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.
Simon Brewster serves as Senior Research Fellow and Consultant Urological Surgeon at the University of Oxford's Hertford College, where he has been Clinical Tutor since 2002 after serving as Lecturer (2002-2011). His clinical work focuses on prostate cancer at Oxford University Hospitals NHS Trust, delivering exam-focused bedside teaching for Years 4-6 medical students. Education: BSc in Anatomy (1st class), Charing Cross Hospital Medical School, 1983 MBBS (Hons. Pathology), Charing Cross Hospital Medical School, 1986 FRCS exams and MD research, Bristol (1988-1998) Brewster's research program demonstrates sequential evolution from basic science (1998-2007) investigating IGF1R targeting and Wnt signaling in prostate cancer to clinical translation (2011-2022) evaluating multiparametric MRI integration in diagnostic pathways and active surveillance protocols. His work bridges laboratory discoveries with patient-centered outcomes, particularly examining how imaging advancements impact treatment decisions and quality of life. The multidisciplinary nature of his clinical research involves close collaboration between urologists, oncologists, radiologists, and pathologists to optimize prostate cancer management. Analysis of his 15 most recent publications reveals three dominant themes: (1) MRI-driven diagnostic refinement including pre-biopsy MRI implementation and active surveillance reclassification, (2) comparative treatment efficacy studies evaluating focal therapy versus radical prostatectomy, and (3) national guideline development through NICE recommendations. His work consistently addresses real-world clinical challenges in prostate cancer management, with strong emphasis on patient satisfaction metrics and practical implementation within the UK healthcare system. Scientific Awards: BJUI Article of the Week (November 18, 2018) for MRI impact on active surveillance As an educator, Brewster has co-supervised four higher research degrees and served as Clinical Supervisor for junior surgical trainees since 1998, including Intercollegiate ISCP Educational Supervisor role since 2009. His grant portfolio includes the UK PART trial (focal therapy vs radical prostatectomy, PI Prof Richard Bryant) and Oxfordshire MRI implementation study (2015-2019). He chairs the Hertford College/Vaughan Williams Prize for Excellence in Clinical Medicine. He leads a multidisciplinary research consortium including Dr. Val Macaulay (basic science) and Prof. W. Bodmer (Wnt pathway research), while coordinating clinical activities through Oxford University Hospitals NHS Trust. His work directly informs national prostate cancer pathways through British Association of Urological Surgeons committees and NICE guideline development.
Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.
Louis Collins is a Professor in the Department of Biomedical Engineering and Department of Neurology and Neurosurgery at McGill University, with associate membership at the Center for Intelligent Machines. His work focuses on advanced medical imaging techniques for neurological applications. Key Expertise: Non-linear image registration, model-based segmentation, neuroimaging, MRI analysis Applications: Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, schizophrenia Methodology: Development of computer vision algorithms for image-guided neurosurgery (IGNS), automated atlasing, and biomarker quantification Collins' research combines computational neuroanatomy with clinical translation, particularly in: Quantifying brain atrophy and anatomical variability across populations Optimizing MRI templates for improved diagnostic accuracy Developing tools like SEEGAtlas for surgical electrode classification Exploring neurophysiological fingerprints of neurodegenerative diseases His lab (NIST-Lab) actively pursues CIHR-funded projects on ultrasound-based image-guided neurosurgery and machine learning applications in clinical trials.
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.
Prof. Kwang W. Oh is a tenured Professor at the Department of Electrical Engineering and Department of Biomedical Engineering within the School of Engineering and Applied Sciences at University at Buffalo (SUNY at Buffalo) . He serves as the Director of Graduate Studies in Electrical Engineering and Director of SMALL (Sensors and MicroActuators Learning Lab) . His academic journey includes PhD and MS in Electrical and Computer Engineering from University of Cincinnati (2001, 1997) and BS in Physics from Chonbuk National University (1995). Prof. Oh's research expertise lies at the intersection of microfluidics , BioMEMS , and lab-on-a-chip technologies. His lab has pioneered vacuum-driven microfluidic devices , PDMS-based systems , droplet manipulation , and chemical-free fabrication techniques . His work enables point-of-care diagnostics , single cell analysis , and wearable medical sensors , with significant contributions to sample-to-answer nanosystems and world-to-chip interfacing . The scientific awards section highlights his excellence in teaching and research: SUNY Chancellor's Award for Excellence in Teaching (2020) Meyerson Award for Undergraduate Teaching (2019) Qualcomm Faculty Award (2019) Senior Teacher of the Year (2017) Royal Society of Chemistry's Emerging Investigators (2013) Samsung Electronics' CEO Honor (2003) His lab has produced numerous PhD and MS students including Dr. Anyang Wang (2020), Dr. Nikhila Nyayapathi (2020), Mr. Liam Christie (2021), and Dr. Domin Koh (2019). As a conference chair , he has organized symposia at NanoTech (2012-2026) and served as editorial board member for Sensors , Micromachines , and Biomedical Engineering Letters .
