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Karoline FaustView profile

Associate Professor

• Microbial community dynamics
• Microbial systems biology
• Bioinformatics
• +6 more

Karoline Faust is an Associate Professor at KU Leuven, affiliated with the Laboratory of Molecular Bacteriology (Rega Institute) and the Faculty of Medicine . She contributes to the iSi Health and Leuven One Health institutes, and serves on senior academic councils. Her research spans microbial systems biology, focusing on community dynamics and network analysis. Education: PhD in bioinformatics (2010, KU Leuven) Affiliations: KU Leuven, ISME Journal editorial board, Belgian Society for Microbiology Her research investigates microbial community dynamics , systems biology approaches to microbiomes, and bioinformatics tool development . She specializes in modeling human gut microbiota , synthetic microbial communities , and environmental microbiomes (e.g., microplastic impacts on Daphnia microbiomes). Her work integrates metabolic modeling , network analysis , and experimental systems to understand microbial interactions. Recent publications highlight her contributions to microbial network inference , 16S rRNA sequencing protocols , microfluidics , and ecological modeling of microbiomes. She develops tools like manta , miaSim , and CoNet to analyze community structures. Teaching: Karoline co-teaches courses in microbiology, bioinformatics, and network analysis at KU Leuven, and has contributed to international workshops on microbial network inference. Scientific Engagement: She serves as Senior Editor at ISME Journal and Secretary of the Belgian Society for Microbiology .

Lauren AndrewsView profile

Associate Professor

• Synthetic Biology
• Genome Engineering
• Metabolic Engineering
• +5 more

Lauren Andrews serves as Associate Professor and Marvin and Eva Schlanger Faculty Fellow in the Department of Chemical Engineering at the University of Massachusetts Amherst. Her research integrates synthetic biology and genetic engineering to develop programmable cellular systems for biotechnological applications. Education: Postdoctoral Training: Massachusetts Institute of Technology (Biological Engineering and Broad Institute of MIT and Harvard) PhD: University of Colorado Boulder, Chemical Engineering (2012) MS: University of Colorado Boulder, Chemical Engineering (2009) BS: Cornell University, Chemical Engineering (2006) Dr. Andrews' research focuses on establishing genetic design rules for reprogramming cellular regulation and metabolism. Her lab pioneers synthetic gene networks, genetically-encoded biosensors, and high-throughput methodologies for optimizing genetic designs in both model and non-model bacteria. This work enables precise control of cellular sensing, memory, and environmental responses through multiplexed DNA assembly and next-generation sequencing. Analysis of her 15 most recent publications reveals dominant themes in bacterial biosensor development (particularly for bioremediation), quorum sensing engineering, and programmable genetic circuits for probiotic applications. Her research consistently bridges fundamental genetic circuit design with practical implementations in bacterial consortia and non-model organisms. Scientific Awards: Marvin and Eva Schlanger Faculty Fellowship NSF CAREER Award (2020) for "Programmable synthetic microbial consortia for complex multicellular functions" Her grant portfolio demonstrates significant funding for collaborative research in bacterial communication systems and model-guided design of synthetic ecosystems. The Andrews Lab maintains active partnerships with the MIT-Broad Foundry and Cold Spring Harbor Laboratory, where she co-founded the Synthetic Biology Summer Course. Current projects focus on CRISPR-based regulation in non-model bacteria and algorithmic programming of sequential logic in probiotic strains. The Andrews Lab operates within the Life Science Laboratories at UMass Amherst, utilizing advanced facilities for genetic prototyping and high-throughput screening. Her team develops multiplexed tools for exploring genetic design spaces, with particular emphasis on soil bacteria and Gram-positive pathogens for environmental and therapeutic applications.

Steffen KlamtView profile

Research Fellow

• Metabolic Engineering
• Systems Biology
• Computational Modeling
• +2 more

Dr.-Ing. Steffen Klamt leads the Research Group 'Analysis and Redesign of Biological Networks' at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany, where he has been employed since 1998. He received his Diplom-Systemwissenschaftler degree from the University of Osnabrück in 1998 and his Dr.-Ing. from the University of Stuttgart in 2005. His research focuses on computational systems biology with emphasis on metabolic engineering, biochemical networks analysis, and bioprocess optimization. He develops computational tools like CellNetAnalyzer for network analysis and StrainDesign for metabolic engineering applications. Key research areas include constraint-based modeling, minimal cut sets analysis, and dynamic optimization of metabolic processes. His recent publications demonstrate strong focus on multi-stage bioprocess optimization, enzyme cascade engineering, and novel strain development strategies for chemical production. Common themes include ATP manipulation strategies, thermodynamic constraints in metabolism, and integration of experimental data with computational models. Scientific Awards: Ernst Dieter Gilles Lecture Award Ernst Dieter Gilles Fellowship He leads a research group developing computational methods for metabolic network analysis and maintains collaborations with experimental groups for model validation and application. The group develops open-source software tools widely used in systems biology research.

