Weiyu Xu is a Professor at the University of Iowa, affiliated with both the Department of Electrical and Computer Engineering and the Department of Applied Mathematical and Computational Sciences. He joined the College of Engineering in 2012 and leads the Intelligent Information Processing Lab (IIPL). Ph.D., Electrical Engineering, California Institute of Technology, 2009 M.S., Electrical Engineering, California Institute of Technology, 2006 M.S., Electronic Engineering, Tsinghua University, 2005 B.E., Information Engineering, Beijing University of Posts and Telecommunications, 2002 His research focuses on compressive sensing, information theory, signal processing, network optimization, and deep learning applications in medical imaging and cybersecurity. He has made significant contributions to adversarial attack robustness, distributed optimization algorithms, and medical imaging techniques for OCT segmentation and brachytherapy. Recent publications highlight trends in Adversarial machine learning Medical imaging algorithms Compressed sensing for diagnostics Optimization in wireless communication He has also contributed to federated learning over tree networks and theoretical guarantees for sparse signal recovery. Weiyu Xu's lab, IIPL, integrates deep learning with classical optimization and signal processing. His work bridges foundational theories (e.g., information-theoretic robustness) with real-world applications in healthcare (e.g., cancer treatment planning) and communication systems (e.g., MIMO channel estimation).
Professor Ravi Shukla is a faculty member at RMIT University's School of Science, holding the title of Professor and Deputy Head of Department (Research). He specializes in Nanobiotechnology, with research spanning biomaterials, drug delivery systems, and medical diagnostics. His work integrates biosciences, materials science, and food technology to advance understanding of nanomaterial-biomolecular interactions. Academic History: Professor Shukla has held roles at RMIT since 2011, progressing from Research Fellow to his current professorship. He also serves as an Adjunct Professor at the University of Missouri and Theme Leader for Nanobiotechnology at RMIT’s Center for Advanced Materials and Industrial Chemistry. His teaching focuses on fostering student belonging and innovation in biotechnology education, including coordinating RMIT’s undergraduate Biotechnology program. Research Interests: His lab explores hybrid biomaterial synthesis, nano-enabled proteomics, and non-viral gene therapy using MOFs. Recent work emphasizes applications in diabetes biosensing, CRISPR/Cas9 delivery, and antimicrobial resistance mitigation through nanostrategies. Over 130+ publications and substantial research funding highlight his interdisciplinary impact. Professional Engagement: Editor roles in Frontiers in Bioengineering and Biotechnology , Co-Editor-in-Chief of Current Research in Nutrition and Food Science , and advisor to the Australasian Association of Ayurveda underscore his leadership. He actively mentors students in projects like nano-antimicrobial wound healing and aptamer-based hepatitis A detection. Key Achievements: Pioneered nucleic acid-encapsulated MOFs for cancer therapy and developed paper-based biosensors for rapid diagnostics. His work aligns with UN Sustainable Development Goals 2 (Zero Hunger) and 3 (Good Health).
Prof. Alexander Geissler holds the position of Full Professor of Health Care Management at the School of Medicine (Med-HSG) within the University of St. Gallen. His research focuses on health systems research, health economics, and health policy, with particular emphasis on digital transformation in healthcare and patient-reported outcomes. He has contributed extensively to studies on healthcare quality improvement, public reporting systems, and the integration of artificial intelligence in medical diagnostics and screening programs. His work spans topics like optimizing hospital digital maturity (e.g., German DigitalRadar project), analyzing surgical outcomes (robotic vs. open prostatectomies), and evaluating patient empowerment through quality information. He has pioneered methodologies for interpreting patient-reported outcomes (e.g., EQ-5D-3L) and designing clinical dashboards to enhance care delivery. Recent research highlights include investigating AI applications in breast cancer screening and cost-effectiveness of remote patient monitoring post-joint replacement surgery. Geissler’s publications demonstrate a strong focus on healthcare policy implications, such as hospital capacity planning, payment systems for specialized care, and cross-country comparisons of healthcare transparency initiatives. His work frequently bridges academic rigor with practical policy recommendations, particularly in Switzerland and Germany. Notably, he has addressed low-value care reduction, price sensitivity in healthcare demand, and the socio-demographic factors influencing healthcare utilization. While no specific awards are listed, his prolific research output reflects sustained leadership in health systems analysis. His academic contributions are disseminated through the Alexandria Research Platform and international peer-reviewed journals.
