Gianluca Mastrantonio is an Associate Professor at the Department of Mathematical Sciences (DISMA) of the Polytechnic University of Turin , Italy. He is a member of the Interdepartmental Center SmartData@PoliTO and actively contributes to the Statistics and Data Science research group. His academic roles include teaching in PhD programs (Mathematical Sciences, 2023-2025) and master's courses such as Statistical Methods in Data Science and Statistical Models/Statistical Learning . His research focuses on Bayesian Statistics and Hierarchical Models , with applications spanning Biostatistics , Environmental Monitoring , Machine Learning , and Computational Statistics . Key keywords include RNA Velocity , Sea Climate Analysis , Animal Movement Modeling , and Bayesian Software Development . Selected Scientific Awards Steering Committee Member, Royal Statistical Society (Emerging Applications, 2021-) Effective Member, International Statistical Institute (ISI, 2019-) Effective Member, Graspa (Italy, 2017-) Associate Editor, Journal of Statistical Computation and Simulation (2020-) Key Research Themes Bayesian Modeling of RNA Dynamics Environmental and Wildlife Behavior Analysis Statistical Software Development (Julia/R packages) Climatic Change-Point Detection Integration of Linear/Circular Data in Ecology Applications in Prostate Cancer Diagnostics
J. Westerweel is a Professor in the Department of Fluid Mechanics at Delft University of Technology, School of Mechanical Engineering. His research focuses on experimental fluid dynamics, particularly in Particle Image Velocimetry (PIV) , turbulent flow , and microfluidic systems . He has contributed extensively to understanding coherent structures , drag forces , and flow measurement methodologies . Research Trends: Recent works emphasize 3D flow reconstruction , microbubble dynamics , programmable hydrodynamics , and industrial fluid applications such as gypsum slurry flow optimization. His studies span both fundamental turbulence analysis and applied techniques in rowing propulsion , compliant coatings , and cavitation mitigation . Editorial Contributions: He has served as an editor for Experiments in Fluids and Flow, Turbulence and Combustion , ensuring quality in experimental methods across fluid mechanics.
Thierry Artières is a University Professor at Aix-Marseille University, primarily affiliated with École Centrale Marseille (ECM), where he holds multiple leadership positions including Head of the Computer Science teaching unit, Head of the IAAA course of the Computer Science Master's degree, and Head of the IAM course of the 3rd year Computer Science option. He is a key member of the QARMA (Machine Learning) research team within the LIS (Laboratoire d'Informatique et Systèmes) and collaborates with several research institutes including the READ laboratory, Institut de Neurosciences de la Timone (INT), and the ILCB (Institute of Language, Communication and the Brain). His research interests span Machine Learning, Deep Learning, and Artificial Intelligence with applications to neuroscience, medical imaging, and computational biology. His work focuses on understanding brain representations of voice and sound, optimizing MRI acquisition through deep learning, and developing novel machine learning techniques for multi-label classification and generative modeling. He has supervised numerous PhD students including Loris Berthelot, Hamed Benazha, Malek Senoussi, Swetali Nimje, and Charly Lamothe. His recent publications reveal a strong focus on applying deep learning to neuroscience problems, particularly in understanding how the brain processes sound and voice. His work combines theoretical machine learning advances with practical applications in medical imaging and cognitive neuroscience, often through collaborations between computer science and neuroscience laboratories. His research demonstrates a consistent trend toward interdisciplinary work that bridges AI with biological and medical domains. Member of QARMA Machine Learning team at LIS Collaborator with Institut de Neurosciences de la Timone Involved with ILCB Institute (Institute of Language, Communication and the Brain) Supervisor of multiple PhD students and Master's interns Regularly posts about internship opportunities in Machine Learning and AI Professor Artières actively mentors students through PhD positions, Master's internships, and engineering student projects. He has secured funding for multiple research projects, including ANR-funded collaborations with neuroscience institutes. His lab regularly offers 5-6 month internships on cutting-edge topics in machine learning, and he has facilitated numerous research opportunities for students interested in AI and data science careers. He also contributes to understanding the French job market for AI and data science professionals.