Bärbel Finkenstädt RandView profile

Tutor

• Statistical Methodology
• Machine Learning
• Bayesian Inference
• +6 more

Bärbel Finkenstädt Rand is a Senior Tutor at the Warwick Medical School , University of Warwick, with extensive research contributions at the intersection of statistics, machine learning, and biomedical sciences. Her work focuses on developing advanced methodologies for analyzing temporal and spatio-temporal data, particularly in circadian rhythms and disease dynamics. Research Themes : Bayesian inference, Hidden Markov Models, circadian rhythm stability, transcriptional bursting, and wearable sensor data analysis. Collaborations : Chronotherapy Group at Warwick, Université Paris-Saclay, and interdisciplinary teams across medicine, genetics, and computational biology. Publications reveal a strong emphasis on circadian health monitoring, gene expression dynamics, and epidemic modeling using stochastic frameworks. Her recent work prioritizes personalized medicine applications through telemonitored biomarkers and IoT platforms . Methodological Innovations include spline-based HMMs, distributed delay systems, and harmonic modeling for nonstationary time series. Applications span oncology, sleep medicine, and population ecology.

Mikail RubinovView profile

Assistant Professor

• Network Neuroscience
• Computational Neuroscience
• Brain Connectivity
• +4 more

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.

Xiaobo LiView profile

Professor

• Neuroscience
• Medical Imaging
• Machine Learning Applications in Neurology
• +3 more

Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.

• Agricultural Development
• Seed Systems
• Plant Genetics
• +3 more

Conny Almekinders is an Assistant Professor in the Department of Knowledge Technology and Innovation at Wageningen University & Research, focusing on seed systems, agricultural development, and participatory research approaches. His work bridges plant genetics, socioeconomics, and institutional dynamics to enhance crop seed systems globally. Key areas include potato and cassava seed systems, gender-responsive agricultural practices, and disease management strategies in developing countries. Research emphasizes seed degeneration, stakeholder collaboration in breeding programs, and policy analysis for resilient seed systems. He has co-supervised 25 PhD projects, including studies on decentralized seed multiplication in Kenya, hybrid potato breeding models, and community-driven development in Ethiopia. Notable contributions include developing diagnostic tools for root/tuber seed systems and evaluating farmer willingness-to-pay for quality planting materials in Southeast Asia. Almekinders has published over 168 peer-reviewed articles, datasets, and reports addressing seed systems' technical and social dimensions. His projects often involve international collaborations, such as the CRRIsP initiative on climate-resilient potato innovations and the RTB Seed System Toolbox. He actively participates in lectures and workshops promoting inclusive seed system transformation.

Yasser Iturria MedinaView profile

Assistant Professor

• Neuroimaging
• Neuroinformatics
• Precision Medicine
• +6 more

Yasser Iturria Medina is an Assistant Professor at the Montreal Neurological Institute (MNI) , McGill University, within the Department of Neurology and Neurosurgery . He is an associate member of the Ludmer Centre for Neuroinformatics and Mental Health and the McConnell Brain Imaging Centre . Academic Rank: Assistant Professor Key Affiliations: MNI, Ludmer Centre, McConnell Brain Imaging Centre His educational background includes: Undergraduate: Nuclear Engineering (2004), Higher Institute for Nuclear Sciences and Technology, Cuba MSc: Neurophysics and Neuroengineering (2006), Cuban Neuroscience Center PhD: Neuroimaging and Neuroinformatics (2013), National Center for Scientific Research and Havana’s University of Medical Science His research focuses on neuroinformatics for precision medicine , particularly in neurodegenerative diseases like Alzheimer's and Parkinson's. His lab develops multiscale brain models integrating molecular, imaging, and cognitive data to characterize pathogenic mechanisms and identify personalized interventions. Key research areas include: Neurodegeneration modeling Neurovascular interactions in Alzheimer's Multi-omics integration for disease subtyping Neuroimaging biomarkers across neurodegenerative spectra Computational modeling of amyloid-beta and tau propagation The article analysis reveals his emphasis on: Alzheimer's disease mechanisms (45% of recent works) Multi-omics and transcriptomic modeling (30%) Neurovascular and white matter pathology (20%) Machine learning applications in neuroimaging (15%) Development of tools like NeuroPM-box and MVComp toolbox His lab has been instrumental in creating NeuroPM-box , a software platform for integrating molecular, neuroimaging, and clinical data to characterize neurodegenerative progression and heterogeneity. He has also contributed to CAPTURE ALS , a comprehensive analysis platform for amyotrophic lateral sclerosis.