Dr. Yfke Ongena is a Senior Lecturer in Communication and Information Sciences at the University of Groningen's Faculty of Arts. She holds a PhD from Vrije Universiteit Amsterdam (2005) and has extensive postdoctoral experience, including at the University of Nebraska-Lincoln (2006). Her research focuses on survey methodology, questionnaire design, and interviewer-respondent interaction. Key projects include the NWO-funded 'Mixed modes in the European Social Survey' (2011–2015) and current work on socially desirable responses in surveys. Education: BSc/MSc in Communication Science (University of Groningen), PhD in Survey Methodology (VU Amsterdam). Awards include the GOR25 Poster Award (2025). She has authored over 55 publications, with recent work addressing AI in medical diagnostics, physician cost awareness, and gender equity in academic careers. Research Interests: Survey design challenges, measurement bias, representation in surveys, and the integration of AI in healthcare. Active in professional networks like the European Survey Research Association and GESIS. Grants and Projects: NWO-funded projects, ESRA conference involvement, and collaborations with institutions like the University of Twente and Groningen Radiology departments. Currently investigates patient perspectives on AI in prostate cancer diagnosis and residency training programs in radiology.
Kiemeney Lambertus is a Professor of Cancer Epidemiology at the Department of Health Evidence, Radboud University Medical Center in Nijmegen, Netherlands. He serves as Chair of the Department of Health Evidence and Scientific Director of the Radboud Institute for Health Sciences. His academic career includes roles as Head of the Department of Cancer Registry and Research, and Director of Research at the Netherlands Comprehensive Cancer Organisation. Lambertus holds a Master's in Biomedical Sciences (cum laude) from Radboud University and a Bachelor's in Physiotherapy (cum laude). His research focuses on urological cancers, genetic and clinical epidemiology, and biobanking. Key areas include prostate and bladder cancer genetics, risk prediction models, and validation of biomarkers like PCA3. He has pioneered studies on FGFR3 and JAG1 genes in bladder cancer susceptibility, and demonstrated the safety of coal tar treatments in dermatology. His over 500 publications include foundational work in genome-wide association studies (GWAS), with a Nature Genetics paper on bladder cancer susceptibility genes cited over 200 times. Lambertus has received global recognition including the Dominique Chopin Distinguished Researcher Award and inclusion in Thomson Reuters' most influential scientists list. Teaching Excellence: Multiple lecturer-of-the-year awards highlight his educational contributions. Leadership: Oversee large research institutes and national cancer initiatives. Global Impact: Collaborates with institutions worldwide, including Johns Hopkins University and the European Institute of Oncology. Current research emphasizes translational studies linking genetic discoveries to clinical outcomes, with a focus on reducing overdiagnosis in prostate cancer screening.
Roxanne Wadia, MD, holds dual roles as a Clinical Fellow in Pathology and Clinical Assistant Professor of Medicine (Medical Oncology) at Yale School of Medicine. Her expertise spans anatomic and cytopathology, with a focus on cancer diagnosis and treatment guidance. She completed her medical degree at Vanderbilt University School of Medicine (2008) and is board-certified in Anatomic Pathology (2023) and Cytopathology (2024). Dr. Wadia’s research integrates oncology, HIV/AIDS, and medical informatics, leveraging large datasets such as the VA’s Million Veterans Program to improve cancer care outcomes. She actively contributes to clinical care at Yale New Haven Hospital, the VA Connecticut Healthcare System, and the Hospital of Saint Raphael’s, emphasizing holistic patient care and interdisciplinary collaboration. Her research explores prostate cancer prognosis, HIV-related malignancies, and healthcare analytics. Key projects include developing predictive tools for nonmetastatic prostate cancer mortality and studying lung cancer surgery outcomes in HIV-positive patients. Dr. Wadia also mentors trainees in oncology and hematology, emphasizing independent clinical decision-making. She participates in the VA’s clinical training programs for medical students, residents, and fellows, fostering a balance between clinical practice and research innovation.
