Dr. Ruchit Agrawal is an Assistant Professor of Computer Science and Head of Computer Science Outreach at the University of Birmingham Dubai. Previously, he held roles including Postdoctoral Research Scientist at the University of Oxford, Marie-Curie Researcher at Queen Mary University of London, and AI Researcher at FBK, Italy. His research focuses on Multimodal Deep Learning, Adaptive AI, NLP, and Clinical Machine Learning. He earned a Ph.D. from Queen Mary University of London and M.S. and B.Tech degrees from IIIT Hyderabad. Education: Ph.D. in Computer Science (Queen Mary University of London), M.S. in Computer Science (IIIT Hyderabad), B.Tech. in Computer Science (IIIT Hyderabad). Research interests include AI-driven healthcare solutions, contextualized machine learning systems, and cross-lingual NLP. His work spans healthcare informatics, music informatics, and machine translation. He has collaborated on projects like MIP-Frontiers and contributed to advancements in multimodal stuttering detection and stock price prediction models. Professional roles also include software development stints at Google Summer of Code and teaching at IIIT Hyderabad. His outreach initiatives aim to promote computer science education and accessibility.
Dr. Ram Kiran Alluri is an Clinical Assistant Professor at the Keck School of Medicine, University of Southern California , specializing in Orthopaedic Spine Surgery at the USC Spine Center . His clinical focus includes cervical spine surgery , minimally invasive techniques , and robotic-assisted procedures for adult spinal deformity and complex revision surgery . Undergraduate & Medical Education: University of California Los Angeles (UCLA) Residency: Orthopaedic Surgery at USC Fellowship: Minimally Invasive & Adult Spinal Deformity at Hospital for Special Surgery Dr. Alluri's research emphasizes clinical outcomes analysis and navigational technology in spine surgery. With over 80 peer-reviewed publications and 130+ conference presentations , his work spans topics like AI in medical education , spin bias in systematic reviews , and robotic surgery efficiency . Key awards include the SMISS Young Surgeon Grant and AAOS Best Poster Award . He holds active memberships in AAOS , NASS , and AO Spine .
Wouter Wilson is a part-time Assistant Professor in the Orthopaedic Biomechanics group at TU/e, where he supervises PhD students in biomechanics and finite element modeling. His research examines cartilage mechanics, orthopedic implants, and tissue engineering solutions. He holds an MSc in Mechanical Engineering and PhD from TU/e, with previous industry experience at GKN/Fokker Landing Gear as Engineering Manager. Current research focuses on computational biomechanics of spinal interventions, biomaterial development, and implant optimization. Publications demonstrate expertise in patient-specific modeling, mechanical testing of engineered tissues, and implant performance analysis. Research consistently addresses clinical translation challenges in orthopedics through experimental and computational approaches. Currently at Sioux Technologies as Senior Group Lead while maintaining academic affiliations.
Riaz Khan is an Adjunct Professor at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing and the Department of Computer Science and Software Engineering. His research focuses on interdisciplinary applications of technology in healthcare, particularly in orthopaedic surgery, biomedical engineering, and medical imaging. He has contributed to advancements in surgical techniques, pain management, and rehabilitation technologies through collaborative projects. Key research areas include total knee arthroplasty optimization, precision bone shaping via laser ablation, smartphone-based motion tracking for post-surgical recovery, and analgesia protocols for orthopaedic patients. He has co-authored over 27 peer-reviewed publications and contributed to a Cochrane Review on patella resurfacing techniques. Dr. Khan has secured research funding, including a grant from Healthway for a smoking cessation initiative linked to hospital admissions (2009–2010). His work aligns with UN Sustainable Development Goals related to good health and well-being. Collaborations span biomedical optics, surgical innovation, and clinical outcome measurement.
Roles & Affiliations: Danial Roshandel holds the position of Adjunct Senior Research Fellow at the University of Western Australia (UWA) Medical School and the Centre for Ophthalmology and Visual Science (affiliated with the Lions Eye Institute). His academic journey includes roles as an Assistant Professor at Shahid Beheshti University of Medical Sciences (2015–2018) and Consultant Ophthalmologist at Kamkar Hospital (2014–2015). Primary University: The University of Western Australia Medical School: UWA Medical School Research Centre: Centre for Ophthalmology and Visual Science Education: Doctor of Philosophy (PhD) in Ophthalmology, UWA (2018–2022) Specialist in Ophthalmology, Shahid Beheshti University of Medical Sciences (2010–2014) Doctor of Medicine (MD), Golestan University of Medical Sciences (2003–2010) Research Interests: Focus on ocular surface disorders, limbal stem cell transplantation, dry eye disease, and genetic eye diseases. Key areas include biobanking for congenital aniridia (2022–present), retinitis pigmentosa progression monitoring (2018–2021), and corneal neovascularization (2014–2018). Publications & Research Trends: Recent work emphasizes stem cell therapies, adaptive optics imaging, and genetic predisposition studies. Notable contributions include studies on limbal stem cell differentiation and clinical applications of induced pluripotent stem cells. Awards: University Postgraduate Award (2018) Professional Activities: Serves as a peer reviewer for Scientific Reports , Retina , and Molecular Vision . Active in professional organizations like the Iranian Society of Ophthalmology and the Islamic Republic of Iran Medical Council. Labs & Collaborations: Collaborates with the Ocular Tissue Engineering Laboratory and the Lions Eye Institute. Research networks span Australia, Iran, and international partners in adaptive optics and genetic studies.