Dr. Xuan Vinh To is a Postdoctoral Research Fellow at the Queensland Brain Institute, The University of Queensland. His research focuses on neuroimaging techniques and their application to traumatic brain injury (TBI), neuroinflammation, and neurodegenerative diseases such as Alzheimer's. He holds a PhD in Neuroscience from The University of Queensland (2021). Education: PhD in Neuroscience, The University of Queensland, 2021 Research Interests: Development of advanced MRI techniques for TBI assessment Neuroinflammation biomarker discovery Functional connectivity analysis in aging and disease Preclinical imaging in rodent models Machine learning applications in neuroimaging Publications Trends: His work shows strong focus on translational neuroimaging, particularly in TBI pathophysiology and recovery mechanisms. Recent studies explore MRI biomarkers linking inflammation, structural changes, and cognitive outcomes in both clinical and preclinical settings. Data Resources: He has contributed multiple datasets on TBI neuroimaging through platforms like The University of Queensland's data repository, enabling reproducible research in rodent models and clinical TBI cohorts.
Jean-François Carrier is a Full Professor at the University of Montreal's Faculty of Arts and Sciences, Department of Physics. He leads research in medical physics, focusing on improving radiation oncology treatments and imaging techniques, with clinical applications in radiation therapy and nuclear medicine. His work integrates advanced technologies like Monte Carlo simulations, dual-energy CT, and AI-driven image analysis. Affiliations: Research Center of the University of Montreal Hospital Center (CRCHUM), Centre Hospitalier de l'Université de Montréal (CHUM). Research Interests: Radiation detectors, dosimetry, Monte Carlo simulations, and translational radiation oncology. His group collaborates with clinical personnel in radiation oncology departments to develop practical solutions for treatment planning and imaging. Grants and Projects: Over 20 active and completed projects funded by CRSNG, FRQS, MITACS, and others. Recent grants include advancements in PET imaging, alpha particle therapy, and AI-driven dose calculation algorithms. Teaching: Courses include Radiology and Radiation Protection, Medical Physics Special Topics, and Medical Imaging modalities. Supervises graduate programs in Biophysics and Molecular Physiology, and Physics Doctorate. Labs and Teams: Part of the TransMedTech Montreal initiative and the Imaging and Engineering Axis at CRCHUM, focusing on engineering solutions for healthcare challenges.
Diego Ulisse Pizzagalli is an independent researcher in Computational Medicine affiliated with the Faculty of Biomedical Sciences at the Università della Svizzera italiana (USI), the Euler Institute, and the Institute for Research in Biomedicine (IRB). He holds roles as a lecturer at USI and teaching assistant, focusing on machine learning in medicine and signal processing. His work bridges immunology and AI, with projects involving wearable device monitoring in chronic diseases and the development of digital biomarkers. His research focuses on applying artificial intelligence to understand immune responses, tissue remodelling, and chronic diseases. Key areas include trajectory analysis of immune cells using intravital microscopy, predictive models for disease complications, and bioimage analysis. He leads the IMMUNEMAP initiative, an open immunology data platform, and collaborates on hardware prototyping for medical applications. Pizzagalli’s recent work emphasizes AI-driven medical imaging tools (e.g., CompositIA for CT scans, deep learning in forefoot morphology analysis) and trajectory-based studies of immune cell behavior. His projects integrate clinical data with computational models, such as predicting hospital admissions in chronic liver disease patients using wearable sensors. He co-leads projects funded by systemsx.ch, Apple Inc., and others. Supervises BSc/MSc students. The Digital Pathophysiology Lab at Euler Institute serves as his primary research base, promoting interdisciplinary collaboration in biomedical imaging, trajectory mining, and wearable sensor development.
Dr. Nathan Curry is an Academic Visitor in the Department of Physics at Imperial College London, within the Faculty of Natural Sciences. He holds a PhD from the University of Cambridge, where his thesis focused on developing correlative STED and atomic force microscopy techniques to study neuronal cells. Currently, he is a research associate in the biophotonics group at Imperial, working on advancing microscopy technologies for biological research. Nathan completed his PhD at the University of Cambridge, titled "Development and application of correlative STED and AFM to investigate neuronal cells" . His research interests revolve around biophotonics, with a focus on developing advanced microscopy techniques such as correlative STED (Stimulated Emission Depletion) and atomic force microscopy (AFM). He investigates cellular processes in neuronal and astrocyte cells, particularly studying the effects of α-synuclein and polyglutamine aggregation in aging Caenorhabditis elegans . His work also includes optimizing hardware and software for high-content screening of cell morphologies, particularly in the context of metastasis research. Nathan collaborates with the Institute of Cancer Research on developing an oblique plane microscope, enhancing its compatibility with live cells and improving 3D cell segmentation and tracking software. His interdisciplinary work bridges biophotonics and cell biology, aiming to advance imaging technologies for biomedical applications.
