Find Academic Supervisors

• Epidemiology
• Genetics
• Medical Biochemistry
• +3 more

Dr. Olga Chervova is a Senior Research Fellow at University College London's Department of Epidemiology & Public Health, specializing in epigenetics and its applications to human health. Her work bridges pure mathematics and clinical sciences, with a focus on cardiovascular medicine, genetics, and oncology. Education: PhD in Pure Mathematics (University College London, 2012); MSc in Clinical Sciences (Saratov State University, 2006). Research interests include epigenetic mechanisms in disease, particularly DNA methylation's role in cancer evolution, cardiovascular conditions, and age-related pathologies. She has pioneered studies on epigenetic clocks, linking accelerated aging metrics to diseases like sarcoma and myocardial infarction. Her work also addresses global health challenges, such as studying war exposure effects in Syrian refugees through epigenetic biomarkers. Key research trends in her publications include integrating multi-omics data (genomic, transcriptomic, epigenetic) for precision medicine, developing diagnostic classifiers using epigenetic signatures, and applying machine learning to interpret clinical time-series data. Her studies often emphasize longitudinal analyses and population-based cohorts. Labs/Teams: Co-founder of the Personal Genome Project-UK (PGP-UK), an open-access multi-omics resource supporting personalized medicine research. She collaborates with international teams on lung cancer evolution and epigenetic biomarker development.

Jun SongView profile

Professor

• Computational Biology
• Genomics
• Epigenetics
• +3 more

Jun Song is a Professor affiliated with multiple departments at the University of Illinois at Urbana-Champaign, including the Department of Statistics, Department of Physics, Carl R. Woese Institute for Genomic Biology, and holds the title of Founder Professor in Physics. His research focuses on computational biology, statistical genetics, and systems biology, with emphasis on understanding regulatory genomics and epigenetic mechanisms. Song leads a research group developing innovative tools for analyzing genomic data, such as CHANCE, NSeq, and ClusterEnG. His work includes studies on chromatin structure, CRISPR prime editing efficiency, cancer-associated genetic variants, and the molecular basis of schwannoma development. He has contributed to high-impact journals in computational biology and genetics. Song’s lab actively investigates barriers to induced pluripotent stem cell (iPSC) generation through genome-wide screens and functional genomics approaches. Key research themes involve integrating multi-omics data, modeling epigenetic evolution in cancer, and developing statistical methods for interpreting regulatory variation. His collaborative projects span epigenetics, cancer biology, and computational tool development for genomics research.

Mariola FerraroView profile

Associate Professor

• Bacterial Infections
• Extracellular Vesicles
• Ubiquitin Pathways
• +4 more

Mariola Ferraro is an Associate Professor in the Department of Microbiology & Cell Science at the University of Florida. Her research focuses on microbial pathogenesis, extracellular vesicles, and host defense mechanisms. She investigates how pathogens such as Salmonella , Staphylococcus aureus , and Yersinia interact with host cells, particularly through ubiquitin pathways and extracellular vesicle-mediated communication. Her work integrates proteomics, immunology, and space biology to study bacterial adaptation under stress conditions like microgravity. Recent studies include the impact of simulated microgravity on Enterobacter cloacae proteomes and the role of cannabinoid receptors in modulating immune responses during infections. Ferraro’s research also explores novel vaccine strategies using extracellular vesicles isolated from wastewater-derived Salmonella strains. Her interdisciplinary approach bridges fundamental biology with translational applications, including drug development targeting deubiquitinating enzymes (DUBs) and exosome-based therapies for neurological disorders. Publications highlight her expertise in multi-omics data analysis, lipidomic profiling, and the development of standardized protocols for extracellular vesicle research (MISEV2023). Collaborations span microbiology, immunology, and space science, reflecting her commitment to advancing understanding of pathogen-host interactions in diverse environments.

