Francis Loth is a Professor at Northeastern University, holding dual appointments in the Mechanical and Industrial Engineering Department and the Bioengineering Department. His research focuses on biological fluid mechanics, particularly cerebrospinal fluid (CSF) dynamics, Chiari malformation, and syringomyelia. He leads studies using advanced imaging (MRI) and computational models to understand CSF flow impedance, brain tissue motion, and surgical outcomes. Education: PhD in Mechanical Engineering from Georgia Institute of Technology (1993). Research Interests: Experimental and computational fluid mechanics in biological systems CSF simulation and brain biomechanics Medical image processing for neurology Cardiac-coupled neural tissue motion Key Work: His team investigates morphometric changes in Chiari patients, develops MRI techniques for brain displacement quantification, and evaluates surgical interventions like posterior fossa decompression. Projects include the Chiari 1000 Registry and collaborations with neurosurgeons to improve clinical outcomes. Labs/Teams: Active in the Institute for Mechanobiology, focusing on translational research between fluid dynamics and neurological disorders.
Dr. Po-Wah So is a Reader in Biomedical Imaging and Spectroscopy at King's College London, affiliated with the School of Neuroscience and Department of Neuroimaging. Her research focuses on brain aging, neurodegenerative diseases (e.g., Alzheimer's), and the role of metals like iron in neurological processes. She employs advanced MRI and NMR techniques to study metabolic diseases and their interactions with brain health. Dr. So holds a PhD in Chemistry from Birkbeck, University of London, and has extensive academic and industry experience in NMR applications. Her work includes pioneering synchrotron-based metallomics mapping and collaborations on translational imaging projects. She has led or co-led multiple grants from bodies like Alzheimer's Research UK and the Wellcome Trust, focusing on iron metabolism, metabolic syndrome, and pediatric neuroimaging. Dr. So also contributes to academic leadership as Departmental Admissions Tutor and module leader in neuroscience and bioimaging programs.
Professor Kate Trinajstic is a vertebrate palaeontologist and holds the rank of John Curtin Distinguished Professor at Curtin University's School of Molecular and Life Sciences (MLS), within the Faculty of Science and Engineering. She also serves in the Office of the Provost. Her academic journey includes a BSc (Hons) from Murdoch University and a PhD in Geology from the University of Western Australia (2000), focusing on microvertebrates from the Gneudna Formation. She joined Curtin in 2009 as a Curtin Research Fellow and secured an ARC QEII Fellowship in 2011 to study early vertebrate musculature and skeleton development. Her current research explores the origins of electroreception and nocturnality in early jawed vertebrates, supported by an ARC Discovery Project (2014). Her research interests span vertebrate paleontology, with a focus on skeletal and muscular evolution in early vertebrates, internal fertilization mechanisms, and the application of advanced imaging techniques like synchrotron scanning. Fieldwork locations include Western Australia’s Kimberley region, Morocco, and South Africa. Notable achievements include discoveries of the world’s oldest vertebrate sexual organs and live birth in Devonian fishes, published in Nature and Science . Professor Trinajstic has received prestigious awards such as the 2010 Malcolm McIntosh Award for Physical Science and the 2009 Top Ten Species Award. Her work bridges paleontology with evolutionary biology, contributing to understanding vertebrate diversification and adaptation. She collaborates widely, with over 80 peer-reviewed publications, and her research often integrates geochemical and morphological analyses to unravel fossil preservation mechanisms.
Matthew Gibbons is a Researcher in the Department of Radiology at the University of California, San Francisco (UCSF) School of Medicine. His work bridges medical imaging, biomedical engineering, and clinical research, with a focus on prostate cancer and bone physiology. Education: PhD in Engineering Applied Science (UC Davis, 1995), MS in Nuclear Science (Air Force Institute of Technology, 1984), BS in Physics (Purdue University, 1982), and MS in Biomedical Imaging (UCSF, 2018). Gibbons specializes in Diagnostic Imaging , particularly MRI applications for prostate cancer detection and cortical bone vessel analysis. His research leverages Neural Networks , Image Processing , and Biomedical Engineering to improve tumor segmentation and histopathology reference standards. Recent publications (2015–2025) highlight his expertise in MRI , Prostate Cancer , and Computational Modeling . Collaborations with institutions like UC Davis and Purdue University underscore his interdisciplinary approach. Gibbons has worked with co-authors including Susan Noworolski, Peter Carroll, Jeff Simko, and John Kurhanewicz. His work spans journals such as Tomography , Magnetic Resonance Imaging , and Journal of Applied Physics , with additional contributions to patents in magnetic recording technology.
