Dr. Saree Alnaghy is an Honorary Associate Professor at the University of Wollongong's Faculty of Engineering and Information Sciences, affiliated with the Centre for Medical Radiation Physics. She holds a Bachelor of Medical and Radiation Physics (Honours) and a PhD in Physics from the University of Wollongong (2009–2017). Her research focuses on advanced medical imaging and radiation therapy technologies, including photon counting detectors, dosimetry systems, and robotic motion phantoms for quality assurance. Key research areas include: Development of novel X-ray detectors for radiotherapy guidance High-resolution dosimetry techniques using silicon and polymer-based systems Integration of real-time imaging in radiation therapy Robotic systems for motion management in oncology Her work has been supported by grants such as 'Sharper Targeting, Brighter Future' (2024) and 'Bringing Colour to Radiotherapy' (2021–2025). She currently supervises PhD and MRes students on projects involving photon counting CT scanners and radiotherapy imaging. Alnaghy also serves as a Radiation Oncology Medical Physics Registrar at the Nelune Comprehensive Cancer Centre.
Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award
Daniel Razansky is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, leading the Professorship for Biomedical Imaging. His research spans engineering, physics, biology, and medicine, focusing on developing advanced in vivo imaging tools like optoacoustic tomography and ultrasound neuromodulation. His recent work emphasizes multi-scale functional and molecular imaging , with applications in neuroscience , Alzheimer’s disease , and stroke diagnostics . Collaborations include National Tsing Hua University and the EU Horizon consortium SWEEPICS. Current projects target hybrid imaging systems (e.g., MRI-MSOT) and image-guided neuromodulation. Scientific awards include the IPPA James Smith Prize for his contributions. His lab has secured significant grants, including a $2.5M NIH award and SNSF funding. He mentors PhD students like Quanyu Zhou and Eva Remlova, who have received accolades for their research. The Razansky Lab at ETH Zurich’s Preclinical Imaging Center explores medical microrobotics , dynamic fluid flow imaging , and neuroimaging techniques , aiming to bridge engineering with clinical applications.
Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.
Dr. Joanna Deaton Bertram is an Assistant Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering. She concurrently holds an Assistant Professor appointment in Surgery, underscoring her interdisciplinary commitment to advancing medical robotics. Dr. Bertram leads a research laboratory devoted to the design, modeling, and control of robotic systems for surgical and interventional applications, working closely with Duke’s clinical and engineering communities. Education Ph.D. in Robotics, Georgia Institute of Technology, 2024 M.S. in Mechanical Engineering, Georgia Institute of Technology, 2024 B.S. in Biomedical Engineering, Georgia Institute of Technology, 2018 Research Interests Dr. Bertram’s research program is centered on medical robotics , with particular emphasis on continuum robotics and image-guided interventions . Her work integrates novel mechanical design with advanced control algorithms and smart materials to create robotic systems capable of navigating complex anatomical pathways. A hallmark of her approach is the incorporation of real-time fiber-optic shape and force sensing (using Fiber Bragg Grating technology) to provide surgeons with unprecedented feedback during procedures. Application domains include steerable needles for brachytherapy , robotic guidewires for endovascular surgery , and pediatric neuroendoscopy . Publication Themes Across more than fifteen peer-reviewed articles, Dr. Bertram has systematically advanced the state of the art in surgical robotics , fiber-optic sensing , and robotic system modeling . Her 2024 tutorial on Nitinol and Tungsten tendon attachment techniques provides practical guidance for building highly articulated continuum robots, while her 2023 series on the COAST guidewire robot demonstrates model-based design and simultaneous shape/force sensing for large-deflection medical devices. Earlier work explored 3D-printed patient-specific robotic tools and carbon-nanotube flexible sensors, illustrating a trajectory from fundamental sensor research to full robotic system integration. Scientific Recognition & Collaboration Although no major external awards are explicitly listed, Dr. Bertram’s publications in top-tier venues such as IEEE Robotics and Automation Letters , IEEE Transactions on Medical Robotics and Bionics , and IEEE/ASME Transactions on Mechatronics attest to strong peer recognition. She actively invites motivated graduate students, post-docs, and research staff to join her lab, fostering an open and interdisciplinary environment. Advising & Grants Dr. Bertram’s lab is presently recruiting trainees at all levels. While specific funded grants are not enumerated, her dual departmental appointments and extensive publication record suggest active federal or foundation support. Prospective students and collaborators are encouraged to contact her directly at joanna.d.bertram@duke.edu . Laboratory & Teams Dr. Bertram directs a laboratory within Duke University’s Pratt School of Engineering that collaborates closely with clinicians in the School of Medicine. The group focuses on rapid prototyping of medical devices, in-vitro and ex-vivo validation, and translation of robotic technologies to the operating room.
Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology
Associate Professor Andre Kyme is an academic staff member in the School of Biomedical Engineering at The University of Sydney. His research focuses on developing enabling technologies for biomedical imaging, including motion compensation in MRI/PET, robotic platforms for image-guided therapy, and cross-disciplinary applications like plant salt uptake analysis using PET. He collaborates with institutions globally and advises students on projects like lameness detection in horses and AI-based motion correction. Research Interests: Kyme's work spans motion correction in medical imaging modalities, medical robotics integration with imaging systems, and innovative applications of imaging technologies in non-traditional fields. His team emphasizes leveraging advancements in computer vision, machine learning, and instrumentation to improve imaging performance and accessibility. Recent Projects: Current research includes MRI-compatible robotic platforms for therapy applications, AI-driven lameness detection in horses, and pediatric neuroimaging improvements. He leads the BREEZE initiative to enhance MRI accessibility for children with cerebral palsy through eye-gaze communication technology. Publications: His work spans 20+ years with over 50 peer-reviewed publications in journals like Physics in Medicine and Biology and IEEE Transactions. Key areas include PET/SPECT/CT motion correction algorithms, robotic systems for medical imaging, and novel imaging applications in plant science. Teaching: Kyme instructs core biomedical engineering courses including thesis supervision and capstone projects at both undergraduate and postgraduate levels. Labs/Teams: Active in the Brain and Mind Centre and Biomedical Imaging, Visualisation and Information Technologies groups at Sydney. Collaborates with industry partners like TeleMedVet and academic institutions including University of California Davis and Chinese University of Hong Kong.
Craig Jones is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering. He is affiliated with the Malone Center for Engineering in Healthcare and contributes to the Precision Medicine Analytics Platform's Imaging and Data Science Subcommittees. BSc in Computer Science and Mathematics from Simon Fraser University MSc in Medical Biophysics from the University of Western Ontario PhD in Physics from the University of British Columbia His research focuses on applying artificial intelligence and neural networks to medical image processing, particularly for MRI, CT, optical coherence tomography (OCT), and ultrasound datasets. Key areas include 2D/3D image processing, anomaly detection, segmentation, and uncertainty quantification, with clinical applications in neurosurgery, ophthalmology, and oncology. Projects span robotic imaging, neuroendoscopic guidance, and cancer boundary detection. Recent publications highlight advancements in vision-language models for 3D medical imaging, automated segmentation of venous malformations, and AI-guided neurosurgical tools. Articles emphasize multimodal data fusion, self-supervised learning, and federated learning for rare cancer analytics. He received a $310,000 Department of Defense grant in 2022 to develop AI-guided treatments for venous malformations. His work bridges clinical imaging domains and computer vision as a member of the Radiology AI Lab (RAIL), a collaborative effort across Johns Hopkins Hospital, the Whiting School of Engineering, and the Applied Physics Laboratory.