Loïc LannelongueView profile

Research Fellow

• Environmentally sustainable computing
• Biomedical data science
• Carbon footprint of computational research
• +3 more

Loïc Lannelongue is a Senior Research Associate at the University of Cambridge, affiliated with the Cardiovascular Epidemiology Unit in the Department of Public Health and Primary Care (School of Clinical Medicine) and the VPD Heart and Lung Research Institute. He serves as a Postdoctoral Associate at Jesus College and is a visiting scientist at EMBL-EBI, leading research at the critical intersection of sustainable computing and biomedical data science. His academic foundation includes: BSc from ENSAE Paris (2017) MSc (Diplôme d’Ingénieur) from ENSAE Paris (2018), majoring in mathematics and statistics with minors in theoretical physics and economics MSc in Statistics and Machine Learning from the University of Oxford (2018) PhD in Health Data Science from the University of Cambridge (2022), supervised by Prof Michael Inouye under MRC-DTP funding Lannelongue’s pioneering work centers on environmentally sustainable computational science , developing tools like the Green Algorithms calculator to quantify and reduce computing’s carbon footprint. His research bridges hardware infrastructure limitations with scientific practice, focusing on AI ethics, carbon accounting methodologies, and policy frameworks like the GREENER Principles. Concurrently, he integrates machine learning with genomics and medical imaging to decode cardiovascular disease mechanisms, exemplified by his Nature-published multi-omic trait atlas. His publication portfolio reveals a decisive shift toward sustainability metrics in computational research, with 2023 marking a peak in high-impact policy-oriented work (Nature journals, Heidelberg Agreement). Key trends include standardizing carbon footprint methodologies, advocating for green procurement in research funding, and translating environmental impact data into actionable researcher guidelines through the Green DiSC certification. Accolades recognizing his dual impact: Susannah Boddie Impact of the Year Award (Health Data Research UK, 2024) for sustainability leadership Software Sustainability Institute Fellowship (2023) Cambridge University Sport’s Outstanding Contribution Award (2022) As an educator, he supervises Computer Science tripos courses in statistics, AI, and bioinformatics while mentoring through the Green Algorithms Initiative. His research is supported by MRC-DTP funding during his PhD and ongoing institutional backing for the Lannelongue Group, which collaborates with European funders to embed sustainability criteria in grant frameworks. He directs the Lannelongue Group—a hub for Environmentally Sustainable Computational Science—managing the Green Algorithms Initiative (open-source carbon calculators), Green DiSC certification scheme, and ESCS Community. The group’s work bridges hardware engineers and domain scientists to operationalize sustainability, evidenced by tools adopted globally and policy agreements like the Heidelberg Agreement signed by major European research funders.

Li MaView profile

Professor

• Statistical Genetics
• Population Genetics
• Genomic Selection
• +5 more

Dr. Li Ma is a Professor in the Department of Animal & Avian Sciences at the University of Maryland, College Park. His research focuses on statistical genetics, population genetics, and genomic selection in livestock, particularly dairy cattle. He develops computational tools to enhance genetic studies using next-generation sequencing data, with applications in improving livestock productivity and disease resistance. His work includes analyzing recombination patterns and PRDM9 alleles in dairy cattle breeding, genomic selection strategies using large-scale datasets, and integrating multi-omic data for precision livestock breeding. He also explores big data analytics in agricultural genomics. Notable research areas include sequence-based genomic discovery, genetic architecture of complex traits, and applying machine learning to genetic studies. His recent articles (2024–2025) show interdisciplinary work in financial econometrics, addressing topics like risk management, stochastic volatility modeling, and option pricing strategies. This dual focus suggests expertise spanning both biological and quantitative finance domains. Dr. Ma collaborates on projects involving genomic databases, statistical method development, and computational biology tools. His work bridges genetic research and advanced financial modeling, though specific student advisement details are not listed here.

