Dr. Najaf Amin is an Associate Professor in Molecular Epidemiology at the Nuffield Department of Population Health, University of Oxford. She holds advanced degrees including an MSc, DSc, and PhD in genetic epidemiology from Erasmus University Rotterdam, Netherlands (2011). Research Interests: Identifying biomarkers for neuro-psychiatric traits using integrative multi-omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics) and brain-based data Investigating causal relationships via Mendelian Randomization Exploring gut microbiome interactions in mental health and aging Developing aging clocks and studying their health implications across populations Article Trends: Recent work focuses on UK Biobank data ethics, metabolomic influences on depression, and multi-omics approaches to neurodegenerative disease biomarker discovery. The research spans molecular epidemiology, public health, and translational neuroscience. Research Groups: Dr. Amin contributes to studies on aging clocks and the depression-dementia risk nexus within the University of Oxford's Molecular Epidemiology department.
Alejo J Nevado-Holgado is an Associate Professor at the University of Oxford, holding joint appointments in the Department of Psychiatry and the Big Data Institute. He is a key member of Dementia Research Oxford and co-heads the Computational and Molecular Neuroscience laboratory alongside Professor Noel Buckley. His interdisciplinary research team comprises approximately 20 scientists specializing in AI, biochemistry, and bioinformatics, working at the intersection of computational methods and neurological health. Dr. Nevado-Holgado completed his PhD in the University of Bristol, Department of Computer Science, under the supervision of Dr. Rafal Bogacz and Dr. John Terry. His doctoral research focused on mathematical modeling, signal analysis, and machine learning applied to the study of the basal ganglia and Parkinson's disease. Following experimental training at Cambridge, he shifted his focus to applying machine learning and bioinformatics to neurodegeneration research, particularly investigating biomarkers and metabolic networks in Alzheimer's and Parkinson's diseases. Dr. Nevado-Holgado's research program centers on leveraging artificial intelligence and bioinformatics to transform mental health care and drug discovery. His laboratory develops and applies state-of-the-art iPSC, AI, and bioinformatic techniques to better understand neurological disorders. The team's work spans multiple domains including: Neural networks applied to genetics, transcriptomics, proteomics, and iPSC cell microscopy Analysis of Electronic Health Records using natural language processing Integration of biotech laboratory data with real-world clinical evidence Development of personalized medicine approaches and drug repurposing strategies High-performance computing solutions for large-scale biomedical data analysis His recent publication record demonstrates a strong focus on applying AI to mental health diagnostics, neurodegenerative disease biomarkers, and multi-omics data integration. Notable trends include the increasing application of large language models to clinical text analysis, the development of proteomic aging clocks, and the integration of environmental and genetic factors in understanding aging and mortality. His work consistently bridges computational innovation with clinical relevance, seeking to detect neurological disorders in their prodromal stages when interventions could be most effective. Dr. Nevado-Holgado leads or participates in multiple significant research initiatives: Virtual Brain Cloud (EU H2020): €1.5 million European consortium investigating computational simulations for Alzheimer's diagnosis Microbiome in depression (NIH U19): $27 million consortium studying microbiome's role in depression Metabolomics in dementia (MOVE-AD): Consortium investigating metabolomics of dementia Industry projects: £900,000 in funding for applying neural networks to genomics data Blood-brain axis of Alzheimer's disease (AMP-AD): $330,000 project investigating protein levels in Alzheimer's His laboratory team includes lead scientists Laura Winchester (bioinformatics) and Andrey Kormilitzin (AI), along with researchers specializing in genetics, AI microscopy, AI NLP, AI imaging, and iPSC models. The lab actively recruits additional researchers in AI NLP and bioinformatics, reflecting the growing scope of their computational neuroscience work.
Mohammad Samsul Alam is a Postdoctoral Associate in the Department of Biostatistics & Bioinformatics at Duke University. His research focuses on advanced statistical methodologies for analyzing complex biomedical and environmental datasets. Research interests include longitudinal data analysis, multi-omics integration, clustering algorithms, and skewed functional data modeling. His work addresses dynamic risk estimation in Alzheimer's disease, rainfall pattern recognition in Bangladesh, and statistical frameworks for heterogeneous data. Recent publications highlight his expertise in integrative data analysis, with applications in both precision medicine and environmental science. He has co-authored methodological advancements in journals such as Statistics in Medicine and Biometrics .