P. Murali Doraiswamy, MBBS, FRCP , is Professor of Psychiatry and Professor in Medicine at Duke University School of Medicine, Director of the Neurocognitive Disorders Program, Senior Fellow at the Duke Center for the Study of Aging and Human Development, and holds affiliate faculty appointments with the Duke Center for Applied Genomics & Precision Medicine, the Duke Microbiome Center, and the Duke Initiative for Science & Society. He is a Faculty Network Member of the Duke Institute for Brain Sciences and has advised major agencies including NIH, FDA, WHO, and the World Economic Forum. Research Focus Dr. Doraiswamy leads a multidisciplinary program that integrates advanced neuroimaging, multi-omics, digital therapeutics, and artificial intelligence to understand, predict, and prevent Alzheimer’s disease and related neurodegenerative disorders. His work spans: Development and validation of blood, CSF, imaging, and digital biomarkers for early detection and staging. Clinical trials of novel pharmacological, lifestyle, and digital interventions in mild cognitive impairment (MCI) and Alzheimer’s dementia. Systems-biology approaches combining genomics, metabolomics, lipidomics, and microbiome data to uncover mechanisms of resilience and risk. Policy translation and global mental health initiatives aimed at reducing disparities and improving brain health worldwide. Publications & Impact With more than 400 peer-reviewed publications and continuous federal and industry funding, his research has shaped current diagnostic algorithms and therapeutic pipelines. Recent work (2023-2025) demonstrates accelerated adoption of deep-learning MRI models for amyloid/tau staging, AI-guided companion robots to combat loneliness, and precision nutrition trials leveraging microbiome signatures. Scientific Leadership & Recognition He has chaired the World Economic Forum’s Global Agenda Council on Brain Research and co-chaired innovation advisory councils for large social-impact funds. His findings have been featured by BBC, The New York Times, Scientific American, TIME, NPR, CBS Evening News, Oprah, The Dr Oz Show , and acclaimed documentaries such as (Dis)Honesty: The Truth about Lies and Mysteries of the Brain . Advocacy & Societal Engagement Beyond the laboratory, Dr. Doraiswamy is a leading advocate for increased public and private investment in brain and behavioral research. He serves on multiple charitable boards and co-authored the popular book The Alzheimer’s Action Plan , translating cutting-edge science into practical guidance for patients and families.
Benoit Rosa is currently a CNRS Researcher within the Robotics, Data science, and Healthcare technologies Team at the ICube Laboratory, University of Strasbourg. Previously, he was a Research Fellow at the Pediatric Cardiac Bioengineering Lab, Boston Children's Hospital, Harvard Medical School (2015-2016), and a postdoctoral fellow in the Robot Assisted Surgery group at the Mechanical Engineering department of KU Leuven, Belgium (2013-2015). He received his Ph.D. in 2013 from Pierre & Marie Curie University (now Sorbonne University) under the supervision of Pr. Guillaume Morel and Pr. Jerome Szewczyk. His PhD was awarded the best PhD thesis award by the CNRS research group on robotics for 2013. Prior to his PhD, he obtained an Engineering Degree (equivalent to a Master's) from Ecole Centrale Paris. Rosa's research focuses on surgical robotics and image-guided control, with particular expertise in the design and control of miniature, distally-actuated and flexible systems for minimally invasive surgery. His work spans from mechatronic design of minimally invasive surgical devices to advanced control algorithms for surgical robots. Key areas include continuum robotics, visual servo control, surgical tool segmentation, and OCT-guided interventions. His research has significant applications in cardiac surgery, endomicroscopy, and various minimally invasive procedures, with a strong emphasis on translating theoretical robotics into practical clinical solutions. His recent publications demonstrate a growing trend toward applying deep learning techniques to enhance surgical robotics, with focus on autonomous systems that improve precision and reduce surgeon cognitive load while addressing challenges in medical imaging and surgical navigation. Scientific Awards: Best PhD thesis award by the CNRS research group on robotics (2013) Rosa has led multiple significant research projects including Image-based tracking of continuum robots (ongoing), Robot-assisted endomicroscopy (2010-2013), Beating heart intracardiac cardioscopy-guided interventions (2015-2019), and Intuitive control of active catheters (2014-2015). His work has resulted in numerous patents and collaborations with leading medical institutions worldwide, securing research funding for advancing surgical robotics technology. He actively participates in the academic community through invited talks and workshops, and maintains strong collaborations with institutions including Harvard Medical School, KU Leuven, and various French research entities, bridging theoretical robotics with practical clinical applications across multiple medical specialties.
