates neuroscientific knowledge with multi-omics data. Recent publications reveal strong trends in network neuroscience methodology development (circular analysis frameworks, unbiased sampling techniques) and translational applications (epilepsy networks, autism spectrum connectomics, gut-brain axis interrogation). His work increasingly incorporates transcriptomic data with neuroimaging at biobank scale. NIH Grant Writing Workshop (June 2022) NIH Workshop Short Talks (April 2023) Rubinov actively mentors graduate and undergraduate students across Biomedical Engineering and Computer Science. His lab maintains collaborations with UCSF, HHMI Janelia Research Campus, Weizmann Institute, and international neuroscience consortia. Current projects include integrative models of large-scale neural data and transcriptomic basis of neural individuality. The Rubinov Lab operates within Vanderbilt's Department of Biomedical Engineering with extensive cross-school collaborations. Technical resources include GitHub repositories for constraint network models (cnm-code), volumetric segmentation (voluseg), and brain connectivity toolboxes.
Guanghao Qi is an Assistant Professor in the Department of Biostatistics at the University of Washington. His research focuses on developing statistical and machine learning methods for multi-omics approaches in genetic studies, particularly integrating single-cell RNA-seq, GWAS, and functional genomic data. Key areas include single-cell eQTL analysis, Mendelian randomization, and multi-trait genetic association analyses. Education: PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (2020), BS in Mathematics from Fudan University (2015). Research interests emphasize high-dimensional data analysis, allele-specific expression in single cells, and causal inference using genetic variants. Notable achievements include a 2025 NIH K01 award for developing methods to integrate single-cell eQTL and GWAS data, and the development of the TWiST method for single-cell transcriptome-wide association studies. Recent work highlights advancements in computational tools like SURGE for context-specific genetic regulation analysis, and evaluations of Mendelian randomization methods in studies of type 2 diabetes and cardiovascular disease. His work often bridges computational biology and statistical theory to address challenges in interpreting large-scale genomic datasets. Awards: NIH K01 Award (2025) Key Contributions: TWiST method (2025), SURGE framework (2024), HIPO power optimization (2018) Labs/Teams: Active collaborations in genomic epidemiology and statistical genetics, with a focus on single-cell multi-omics integration and causal inference methodologies.
Doron Betel serves as an Assistant Professor at Weill Cornell Medicine's Graduate School of Medical Sciences, with affiliations in both the Physiology, Biophysics & Systems Biology and Computational Biology programs. He directs the Applied Bioinformatics Core (ABC), a central service group providing specialized computational and analytical support for biomedical research across multiple institutions. Dr. Betel's research focuses on developing computational genomic tools for studying human diseases and cellular development, with emphasis on integrative analyses of genomic and epigenomic data from high-throughput assays. His work addresses specific questions related to disease progression, treatment response, stem cell differentiation, and neurological processes through two closely interacting research groups: the Applied Bioinformatics Core and his independent research lab. The analysis of his recent publications reveals a strong emphasis on single-cell and spatial genomics, cross-species data integration, and machine learning applications in cancer immunology and neurodegenerative disease modeling. His research spans multiple high-impact areas including cancer immunotherapy, stem cell biology, diabetes research, and cardiovascular regeneration, with numerous publications in top journals like Nature, Cell, and Nature Immunology. Through the Applied Bioinformatics Core, Dr. Betel provides extensive analytical support across various genomic platforms including single-cell RNA-seq, spatial transcriptomics, ChIP-seq, ATAC-seq, and variant calling. The Core serves as a vital resource for researchers at Weill Cornell Medicine and the broader Tri-Institutional network, offering specialized analysis, computational pipelines, and training services. Dr. Betel maintains extensive collaborations with leading researchers including Lorenz Studer at MSKCC for stem cell and neurodegenerative disease research, Tuomas Tammela for cancer genomics, and multiple immunology researchers studying T cell function in autoimmunity and cancer. His work bridges computational methodology development with direct biomedical applications across multiple disease areas.