Jonathan SwannView profile

Professor

• Biomolecular Medicine
• Metabolomics
• Gut Microbiota
• +5 more

Dr. Jonathan Swann is a Professor of Biomolecular Medicine at the University of Southampton's Faculty of Medicine, School of Human Development and Health. He leads a metabolomics-focused research program investigating gene-environment interactions in health and disease. His work spans two main themes: (1) Gut microbiota's role in host health/disease, including gut-brain axis communication, microbiome-cancer interactions, and microbial metabolism; (2) Early-life environmental influences on development, particularly in global health contexts. He supervises multiple PhD students and collaborates with institutions like Imperial College London and Karolinska Institute. Research affiliations: Institute for Life Sciences, Nutrition, Endocrinology and Metabolism, and 8 other interdisciplinary groups Funding: BBSRC, MRC, Wellcome Trust, NIH, Gates Foundation Recent studies explore microbiota's impact on neurological disorders, malnutrition consequences in children, and metabolic biomarkers in pancreatic cancer. His 2024 Nature Communications paper revealed circadian bile acid rhythms in humans, while 2025 cancer biomarker work advances early detection methods. External roles include editorial board positions for Microbiome and Frontiers in Endocrinology , plus expert advisory roles with the British Society of Gastroenterology.

Blaž ZupanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +4 more

Blaž Zupan is a Full Professor at the University of Ljubljana, also affiliated with Baylor College of Medicine in Houston, teaching artificial intelligence and machine learning. He leads a fifteen-member bioinformatics laboratory and has authored over one hundred publications with more than ten thousand citations, reflecting significant impact in computational sciences. His research centers on explainable AI, machine learning, and data visualization, with deep applications in bioinformatics. He pioneered the Orange data mining suite—a visual programming tool for accessible analytics—and bioinformatics platforms like dictyExpress for gene expression analysis and GenePath for genetic network discovery, emphasizing user-friendly interfaces and real-world usability. Analysis of his recent publications reveals a trajectory from foundational AI (e.g., concept hierarchies in 1999) to contemporary bioinformatics, with current work focusing on single-cell genomics, batch-effect correction, and democratizing AI through visual programming. His tools bridge computational methods and biological discovery, particularly in systems biology and genomics. Zupan’s accolades include the Zois Award (2010), two University of Ljubljana Golden Plaques (2011, 2019), a Fulbright Scholarship (2013), and six student-voted Best Teacher Awards (2008–2017). He was named among the Top 100 Most Influential Innovators of Central and Eastern Europe (2016) by Res Publica, Financial Times, and Google. He secures substantial research funding, currently leading projects like KATARINA (computer science education for all), DALI4US (data literacy for primary schools), DANIO-ReCODE (genomics of regeneration), and L2-60154 (explainable AI for gene expression). Past projects span AI systems (2009–2020), drug discovery, and systems biology, demonstrating sustained interdisciplinary impact. As head of the Bioinformatics Laboratory, he fosters collaboration between computer science and biology, developing open-source tools that empower researchers globally. His consulting for industry and public organizations extends his influence into data-driven decision support and AI training.

Pablo Perez PineraView profile

Associate Professor

• Genome Editing
• Biomedical Engineering
• Molecular Biology
• +3 more

Pablo Perez Pinera is an Associate Professor in the Department of Bioengineering at the University of Illinois, with additional affiliations in Biomedical and Translational Sciences, Molecular and Integrative Physiology, and the Carl R. Woese Institute for Genomic Biology. His work focuses on advancing genome editing technologies, particularly CRISPR-based systems and base editing tools. Research interests include developing precision gene therapies, optimizing viral vector delivery for in vivo applications, and understanding how chromatin structure influences editing efficiency. Recent contributions highlight the creation of the SPLICER base editing toolbox for therapeutic exon skipping and advancements in protein engineering for next-generation genome editors. His articles emphasize innovations in genome editing applications, such as reducing Alzheimer’s disease precursors in mice via gene editing. Collaborations span interdisciplinary teams addressing biomedical challenges through molecular and genetic approaches.

