Serghei Mangul, PhD, University of Southern California, USA
Dr Mangul is an Assistant Professor of Clinical Pharmacy and Computational Biology at the University of Southern California. He specializes in the design, development, and application of novel data-driven computational approaches to accelerate the diffusion of genomics and biomedical data into translational research and education. Dr Mangul is a passionate advocate for promoting transparency and reproducibility in data-driven biomedical research, as well as for making bioinformatics education accessible to all. His work is dedicated to advancing the principles of reproducibility, data sharing, and software usability, with the ultimate goal of shaping a more equitable and impactful future for the field of bioinformatics. Dr Mangul received his PhD in Bioinformatics from Georgia State University, and he holds a BSc in Applied Mathematics from Moldova State University, Chisinau, Moldova. He completed his postdoctoral training in computational genomics with Prof Eskin at the University of California Los Angeles (UCLA). Dr Mangul is the recipient of the prestigious National Science Foundation CAREER and Fulbright US Scholar Program awards. He serves as a mentor for the NIH AIM-AHEAD Leadership Fellowship and NCATS Training Program in Advanced Data Analysis.
Mark Robinson, PhD, University of Zurich, Switzerland
Mark Robinson leads the Statistical Bioinformatics research group at the University of Zurich. The group brings considerable expertise in the analysis of omics data of various types and in particular, RNA sequencing data and methods for differential analysis (e.g., differential abundance, differential expression, differential splicing). Their core strength is in the development and use of advanced statistical techniques to robustly process and interpret large molecular datasets. The group also maintains a firm stance on open and reproducible science. The default policy is: i) to produce documented open-source software for our developed statistical and computational frameworks (typically via the Bioconductor project, but several are now done in Python and Snakemake); ii) to create code repositories for manuscript analyses; and, iii) post manuscripts as preprints.
Fritz Joachim Sedlazeck, PhD, Baylor College of Medicine, USA
Dr Fritz Sedlazeck is an Associate Professor at Baylor College of Medicine and an adjunct Associate Professor at Rice University. He has led a research group at the Human Genome Sequencing Center at Baylor since 2018, where his work has become central to advancing bioinformatics approaches for the detection and analysis of genomic variation—particularly structural variants (SVs). These complex genomic events span multiple positions in the genome and are critical to understanding evolution, disease mechanisms, gene regulation, and phenotypic diversity. Dr Sedlazeck is widely recognized for developing cutting-edge computational tools, including the popular Sniffles SV caller, and for his leadership in benchmarking methods to ensure robust and reproducible variant detection. His group has been instrumental in uncovering the mechanisms of SV formation across species and in diverse human populations, participating in major international efforts such as TOPMed, CCDG, CARD, and All of Us. Through this work, he has helped set standards for the field while deepening our understanding of how complex alleles evolve and contribute to human biology.
Hong-Bin Shen, PhD, Shanghai Jiao Tong University, China
Hong-Bin Shen is a distinguished professor of Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University. The long-term of interest of his lab is pattern recognition and bioinformatics, to the development of advanced machine learning/artificial intelligence-based algorithms for understanding the complex knowledge of proteomics and genomics, including predicting and annotating molecule structures, functions, and interactions.
Jean Yee Hwa Yang, PhD, The University of Sydney, Australia
Dr Jean Yang is a Professor of Statistics and Bioinformatics at the University of Sydney. She is currently the Director of Sydney Precision Data Science and a NHMRC Investigator Leadership Fellow. She received her BSc in Mathematical Statistics from the University of Sydney in 1996 and her PhD in Statistics from the University of California, Berkeley in 2002. Her research lies at the intersection of medicine and methodological development, focusing on the creation of statistical methods and their application to challenges in -omics and biomedical research. Her work has led to advances in integrating multi-layered biological data by reducing extraneous variability and accounting for heterogeneity. She has also made significant contributions to scalable data integration in single-cell transcriptomics sequencing. More recently, her research has focused on integrating multiple biotechnologies with clinical data to develop novel approaches in statistical machine learning and network analysis. In 2022, she was elected a Senior Fellow of the Australian Bioinformatics and Computational Biology Society. In 2015, she was awarded the Moran Medal in Statistics by the Australian Academy of Science in recognition of her contributions to the development of methods for analyzing molecular data in cutting-edge biomedical research. As a statistical data scientist working at the intersection of statistics, biomedicine, and health, Dr Yang enjoys developing innovative methods with translational potential in collaborative settings, working closely with investigators from diverse disciplines.
Xin Maizie Zhou, PhD, Vanderbilt University, USA
Xin Maizie Zhou is an Assistant Professor of Biomedical Engineering and Computer Science at Vanderbilt University. She is a core faculty member of the Data Science Institute and serves on the leadership teams of the Center for Computational Systems Biology (CCSB) and the Vanderbilt Lab for Immersive AI Translation (VALIANT). Her research focuses on structural variation in personal and cancer genomes, single-cell and spatial DNA/RNA sequencing technologies, and the analysis of neural circuits in both biological and artificial systems, using methods at the intersection of computational biology, neuroscience, and machine learning. Maizie earned her undergraduate degree from Huazhong University of Science and Technology (HUST), an MSc in Computer Science from Wake Forest University, a PhD in Neuroscience from Wake Forest School of Medicine, and a PhD in Computer Science from Stanford University. Her doctoral work at Stanford was supported by fellowships from the U.S. National Institute of Standards and Technology and the Enlight Foundation. She joined the Vanderbilt faculty in August 2020 and is the recipient of an NIH Outstanding Investigator Award (R35).