Zelmina Lubovac is a Senior Lecturer in BioInformatics at the School of Bioscience, University of Skövde. She serves as both a Course Coordinator for multiple undergraduate and graduate courses in bioinformatics and a Programme Coordinator for Master's level programs. Her academic work focuses on the intersection of computational methods and biological applications, particularly in disease analysis and biomarker discovery. Dr. Lubovac's research spans several key areas in bioinformatics and systems biology: Disease module identification in complex biological networks Multi-omics integration (genomics, proteomics, metabolomics) for biomarker discovery Machine learning applications in RNA-seq and other high-throughput biological data Development of bioinformatics software tools for network analysis miRNA analysis in cancer and neurological disorders Her recent publications (2022-2024) demonstrate a strong focus on applying computational approaches to understand disease mechanisms, particularly in pancreatic cancer and multiple sclerosis. She has developed several widely-used bioinformatics tools including MODalyseR, MODifieR, and TFTenricher that facilitate disease module analysis and gene network interpretation. Her work often involves collaborative research with clinical teams to translate computational findings into potential diagnostic applications. Dr. Lubovac has been involved in significant research projects including: BIO-AID (Biomedical AI-driven data analytics): Oct 2020 - Sep 2024 Systems Biology DMDPipe: Mar 2018 - Feb 2021 She actively contributes to both undergraduate and graduate education at the University of Skövde, coordinating multiple courses and programs in bioinformatics and bioscience, with a clear emphasis on preparing students for careers at the intersection of biology and computational science.
Carlos Salomon Gallo is a Professor and NHMRC Investigator Fellow (EL2) at The University of Queensland's Centre for Clinical Research, affiliated with the School of Biomedical Sciences. He directs the Centre for Extracellular Vesicle Nanomedicine and leads the Exosome Biology Laboratory. A globally recognized key opinion leader in extracellular vesicles (ranked 3rd worldwide by Expertscape), his research focuses on EV biology for diagnostic and therapeutic applications in ovarian cancer, gestational diabetes, preeclampsia, and other obstetrical syndromes. His research integrates proteomics (SWATH-MS), miRNA analysis, and advanced isolation techniques to develop liquid biopsies. Core interests include: EV biomarker discovery and validation for early disease detection. Mechanisms of EV-mediated signaling in metabolic and oncological pathologies. Engineering EVs for targeted drug delivery and CRISPR-Cas therapeutics. Clinical translation of EV-based diagnostics (IVDs) and therapeutics. Analysis of his recent articles reveals a dominant focus on EV profiling in pregnancy complications (gestational diabetes, preeclampsia) and oncology (ovarian cancer), utilizing multi-omics approaches. Key trends include developing high-sensitivity EV biosensors, understanding hypoxia-induced EV signaling, and exploring 3D models for EV research. He has received significant recognition, including: NHMRC Emerging Leadership Fellow NHMRC Investigator Fellow (EL2) He leads the Exosome Biology Laboratory and the UQ Centre for Extracellular Vesicle Nanomedicine, fostering cross-disciplinary collaboration. His work involves extensive national and international partnerships, evidenced by leadership roles in the Centre for Clinical Diagnostics and over 20 invited international talks in 5 years. He actively mentors HDR students and contributes to global EV research standards (MISEV2023).
Dr. George C Tseng serves as Professor and Vice Chair for Research in the Department of Biostatistics at the University of Pittsburgh School of Public Health, with secondary appointments in Human Genetics and Computational and Systems Biology. His educational background includes a BS (1997) and MS (1999) in Mathematics from National Taiwan University and an ScD (2003) in Biostatistics from Harvard School of Public Health. Dr. Tseng's research focuses on developing statistical methodologies for genomic and bioinformatic applications to advance precision medicine. His work spans multiple high-impact areas including multi-omics data integration, machine learning for high-dimensional data, cluster analysis for disease subtyping, and statistical methods for experimental design in omics studies. His approach emphasizes close collaboration with biological and clinical researchers to ensure methodological relevance to real-world problems. His publication record demonstrates consistent contributions to top statistical and bioinformatics journals, with recent work focusing on congruence analysis between animal models and humans, outcome-guided clustering methods, and high-dimensional causal mediation analysis. Elected Fellow, American Statistical Association (2017) Statistician of the Year, ASA Pittsburgh Chapter (2017) Provost's Award for Excellence in PhD Mentoring, University of Pittsburgh (2019) Clinical Research Scholar (K12) Award, NIH (2007-2009) Elected Member, International Statistical Institute (2012) Dr. Tseng has successfully mentored over 25 PhD students who have secured positions in academia, industry, and government agencies. His laboratory has maintained continuous NIH funding as principal investigator since 2012, including current grants R01CA285337 (2025-2030) and R01LM014142 (2023-2026). The Tseng Lab operates as a collaborative research environment focused on translating statistical innovations into practical solutions for biological and medical challenges, with strong connections to multiple research centers and clinical departments at the University of Pittsburgh.