Bing Ma is an Assistant Professor at the University of Maryland School of Medicine, affiliated with both the Institute for Genome Sciences and the Department of Microbiology and Immunology. As a computational biologist, Dr. Ma specializes in integrating advanced 'omics' technologies to study host-microbe ecosystems. Education: PhD in Computational Biology from University of Wisconsin-Madison Postdoctoral Training: Laboratory of Dr. Jacques Ravel Research focuses on Microbiome analysis using Multi-‘Omics approaches, with particular emphasis on: Microbe-Host Interactions Biomarker discovery Live biotherapeutics development Systems Biology applications GI Health research Recent publications highlight trends in Metagenomics , Metatranscriptomics , and Metabolomics applied to vaginal microbiota characterization and preterm infant gut development.
Dr. Paula Perez Pardo is an Assistant Professor at the Faculty of Science, Utrecht University , specializing in Pharmacology within the Department of Pharmaceutical Sciences. Her research focuses on the microbiota-gut-brain axis in neurological disorders, including Parkinson's disease, Autism Spectrum Disorder (ASD), and Chronic Fatigue Syndrome. She investigates how gut bacteria and their metabolites trigger brain disorders using innovative techniques and collaborates with clinicians, patient organizations, and companies to translate findings into diagnostic biomarkers and therapeutic targets. Education: Molecular Biology (BSc), Autonoma University Madrid Molecular Biology (MSc), Free University Berlin PhD in Pharmaceutical Sciences, Utrecht University (2017) Research Interests center on: Gut-brain communication mechanisms Role of bacterial metabolites (e.g., p-Cresol, butyrate) Neuroimmune interactions in ASD and Parkinson's Food-based therapies and microbiome modulation ADAM protease regulation in neuroinflammation Her recent publications highlight: Mouse strain-specific microbiota responses in autism models TLR4 signaling in Parkinson's disease Prebiotic diets normalizing immune/behavioral defects Machine learning approaches to microbiome signatures Pharmacological validation of TDO inhibition She actively contributes to translational research through collaborations like the GEMMA Consortium and works with international institutions including the Institut de la Recherche Agronomique in Paris.
Caspar Safarlou is a Postdoctoral researcher in Ethics at the Department of Global Public Health and Bioethics of the Julius Center at the University Medical Center Utrecht, and a guest researcher at the Institute for Risk Assessment Sciences (IRAS) of Utrecht University. He is currently completing his PhD, expected in 2025. His academic work bridges philosophy, ethics, and environmental health sciences. Dr. Safarlou's research primarily focuses on the ethical dimensions of exposome research, which examines environmental exposures and their biological responses throughout the human lifespan. His expertise spans Value Theory, Meta-Ethics, Normative Ethics, and Social and Political Philosophy. His work addresses critical issues at the intersection of environmental health, data science, and ethical considerations, with particular attention to the actionability of research findings and participant relations. His publications reveal a strong emphasis on reconceptualizing exposomics as a distinct research program, examining ethical aspects of big data approaches in environmental health, and exploring the philosophical foundations of ethical decision-making in scientific contexts. His work often investigates how ethical frameworks can guide the development and application of new research methodologies in environmental health sciences. Dr. Safarlou has received recognition for his contributions to bioethics, particularly in the emerging field of exposome ethics. His systematic review on the ethical aspects of exposome research has been influential in identifying key ethical themes that require attention in this rapidly developing field. His collaborative work demonstrates strong partnerships with researchers including Karin R. Jongsma, Roel Vermeulen, and Annelien L. Bredenoord, reflecting an interdisciplinary approach that combines philosophical rigor with practical applications in environmental health research.
John P. Wikswo is the University Distinguished Professor at Vanderbilt University, holding appointments in Biomedical Engineering, Molecular Physiology & Biophysics, and Physics. He founded the Vanderbilt Institute for Integrative Biosystems Research and Education (VIIBRE) in 2001, which has become a leader in microfluidic organs-on-chips and systems biology tools. B.A. in Physics, University of Virginia (1970) M.S. and Ph.D. in Physics, Stanford University (1973, 1975) For 47 years, he has pioneered biomagnetic measurements in cardiology, including the first detection of magnetic fields from single axons and cardiac virtual electrode effects. His current work focuses on autonomous robot scientists like Genesis, integrating microfluidics with AI for yeast chemostat studies, and microphysiological systems for drug discovery and toxicology. His 250+ publications and 47 patents span biomedical instrumentation , microfabrication , and interdisciplinary education via the SyBBURE program, which has mentored over 400 students. Awards include the 2017 R&D 100 Award and fellowships in seven societies.