Andrew Ng serves as a Clinical Assistant Professor of Neurology (Clinician Educator) at the University of Southern California, focusing on the integration of artificial intelligence and robotics into surgical practice. His clinical educator role emphasizes advancing surgical training methodologies and performance assessment systems. His primary research interests include: Robotic surgery systems and implementation Artificial intelligence applications in surgical performance analysis Telesurgery frameworks and safety protocols Surgical training feedback mechanisms Urological and neurological surgical outcomes Objective assessment of surgical skills Analysis of his 2022-2025 publications reveals a dominant focus on AI-driven surgical performance metrics, particularly in robotic prostatectomy. His work establishes critical links between intraoperative technical skills and long-term patient outcomes, with significant contributions to nerve-sparing quantification and real-time feedback systems. The research consistently bridges computer vision, machine learning, and clinical surgical practice to develop predictive models for recovery metrics. Dr. Ng actively contributes to surgical consensus statements through organizations like the Society of Robotic Surgery, developing standardized frameworks for telesurgery implementation and surgical gesture analysis. His collaborative work spans engineering teams for AI tool development and clinical teams for surgical outcome validation, positioning him at the forefront of surgical technology translation.
Daniel A. Oakes, MD, is a Professor of Clinical Orthopaedic Surgery at the Keck School of Medicine of USC and serves as Director of the USC Joint Replacement Program. With expertise in knee and hip replacement/reconstruction, he pioneered robotic total knee surgery at Keck Medical Center in 2018 and focuses on computer-assisted navigation technologies, porous metal implants, and infection prevention. Harvard Medical School graduate Mayo Clinic fellowship (recipient of Mark B. Coventry Award) Dual expertise in clinical practice and translational research His research spans robotic surgery outcomes , implant longevity , and gene therapy for bone repair (notably AAV-based delivery systems). Publications from 2000-2025 reflect evolving priorities in joint replacement, including pandemic-era surgical trends and biofilm disruption technologies. Recognized as a Top Doctor by multiple organizations, he has received teaching excellence awards and served as Joint Replacement Fellowship Director. Current work emphasizes 3D-printed scaffolds , non-cemented stems , and metal artifact reduction MRI for prosthetic evaluation.
Mamoun T. Mardini is an Assistant Professor at the Institute on Aging , University of Florida. His research focuses on leveraging data science, wearable technologies, and machine learning to address challenges in gerontology and healthcare. Education : Ph.D. Computer Science (2018), University of North Carolina M.S. Computer Engineering (2013), American University of Sharjah B.S. Computer Engineering (2009), Jordan University of Science and Technology Research Interests : Dr. Mardini’s work emphasizes: Development of AI-driven frailty assessment tools for surgical patients Applications of smart wearables for monitoring mobility and pain in older adults Machine learning models for predicting cardiovascular outcomes and disparities Analysis of social determinants of health using real-world data Key Contributions : His studies include: Pioneering work on reducing unnecessary TEE procedures using AI Development of the ROAMM smartwatch platform for ecological momentary assessment Investigations into racial disparities in healthcare outcomes using ML Awards : No awards explicitly mentioned in the provided data. Labs/Teams : Active in the Division of Epidemiology and Data Science in Gerontology (EDGE) .
Linus Ohlsson is a Research intern physician and PhD-student at Linköping University Hospital and Linköping University. He is affiliated with the Department of Health, Medicine and Caring Sciences (HMV), specifically within the Division of Diagnostics and Specialist Medicine (DISP) and the Unit of Cardiovascular Sciences. His primary research explores computed tomography and visualization techniques to better understand the heart during mechanical assisting pump therapy for advanced heart failure patients. He has also conducted significant research on medical education, particularly on revitalizing pedagogy in medical problem-based learning curricula and enhancing students' understanding of cardiac physiology through 4D visualization techniques. Ohlsson completed his medical degree (MD) from Linköping University in 2024 and has held various positions including Research intern Physician at Linköping University Hospital, Junior Physician at the Department of Cardiothoracic Surgery and Department of Clinical Physiology, and Chairman of the CMIV Research School. He served as a Visiting Scientist at the CT Clinical Innovation Centre at Mayo Clinic in 2024. His publications focus on echocardiographic hemodynamic monitoring in HeartMate 3 therapy, haemodynamic significance of extrinsic outflow graft stenoses, and educational approaches in medical training. His work has appeared in journals such as ESC Heart Failure, European Heart Journal, and Clinical Anatomy. Ohlsson is actively involved in the Network for Medical Image Science and Visualization (CMIV), the CMIV-Research School, the Cardiovascular Imaging and Modelling research cluster (CIM), and the Circulation and Metabolism research group (CircM), contributing to interdisciplinary research that bridges medical practice and technological innovation.
