Anne de Jong is a researcher in the Molecular Genetics department at the University of Groningen, specializing in bioinformatics and computational biology. Her work focuses on developing user-friendly pipelines and web servers for integrating data mining and statistics, particularly in bacterial genetics and transcriptomics. She contributes to tools like BAGEL3, PePPER, and Genome2D, which aid in analyzing prokaryotic genome and transcriptome data. Her group utilizes Linux servers to manage large datasets from techniques such as Next Generation Sequencing and proteomics analysis. Her research interests include RNA folding, biospectroscopy, and translating big-data into biological knowledge. She has published extensively on bacteriocin detection, promoter prediction, and data visualization frameworks for prokaryotic systems biology.
Suhail Ashraf is a researcher at the University of Bielefeld, affiliated with both the Faculty of Biology and the Center for Biotechnology (CeBiTec). His primary research unit is the Plant Genetics and Genomics Group, where he investigates plant-microbe interactions and develops biological solutions for agricultural challenges. Based in office UHG G0-107, he maintains active research collaborations across multiple disciplines. Dr. Ashraf's research spans several critical areas in plant science and agricultural biotechnology. His work demonstrates expertise in both experimental and computational approaches to solving agricultural problems. He has made significant contributions to understanding plant-pathogen interactions, developing biological control agents, and applying genomic techniques to crop improvement. His research shows particular strength in integrating laboratory findings with computational modeling to develop practical agricultural solutions, especially for banana cultivation and other economically important crops. Analysis of his publication record reveals a strong focus on utilizing beneficial microorganisms like Bacillus species for crop protection. His work spans nematode management, antifungal compound discovery, viral resistance in plants, and pesticide biodegradation. The interdisciplinary nature of his research connects microbiology, genomics, computational biology, and agricultural science to address real-world farming challenges while promoting sustainable practices. Dr. Ashraf maintains active collaborations within the University of Bielefeld's research ecosystem, particularly with the Center for Biotechnology which serves as a hub for interdisciplinary life science research. His work contributes to Bielefeld's strategic focus areas that bridge fundamental research with practical applications in agriculture and environmental sustainability.
Sigrid Neuhauser is a Full Professor (Univ.-Prof. Mag. Dr.) at the Institute of Microbiology , University of Innsbruck , where she also serves as Deputy Head of the Institute. She is a START-Fellow of the Austrian Science Fund (FWF) and leads a vibrant research group focused on Phytomyxea , phytopathology, and marine and soil microbiology. Education & Career: Current: Professor and Deputy Head, Institute of Microbiology, University of Innsbruck START-Fellow, Austrian Science Fund (FWF) Research Interests: Prof. Neuhauser’s work spans taxonomy and systematics of Phytomyxea , host-parasite interactions , phytopathology , and marine and soil microbiology . Her group uses cutting-edge molecular tools, including transcriptomics and single-cell RNA visualization (FISH), to unravel how biotrophic parasites interact with their hosts at the cellular level. She has pioneered studies on Plasmodiophora brassicae (clubroot) and Maullinia parasites of brown algae, and she leads the EU Marie Curie ITN ALFF – Algal friends and foes . Scientific Awards: START-Fellowship, Austrian Science Fund (FWF) Leadership & Grants: Principal Investigator, HiPhy – Understanding Phytomyxid-Host-Interactions (START-Grant, FWF) Partner, ALFF – Algal friends and foes (EU Marie Curie ITN) Coordinator, YETIS: Yeast Endophytes of VitIS sp. (applied research) Labs & Teams: She heads the Phytomyxea Research Group at the Institute of Microbiology, comprising post-docs, PhD students, and technical staff. The group operates state-of-the-art microscopy, molecular biology, and bioinformatics facilities.