Xiaoyu Che is an Assistant Professor of Biostatistics at Columbia University's Mailman School of Public Health, where he serves as the principal biostatistician in the Center for Infection and Immunity (CII) at Columbia University Irving Medical Center. His work bridges statistical methodology with biomedical research, focusing on complex disease mechanisms through advanced data analysis approaches. Dr. Che received his academic training at prestigious institutions: BS in Mathematics from Zhejiang University (2006) PhD in Mathematics from Claremont Graduate University (2013) Dr. Che's research program centers on the development and application of statistical methods for multi-omics analyses, with particular focus on understanding the pathogenesis of chronic and neurodevelopmental conditions. His work spans multiple domains including Autism Spectrum Disorder (ASD), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and Gulf War Illness (GWI). He employs sophisticated biostatistical approaches to integrate diverse biological data types, revealing novel insights into disease mechanisms. His methodological expertise includes Bayesian statistics, metabolomic analysis, immune signature identification, and microbiome characterization, all aimed at translating complex biological data into meaningful clinical insights. Analysis of Dr. Che's publication record reveals a strong thematic focus on applying advanced statistical methods to understand complex disease mechanisms. His work consistently bridges biostatistical innovation with biomedical discovery, particularly in the areas of neurodevelopmental disorders and chronic fatigue conditions. The publications demonstrate progression from foundational methodological work to increasingly sophisticated multi-omics integration approaches, reflecting both technical growth and expanding research impact. His collaborative approach is evident through numerous high-impact publications with interdisciplinary teams across Columbia University and beyond. Dr. Che teaches BIST P8104: Probability in the Biostatistics MS degree program at Columbia, demonstrating his commitment to training the next generation of biostatisticians. While specific grant information isn't detailed in the provided materials, his extensive publication record across multiple high-impact journals suggests successful grant funding supporting his research program. As principal biostatistician in the Center for Infection and Immunity, Dr. Che plays a critical role in the analytical framework of the center's research initiatives. His work supports the center's mission to understand the relationship between infectious agents and human health through rigorous quantitative analysis. The collaborative nature of his research is evident in the diverse range of co-authors spanning immunology, virology, microbiology, and clinical medicine.
Prof. Enkelejda Miho is a Professor of Digital Life Sciences at the School of Life Sciences, FHNW, leading the aiHealthLab. Her work bridges computer science/AI with life sciences, focusing on drug discovery, personalized medicine, and immunology. She holds roles as Team Leader at aiHealthLab and Group Leader at the Swiss Bioinformatics Institute. Research Interests : She applies machine learning to analyze immune repertoires, antibody engineering, and autoimmunity diagnostics. Her lab develops computational tools like the RWD-Cockpit for real-world data analysis and synthetic antibody-antigen models (Absolut!) to advance biotherapeutics. Her work on dengue immunity and monoclonal gammopathies highlights translational applications. Key Projects : The aiHealthLab focuses on AI-driven diagnostics and therapeutics. Her contributions include AI frameworks for antibody specificity prediction, age-related immune repertoire changes, and large-scale network analysis of antibody repertoires. Labs/Teams : Leads aiHealthLab and collaborates with the Swiss Bioinformatics Institute, integrating computational and experimental immunology.
Zhandong Liu is an Associate Professor at Baylor College of Medicine with joint appointments in the Department of Pediatrics and Department of Neurology . He serves as Chief of Computational Sciences at Texas Children's Hospital and co-directs the Quantitative & Computational Biosciences Graduate Program at Baylor. Education: B.S. in Computer Science, Nankai University (2001) M.S. in Computer Science, Wayne State University (2003) Ph.D. in Genomics and Computational Biology, University of Pennsylvania (2010) Dr. Liu's research integrates genomics , machine learning , and bioinformatics to advance understanding of neurological diseases. His work focuses on: Multi-omics data integration for disease mechanism discovery Development of cloud-based CRISPR analysis tools like CRISPRcloud Augmented reality platforms for biomedical data visualization Identification of disease genes through computational models Alternative splicing analysis in cancer and neurodegeneration Single-cell and spatial transcriptomics algorithms His recent publications emphasize Alzheimer's disease , MECP2 syndromes , and computational therapy prediction across multiple domains. Scientific awards include the 2018 Outstanding Service Award from the International Association for Intelligent Biology and Medicine. He has secured major grants from NIH, CPRIT, and NSF for projects including: NSF grant #199977 (2018-2020): Augmented reality therapy platforms CPRIT grant #RP170387 (2016-2019): Network-guided cancer analysis NIH #1R01AG057339 (2017-2022): Alzheimer's disease networks As head of the Liu Lab , he leads teams developing tools like: MARRVEL : Human-model organism gene variant integration CRISPRcloud : Secure CRISPR screen analysis platform CrypSplice : Cryptic splicing detection algorithm