Filip Szczepankiewicz is an Associate Professor and Associate Senior Lecturer in Medical Radiation Physics at Lund University, Sweden. He serves as Principal Investigator for eSSENCE: The e-Science Collaboration and LUCC: Lund University Cancer Centre, with active research projects spanning advanced MRI techniques for neuroscience, cancer imaging, and microstructure analysis. His research focuses on several key areas: Advanced diffusion MRI techniques for microstructure imaging Brain imaging applications in neuroscience and cognitive disorders Prostate cancer imaging and biomarker development Cardiac diffusion tensor imaging MRI physics and methodology development Dr. Szczepankiewicz's recent publications demonstrate sophisticated multi-dimensional MRI approaches that separate different biophysical processes within tissues. His work bridges physics, engineering, and clinical applications, particularly in neurological disorders and cancer diagnostics, with increasing emphasis on techniques that probe tissue microstructure with unprecedented detail. He leads significant research projects including: eSSENCE@LU 9:2 - Establishing the link between prostate cancer microstructure and MRI (2023-2026) Prostate Cancer Imaging Group: Multidimensional MRI-based biomarkers (2022-2030) eSSENCE@LU 6:4 - Accelerated microstructure imaging (2020-2021) His work contributes to UN Sustainable Development Goals related to good health and well-being, with documented research collaborations across multiple institutions and countries.
Sabina Hrabetova, MD, PhD, serves as a Professor in the Department of Cell Biology at the State University of New York Downstate Health Sciences University's College of Medicine. Her research centers on the brain's extracellular space (ECS), investigating its structural properties and functional implications for neural communication and therapeutic delivery. Her primary research interests focus on brain extracellular space dynamics , where she combines experimental diffusion measurements with mathematical modeling to characterize ECS structure. Her work examines how ECS geometry, pore width, and extracellular matrix composition influence diffusion processes critical for intercellular signaling, nutrient delivery, and drug transport. This research spans neuropharmacology , computational neuroscience , and biophysical modeling , with significant implications for neurological disorders and brain tumor treatments. Analysis of her recent publications reveals an evolving research trajectory from fundamental ECS characterization toward translational applications. Her work increasingly integrates glymphatic system research with traditional diffusion studies, while maintaining focus on structural determinants of brain microenvironment function. Notably, her 2025 publication extends into oncology collaboration, demonstrating interdisciplinary reach. Her laboratory develops specialized methodologies for brain tissue analysis, including novel incubation chambers for acute brain slices and optical sensing techniques for neuropeptide transmission studies. These technical innovations support her core mission of creating realistic three-dimensional models of brain ECS to predict structural impacts on molecular transport.
Parag Chitnis is an Associate Professor in the Department of Bioengineering at George Mason University's College of Engineering, where he has served since Fall 2014. He also acts as Principal Investigator at the Krasnow Institute of Advanced Study, focusing on systems science and bioengineering innovation through interdisciplinary research. PhD, Mechanical Engineering - Boston University MS, Mechanical Engineering - Boston University BS, Engineering Physics and Mathematics - West Virginia Wesleyan College His research centers on medical ultrasonics and photoacoustics, pioneering non-invasive biomedical imaging techniques for tissue characterization, tumor detection, and neural signal monitoring. He integrates DNA-based nanosensors, NIR-II fluorescent probes, and machine learning algorithms to advance diagnostic precision. Recent articles highlight innovations in photoacoustic tomography, wearable ultrasound systems, and ultrasound-responsive drug delivery. Key trends include transcranial imaging, vascular mapping, and real-time muscle fatigue monitoring using portable devices. His work extends to the Center for Advancing Systems Science and Bioengineering Innovation (CASSBI), with clinical collaborations focused on pelvic floor elastography, neuroinflammation diagnostics, and implantable device actuation via ultrasound.
Samantha Zambuto is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Kentucky , focusing on tissue engineering and biomaterials for reproductive health. She was co-advised by Dr. Michelle Oyen and Dr. Jerry Lowder during her T32 postdoctoral fellowship at Washington University in St. Louis, which focused on female lower urinary tract disorders. PhD in Bioengineering, University of Illinois Urbana-Champaign (2022) MPHS in Population Health Sciences, Washington University School of Medicine (2024) Master of Biomedical Engineering, Brown University (2017) Bachelor of Biological Engineering, Cornell University (2015) Her research centers on creating gelatin methacryloyl (GelMA) hydrogel platforms to model the endometrium, vagina, and female lower urinary tract. These systems investigate: Cell-cell and cell-matrix interactions Hormone dynamics during pregnancy Biomechanics of vaginal tearing Tissue regeneration applications Her recent publications focus on 3D biomaterial modeling of reproductive tissues, including studies on trophoblast invasion, endometrial decidualization, and placental microstructure. She has secured multiple NIH grants and awards, including the BJC Healthcare Scholarship for public health research. NIH Extramural Loan Repayment Program (2024–2026) T32 Clinical Outcomes Research Training Program (2022–2025) T32 Tissue Microenvironment Training Program (2020–2022) Zambuto leads a multidisciplinary lab exploring the intersection of engineering and reproductive biology , with emphasis on health equity and regenerative medicine.