Dr. Chad J. Brenner is an Associate Professor in the Department of Otolaryngology-Head and Neck Surgery and Pharmacology at the University of Michigan Medical School. He also directs the U-M Program in Cellular and Molecular Biology, the Otolaryngology Clinical Laboratory (CLIA), and the Head & Neck Oncology Program. His research focuses on developing liquid biopsy tools for cancer detection and precision therapy, particularly in HPV-driven head and neck cancers. Key roles include membership in the Rogel Cancer Center, Kresge Hearing Research Institute, and Center for Computational Medicine & Bioinformatics. Education: B.S. in Biomedical Engineering, M.S. in Bioelectrical Engineering, and Ph.D. in Cellular and Molecular Biology from the University of Michigan, with doctoral work on prostate cancer mechanisms. Research Interests: HPV integration and cancer heterogeneity Urine- and blood-based liquid biopsies for real-time cancer monitoring Combination immunotherapy strategies for improving checkpoint inhibitor responses Genetic engineering to identify cancer vulnerabilities Clinical trial innovation for adaptive therapies Recent Work Highlights: Development of the MyHPVscore blood test for HPV-related head and neck cancer detection, and exploration of tumor-immune interactions through PD-L1 and T-cell profiling. Ongoing projects include PET-guided radiotherapy optimization and multi-omics analyses of tumor heterogeneity. Labs & Teams: Leads the Michigan Otolaryngology and Translational Oncology (MiOTO) lab and collaborates with interdisciplinary teams across computational medicine, immunology, and clinical oncology.
Yang Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI), working under Prof. Katya Krupchyk. His research focuses on inverse problems in imaging sciences, nonlinear hyperbolic equations, and medical imaging applications. He previously held a postdoctoral position at the University of Washington, Seattle, under Prof. Gunther Uhlmann. His work integrates microlocal analysis and partial differential equations to address challenges in wave propagation, nonlinear acoustics, and elasticity. Education & Career: PhD from Purdue University (advisor: Prof. Plamen Stefanov) Postdoc: University of Washington, Seattle (2020–2024) Research Interests: Dr. Zhang's work spans inverse problems for nonlinear hyperbolic equations, acoustic imaging, and integral transforms in medical contexts. He develops novel methodologies using multi-fold linearization, wave interactions, and advanced calculus techniques. His studies on Rayleigh and Stoneley waves in elasticity further demonstrate his expertise in microlocal analysis. Key Contributions: His research bridges theoretical mathematics and applied imaging, with notable publications on inverse scattering, damping effects in wave equations, and Compton camera imaging. He is an active member of the Inverse Problems International Association (IPIA). Grants & Collaborations: Collaborations with prominent figures like Prof. Gunther Uhlmann and Prof. Katya Krupchyk highlight his network in inverse problems. His work often involves both analytical and computational approaches, with applications in medical diagnostics and geophysics.
Indrani Bhattacharya, PhD, is an Assistant Professor in the Department of Biomedical Data Science and the Center for Precision Health and Artificial Intelligence (CPHAI) at Dartmouth College's Geisel School of Medicine. Her research focuses on developing human-centered AI systems for healthcare, particularly in multimodal medical imaging and behavioral health analytics. She holds a BS in Electrical Engineering from Jadavpur University (India), and MS/PhD from Rensselaer Polytechnic Institute (USA). Postdoctoral training at Stanford University's Department of Radiology further specialized her in biomedical imaging informatics. Research interests include: Integrating imaging and non-imaging data for precision medicine AI-driven prostate cancer detection/classification Multimodal behavior estimation for doctor-patient interactions Privacy-preserving sensor systems for group interaction analysis Her work bridges computer vision, medicine, and social science, with recent breakthroughs in MRI-ultrasound fusion AI outperforming radiologist interpretations in multi-center studies. Active in AI ethics and translational research, she leads teams developing clinical decision support tools for oncology and behavioral health. Key career milestones include: Postdoctoral scholar at Stanford University School of Medicine (2016-2021) Academic research staff at Stanford Radiology (2021-2022) Founding member of Dartmouth CPHAI precision health initiatives Labs/Teams: Leads the Biomedical AI for Healthcare group at Dartmouth, collaborating with Stanford and industry partners on AI-driven diagnostic systems.
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.