Dr. John D. Fryer is a Professor and the inaugural Director of the Center for Accelerated Nanotherapeutics at the Translational Genomics Research Institute (TGen) within the Bioinnovation and Genome Sciences Division. His research focuses on translational neuroscience with emphasis on Alzheimer's disease and related dementias, neuroinflammation mechanisms, and development of novel biologics including nanobodies and picobodies. Dr. Fryer's laboratory pursues NIH-funded research at the intersection of genetics, aging, and neuroinflammation. His work spans multiple critical areas of neuroscience including: Alzheimer's disease and related dementias, particularly the inflammatory aspects of neurodegeneration Nanobody and picobody development for therapeutic targeting of disease-critical proteins Sepsis and acute inflammation and their impact on brain function in aged individuals Psilocybin and mood disorders, studying differential brain responses to micro- versus macro-dosing Brain tumor interactions with the immune system and neurons, including the intriguing inverse relationship between Alzheimer's disease and brain tumor susceptibility Analysis of Dr. Fryer's publication record reveals a strong focus on APOE variants, microglial responses in neurodegeneration, and innovative biologics development. His research increasingly integrates multi-omic approaches including single-cell RNA sequencing and spatial transcriptomics to uncover novel therapeutic targets. His lab has developed searchable databases like www.fryerlab.com/ribotag and https://fryerlab.shinyapps.io/LBD_CWOW/ to share research data with the scientific community. Dr. Fryer has published extensively in high-impact journals including Nature Immunology, Nature Neuroscience, Science Translational Medicine, Science, and Neuron. His recent work on nanobody development for targeting Alzheimer's pathology represents a promising translational approach with potential clinical applications. His laboratory maintains active collaborations, as evidenced by the extensive co-author networks in his publications, and continues to secure NIH funding for innovative neuroscience research addressing critical challenges in neurodegenerative disease, brain inflammation, and novel therapeutic development.
Jonathan Keats, Ph.D., serves as an Assistant Professor at the Translational Genomics Research Institute (TGen) and holds the position of Scientific Director at the Judy and Bernard Briskin Center for Multiple Myeloma Research, City of Hope. Additionally, he directs TGen's Bioinformatics division and the Collaborative Sequencing Center, which provides advanced genomic sequencing services to researchers worldwide. His work bridges cutting-edge genomics with clinical applications in cancer. Dr. Keats' educational journey includes: Ph.D. from the University of Alberta (2005), where he investigated the clinical and biological consequences of the t(4;14)(p16;q32) translocation in multiple myeloma under Dr. Linda Pilarski. Postdoctoral training at the Mayo Clinic with Dr. Leif Bergsagel, focusing on identifying novel genetic events in multiple myeloma pathogenesis. His research is predominantly centered on multiple myeloma, with secondary interests in other hematological malignancies and immunodeficiency syndromes. Dr. Keats employs innovative genomic and bioinformatic methodologies to dissect the molecular underpinnings of these diseases, particularly targeting pathways like NF-kB that drive tumor development, progression, and therapeutic resistance. His work aims to translate genomic discoveries into immediate clinical impact, aligning with TGen's ethos of 'helping someone today.' Analysis of Dr. Keats' publication record reveals a trajectory from early investigations of specific cytogenetic abnormalities (e.g., t(4;14)) to comprehensive genomic analyses. His landmark studies include the identification of NF-kB pathway mutations and participation in the Multiple Myeloma Genomics Initiative. Recent work leverages advanced sequencing technologies to uncover structural variants and epigenetic modifications, demonstrating a consistent focus on integrating multi-omics data to refine disease subtyping and identify therapeutic vulnerabilities in myeloma. Dr. Keats leads the Collaborative Sequencing Center at TGen, which utilizes state-of-the-art short-read, long-read (Oxford PromethION), and intermediate-read sequencers to provide gapless human genome assemblies. His laboratory at the Briskin Center conducts research on multiple myeloma genomics and is the lead site for the CoMMpass study, which has identified distinct myeloma subtypes based on genomic profiles. This work directly informs precision medicine approaches for myeloma patients.