Tanja LaskeView profile

Researcher

• Systems Medicine
• Computational Biology
• Virology
• +3 more

Dr. Tanja Laske is a Group Leader at the Cosy.Bio lab within the Faculty of Mathematics, Informatics and Natural Sciences at the University of Hamburg. Her research focuses on computational systems biology, integrating multi-omics data with mechanistic models and machine learning to address challenges in systems medicine. She holds a PhD in Bioprocess Engineering from the Max Planck Institute for Dynamics of Complex Technical Systems and a Master’s in Biosystems Engineering from Otto von Guericke University Magdeburg. Her work includes experimental studies on adenoviral vector production and mathematical modeling of virus replication dynamics. Her research interests span systems biology, virology, and bioprocess optimization. Key projects include analyzing defective interfering particles in influenza viruses, modeling virus-host interactions, and developing algorithms for differential co-expression analysis (e.g., DRaCOon). She also explores metabolic dysregulation in diabetic bone regeneration and proteomic biomarker discovery for bone-related diseases. Recent work emphasizes computational methods like FedProt for federated proteomic analysis and ProHarMeD for drug repurposing. Her publications highlight contributions to normalization techniques in proteomics, patient stratification via UnPaSt, and mechanistic modeling of viral dynamics. No scientific awards or grants are explicitly listed. She advises no students, though her group contributes to collaborative research projects. Her lab, Cosy.Bio, focuses on systems medicine applications and interdisciplinary approaches to biomedical challenges.

Silke Van ElferenView profile

Researcher

• Biomarkers
• Cardiomyopathies
• DNA Methylation Analysis
• +3 more

Silke Van Elferen is a Research Associate at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, within the Computational Systems Biology department. She is concurrently completing a Master's degree in Drug Development and Neurohealth at Maastricht University. Her research focuses on biomarker identification in cardiomyopathies and federated DNA methylation data analysis, conducted under collaborations with CoSy.Bio. Education: BSc (Hons) in Forensic Science, Anglia Ruskin University MSc in Systems Biology, Maastricht University (thesis: federated DNA methylation workflow implementation) Research Interests: Silke specializes in translational bioinformatics, particularly in developing computational frameworks for multi-omics data integration. Her current work targets tissue-specific and blood-based biomarkers for cardiomyopathies, leveraging federated learning approaches to enhance data privacy in genomic studies. She also explores applications of systems biology in drug development pathways. Labs/Teams: Active contributor to CoSy.Bio, collaborating with Dr. Olga Tsoy and Lena Hackl on thesis projects. Previously mentored by Dr. Olga Zolotareva during her MSc thesis.

• Pain Management
• Sleep Disorders
• Machine Learning in Healthcare
• +5 more

Carl Saab is an Adjunct Professor of Engineering at Brown University's School of Engineering. His research focuses on neurophysiological mechanisms of pain and sleep, leveraging machine learning, quantum computing, and advanced neuroimaging techniques. He explores biomarker discovery for chronic conditions like neuropathic pain and migraine, and develops innovative treatments such as spinal cord stimulation protocols. Education details are not explicitly provided in the text, but his extensive publication record indicates expertise in neuroscience, biomedical engineering, and computational biology. His work bridges clinical applications and technological innovation, addressing challenges in pain management, sleep medicine, and neurological disorders. Research interests include: Neuropathic pain mechanisms and therapies Sleep-disordered breathing and cardiovascular risk stratification Machine learning in biomarker identification Quantum computing's role in multi-factorial disease analysis EEG/EMG-based pain assessment systems Recent publications emphasize foundational models for clinical decision-making, spinal cord stimulation efficacy, and migraine neurophysiology. Though awards are not listed, his prolific output suggests significant contributions to translational neuroscience. Advising and grant details are not specified, but his work likely involves interdisciplinary collaborations across engineering and medical fields.