David Albrecht is a postdoctoral researcher at the Max Planck Institute for the Science of Light (MPL) in Erlangen, Germany, affiliated with the Department Sandoghdar under the supervision of Dr. Vahid Sandoghdar. His research focuses on viral membranes and host interactions using advanced optical microscopy techniques, including interferometric scattering (iSCAT) and super-resolution microscopy. Education: BSc in Biochemistry, Hanover University MSc in Biochemistry, ETH Zurich PhD in Biochemistry, ETH Zurich (2016), thesis on 'The axon initial segment diffusion barrier at the nanoscopic level' under Dr. Helge Ewers David Albrecht's research interests lie at the intersection of virology, biophysics, and optical imaging. He investigates virus infection mechanisms, particularly how viruses exploit cellular processes and organize their fusion machinery at the nanoscale. His work emphasizes live imaging with molecular specificity and high spatiotemporal resolution. He has made significant contributions to understanding the spatial organization of viral proteins and the development of advanced microscopy methods for label-free and quantitative imaging. The recent trend in his publications (2019–2024) reflects a strong focus on high-resolution optical techniques applied to virology and nanoparticle analysis. His work spans from fundamental virology—such as vaccinia virus entry mechanisms—to methodological innovations in interferometric microscopy and drift correction. These studies demonstrate a consistent interdisciplinary approach combining biological questions with physical and engineering solutions. Scientific Awards: Marie Sklodowska-Curie Fellowship (awarded during postdoctoral work at University College London) David Albrecht has been involved in significant research projects funded by competitive fellowships and institutional support. While no formal advising roles are mentioned, his collaborative publications suggest active participation in team-based research. He has contributed to studies involving virus-host interactions, membrane dynamics, and imaging technology development. His current work at MPL is likely supported by institute funding and collaborative grants. Labs and Research Teams: He has worked in multiple high-profile research environments: the lab of Dr. Helge Ewers (ETH Zurich), the joint group of Dr. Jason Mercer and Dr. Ricardo Henriques (University College London), and currently in the group of Dr. Vahid Sandoghdar at the Max Planck Institute for the Science of Light. These labs specialize in advanced microscopy and biophysical analysis of cellular and viral systems.
Jean-Yves Tinevez is the Head of the Image Analysis Hub (IAH) at the Institut Pasteur in Paris, France. He serves as a Principal Investigator for key image analysis software projects and is a central figure in the institute's bioimage analysis community. His research is fundamentally centered on bioimage analysis and open-source software development . His primary interests include single-particle tracking , machine and deep learning for image segmentation , the creation of custom analysis pipelines , and the development of extensible software platforms like TrackMate, MaMuT, and JDLL. His work bridges the gap between complex biological imaging data and quantitative scientific discovery. The recent publications he is associated with highlight a strong trend in methodological innovation for bioimage analysis. The focus is on creating powerful, accessible software tools (e.g., SAMJ, CellTracksColab, JDLL, GeNePy3D) and enhancing existing ones (e.g., TrackMate 7) to leverage state-of-the-art AI and computational techniques. While his core expertise is in developing the tools, these tools are applied to diverse biological problems, such as host-pathogen interactions , single-cell analysis in microfluidics , and cellular and tissue morphometrics . Head of Facility, Image Analysis Hub, Institut Pasteur (Current) Principal Investigator, TrackMate, Institut Pasteur (Completed) Principal Investigator, MaMuT, Institut Pasteur (Completed) Member, Advanced Light Microscopy initiative, Institut Pasteur Member, Artificial Intelligence at the Institut Pasteur initiative Steering Committee Member, NEUBIAS Tinevez plays a vital role in training and knowledge dissemination . He is a regular instructor for the Institut Pasteur's PhD training programs and has led numerous workshops on Fiji/ImageJ, Python for image analysis, and advanced bioimage analysis. The IAH, under his leadership, operates as a collaborative core facility with a strong commitment to open science and quality management (ISO-9001:2015 certified). He fosters extensive collaborations with research units across the Institut Pasteur and beyond. The facility provides infrastructure, walk-in support, and develops custom tools to empower researchers, effectively acting as a grant-funded service that enables a vast amount of research across the campus. The Image Analysis Hub, led by Tinevez, is a core technological facility within the Institut Pasteur's Center for Technological Resources and Research (C2RT) and the Research and Resource Centre for Scientific Informatics (C2RI). It operates a dedicated analysis room with specialized workstations and offers remote access via virtual machines. The hub is involved in several transversal projects, including the Advanced Light Microscopy initiative and the application of Artificial Intelligence in biomedical research at the institute.