Daylon James is an Assistant Professor in Stem Cell Biology at Weill Cornell Medicine , where he serves as Director of the Reproductive Endocrinology Laboratory and Manager of the Tri-Institutional Stem Cell Derivation Laboratory . His research program focuses on regenerative medicine and cell-based therapies for infertility , leveraging expertise in stem cell biology and xenograft models. PhD: The Rockefeller University Postdoctoral Training: Weill Cornell Medical College Research interests include: Stem cell differentiation for vascular and ovarian tissue regeneration Modified RNA delivery systems for fertility preservation Xenograft models in reproductive endocrinology Lipid metabolism in pluripotent stem cells Angiogenic and paracrine mechanisms for graft survival His publications reveal a focus on ovarian reserve disorders , anti-Müllerian hormone (AMH) applications , and endothelial cell engineering , with recent work exploring JAK inhibition for chemotherapy protection and cytoskeletal regulation in oocyte maturation. Labs and teams: Reproductive Endocrinology Laboratory Tri-Institutional Stem Cell Derivation Laboratory
Carman Man-chung Li is an Assistant Professor of Cancer Biology at the Perelman School of Medicine, University of Pennsylvania. He is also an Assistant Investigator at the Abramson Family Cancer Research Institute and a Core Investigator at The Basser Center for BRCA. His research focuses on hereditary cancer mechanisms, particularly how heterozygous loss-of-function mutations in tumor suppressor genes drive early tumorigenesis beyond the classical 'two-hit' hypothesis. Education: A.B. in Molecular Biology (High Honors), Princeton University (2009); Ph.D. in Biology, Massachusetts Institute of Technology (2015) Using genetically engineered mouse models, organoid cultures, and multi-omics, his lab investigates gene haploinsufficiency effects, epigenetic alterations, and stromal-epithelial interactions. Recent work includes mapping early tumor drivers in BRCA1-mutant models and cross-ancestry cancer risk stratification. Scientific Affiliations: Abramson Family Cancer Research Institute The Basser Center for BRCA Graduate Groups: Pharmacology, Cell and Molecular Biology His lab collaborates with the Penn Medicine Biobank, VA Million Veterans Program, and EDISYN Consortium for translational studies in Li-Fraumeni Syndrome and BRCA-related cancers. Funding sources include the National Cancer Institute, Prostate Cancer Foundation, and Li Fraumeni Syndrome Association.
Professor Dennis Lo is a renowned Hong Kong molecular biologist and the Vice-Chancellor and President of the Chinese University of Hong Kong (CUHK) since 2025. He holds the Li Ka Shing Professor of Medicine title and serves as Associate Dean (Research) and Director of the Li Ka Shing Institute of Health Sciences at CUHK's Faculty of Medicine . His groundbreaking work in non-invasive prenatal testing (NIPT) and cell-free fetal DNA detection has revolutionized prenatal diagnostics and cancer genomics. Research Interests : Dennis Lo's research spans molecular biology, focusing on cell-free DNA analysis in maternal blood plasma for prenatal testing and cancer profiling. He pioneered NIPT using next-generation sequencing (NGS) and DNA methylation differences to detect fetal and tumor mutations. His work also includes SARS virus sequencing during the 2003 outbreak and exploring fetal RNA expression in maternal circulation. Key Awards : Breakthrough Prize in Life Sciences (2021) Royal Medal (2021) Lasker-DeBakey Clinical Medical Research Award (2022) Future Science Prize (2016) Scientific Leadership : Lo co-founded biotech companies Cirina (acquired by GRAIL) and Xcelom to commercialize his discoveries. He serves as Associate Editor of Clinical Chemistry and has held leadership roles in Hong Kong's Technology and Innovation Functional Constituency.