Yike ShenView profile

Assistant Professor

• Environmental Health Sciences
• Microbiome Research
• Environmental Toxicology
• +3 more

Dr. Yike Shen is an Assistant Professor of Earth and Environmental Sciences at the University of Texas at Arlington. Their research focuses on integrating environmental exposures, multi-omics data, and health outcomes through computational precision environmental health. They hold a PhD in Environmental Science & Policy and Environmental Toxicology from Michigan State University and a BS in Environmental & Conservation Sciences from the University of Alberta. Research interests include environmental health sciences, microbiome studies, and computational methods for risk assessment. They have published extensively on topics like pesticide dissipation, chemical ecotoxicity prediction, and cohort networks for data-driven discovery. Notable awards include the University of Texas System Rising STARs award and the College of Agriculture and Natural Resources Food Systems Fellowship. Dr. Shen has secured multiple grants, including a federal HUD grant for indoor air quality interventions and a USDA-funded summer internship program. They teach courses like Machine Learning for Environmental Science and Environmental Data Science. Their lab, the Shen Research Group, emphasizes interdisciplinary approaches to environmental health challenges.

• Cancer Genomics
• Transcriptomics
• Next-Generation Sequencing
• +4 more

Stephen Madden is Senior Lecturer in Computational Biology at RCSI's Data Science Centre, holding a PhD from University College Dublin. His research applies bioinformatics and statistical approaches to cancer genomics and precision medicine. Key research areas include multi-omics integration in breast cancer, therapeutic target discovery, and predictive modeling of treatment response. Recent work examines extracellular vesicles in neonatal development, immunothrombosis mechanisms in hematologic malignancies, and implantable sensor technologies. Publications demonstrate expertise in proteomic profiling, preclinical model development, and computational method implementation. Teaching focuses on programming, transcriptomics, and genomics for biomedical students. He serves as Deputy Director for the MSc in Technologies and Analytics in Precision Medicine and supervises PhD projects in cancer systems biology.

Susanta BehuraView profile

Assistant Professor

• Computational Biology
• Developmental Genomics
• Fetal Brain Development
• +3 more

Dr. Susanta Behura is an Assistant Professor in the Division of Animal Sciences at the University of Missouri's College of Agriculture, Food and Natural Resources. His research integrates computational approaches with multi-omics data to investigate fundamental questions in animal genetics, genomics, and reproductive biology. Research Focus: Computational analysis of fetal brain development pathways Epigenetic regulation of brain-placental axis Single-cell transcriptomics of developmental processes His recent publications utilize transcriptomic, epigenetic, and metabolomic approaches to study sex-biased metabolic regulation and placental adaptation in mammalian development. Dr. Behura teaches Functional Genomics of Mammals and mentors graduate students in bioinformatics approaches for biological discovery.

Matti van WelzenView profile

Research Fellow

• Systems Biology
• Bioinformatics
• Inflammation Resolution
• +2 more

Dr. Matti van Welzen is a Research Fellow at the Department of Systems Biology and Bioinformatics at the University of Rostock. His research focuses on computational modeling of biological systems with emphasis on inflammation pathways and disease networks. Current projects include SYLOBIO, MASLD Map, and the Atlas of Inflammation Resolution. He develops network-based approaches for analyzing complex biological processes and disease mechanisms, particularly at the intersection of inflammation, metabolism and aging. Recent publications demonstrate advanced computational techniques for mapping disease phenotypes. Dr. van Welzen collaborates with Biologische Heilmittel Heel GmbH on inflammation research and has presented work at international systems biology conferences.