Ziming Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI) , with additional affiliations in Data Science and Robotics Engineering. He previously held research roles at Mitsubishi Electric Research Laboratories (MERL) and Boston University. PhD in Computing (2013) from Oxford Brookes University , UK MS in Computing Science (2010) from Simon Fraser University , CA BS in Computer Science and Technology (2005) from Northeastern University , China Research interests span computer vision , machine learning , and their applications in point cloud processing , medical imaging , autonomous driving , and IoT . He leads the Vision, Intelligence, and System Laboratory (VISLab) at WPI. Recent publications focus on 3D reconstruction , hyperbolic learning , and robust classifiers . Awards include the R&D100 Award 2018 and NSF funding for data-efficient deep learning. PhD Students: Yecheng Lyu (co-supervised), Guojun Wu (co-supervised), Hangrui Zhang, Xuechu Yu Master's Students: Yun Yue, Yuping Shao Visiting Scholars: Fangzhou Lin His lab partners with industry and academic institutions, focusing on autonomous systems , robotics , and scientific imaging projects.
Wei Pang is a Professor of Computer Science and Bicentennial Research Leader at the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh. He leads the BCML Lab and is affiliated with the Edinburgh Centre for Robotics and National Robotarium. His expertise spans bio-inspired computing, machine learning, and AI applications in healthcare, robotics, and sustainability. Pang holds a PhD in Computing Science from the University of Aberdeen, with prior roles including Senior Lecturer at the University of Aberdeen and research fellowships in systems biology. Affiliations: Heriot-Watt University, Edinburgh Centre for Robotics, National Robotarium Education: PhD in Computing Science (2009), MEng (by research), BSc (Jilin University, China) Research Interests: Bio-inspired computing (e.g., artificial immune systems, swarm intelligence), machine learning (deep learning, explainable AI), healthcare applications (medical imaging, disease detection), and interdisciplinary projects in robotics and environmental science. His work addresses challenges in robust AI, fairness, and accountable machine learning. Recent Projects: EPSRC-funded RAIns and MI projects, CRUK-funded Endo.AI, and PRIME project on minority ethnic communities' digital experiences. His research has secured over £10M in grants, including £3.5M institutional funding. Awards: Scottish Crucible Award (2015), ADMA Best Paper Runner-Up (2016), EPSRC PRIME Award (2024) Grants/Advising: Supervised 12 PhD completions; contributed to £10M+ external funding. Labs/Teams: BCML Lab (focusing on bio-inspired AI), collaborations with Oxford, Cambridge, and industrial partners like Weather2 and Data2Text.
Roman Kuc is a Professor of Electrical Engineering at Yale University, affiliated with the School of Engineering & Applied Science. He directs the Intelligent Sensors Laboratory, focusing on biomimetic sensors for robotics and bioengineering. His research explores brain-based devices (BBDs), sonar sensing, and neuromorphic processing inspired by biological systems. He holds a BSEE from Illinois Institute of Technology and a PhD from Columbia University. Dr. Kuc’s work bridges signal processing, robotics, and bioengineering, with applications in autonomous systems and clinical diagnostics. He has published over 200 papers and authored textbooks like Electrical Engineering in Context and The Digital Information Age . Notable honors include an honorary doctorate from the Glushkov Institute of Cybernetics and the Yale Sheffield Distinguished Teaching Award. His research themes include cognitive mapping via sonar echoes, neural network-based classification of environmental features, and biomimetic approaches to echolocation. Recent work emphasizes sensorimotor integration and robust performance in uncertain environments. Scientific awards highlight his contributions to robotics, signal processing, and education. His lab develops systems that emulate biological sensory mechanisms, aiming to advance robotics, medical applications, and assistive technologies.
Professor Da-Wen Sun is a globally recognized authority in food and biosystems engineering at the UCD School of Biosystems & Food Engineering , University College Dublin. His research focuses on enhancing food preservation through innovative technologies like ultrasound-assisted freezing to minimize nutrient loss and structural damage in frozen foods. Key contributions: Developed ultrasound freezing methods to reduce ice crystal damage Editor of seminal texts including Handbook of Frozen Food Processing Founded the journal Food and Bioprocess Technology His work bridges computational modeling (e.g., CFD simulations , machine learning ) with industrial applications, particularly in freezing, drying, and vacuum cooling. Recent studies explore terahertz imaging for pest detection, deep eutectic solvents for moisture control, and cold plasma for allergen reduction. Scientific awards include: Frozen Food Foundation Freezing Research Award (2013) - First non-US recipient CIGR Honorary President title (2016) for leadership in agricultural engineering He leads the UCD Food Refrigeration & Computerised Food Technology group , collaborating internationally on technologies like nanosensors and green cryoprotectants to advance sustainable food systems.
Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building
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.
Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Department Head at Carnegie Mellon University. She holds a PhD in Statistics from the University of Washington (2006), an MS in Statistics from Stanford (2006), and a BA in Mathematics, Statistics, and Spanish from Rice University (2002). Her research spans clustering methodology , record linkage , educational data mining , public health , and semantic organization , with a focus on high-dimensional data and adaptive learning environments. She leads the Integrated Statistics Learning Environment (ISLE) and Corporate Capstone programs, emphasizing low-barrier data platforms for education and industry collaboration. Academic Roles : Department Head, Carnegie Mellon; Affiliated Faculty, Block Center for Technology and Society Research Grants : NSF (2017-2019), NIH (2018), Carnegie Mellon ProSEED/Simon Initiative (2020, 2018), Berkman Fund (2014) Her 15 most recent publications focus on data science pedagogy, clustering algorithms, record linkage applications in historical and medical data, educational data mining, and semantic organization studies. Awards include the ASA Waller Education Award (2015) and the William H. and Frances S. Ryan Award (2015) . She mentors a diverse group of PhD, Master's, and undergraduate students, with alumni pursuing careers in academia, industry, and sports analytics.
Dr. Richard H. Wiggins serves as Professor of Radiology and Imaging Sciences at the University of Utah School of Medicine, where he also holds the position of Associate Dean of Continuing Medical Education. He maintains adjunct professorships in Otolaryngology, Head and Neck Surgery, and Biomedical Informatics. His educational background includes a BA in Biochemistry from the University of Texas at Austin and an MD from the University of Texas at Houston Health Sciences Center, followed by specialized training at the University of Florida and University of Utah. Dr. Wiggins's research spans neuroradiology, biomedical informatics, and medical education innovation. His work explores advanced imaging techniques for head and neck pathologies, PET-CT applications in oncology, and the development of novel educational platforms like the RadDiscord community. He maintains particular expertise in temporal bone imaging, paragangliomas, and thyroid nodule classification systems. Analysis of his recent publications shows strong emphasis on: (1) optimizing oncologic imaging protocols, especially PET-CT applications across cancer types; (2) advancing head and neck radiology through volumetric analysis and radiomics; and (3) transforming radiology education via gamification and digital communities. His scientific honors include: Distinguished Teaching Award (Otolaryngology, University of Utah) Honored Educator Award (Radiological Society of North America) Inaugural Fellow, Academy of Health Science Educators 25th Fellow of the Society of Imaging Informatics in Medicine Fellow of the American College of Radiology He leads the Utah Head and Neck Imaging Center of Excellence, which includes an educational YouTube channel (@UHNICE). While specific grants aren't detailed, his extensive lecture record (600+ presentations) and leadership roles in professional societies like SIIM (Program Chair 2014-2020), ASHNR (Past President), and WNRS (Past President) demonstrate significant academic influence.
Maryellen L. Giger, Ph.D. is the A.N. Pritzker Distinguished Service Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago. She serves as Vice-Chair of Radiology (Basic Science Research) and was the immediate past Director of the CAMPEP-accredited Graduate Programs in Medical Physics/Chair of the Committee on Medical Physics. Her career spans over 30 years of pioneering research in computer-aided diagnosis, machine learning, and deep learning applications in medical imaging. Dr. Giger's research focuses on computational image-based analyses for cancer risk assessment, diagnosis, prognosis, and response to therapy, particularly in breast cancer, lung cancer, prostate cancer, lupus, bone diseases, and more recently, COVID-19. Her work has evolved from developing computer-aided diagnosis systems to utilizing 'virtual biopsies' in imaging genomics association studies for discovery. She has made significant contributions to quantitative imaging, radiomics, and AI applications in medical imaging, with emphasis on translating research into clinical practice. Her publication record shows a clear trajectory from foundational work in computer vision for medical imaging to cutting-edge AI and deep learning applications. The recent publications demonstrate her leadership in large-scale collaborative efforts like the Medical Imaging and Data Resource Center (MIDRC), focus on health equity through AI analysis, and expansion into diverse applications including gynecological imaging, lung cancer screening, and trauma assessment. Her work consistently bridges technical innovation with clinical relevance. Dr. Giger has received numerous prestigious honors including membership in the National Academy of Engineering, the William D. Coolidge Gold Medal (the highest award from AAPM), and being named one of the 50 most impactful medical physicists in the last 50 years. She is a Fellow of multiple professional societies including AAPM, AIMBE, SPIE, SBMR, and IEEE. Her 2019 TIME magazine recognition for QuantX, the first FDA-cleared machine-learning-driven system for cancer diagnosis, highlights her translational impact. As an educator and mentor, Dr. Giger has guided over 100 graduate students, residents, and medical students throughout her career. She has secured substantial research funding including NIH R01 grants and serves as contact PI for the NIH NIBIB-funded & ARPA-H-funded Medical Imaging and Data Resource Center (MIDRC). Her leadership extends to former presidencies of the American Association of Physicists in Medicine and SPIE, and she was the inaugural Editor-in-Chief of the SPIE Journal of Medical Imaging. Dr. Giger co-founded Quantitative Insights, Inc. through the University of Chicago's New Venture Challenge, which developed QuantX - the first FDA-cleared AI system for cancer diagnosis. She leads the Medical Imaging and Data Resource Center (MIDRC), a critical resource for AI development in medical imaging that received the 2023 DataWorks Prize. Her research laboratory bridges engineering, physics, and clinical medicine to develop and validate quantitative imaging biomarkers and AI tools for precision medicine.