Claudia Redenbach is a Professor and Dean of Mathematics at RPTU Kaiserslautern, Germany. She is affiliated with the Department of Mathematics and leads the Statistics Working Group (AG Statistik), with additional connections to the Department of Statistics and the Graduate School 'Mathematics as a Key Technology.' Position: Dean of Mathematics Institution: RPTU Kaiserslautern Location: Building 48, Room 534, Gottlieb-Daimler-Straße, 67663 Kaiserslautern Contact: claudia.redenbach@rptu.de | +49 (0)631 205 3620 Professor Redenbach's research focuses on the intersection of stochastic geometry, spatial statistics, and image analysis with applications in materials science. Her work bridges theoretical mathematics with practical engineering applications, particularly in analyzing microstructures of concrete, foams, composites, and other materials. She has developed innovative methods for analyzing spatial point patterns, directional data, and 3D image data from various microscopy techniques including CT, FIB-SEM, and light-sheet microscopy. Her recent publications (2024-2025) reveal a strong emphasis on computational methods for materials analysis, including crack detection in concrete, fiber orientation analysis, artifact removal in imaging, and the development of mathematical morphology techniques for directional data. She frequently collaborates with materials scientists and engineers, with K. Schladitz appearing as a consistent co-author across numerous publications. Her work demonstrates a consistent focus on developing mathematical tools that solve practical problems in materials characterization and analysis. Professor Redenbach has made significant contributions to spatial statistics, particularly in anisotropy analysis of point patterns, and to the application of stochastic geometry models for material microstructure analysis. Her research spans both theoretical developments in statistical methods and their practical implementation for real-world materials science problems.
Dr. Armin Hochreiner is an Assistant Professor at Linz University of Applied Sciences, specializing in Medical Engineering and Thermography. He leads research projects at the Research Center Linz and TIMed Center, focusing on advanced optical setups, bioprinting, and non-destructive testing. Projects: GBOLF (Greiner Bio One Leichtfried), BF-ABSAOS (Adaptive Beam Shaping), TC-LOEM (Laser-Induced Surface Modification). Research Areas: Optical coherence tomography, two-photon lithography, fiber lasers, and data acquisition systems. His recent work includes a 2025 publication on low-cost pulse generation for 3D-printed microfluidics and a 2024 study on lipid properties in high-density lipoproteins. Grants from Land OÖ Basisfinanzierung and FTI-Strukturförderung support his research on adaptive optics and biocompatible microstructures. Collaborations span biomedical imaging, machine learning integration in optical systems, and interdisciplinary projects with institutions like Greiner Bio One and Springer.
Professor Brian Rodriguez is a full-time faculty member in the School of Physics at University College Dublin, based at the Conway Institute in Belfield, Dublin 4. His research bridges nanoscale materials physics and biomedical applications, with expertise in scanning probe microscopy techniques. He maintains an active teaching schedule across multiple modules and can be contacted via brian.rodriguez@ucd.ie or phone at 01 716 6744. His academic credentials include: BS from University of North Carolina MS from North Carolina State University, USA PhD in Physics from North Carolina State University, USA (2003) Professional Certificate in University Teaching & Learning from University College Dublin Rodriguez specializes in piezoresponse force microscopy (PFM) and atomic force microscopy (AFM) for characterizing ferroelectric materials, polar nitride semiconductors, and biological systems. His work has expanded into bio-inspired nanomaterials development, including sustainable peptide semiconductors for sensing, amorphous alloys for antibacterial implants, and electrocatalysts for green hydrogen. He actively integrates machine learning with AFM data to advance cancer diagnostics, emphasizing translational applications in biomedicine and energy. Recent publications (2024-2025) demonstrate interdisciplinary momentum toward sustainable nanomaterials for energy conversion and biomedical sensing. Key trends include seawater electrolysis catalysts using MBenes/borides, metal-free SERS platforms with peptide semiconductors, and electric-field-activated pathogen detection. His group pioneers techniques like fluid-phase 3D printing for hydrogel patterning and high-voltage KPFM adaptations, with consistent focus on fundamental electromechanical property characterization. Award highlights include: UCD College of Science Women in Science Mentoring Award (2024) Alexander von Humboldt Fellowship (2007) RMIT Foundation International Research Exchange Fellowship (2010) Two UT Battelle Team Awards (2006) Rodriguez coordinates core modules including Bio-inspired Technologies (2016-2025), AFM for Bionano (2016-2025), and Nanomechanics (2017-2025), using active-learning pedagogies. His research is funded through Science Foundation Ireland's GROW Supplement (2017), the OBRSS Research Support Scheme (2016-2023), and the Resolve-NP project grant (2023-2025) targeting lipid nanomedicine characterization at single-particle resolution. He leads the NanoFunction research group (nanofunction.org) within UCD's Conway Institute, focusing on nanoscale functional materials. Current projects involve peptide-based sensors, antimicrobial coatings, and electrocatalyst engineering, supported by collaborations with Zhang, Bao, and Brennan on the Resolve-NP grant. Rodriguez also contributes to SIMUFER (Single- and Multiphase Ferroics with Restricted Geometries) and maintains active professional society memberships.