Hao Wu, PhD is an Associate Professor of Genetics at the University of Pennsylvania's Perelman School of Medicine. He is a Core member of the Penn Epigenetics Institute, Member of the Penn Cardiovascular Institute (CVI), Member of the Penn Institute of Regenerative Medicine (IRM), and Member of the CHOP Center for Mitochondrial and Epigenomic Medicine (CMEM). His educational background includes: B.S. in Biological Sciences and Biotechnology from Tsinghua University (2002) Ph.D. in Epigenetic regulation of neural stem cell differentiation from University of California Los Angeles (2009) Dr. Wu's research focuses on understanding how epigenetic processes regulate gene expression to establish diverse cell types and respond to environmental signals. His lab combines experimental approaches with bioinformatics to study cell-type specification and maturation from mammalian stem cells, particularly in cardiovascular and neural lineages. The Wu lab investigates molecular mechanisms regulating the interaction between environment and epigenome, and how extrinsic signals modify epigenetic marks to influence development or disease. Their long-term goal is to quantitatively analyze and engineer cell-type specific epigenomes to inform therapeutic approaches for human diseases. Analysis of Dr. Wu's recent publications reveals a strong focus on single-cell and spatial multiomic technologies to investigate epigenetic regulation. His work spans cardiovascular biology, neuroscience, and stem cell biology, with emphasis on DNA methylation/demethylation, transcriptional control, and development of novel genomic sequencing methods. Recent publications highlight advances in single-cell epigenomic profiling, time-resolved RNA sequencing, and their application to neural development, cardiac maturation, and disease mechanisms. As a mentor, Dr. Wu oversees a diverse research group including postdoctoral fellows, graduate students, research specialists, and undergraduates. His lab members work on various projects related to epigenomic profiling, epigenome editing, and single-cell technologies. Dr. Wu's research is supported by grants enabling the development of innovative genomic technologies and their application to understand fundamental biological processes. The Wu Lab maintains a collaborative environment focused on developing high-precision single-cell epigenomic profiling methods, novel epigenome editing tools, and time-resolved single-cell RNA sequencing approaches. Current projects investigate neural development, cardiac lineage specification, and the environment-epigenome interaction, bridging fundamental epigenetic mechanisms with translational applications.
Sophia Tsoka is a Reader in Bioinformatics at King's College London specializing in computational genome analysis, network reconstruction, and machine learning applications in cancer immunology and microbiome research. She leads the 'Algorithms for Antibodies' project funded by the Royal Society and serves as Co-Investigator on multiple research projects including 'Understanding the significance of patient B cells and expressed antibodies in melanoma' supported by the British Skin Foundation. Dr. Tsoka's research focuses on computational genome analysis, genome data mining, network analysis and reconstruction, metabolic networks, protein interaction networks, and the evolution of genome properties and dynamics. Her work bridges bioinformatics, machine learning, and immunology, with particular emphasis on applying computational approaches to understand antibody mechanisms, tumor microenvironments, and microbiome dynamics. She has developed innovative algorithms for network analysis, classification, and multi-omics data integration that have advanced our understanding of complex biological systems. Her recent publications demonstrate a strong trajectory in applying computational methods to cancer immunology, with multiple high-impact papers in 2025 spanning IgE antibody therapeutics, tumor microenvironment analysis, and machine learning approaches for biomedical data. These works reveal a consistent focus on developing interpretable computational models that can translate complex biological data into clinically relevant insights. Best paper award (2022) Best Paper Award (2020) Dr. Tsoka supervises numerous research projects and has secured significant grant funding from prestigious organizations including the Royal Society and British Skin Foundation. Her collaborative work spans multiple disciplines, connecting computational scientists with immunologists and clinicians to advance cancer therapeutics. She has established herself as a key contributor to the field of computational immunology with over 4,800 citations to her work. Her laboratory focuses on developing and applying advanced computational methods for analyzing complex biological networks, with particular emphasis on cancer immunology applications. The team combines expertise in algorithm development, machine learning, and biological data analysis to address challenging problems in antibody engineering and tumor microenvironment characterization.