Eric Barth is Professor of Mechanical Engineering and Professor of Neurological Surgery at Vanderbilt University's School of Engineering. He serves as Director of the C* Control laboratory (also known as the Laboratory for the Design and Control of Energetic Systems) and is affiliated with the Vanderbilt Institute for Surgery and Engineering (VISE), an interdisciplinary entity bringing engineers and physicians together to impact healthcare. His educational background includes: Ph.D. in Mechanical Engineering from Georgia Institute of Technology M.S. in Mechanical Engineering from Georgia Institute of Technology B.S. in Engineering Physics from University of California - Berkeley Professor Barth's research focuses on dynamic systems and control with applications spanning multiple domains. His primary interests include the design, modeling and control of mechatronic and fluid power systems, free-piston internal combustion and free-piston Stirling engines, energy storage and harvesting systems, and MRI compatible pneumatic robots for medical applications. His work applies a system dynamics and control perspective to problems involving the control and transduction of energy, encompassing multi-physics modeling, control methodologies formulation, and model-based design. His recent publications reveal a strong trajectory connecting mechanical engineering principles with medical applications, particularly in neurosurgery. The research spans energy systems (especially Stirling engines and novel energy storage approaches) and advanced medical robotics for MRI-guided interventions. This dual focus demonstrates his ability to bridge theoretical control systems with practical applications in both energy and healthcare domains. Professor Barth actively advises several doctoral students including David Comber, Joshua J Cummins, Alexander V. Pedchenko, and E. Bryn Pitt. His research is supported by significant funding, notably from the Center for Compact and Efficient Fluid Power, an NSF Engineering Research Center. The C* Control laboratory he directs occupies approximately 1000 square feet and contains specialized equipment including an 8-camera high-bandwidth optical tracking system, mechanical breadboard tables, pneumatic equipment with high-bandwidth servo-valves, specialized pressure sensors, a thermographic camera, high-speed video equipment, 3D printers, and a 2D laser cutter. Computational facilities include a network of approximately 20 machines running MATLAB/Simulink and SolidWorks, with access to additional CNC machining resources through the School of Engineering and the University.
Jon Heiselman is a Research Assistant Professor in the Department of Biomedical Engineering at Vanderbilt University School of Engineering. He serves as Associate Director of the Master of Engineering in Surgery and Intervention Program and leads research in image-guided surgical technologies. His work focuses on soft tissue deformation modeling, augmented reality applications, and computational frameworks for precision surgery. Education: PhD in Biomedical Engineering (Vanderbilt University, 2020) Advisor: Michael Miga, Harvie Branscomb Professor Research interests span image-guided surgical navigation, deformable registration algorithms, and digital twin modeling for therapeutic forecasting. Articles highlight advancements in soft tissue deformation correction, augmented reality integration, and machine learning approaches for real-time surgical guidance. Current affiliations include Vanderbilt's Biomedical Modeling Laboratory (BML) and the VISE Steering Committee. He contributes to NIH-funded training programs and has received recognition for his work in surgical data science and computational oncology.