Dr. Tong Sun is an Assistant Professor of Pathology at Yale School of Medicine, specializing in surgical pathology and cytopathology with a focus on gynecological and genitourinary pathology. Her research emphasizes translational and molecular studies in cancer diagnostics, including the application of next-generation sequencing and molecular markers in cytology. Education & Training: M.D. from Peking University Health Science Center (2002) Ph.D. in Molecular Oncology from Peking Union Medical College (2007) Postdoctoral Research Fellow at Dana-Farber Cancer Institute (2011) Clinical and Anatomic Pathology Residency at University of Massachusetts Medical School (2019) Cytopathology Fellowship at Massachusetts General Hospital (2020) Research Interests: Dr. Sun’s work bridges clinical practice and research, focusing on improving diagnostic accuracy through molecular profiling and cytological techniques. Key areas include: Genetic markers in castration-resistant prostate cancer Application of the Paris and International Systems for Urine/Serous Fluid Cytopathology Next-generation sequencing in urothelial and gynecologic malignancies Cytomorphologic challenges in transgender health and histological correlation Publications & Trends: Over 50 peer-reviewed articles and 5 book chapters, highlighting advancements in cervical cytology, thyroid nodules, endometrial cancer prognosis, and ovarian cyst diagnostics. Recent work emphasizes molecular features of pancreatic and lung carcinomas. Awards: Warren R. Lang Resident Physician Award (2020) Journal of American Society of Cytopathology Physician-in-training Award (2022) Grants & Labs: Collaborates with Yale’s Gelb Genitourinary Translation Research Center and frequently publishes with co-authors like Guoping Cai and Adebowale Adeniran. No specific lab mentioned, but active in interdisciplinary cancer research teams. Future Work: Expanding studies on KRAS mutations in biliary stenosis, SMARCA4-deficient lung carcinomas, and clinical validation of novel diagnostic tools in serous fluid cytology.
Daniel B. Vigneron, PhD is a Professor at the University of California, San Francisco (UCSF) Department of Radiology and Biomedical Imaging. He serves as Director of the Hyperpolarized MRI Technology Resource Center (HMTRC), Director of Human Imaging Core Services, Director of Advanced Imaging Technologies SRG, and Operations Director of the Surbeck Laboratory for Advanced Imaging. As a core member of the UCB/UCSF Graduate Group in Bioengineering, Vigneron has established himself as a leader in molecular imaging research with over three decades of experience at UCSF. Vigneron's research focuses on developing advanced functional and metabolic MRI techniques, particularly hyperpolarized carbon-13 technology, for studying prostate cancer, brain tumors, and other diseases. His work enables non-invasive imaging of metabolic processes, allowing clinicians to monitor therapy effectiveness and guide treatments. The HMTRC, which he founded in 2011 with NIH funding and recently secured a 5-year renewal for, has supported 20 external projects domestically and 15 internationally, produced 239 publications, and trained 149 researchers. Vigneron's lab develops novel coil and software techniques for high-field MRI, MR spectroscopy, and diffusion imaging at 3T and 7T for studying brain, prostate cancer, and other organs. His recent publications demonstrate a clear trajectory toward clinical translation of hyperpolarized carbon-13 MRI across multiple organ systems. The research spans abdominal imaging with advanced denoising techniques, cardiac metabolism studies, whole-brain coverage applications, and cerebral perfusion analysis. This work represents a significant shift from basic science toward practical clinical applications in oncology, cardiology, and neurology, with particular emphasis on standardization for multi-center studies. Scientific Awards: 2022 Outstanding Faculty Mentoring Award from UCSF Department of Radiology and Biomedical Imaging Vigneron has mentored 149 trainees throughout his career, with several former students now serving as faculty members including Duan Xu, Peder Larson, and Susan Noworolski. As Principal Investigator overseeing eight grants, he has secured significant NIH funding for the HMTRC and other research initiatives. His administrative leadership extends to co-chairing the department's Safety and Compliance Committee, where he has helped establish robust safety protocols for PET-MR programs. Vigneron's mentoring philosophy emphasizes adapting to individual needs at different career stages, moving from instructor to coach to manager to cheerleader as trainees progress. The Vigneron Lab, located in Byers Hall on the UCSF Mission Bay campus, operates within the Surbeck Laboratory for Advanced Imaging. The lab group develops novel acquisition techniques and hardware for multinuclear MR spectroscopy, with particular focus on hyperpolarized carbon-13 metabolic imaging. The HMTRC serves as a hub for team science, bringing together researchers from diverse disciplines to advance metabolic imaging technology and its clinical applications.