Professor Huiru (Jane) Zheng is a Professor of Computer Science at the School of Computing, Ulster University. She serves as Theme Leader of Data Analytics and Systems in the AI Research Centre and is a full member of the Computer Science Research Institute. As a Fellow of the UK Higher Education Academy and Senior Member of IEEE, she has established herself as a leading researcher with significant contributions to bioinformatics and healthcare informatics. Her educational background includes: PhD in Bioinformatics (2003) from Ulster University Postgraduate Certificate in Teaching in Higher Education (2005) from Ulster University Professor Zheng's research spans multiple domains of data science with applications in healthcare, agriculture, and environmental monitoring. Her primary interests include integrative data analytics in systems biology, machine learning for healthcare decision support, and assistive technology development. She has particular expertise in applying advanced data mining techniques to complex biological datasets, with a focus on improving healthcare outcomes and supporting independent living through technology. Her extensive publication record demonstrates a clear trend toward interdisciplinary research that bridges computer science with practical applications. Recent work shows increasing focus on real-world implementations of AI in digital health, precision agriculture, and environmental monitoring systems. The breadth of her research interests is evident in publications ranging from gait analysis using smart insoles to methane prediction in dairy farming and wildfire monitoring using UAVs. Professor Zheng has received notable recognition for her contributions: Fellow of the UK Higher Education Academy Senior Member of IEEE As a principal investigator, Professor Zheng has successfully secured substantial research funding from diverse sources including EPSRC, TSB, DEL, NHS, Invest NI, Innovation UK, and the European Commission. Her leadership extends to editorial roles for international journals and organizing major conferences such as the UK Workshop on Computational Intelligence. She has supervised numerous research students through her various projects. Professor Zheng leads several active research initiatives including the AI Research Centre's Data Analytics and Systems theme. Her current projects involve developing digital twin technology for personalized healthcare, AI-assisted systems for post-stroke rehabilitation, methane prediction models for sustainable dairy farming, and age-friendly built environment assessment systems. These projects often involve multidisciplinary collaboration across computer science, healthcare, agriculture, and environmental science domains.
Kristian Gurashi is a Postdoctoral Researcher in the Mead Group at the University of Oxford, focusing on Normal and Malignant Haematopoietic Stem Cell Biology . His work integrates multiomic approaches to study myeloid malignancies such as chronic myelomonocytic leukemia (CMML), myelofibrosis, and myelodysplastic syndromes (MDS). Key Research Areas: Haematopoietic Stem Cell Biology Myeloid Malignancies Single-Cell Genomics Organoid Modeling Precision Medicine Immune Suppression Mechanisms His recent publications highlight the application of single-cell sequencing , spatial transcriptomics , and organoid models to dissect clonal evolution, microenvironmental interactions, and therapeutic resistance. Notable trends include studies on TP53 mutations , JAK2 allelic burden , and MDMX expression as prognostic and therapeutic indicators. His work also explores inflammatory signaling and aberrant cell communication in disease progression.
Alisa Yurovsky is a Lecturer and IDEA Fellow at the Department of Biomedical Informatics at Stony Brook University. Her research focuses on computational biology at the intersection of computer science, genetics, and statistics to advance precision medicine. She earned her PhD in Computer Science from Stony Brook University under Steven Skiena, an M.S. in Computational Biology from EPFL under Bernard Moret, and a B.S. in Computer Science with a Mathematics minor from Carnegie Mellon University. Research Interests: Alisa develops computational algorithms for precision medicine applications, particularly addressing racial disparities in health data. Her current projects include compartment deconvolution in mixed tissue samples, spatial transcriptomics analysis, small-size differential expression studies, and machine learning in biomedical informatics. Publications Trends: Her recent work spans spatial transcriptomics, survival analysis, and machine learning applications in genomics. She has contributed to algorithms for cell-cycle phase detection, ribosomal frameshift identification, and non-negative matrix factorization. Scientific Awards: 2023 IDEA Fellowship 2020 NSF/CRA/CCC Computing Innovation Postdoctoral Fellow 2017 CEWIT Best Poster Award 2016 NSF Graduate Research Fellowship 2010 Prix Annaheim-Mattille for Master’s Thesis 2007 Cadence Design Systems Award Advising and Grants: She mentors undergraduate and high school students through Stony Brook’s VIP Webgen team and Simons Summer Research Program. Her research has received support from NSF and institutional grants.