Jef Vandemeulebroucke is a researcher at the Department of Electronics and Informatics , Vrije Universiteit Brussel (VUB) , specializing in medical imaging, computer vision, and augmented reality applications in healthcare. His work bridges artificial intelligence with radiology and biomechanics , focusing on automated segmentation, predictive modeling, and real-time surgical navigation systems. Research interests include: Medical image analysis for disease prognosis (e.g., COVID-19 severity , neurosurgical drains ) Development of MedShapeNet , a 3D medical shape dataset for computer vision Augmented reality systems in orthopedic and neurosurgical interventions AI-driven fluorescence endoscopy and dynamic CT for joint kinematics Key trends in his 140+ publications emphasize deep learning , image registration , and 4D-CT applications . Supervised theses include brain age prediction and chest radiography automation. Active in 38 projects (e.g., AI-NIMO , TumorScope ), he collaborates with institutions like the Universitair Ziekenhuis Brussel (UZB) and FWO (Fund for Scientific Research-Flanders).
Dr. Mihailo Ristic is a Senior Lecturer in the Department of Mechanical Engineering at Imperial College London, part of the Faculty of Engineering. He holds a First Class Honours Degree in Mechanical Engineering from University College London (1981), an M.Sc. in Control Systems (Imperial College London, 1982), and a Ph.D. in Robotics (Imperial College London, 1986). His research spans control systems, CAD/CAM, robotics, and medical engineering, with recent focus on Magnetic Resonance Imaging (MRI) systems and mechatronic devices for clinical applications. Dr. Ristic is a Chartered Engineer and Fellow of the Institution of Mechanical Engineers. He co-founded Turbo Power Systems, specializing in high-speed electric machines and power electronics. His work integrates robotics, medical imaging, and advanced manufacturing, addressing challenges in distributed power generation and biomedical device design. Key projects include novel MRI magnet configurations, intraoperative MRI tools, and robotic systems for MRI-guided interventions. Education: B.Sc. (First Class Honours) Mechanical Engineering, University College London (1981) M.Sc. Control Systems, Imperial College London (1982) Ph.D. Robotics, Imperial College London (1986) Affiliations: Robotics Forum CAD/CAM Research Group Mecheatronics in Medicine His research interests include medical engineering innovations, such as MRI system design, collagen fiber analysis, and robotic-assisted surgery. He has contributed to over 50 publications in journals and conferences, focusing on imaging technologies, robotics, and mechatronics. His work bridges academic research and industrial applications, particularly in energy systems and biomedical devices. Dr. Ristic’s awards include the Fellowship of the Institution of Mechanical Engineers. His grants and projects involve collaborations with industry partners to advance MRI hardware, teleoperated surgical systems, and energy-efficient power solutions.
Professor Pietro Valdastri is a Full Professor and Chair in Robotics & Autonomous Systems at the University of Leeds. He directs the Science and Technologies Of Robotics in Medicine (STORM) Lab, the Institute of Robotics, Autonomous Systems and Sensing (IRASS), and the Robotics at Leeds network. His expertise spans surgical robotics, robotic endoscopy, and magnetic manipulation, with a focus on developing soft magnetic surgical robots (SMSRs) for minimally invasive medical procedures. Valdastri holds a Laurea in Electronic Engineering (University of Pisa, 2001) and a PhD in Biomedical Engineering (Scuola Superiore Sant’Anna, 2006). He previously served as Assistant Professor at Scuola Superiore Sant’Anna (2006–2011) and Vanderbilt University (2011–2016) before joining Leeds in 2016. His research aims to leverage magnetic actuation for surgical navigation, with applications in cancer therapy and endoscopy. Key awards include the NSF CAREER Award (2015), ERC Consolidator Grant (2019), and KUKA Innovation Award (2019). He is a Royal Society Wolfson Research Fellow, IEEE Fellow, and editor for IEEE Robotics and Automation Letters. His work has been featured in major media outlets like the BBC, New Scientist, and WIRED. Valdastri has secured over £25M in grants from NSF, NIH, ERC, EU-H2020, and industry partnerships. He co-founded WinMedical srl (acquired in 2017) and Atlas Endoscopy Limited, advancing robotic colonoscopy platforms. Current research emphasizes autonomous robotic systems, magnetic tentacles for lung therapy, and patient-specific surgical tools.