Dana Kulic is a Professor in the Department of Electrical and Computer Systems Engineering at Monash University. She is also a Global Innovation Research Visiting Professor at the Tokyo University of Agriculture and Technology and the August-Wilhelm Scheer Visiting Professor at the Technical University of Munich. Her research focuses on developing autonomous systems for human-robot interaction (HRI), emphasizing safety, continuous learning from demonstrations, and rehabilitation technology. She co-founded the Adaptive Systems Lab at the University of Waterloo and co-led the NSERC Canadian Robotics Network. Her work addresses challenges in collaborative robotics, including safety quantification, non-expert learning, and rehabilitation applications. Projects include 'Natural Interaction Strategies for Long-Term Robot Use for Elderly Care' and 'Intelligent Robotics for Pharmaceutical Formulation Development.' Key research interests include human motion analysis, robot learning, and HRI. Recent studies explore auditory assistance for gait modification, contingent autonomy in uncertain environments, and multimodal communication for multi-robot systems. Her contributions align with UN Sustainable Development Goals, particularly in healthcare and innovation. Dr. Kulic has led multiple industry-collaborative grants, such as strategic projects in collaborative assembly and rehabilitation automation. She actively participates in interdisciplinary initiatives, including public engagement through events like 'Living with Robots' and media contributions on robotic limitations. Her laboratories, including the Waterloo Robohub, focus on advancing robotic capabilities through experimental methodologies and ethical frameworks. Current projects emphasize adaptive systems, shared autonomy, and robotic applications in healthcare and industrial settings.
Dr. Dema Govalla is a Visiting Assistant Professor in Electrical and Computer Engineering at University of Nevada, Las Vegas. Her research focuses on medical device development with expertise in haptic feedback systems for robotic surgery. Research areas include biosymbiotic interfaces, tactile feedback mechanisms for minimally invasive procedures, and AI-assisted surgical systems. Publications demonstrate consistent focus on improving human-machine interactions in medical contexts through sensor technology innovations. Expert areas include: Research and Development of Medical Devices, Integrated Circuit Design, Digital Signal Processing, Artificial Intelligence, Sensor Design, Human-robot Interaction and Python Development.
Prof. Dr. William R. Taylor is a Professor of Movement Biomechanics at ETH Zürich's Department of Health Sciences and Technology, where he also serves as the Head of the Department and Deputy Head of the Institute for Biomechanics. His primary focus is on developing medical engineering concepts to assess musculoskeletal deficits and movement pathologies, with applications in clinical decision-making and targeted therapies. He has pioneered techniques such as the REFRAME approach for knee implant design evaluation and dynamic videofluoroscopy for joint analysis. William R. Taylor holds a Ph.D. in Biomedical Engineering from the University of Bath (1999). His academic journey includes postdoctoral research at the Biomechanics Research Lab, Prince of Wales Hospital, Sydney, Australia, and leadership roles at the Julius Wolff Institute, Charité – Universitätsmedizin Berlin (2001–2012). His research interests span biomechanics of aging, Parkinson’s disease gait analysis, spinal alignment in scoliosis, and wearable sensor technologies. He leads the Laboratory for Movement Biomechanics, which develops innovative solutions for musculoskeletal and neurological conditions, including wheelchair steering systems and fall prevention interventions for the elderly. Prof. Taylor has received prestigious awards, including the European Society of Biomechanics SM Perren Award (2022) and ISB Clinical Biomechanics Award (2021). His work has produced over 160 peer-reviewed journal contributions, emphasizing interdisciplinary collaboration between engineering, clinical neuroscience, and data science. Current projects include optimizing subthalamic nucleus DBS for Parkinson’s gait improvement, assessing stress-related low back pain in occupational settings, and validating spinal alignment models like ScolioSIM. He also explores the impact of electrode placement, sensor technologies, and machine learning in functional outcome predictions.