Wanding Zhou is an Assistant Professor at the Perelman School of Medicine, University of Pennsylvania, and affiliated with Children's Hospital of Philadelphia in the Department of Pathology and Laboratory Medicine. His research focuses on computational epigenetics, leveraging DNA methylation as a robust readout of chromatin state and cell identity. Ph.D. in Bioengineering from Rice University (2013) His work spans epigenetics and chromatin , bioinformatics and genomics , with applications in cancer genetics , developmental genetics , and human genetics . Zhou develops computational methods for DNA methylation assays, including Infinium microarrays and bisulfite-sequencing, targeting single-cell and low-input experiments. Recent publications highlight advances in DNA methylation harmonization , single-cell analytics , and multi-omics integration for studying cellular aging, tumor heterogeneity, and developmental abnormalities. His lab emphasizes data science and statistical machine learning in biomedical research.
Professor Bernhard Kainz is a leading academic in the Department of Computing at Imperial College London and Professor at Friedrich-Alexander-University Erlangen-Nuremberg. He heads the Image Data Exploration and Analysis Lab (IDEA Lab) and co-leads the Biomedical Image Analysis (BioMedIA) group , focusing on human-in-the-loop computing for healthcare applications. His research develops intelligent algorithms for multi-modal healthcare , emphasizing generative and discriminative machine learning methods to enhance diagnostic decision-making and provide real-time guidance during medical procedures. Current projects address democratizing rare healthcare expertise through AI, normative learning for human-like data analysis, and interpretable medical machine learning. Co-founder of Fraiya Ltd. for medical AI solutions Scientific advisor to ThinkSono Ltd. Leader in EPSRC Centre for Doctoral Training in Smart Medical Imaging and UKRI AI for Healthcare CDT He has received numerous accolades including MICCAI best paper awards, IEEE TMI Distinguished Reviewer status, and Imperial President’s Research Team Award. His team's work spans fetal imaging , cardiac ultrasound , digital pathology , and AI explainability in healthcare.
Prof. Dr. Mathias Wilhelm is a Professor of Computational Mass Spectrometry at the Chair of Proteomics and Bioanalytics at the Technische Universität München (TUM) . His research focuses on computational proteomics , machine learning applications in mass spectrometry , and multi-omics data integration , particularly through projects like ProteomicsDB and Prosit . He leads a multidisciplinary team developing open-source software for proteomics data analysis and co-founded MSAID and OmicScouts . His teaching includes advanced bioinformatics courses and problem-based learning modules at TUM. He serves on the scientific advisory board of Momentum Biotechnologies and collaborates extensively in quantitative proteomics , phosphoproteomics , and immunopeptidomics . His work emphasizes FAIR data principles and high-throughput experimental frameworks .
Jihoon Kim, PhD is an Assistant Professor of Biomedical Informatics and Data Science at Yale School of Medicine. As a founding faculty member of the Section of Biomedical Informatics and Data Science, Dr. Kim leads innovative research at the intersection of bioinformatics, data science, and pediatric medicine. His work focuses on applying multi-omics approaches to understand complex diseases, particularly Kawasaki Disease (KD). Assistant Professor of Biomedical Informatics and Data Science (Primary Appointment) Faculty member of the Yale Combined Program in the Biological and Biomedical Sciences (BBS) Researcher at Human Genome Sciences Dr. Kim earned his PhD from the University of California San Diego, an MS from the University of Wisconsin, and another MS from Seoul National University. His educational background provided the foundation for his expertise in bioinformatics and computational approaches to biomedical problems. Dr. Kim's research interests center on developing and applying bioinformatics tools to understand the genetic and molecular basis of diseases, with particular focus on Kawasaki Disease. His work integrates DNA, RNA-Seq, microRNA, proteome, and metabolome data from the same patients, linked with electronic health records. He has developed several bioinformatics software tools and analysis pipelines using KD omics datasets, including the first whole genome sequencing of African KD families. His expertise extends to distributed computing environments, secure genomic data analysis, and federated learning approaches for multi-institutional studies. Dr. Kim also contributes to cancer research, inflammatory bowel disease studies, and COVID-19 data analysis through collaborative projects. His publication record demonstrates expertise across multiple domains including bioinformatics tool development, multi-omics integration, genetic studies of rare diseases, and clinical applications of machine learning. Recent work shows increasing focus on privacy-preserving analytics, blockchain applications in healthcare, and addressing health disparities through data science approaches. His research spans pediatric diseases, cardiovascular conditions, and inflammatory disorders, with strong emphasis on translational applications. Rising Star Award at the 14th International Kawasaki Disease Symposium (February 2025) Dr. Kim's research is supported by multiple NIH grants, The Gordon and Marilyn Macklin Foundation, and resources from Illumina. He collaborates extensively with clinicians and researchers across institutions, particularly with Dr. Jane Burns on Kawasaki Disease research and with Dr. Lucila Ohno-Machado on multiple projects including the All of Us research program. His work demonstrates successful translation of bioinformatics methods to address clinical challenges in rare diseases where patient samples are scarce and analysis methods are not well established. As a founding faculty member of Yale's Section of Biomedical Informatics and Data Science, Dr. Kim contributes to building research infrastructure and collaborative networks focused on applying data science to biomedical challenges. His work bridges computational methods development with direct clinical applications, particularly in pediatric vasculitis research.