• Bioinformatics
• Radiation Biology
• Biodosimetry
• +3 more

Sonja Strunz is a researcher at the Department of Systems Biology and Bioinformatics, affiliated with the Faculty of Computer Science and Electrical Engineering at the University of Rostock. Her work focuses on radiation biodosimetry and biomarker discovery through transcriptomic analysis of radiation-exposed human peripheral blood lymphocytes. Doctorate in Computer Science (2007-2014), University of Rostock Masters in Bioinformatics (2000-2007), University of Tübingen (inferred) Her research investigates radiation-induced transcriptional changes, dose-dependent gene expression patterns, and development of computational frameworks for radiation dose prediction. She has contributed to understanding radiation's effects on apoptosis, DNA damage, and cell-cycle regulation through bioinformatic analysis. Analysis of her publications reveals expertise in radiation biology, transcriptomics, and systems modeling. Her work combines p-value/fold-change methods with network approaches to identify robust gene signatures for dose estimation and develops computational workflows for molecular biodosimetry applications. She maintains collaborations with institutions including Forschungszentrum Jülich and University Medical Center Hamburg-Eppendorf, supported by funding from Germany's Federal Ministry for Education and Research (BMBF) and Federal Ministry for the Environment.

Alioune NgomView profile

Professor

• Bioinformatics
• Machine Learning
• Computational Biology
• +3 more

Dr. Alioune Ngom is a Professor and Graduate Program Chair at the School of Computer Science, University of Windsor. He holds a Ph.D. in Computer Science from the University of Ottawa with additional graduate degrees in the field. Dr. Ngom founded Genesis Genomics Inc. and maintains affiliations with this organization. His research spans multiple interdisciplinary domains with primary focus areas including: Bioinformatics and computational biology (DNA microarray analysis, mitochondrial genomics) Machine learning applications (clustering, classification, neural networks) Network optimization (wireless mobile networks, scheduling) Computational methods in biomedicine (protein structure prediction, drug discovery) Recent publications demonstrate strong focus on AI integration in life sciences, particularly graph neural networks for drug discovery and multi-modal learning frameworks. Publication trends show increasing emphasis on transformer architectures, trustworthy AI systems, and clinical applications of deep learning. Dr. Ngom mentors graduate students as evidenced by his role as Graduate Program Chair.

• Systems Biology
• Bioinformatics
• Computational Biology
• +9 more

Mario Lauria is an Associate Professor at the Department of Mathematics of the University of Trento, also affiliated with the Interdepartmental Center for Mind/Brain Sciences (CIMEC). He holds a PhD in Electrical and Computer Engineering from the University of Naples Federico II and conducted postdoctoral research at the University of Illinois and UC San Diego. He has held academic positions at The Ohio State University, TIGEM in Naples, and the Microsoft Research-COSBI Centre. His research focuses on computational methods for biomarker discovery, systems biology, and gene regulatory network analysis. Lauria has coordinated multiple EU and industry-funded projects, including work on metabolic markers of pre-diabetes and systems biology approaches to neurodegenerative diseases. Education: PhD in Electrical and Computer Engineering, University of Naples Federico II (1997) M.S. in Computer Science, University of Illinois at Urbana-Champaign (1996) Laurea in Electrical Engineering, University of Naples Federico II (1992) Research interests emphasize bioinformatics , systems biology , and high-performance computing , with applications to gene regulatory networks, diagnostic biomarkers, and multi-omics data integration. His work bridges computational methods with biological systems, particularly in aging, neurodegenerative diseases, and metabolic disorders. Publications reflect a focus on Alzheimer’s biomarkers , diabetes , and gene network analysis . Key contributions include rank-based biomarker algorithms and consensus clustering methods for metabolic profiles. His interdisciplinary approach spans computational tools (e.g., SCUDO, rScudo) and collaborative projects with pharmaceutical and academic partners. Awards include the 2012 SBV IMPROVER Challenge win and IEEE Senior Membership. He serves on editorial boards for journals like IEEE Transactions on Parallel and Distributed Systems and BMC Bioinformatics . Grants and collaborations include leadership in the EarlyBird pre-diabetes project and contributions to EU-funded initiatives like AnEUploidy . His academic service roles include coordinating the Data Science and Quantitative Biology master’s programs at Trento University. Labs/Teams: Active in the Microsoft Research-COSBI Centre for systems biology and the CIMEC interdisciplinary neuroscience hub.