Mehdi Damaghi, PhD is an Assistant Professor in the Department of Pathology at the Renaissance School of Medicine, Stony Brook University, and a faculty member at the Stony Brook Cancer Center. His research focuses on understanding cancer through ecological-evolutionary principles, with particular emphasis on breast and ovarian cancers. Dr. Damaghi received his BSc in Cell & Molecular Biology-Genetics from Chamran University in Iran (1998-2002), his MSc in Biochemistry from Tarbiat Modares University in Iran (2002-2005), and his PhD in Cell Biology and Genetics from the Max Planck Institute in Dresden, Germany (2008-2012). He completed postdoctoral training at the Moffitt Cancer Center (2012-2017) and served as a Research Scientist (2017-2021) and Instructor (2021) there before joining Stony Brook University. Dr. Damaghi's research applies ecological and evolutionary principles to understand cancer initiation, progression, and metastasis. His lab investigates how tumor cells adapt to variable microenvironments, leading to metabolic reprogramming linked to epigenetics and transcription factors. He studies the interplay between tumor cells and their microenvironment, which drives genotypic heterogeneity and phenotypic plasticity. His work integrates single-cell multi-omics approaches (genome, epigenome, transcriptome, proteome, and metabolome) with pathomics analysis to capture cancer cell heterogeneity in their natural context. An analysis of Dr. Damaghi's recent publications reveals a strong focus on cancer metabolism, particularly how tumor acidosis and hypoxia influence cancer progression and treatment resistance. His work bridges evolutionary biology with cancer research, examining how selective pressures in the tumor microenvironment drive adaptive changes in cancer cells. Key themes include metabolic reprogramming, lysosomal function in acidic environments, and the application of ecological principles to improve cancer treatment strategies, especially for breast and ovarian cancers. NIH/NCI U01CA261841-01 as PI: "Ecology and Evolution of Breast Cancer" (2021-2026) NIH/NCI R01CA249016-01 as Co-PI: "Radiomics and Pathomics to Predict Progress of DCIS Lesion" (2021-2026) Dr. Damaghi actively mentors students and researchers, with openings available for MSc, PhD, MD/PhD, and postdoctoral positions in his lab. His lab, the Damaghi Research Lab, focuses on four main projects: 1) Ecology and Evolution of Breast Carcinogenesis, 2) Metabolic phenotypes in DCIS to stratify disease progression, 3) Co-evolution of tumor and stroma in breast cancer, and 4) Evolution of resistant phenotype to PARPi in ovarian cancer.
Rohit Singh is an Assistant Professor at Duke University with appointments in the Departments of Biostatistics & Bioinformatics, Cell Biology, Computer Science, and Electrical and Computer Engineering. He is a member of the Duke Cancer Institute and the Division of Integrative Genomics. Academic Rank : Assistant Professor Departments : Biostatistics & Bioinformatics, Cell Biology, Computer Science, Electrical and Computer Engineering University Affiliations : Duke Cancer Institute, Division of Integrative Genomics His research focuses on computational biology, machine learning for drug discovery, and decoding disease mechanisms through single-cell genomics and protein language models. Current projects include SAME, Velorama, Raygun, and Allo-Allo. Recent publications span single-cell analysis, protein language models, and causal inference. He develops tools like Schema , D-SCRIPT , and Sceodesic . Grants : NIAID, Chan Zuckerberg Initiative, Foundation for Prader-Willi Research Awards : MIT Sprowls Award, Stanford Stephenson Award, RECOMB Test of Time Award He advises students in computational biology and has contributed to patents in protein interaction modeling and viral detection. The lab welcomes postdocs, rotation students, and collaborators.