Ben Raphael is a Professor in the Department of Computer Science at Princeton University, with affiliations at the Lewis-Sigler Institute for Integrative Genomics, Omenn-Darling Bioengineering Institute, and Center for Statistics and Machine Learning. He is also an Affiliate Faculty member at the Rutgers Cancer Institute of New Jersey, Irving Institute for Cancer Dynamics at Columbia University, and New York Genome Center. His research focuses on computational methods for analyzing large-scale biological data, emphasizing cancer evolution, network/pathway analysis, and structural variation in genomes. Research Trends: His recent work spans cancer lineage trees, spatial transcriptomics, optimal transport for developmental models, and network analysis of mutations. Articles highlight applications in prostate cancer, pancreatic cancer, and single-cell genomics. Scientific Awards: 2024 ACM Fellow 2023 RECOMB Test of Time Award 2022 RECOMB Test of Time Runner-Up 2021 ISCB Innovator Award 2021 RECOMB Best Paper Runner-Up 2020 ISCB Fellow 2020 AACR Team Science Award 2011 NSF CAREER Award 2013 RECOMB Best Paper 2010-2012 Sloan Research Fellowship Advising: He has mentored numerous Ph.D. students and postdoctoral fellows, many of whom have transitioned to academic and industry roles. Current advisees include Uthsav Chitra, Gillian Chu, and Alexander Strzalkowski. Labs & Teams: Raphael leads the Raphael Lab at Princeton, developing tools like HotNet2, CHISEL, and HATCHet for cancer genomics and network analysis.
Dr. Ulas Bagci is an Associate Professor at Northwestern University's Feinberg School of Medicine, Department of Radiology. He holds courtesy appointments in Biomedical Engineering (BME), Electrical and Computer Engineering (ECE) at Northwestern, and Computer Science at the University of Central Florida. As the director of the Machine and Hybrid Intelligence Lab, his research focuses on AI and machine learning applications in biomedical and clinical imaging. Education: BS: Bilkent University (2003) MS: Koç University (2005) Fellow: University of Pennsylvania (2009) PhD: University of Nottingham (2010) ISTP Fellow: NIH (2012) Research Interests: Dr. Bagci’s work spans artificial intelligence, machine learning, and their integration into medical imaging workflows. His lab develops algorithms for tumor segmentation, radiomics analysis, and ethical AI frameworks in healthcare. Notable projects include large-scale MRI segmentation of cirrhotic livers and predictive models for clinical outcomes in oncology and cardiology. Publications: His recent work emphasizes AI-driven solutions for challenges in radiology, including lung disease detection, pulmonary embolism mortality prediction, and ethical considerations in foundational AI models. His articles reflect a focus on bridging clinical needs with advanced computational methods. Lab & Affiliations: The Machine and Hybrid Intelligence Lab collaborates with the Robert H. Lurie Comprehensive Cancer Center. Research themes include federated learning, medical image synthesis, and AI ethics in clinical decision-making.
Brian Kirby is the Meinig Family Professor in the Department of Mechanical Engineering at the College of Engineering, Cornell University. He is a leading researcher in microfluidics, biomedical engineering, and cancer diagnostics, with a strong emphasis on circulating tumor cells (CTCs), rare cell isolation, and biophysical forces in disease. His work bridges engineering, biology, and clinical medicine. Institution: Cornell University School: College of Engineering Department: Mechanical Engineering Rank: Professor Education: Stanford University, 2001 Brian Kirby's research focuses on developing and applying microfluidic technologies to solve biomedical challenges. His work centers on microfluidic rare cell capture , particularly circulating tumor cells (CTCs) , enabling early cancer detection and monitoring treatment response. He investigates biophysical forces such as shear stress and surface interactions in conditions like thrombosis and cancer metastasis. His lab also works on dielectrophoresis , acoustophoresis , and electrokinetics for cell separation and analysis. Additional interests include bioinstrumentation , lab-on-a-chip devices , and fluid mechanics in biological systems . His recent publications show a consistent focus on microfluidic diagnostics, cancer biophysics, and smart fluid systems. Articles span topics from CTC isolation in prostate and pancreatic cancers to thrombosis in medical devices and programmable viscosity metamaterials . The research integrates engineering design with clinical applications, often involving interdisciplinary collaboration. Scientific Awards: Creative Teaching Award, Cornell Center for Teaching Innovation Advising Award, College of Engineering, Cornell University, 2015 Research Award, College of Engineering, Cornell University, 2015 Brian Kirby is actively involved in advising and research mentorship. While specific student names are not listed in the provided text, his extensive publication record and leadership of a research group indicate active supervision of graduate students and postdoctoral researchers. His research is supported by grants related to cancer diagnostics, microfluidics, and biomedical engineering, though specific grant details are not provided. He has contributed to the development of novel microfluidic devices such as the GEDI (Geometrically Enhanced Differential Immunocapture) platform for CTC capture and functional analysis. Labs and Teams: Kirby leads a research laboratory at Cornell focused on microfluidics and biomedical instrumentation. His team develops and applies microfluidic platforms for clinical diagnostics, particularly in oncology and hematology. The lab collaborates with clinicians and scientists across disciplines to translate engineering innovations into medical applications.