Wally Block is a full Professor in the Department of Biomedical Engineering at the University of Wisconsin–Madison, where he has led an MRI-focused laboratory since 2000. Previously, he was a systems engineer at GE Healthcare on the first commercial MRI scanners, and he earned his PhD from Stanford University in 1998. Education PhD 1998 – Stanford University MS 1988 – Stanford University BS 1986 – University of Illinois, Urbana-Champaign Research Focus Professor Block’s laboratory pioneers ultra-fast MRI acquisition and reconstruction techniques that dramatically shorten scan times and simplify workflows. Central to his current agenda is image-guided, minimally invasive brain therapy , particularly leveraging intraparenchymal drug-delivery routes to bypass the blood–brain barrier. This highly interdisciplinary program integrates signal processing, machine learning, mechanical engineering, biophysics, and advanced image processing to enable transformative treatments for neurological diseases. Scientific Recognition Fellow, American Institute for Medical and Biological Engineering Senior Fellow, International Society for Magnetic Resonance in Medicine Multiple Distinguished Reviewer awards from Magnetic Resonance in Medicine and Journal of Magnetic Resonance Imaging Vilas Associate Professorship, UW-Madison Honored Instructor Award, UW Housing Whitaker Foundation Grantee Teaching & Mentorship Professor Block regularly teaches cornerstone courses such as Biomedical Engineering Capstone Design (B M E 400/402), Medical Devices Ecosystem: The Path to Product (B M E 640), and directs graduate research credits ( MED PHYS 990 ). He also offers advanced independent study opportunities through B M E 799 , fostering the next generation of engineers and physician-scientists. Laboratory & Collaborative Environment His lab, located in the Wisconsin Institute for Medical Research (WIMR), hosts a multidisciplinary team of graduate students, post-doctoral researchers, and clinical collaborators. The group maintains active partnerships with neurosurgeons, radiologists, and industry leaders to translate novel MRI methods into first-in-human trials.
Gabriel Neurohr serves as Assistant Professor in the Department of Biology at ETH Zurich, Switzerland, where he leads research on the physiological consequences of cell size dysregulation. His work bridges fundamental cell biology with implications for aging and cancer, investigating how deviations from species-specific cell size norms disrupt cellular homeostasis. Academic training includes a BSc in Biochemistry from ETH Zurich (2003-2006), PhD at Barcelona's Center for Genomic Regulation (2008-2012), and postdoctoral fellowship at MIT's Koch Institute (2013-2020). His research program addresses four core questions: (1) functional size limits of cells, (2) size-senescence relationships, (3) macromolecule-volume coordination, and (4) density-dependent functional impacts, employing genetic, live-imaging, and computational approaches in yeast and mammalian systems. Recent publications reveal a methodological evolution toward quantitative analysis, with machine-learning-enhanced imaging (2023) complementing mechanistic studies linking genome instability to size-induced senescence (2023). His 2024 work establishes cell enlargement as a primary driver of senescence through cytoplasmic dilution and organelle dysfunction, demonstrating conserved principles across eukaryotes. Major recognitions include: SNF Eccellenza Fellowship (2020-2025) EMBO Long Term Fellowship (2013-2014) ETH Medal for Master's excellence (2008) Neurohr's lab develops innovative tools like holotomography-based vacuole quantification while maintaining focus on fundamental size-regulation mechanisms. He teaches advanced courses on biomolecular condensates and cellular matter properties, mentoring through ETH's structured graduate programs. Current work explores therapeutic targeting of size-dependent senescence pathways in age-related diseases.