Dr. Gene Kim is a Professor of Biomedical Engineering in Radiology at Weill Cornell Medicine . His research focuses on developing advanced magnetic resonance imaging (MRI) techniques for cancer diagnosis and treatment monitoring, particularly in breast and head/neck cancers. The Kim Laboratory investigates tumor microenvironment through dynamic contrast-enhanced (DCE-MRI) and diffusion MRI (dMRI) methodologies. Ph.D. in Biomedical Engineering, University of Southern California Postdoctoral Training in Cancer Imaging, University of Pennsylvania Key research areas include: Tumor vascular properties and water exchange kinetics Quantitative dMRI for cell viability assessment Adipose-tissue interactions in breast cancer Development of active contrast encoding (ACE-MRI) for comprehensive imaging Recent publications demonstrate expertise in high-resolution 3D imaging , deep learning applications , and temporal resolution optimization . The lab has received continuous funding from the National Cancer Institute through grants like R01CA219964 and UG3/UH3CA228699 . Notable methodological contributions include: Golden-angle radial sparse parallel (GRASP) MRI POMACE framework for cell size measurement ACE-MRI for integrated T1/flip angle quantification
Prof. Assaf Tal is a faculty member in the Department of Bio-Medical Engineering at The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University. His research focuses on developing advanced neuroimaging methodologies using magnetic resonance spectroscopy (MRS) and imaging (MRI) to investigate brain function and disease mechanisms. His primary research interests center on neuroimaging physics and brain disease monitoring , with specific emphasis on: Developing novel MRS/MRI techniques combining spin physics and signal processing Tracking neurochemical changes during cognitive processes Detecting and monitoring neurodegenerative diseases including multiple sclerosis, traumatic brain injury, and Alzheimer's Disease Understanding brain encoding mechanisms across neurochemical, electrophysiological, and structural levels His work bridges biomedical engineering with clinical neuroscience to create improved diagnostic and monitoring tools. Prof. Tal's recent publications (2022-2025) demonstrate strong focus on functional MRS and advanced spectral-temporal analysis , with significant contributions to motion correction, uncertainty estimation, and microstructural modeling in neuroimaging. His research shows increasing integration of computational methods like Bayesian inference and machine learning for precision neuroimaging. His laboratory develops specialized software tools including the Visual Display Interface (VDI) for MRS data processing and simulation, supporting both preclinical and clinical neuroimaging research.
Jean-Baptiste Colliat is a Full Professor at Polytech Lille , Lille University, specialized in Numerical Simulation of Materials and Structures within Civil Engineering. His work bridges computational mechanics with practical applications in concrete durability, geothermal systems, and biomedical simulations. Develops Enriched Finite Element Methods for heterogeneous materials Focuses on Uncertainty Quantification in multi-scale systems Key applications: Nuclear Waste Containment , Rockfill Stability , and Obstetric Biomechanics Recent research trends include 3D fracture network modeling, stress-permeability coupling in porous media, and stochastic analysis of material heterogeneity. His work integrates X-ray Micro-CT , DEM , and Multiscale Homogenization techniques. Professor Colliat leads computational frameworks for Embedded Finite Element Methods and Excursion Set Theory applications. He actively collaborates with LaMcube (Laboratoire de Mécanique Multi-physique Multiéchelle) on problems ranging from microstructural evolution to large-scale infrastructure failure.
Nathan Castro is a researcher affiliated with Queensland University of Technology (QUT) , focusing on biomedical engineering and regenerative medicine. His work bridges advanced manufacturing techniques like 3D/4D printing with biomaterials to develop functional tissue engineering solutions. Research Interests Castro specializes in 3D/4D bioprinting of patient-specific scaffolds Biomimetic and bioactive material design Smart polymers for dynamic tissue regeneration Multi-scale structural optimization in regenerative medicine Recent Publication Trends Castro's articles between 2015-2021 demonstrate expertise in additive manufacturing applications for osteochondral regeneration , neural engineering , and cancer-bone interaction modeling . His work integrates computational modeling with experimental validation, emphasizing mechanical property optimization and biological functionality. Collaborations He has collaborated extensively with Professor Dietmar Hutmacher (QUT) and Professor Lijie Grace Zhang (George Washington University), contributing to over 46 publications in high-impact journals like Advanced Functional Materials , Nanoscale , and ACS Applied Materials and Interfaces .