Yang Zhang is a Professor at Tianjin University and has held academic positions at the Harbin Institute of Technology (2015-present) and The University of Tokyo (Visiting Professor, 2024). Education : PhD in Pathology from University of Cambridge (2012-2015) MPhil from University of Hong Kong (HKU-Pasteur Research Center, 2009-2011) His research bridges computational biology and experimental microbiology , focusing on: AI-powered microscopic imaging for disease diagnostics Deep learning analysis of multi-omics data (proteins, RNAs, etc.) Development of biosensors for pathogen and cancer detection Research Trends from 2018-2025: Consistent focus on deep learning applications Expanding from parasitology to broader cancer research Integration of electrochemical and optical biosensors Scientific Recognition : Elected Fellow of The Royal Society of Chemistry (2025) Elected Fellow of the Royal Society of Biology (2023) His work combines computational approaches (AI, protein language models) with experimental techniques (high-throughput sequencing, mass spectrometry) to address fundamental questions in disease mechanisms at the molecular level.
Rachel Eddy is an Assistant Professor in the Faculty of Medicine at the University of British Columbia (UBC), with dual appointments in the Department of Radiology and Department of Pediatrics . As a James Hogg Young Investigator in Pulmonary Imaging and Director of the MRI Core at the Centre for Heart Lung Innovation (HLI), she bridges biomedical engineering and clinical research to advance lung imaging methodologies. Education: BEng in Electrical and Biomedical Engineering (McMaster University), PhD in Medical Biophysics (Western University), postdoctoral training at UBC/HLI/BCCH Research Focus: Development of hyperpolarized 129Xe MRI and quantitative CT for heterogeneous lung disease characterization, with applications in asthma, COPD, long COVID, and vaping-related lung injury Team: Supervises MSc and MASc candidates, including Alexandra Schmidt and Lixin Chu Her work integrates single-cell sequencing with AI-driven imaging analysis to uncover cellular and structural pathologies in respiratory conditions. Collaborations include the BC Children's Hospital Research Institute and cross-institutional trials. Recent publications highlight novel asthma phenotyping, cannabis-induced lung changes, and multi-center imaging standardization efforts.
Ariangela J. Kozik is an Assistant Professor in the Department of Molecular, Cellular, and Developmental Biology at the University of Michigan, where she leads research on host-microbe interactions in the human respiratory tract with emphasis on asthma pathogenesis and health equity. Education: PhD, Purdue University Dr. Kozik's research centers on deciphering the role of Prevotella species in airway inflammation and asthma, investigating how environmental factors and systemic inequities impact microbiome-host dynamics. Her lab employs multi-omic approaches integrating microbiome, genomic, transcriptomic, metabolomic, and proteomic data to map biological networks in chronic respiratory diseases. She champions interdisciplinary strategies to address health disparities in underserved populations affected by respiratory conditions. Analysis of her publication record reveals consistent focus on respiratory microbiome-asthma relationships, with growing emphasis on social determinants of health. Her work bridges microbial ecology, immunology, and health equity, demonstrating how microbiome heterogeneity contributes to disease phenotypes across diverse populations. Scientific Awards: William A. Hinton Award (2023) Thomas L. Petty Aspen Lung Conference Travel Award (2023) The Kozik Lab operates under core values of inclusivity, collaborative synergy, open communication, and community engagement. Their research program actively develops novel methodologies to interrogate microbiome-host-environment relationships in populations impacted by systemic inequity, aiming to catalyze innovative interventions for chronic lung diseases through equitable scientific practices.
Susan Galloway Hilsenbeck, Ph.D., is a Professor at Baylor College of Medicine and Director of the Quantitative Sciences Shared Resource at the Dan L Duncan Comprehensive Cancer Center. She specializes in biostatistics and informatics collaboration in cancer research, with a focus on clinical trials and biomarker analysis. Education: Ph.D. in Applied Biostatistics, University of Miami (1990) Her research interests include the design and statistical analysis of translational experiments, clinical trials, and prognostic/predictive biomarker studies. She plays a critical role in multi-center clinical trials and leads biostatistics and informatics initiatives at Baylor. Scientific Awards: Fellow, American Statistical Association Certified Professional Statistician (PStat), American Statistical Association Fellow, American Association for Cancer Research Hilsenbeck actively mentors students, teaches biostatistics courses for biomedical graduate students, and contributes to national and international workshops. She collaborates with teams such as the Advanced Technology Cores at BCM and the Smith Breast Center.