Russell CoxView profile

Professor

• Organic Chemistry
• Synthetic Biology
• Fungal Biosynthesis
• +4 more

Prof. Russell Cox holds the position of Professor of Microbiological Chemistry at the Institute of Organic Chemistry, Leibniz University Hannover, Germany. He leads the Synthetic Biology Group and serves on the Executive Board of the Institute. His academic journey includes prior roles as Full Professor at the University of Bristol (UK) and Reader/Senior Lecturer in Organic and Biological Chemistry. He directs the Biomolecular Drug Center (BMWZ) and chairs international conferences on biosynthetic research. Research focuses on elucidating fungal biosynthetic pathways, isolating natural products for pharmaceutical/agrochemical applications, and engineering metabolic pathways to produce novel bioactive compounds. Key areas include tropolone sesquiterpenoids, maleidrides, and polyketide synthase mechanisms. He publishes in top journals like Nature Reviews Chemistry and collaborates globally on projects involving fungal genomics and drug discovery. Teaching responsibilities include Organic Chemistry II (English), Biomolecular Chromatography, and practical courses. Administrative roles span admissions committees for M.Sc. programs in Chemistry, Life Science, and Molecular Microbiology. His lab integrates synthetic biology with genomic approaches to engineer microbial production systems for high-value compounds. Publications since 2020 highlight advances in fungal secondary metabolite engineering, SARS-CoV-2 inhibitor discovery, and genomic studies of Hypoxylaceae. Research emphasizes translating fundamental biosynthetic insights into practical applications for medicine and agriculture.

Jan De BeenhouwerView profile

Professor

• X-ray Imaging
• Computed Tomography
• Image Processing
• +3 more

Prof. Jan De Beenhouwer is a faculty member at the University of Antwerp, affiliated with the Department of Physics and the imec Vision Lab. His research focuses on advanced computational imaging techniques, particularly in X-ray tomography, phase contrast imaging, and reconstruction algorithms for medical and industrial applications. His primary research interests include: Development of novel X-ray imaging methodologies like edge illumination phase contrast Advanced CT reconstruction algorithms for sparse-view and dynamic systems Integration of deep learning with tomographic reconstruction Industrial applications including defect detection and material characterization Biomedical imaging such as bone structure analysis and tissue modeling Analysis of recent publications (2024-2025) reveals strong emphasis on: Innovations in phase contrast imaging hardware and simulation tools Advanced reconstruction techniques for motion compensation and sparse data AI-powered approaches for industrial inspection and biomedical research Development of open-source tools (CAD-ASTRA) for the tomography community He leads research at imec Vision Lab, focusing on both fundamental imaging physics and practical applications. The lab collaborates extensively with industrial partners on non-destructive testing solutions.

Jan SijbersView profile

Professor

• Image Reconstruction
• Medical Imaging
• Quantitative MRI
• +3 more

Jan Sijbers is a Full Professor at the University of Antwerp , affiliated with the Department of Physics . He serves as Spokesperson of the imec-Vision Lab and is a co-founder of spin-offs IcoMetrix and Deltaray . His academic role includes a 10% position as Research Director at imec. Research Interests: Quantitative Magnetic Resonance Imaging (MRI), Tomographic Image Reconstruction, Medical Imaging Algorithms Editorial Roles: Senior Area Editor (IEEE Transactions on Image Processing), Associated Editor (IEEE Transactions on Computational Imaging) Teaching: Courses in Physics, Mathematics, Signal/Image Processing, and X-ray Microtomography Research Trends: His 2025 publications focus on X-ray phase contrast imaging, zebrafish models for bone disease, mesh-based CT reconstruction, and AI-driven biomedical imaging. Earlier works emphasize statistical signal processing in MRI and discrete tomography algorithms. PhD Advisees: He has supervised 68 PhD theses since 2004, with recent topics spanning edge illumination X-ray imaging, 3D knee morphology, and advanced MRI relaxometry. Laboratory Leadership: As head of imec-Vision Lab, he leads research in computational imaging, with applications in medical diagnostics and industrial X-ray systems.

• Forest Management
• Wood Quality
• Dendrochronology
• +3 more

David Auty is an Associate Professor and Executive Director in the School of Forestry at Northern Arizona University. His research focuses on wood properties, forest management practices, and innovative applications of LiDAR technology in forestry. He leads projects investigating the impact of environmental factors on tree physiology, wood quality, and forest dynamics. Notable contributions include developing the sgsR toolbox for LiDAR-based forest inventories and studying radial profiles of specific gravity in conifers. His work bridges ecological and engineering disciplines, addressing challenges in sustainable forestry and climate resilience. Collaborations include international studies on wildfire impacts, carbon modeling, and wood mechanics. David actively contributes to datasets on tree growth dynamics and has authored over 45 scholarly works, emphasizing practical solutions for forest management and conservation. Key Focus Areas: Forest Management, Wood Quality, Remote Sensing, Climate Adaptation Tools & Innovations: sgsR LiDAR Toolbox, Acoustic Wood Testing, Stand Dynamics Models Collaborations: Global networks studying boreal forests, wildfire impacts, and conifer physiology David’s research highlights the interplay between ecological processes and industrial forestry needs, with implications for bioenergy, carbon sequestration, and resilient forest systems.