Prof. Dr.-Ing. Tim Wilhelm Nattkemper leads the Biodata Mining Group at the Faculty of Engineering , Universität Bielefeld , while holding affiliations with the Center for Biotechnology (CeBiTec) and the Institute for Bioinformatics Infrastructure . His work bridges bioinformatics with marine environmental monitoring , focusing on machine learning and computer vision applications. The group specializes in multivariate bioimage analysis , developing platforms like BioIMAX for web-based high-dimensional data exploration. Research spans from MALDI imaging to deep-sea megafauna classification , integrating information visualization and web technologies . Recent projects address seafloor macrolitter monitoring , coral stress response analysis , and self-supervised learning for diatom classification. Their 15 most recent publications (2023-2025) highlight advancements in marine imaging , automated annotation systems , and AI-driven biodiversity assessment , particularly in polymetallic nodule fields. The group also tackles technical challenges like data imbalance in marine image classification and FAIR data principles implementation. As module responsible for courses like Information Visualization and Introduction to Bioinformatics , Nattkemper contributes to academic training in bioinformatics and data science . His interdisciplinary collaborations span physics , chemistry , and ecology within Bielefeld's Material World strategic research area.
Dr. Sara Atito Ali Ahmed is a Surrey Future Fellow at the University of Surrey's Faculty of Engineering and Physical Sciences , specifically within the Centre for Vision, Speech and Signal Processing (CVSSP) . She holds a PhD in Computer Science and specializes in advanced machine learning techniques, with a focus on computer vision, medical imaging, and audio signal processing. Her research emphasizes self-supervised learning, multimodal data fusion, and deep learning ensembles for robust decision-making. Her academic journey includes contributions to healthcare AI through projects like SS-CXR for medical image pretraining and DeepChest for multi-task learning in chest X-ray diagnostics. She has also developed innovative models such as the ASiT audio-spectrogram transformer and DailyMAE for efficient masked autoencoder training. Her work spans diverse domains including robotics perception, SAR target recognition, and plant species identification via ensemble methods. Awards and recognitions are not explicitly mentioned in the provided texts, but her prolific publication record highlights her impact in top-tier venues. She has collaborated on interdisciplinary projects ranging from biomedical applications to environmental sensing, demonstrating a commitment to bridging theoretical research with real-world applications.
Kristen M. Meiburger is a Tenure-track Assistant Professor at the Department of Electronics and Telecommunications, Politecnico di Torino. She holds a Master’s in Biomedical Engineering (2010) and a Ph.D. in Biomedical Engineering (2015) from Politecnico di Torino. She has conducted research at the University of Texas at Austin (2013–2014) and the University of Toronto (2015). Her work focuses on biomedical image processing, including ultrasound and photoacoustic imaging, radiomics, deep learning, and signal reconstruction methods. Her research interests emphasize innovative imaging techniques, non-invasive vascular analysis, and dermatological applications. She leads ongoing projects like REAP (cancer imaging with optical coherence/photoacoustic tomography), AI-VASCUES (vascular dysregulation analysis), and ImPACT-AI (ethical AI in photoacoustic imaging). She teaches and proposes thesis topics at Politecnico di Torino’s DET (Department of Electronics and Telecommunications). Recent publications highlight advancements in AI-driven imaging, such as texture analysis in OCT, hybrid deep learning frameworks for microscopy, and GANs for color normalization. Her work bridges medical imaging innovation with clinical applications, focusing on disease diagnosis and treatment monitoring. Ongoing efforts integrate ethical AI practices and multi-center imaging harmonization to improve medical decision-making.
Erlend Hodneland is a Researcher at the University of Bergen's Department of Informatics, affiliated with the Mohn Medical Imaging and Visualization Centre (MMIV). His work bridges medical imaging, radiomics, and machine learning, focusing on tumor analysis and prognostication in cancers like cervical and endometrial cancers. He specializes in developing automated tumor segmentation techniques and tools for radiomic signature analysis, integrating visualization and computational methods to enhance clinical decision-making. His research interests include medical imaging analytics, radiomics for cancer prognosis, and interactive visualization frameworks for multiparametric medical data. Collaborations involve radiologists, oncologists, and computer scientists to translate imaging data into actionable insights. Key projects include the RadEx platform for radiomic tumor profiling and automated segmentation algorithms for cervical cancer studies. Notable contributions include MRI-based delta radiomics for tracking tumor responses during chemoradiotherapy and fully automatic whole-volume tumor segmentation. His work emphasizes precision medicine through quantitative imaging biomarkers and interdisciplinary approaches to medical data analysis.