Jiwei Zhao is an Associate Professor at the University of Wisconsin-Madison, jointly appointed in the Department of Statistics (School of Computer, Data & Information Sciences) and the Department of Biostatistics & Medical Informatics (School of Medicine & Public Health). He leads the Zhao Research Group, focusing on statistical and machine learning methods for biomedical data analysis. His work is supported by multiple NIH and NSF grants, including an NIH R01 project on patient-reported outcomes in vocal fold paralysis and a Distinguished Research Fellows Program at UW-Madison. Dr. Zhao earned his PhD in Statistics from UW-Madison in 2012 under Dr. Jun Shao. Prior to his current position, he was a Postdoctoral Researcher at Yale University (2012-2013) and held Assistant and Associate Professor roles at SUNY Buffalo (2014-2020). His academic leadership includes serving as Associate Editor for journals like the Annals of Applied Statistics and Scandinavian Journal of Statistics , and as Area Chair for AISTATS (2024-2025). His research interests span semiparametric methodologies, missing data analysis, causal inference, and domain adaptation. Key applications include patient-reported outcomes, electronic health records, clinical trials, and studies on aging, mental health, and cancer. His group collaborates with institutions like the Data Science Institute, Institute on Aging, and Center for Demography of Health and Aging. Recent work emphasizes efficient parameter estimation under privacy constraints, robust learning in nonignorable missing data scenarios, and leveraging machine learning for medical imaging and genetic studies. He actively recruits researchers across postdocs, PhD, and undergraduate levels to join his team.
Holden H Wu is a Professor in the Department of Radiological Sciences at the University of California Los Angeles (UCLA) School of Medicine. His research focuses on advanced medical imaging techniques, particularly in quantitative MRI, artificial intelligence applications, and image-guided interventions. He leads the UCLA MRRL Wu Lab and has established himself as a leading researcher in free-breathing MRI techniques for body composition analysis and disease quantification. Dr. Wu's research interests span nanotheranostics, quantitative imaging, MRI technology development, artificial intelligence applications in medical imaging, and image-guided interventions. His work particularly emphasizes developing motion-robust techniques for abdominal and pediatric imaging, with applications in liver fat quantification, body composition analysis, and prostate cancer imaging. He has pioneered several free-breathing MRI techniques that have eliminated the need for breath-holding in patients, significantly improving clinical applicability, especially for pediatric populations and patients with limited breath-holding capacity. His recent publications demonstrate a strong focus on integrating deep learning with physics-based modeling to improve quantitative MRI techniques. Major trends in his research include the development of self-gated radial MRI techniques, motion-compensated imaging methods, and AI-powered analysis tools for medical imaging data. His work bridges engineering innovation with clinical applications, particularly in liver disease, metabolic disorders, and oncology. New Technologies for Real-Time MRI-Guided Robotic-Assisted Abdominal Interventions (NIH R01EB031934, 2022-2026) - Principal Investigator Quantitative MRI and Deep Learning Technologies for Classification of NAFLD (NIH U01EB031894, 2022-2027) - Principal Investigator Quantifying Body Composition and Liver Disease in Children using Free-Breathing MRI and MRE (NIH R01DK124417, 2020-2024) - Principal Investigator Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification (NIH R01CA248506, 2020-2025) - Co-Principal Investigator Dr. Wu has mentored numerous students and researchers through his active laboratory, focusing on training the next generation of biomedical imaging scientists. His research group, the UCLA MRRL Wu Lab, develops innovative imaging technologies with direct clinical translation potential. Current projects include developing real-time MRI-guided robotic interventions, advanced quantitative techniques for liver fat and fibrosis assessment, and AI-powered prostate cancer detection methods.
C. Alberto Figueroa is the Edward B. Diethrich Professor of Surgery and Professor in the Department of Biomedical Engineering at the University of Michigan. His primary affiliation is with the College of Engineering, where he leads the Computational Vascular Biomechanics Lab. He holds a Ph.D. in Mechanical Engineering from Stanford University (2006). Research interests focus on computational modeling of vascular systems, including hemodynamics, biomechanics, and medical imaging analysis. His work integrates machine learning, computational fluid dynamics (CFD), and multi-scale modeling to study diseases such as aortic aneurysms, pulmonary hypertension, and cerebrovascular disorders. Key areas include vascular deformation mapping, surgical intervention planning, and AI-driven diagnostics for coronary and cerebrovascular diseases. Recent publications emphasize predictive models for aneurysm growth, hemodynamic effects in carotid stenosis, and AI-based 3D reconstruction from medical imaging. His lab develops tools like AngioNet for vessel segmentation and CRIMSON software for cardiovascular modeling. Collaborative projects address clinical challenges in endovascular repair, myocardial perfusion, and hypertension pathophysiology. Dr. Figueroa’s research spans academia and clinical practice, with applications in surgical decision-making, device design, and precision medicine. His work bridges engineering and medicine, aiming to translate computational insights into patient-centered solutions.
