The Center is led and run by the Director (Shandong Wu, PhD) and five Associate Directors (Ashok Panigrahy, MD; Douglas J. Hartman, MD; Liang Zhan, PhD; Kayhan Nematollah Batmanghelich, PhD; and Priyanga Gunarathne, PhD, MBA ). The directors hold biweekly meetings.


Shandong Wu, PhD, is an Associate Professor (with tenure) in Radiology (primary), Biomedical Informatics, Computer Science, Bioengineering, Intelligent Systems, and Clinical and Translational Science, at the University of Pittsburgh (Pitt), and is an Adjunct Professor in the Machine Learning Department at the Carnegie Mellon University (CMU). Dr. Wu leads the Intelligent Computing for Clinical Imaging (ICCI) lab (16 trainee members and >20 clinician collaborators) and serves as the Technical Director for AI Innovations in Radiology at Pitt/UPMC. He is the founding director of the Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging (CAIIMI), which includes more than 98 multidisciplinary members from Pitt, UPMC, and CMU, working on advancing AI research and clinical translation. Dr. Wu’s background is in Computer Science (Computer Vision) with additional clinical training in radiology research. Dr. Wu’s main research areas include computational biomedical imaging analysis, artificial intelligence in clinical/translational applications, big (health) data coupled with machine/deep learning, imaging-based clinical studies, and radiomics/radiogenomics/radioproteomics. Dr. Wu’s research grows from primarily on breast cancer imaging (screening, risk assessment, diagnosis, prognosis, and treatment) to cover many other diseases/organs as well, such as brain injury, gastric cancer, intestinalis, orthopedics, liver cancer and transplantation, pancreatic cancer, lung cancer, cardiac arrest, obesity, etc. Dr. Wu is an advocator of and passionate about developing trustworthy and accessible medical imaging AI for clinical/translational applications. Dr. Wu received the Pitt Innovator Award in 2019. His lab received the prestigious "RSNA (Radiological Society of North America) Trainee Research Award" twice in 2017 and 2019, and the Natus Resident/Fellow Award for Traumatic Brain Injury by 2021 AANS (American Association of Neurological Surgeons). Dr. Wu’s research is supported by NIH/NCI, NSF, RSNA, UPMC Enterprise, Pittsburgh Health Data Alliance, Pittsburgh Foundation, Stanly Marks Research Foundation, Amazon AWS, Nvidia, and many internal funding sources. As a PI he has received more than 5.5 million dollars in research funds.

Associate Directors

Ashok Panigrahy, MD, is Radiologist-in-Chief, Children’s Hospital of Pittsburgh and Vice Chair of Clinical and Translational Imaging Research for the Department of Radiology of the University of Pittsburgh Medical Center (UPMC). Dr. Panigrahy is also a tenured Full Professor and holds the John F. Caffey Endowed Chair of Pediatric Radiology at the University of Pittsburgh with secondary appointments in the Department of Developmental Biology, Biomedical Informatics and Bio-engineering.  Continuously funded by the NIH since 2004, he is known for translating novel imaging techniques in developing brain disorders in relation to neuroprotection and targeted therapies. Dr. Panigrahy’s funding portfolio includes the Department of Defense, UPMC Enterprises and Pitt Momentum Funds.  His laboratory is involved with neuroimaging harmonization for the Pediatric Heart Network (NHLBI), the HEALthy Brain and Child Development Study (NIDA) , the Pediatric Brain Tumor Consortium (NCI) and multiple other multi-centered pediatric related consortiums. He is past president of  the American Society of Pediatric Neuroradiology and a chartered member of the Developmental Brain Disorder study section. Dr. Panigrahy has published over 175 peer-reviewed manuscripts, reviews and book chapters, and mentored over 30 students and postdocs.

Douglas J. Hartman, MD, is an Associate Professor of Pathology, the Vice Chair of the Division of Pathology Informatics at the University of Pittsburgh Medical Center (UPMC) and a practicing gastrointestinal pathologist. He is board certified in both AP/CP and Clinical Informatics. His gastrointestinal research area includes molecular and expression abnormalities unique to neoplasia that arises in patients with inflammatory bowel disease as well as inflammatory cell populations in this population. In addition, he has been implementing digital pathology for primary signout and automated image analysis as well as for telepathology at UPMC. In September 2018, he introduced automated image analysis to quantitate CD8 inflammatory cells within oropharyngeal squamous cell carcinoma. He has participated in implementing two different digital pathology solutions for routine anatomic pathology. Dr. Hartman’s research in informatics focuses on practical application of informatics and artificial intelligence. He has published on informatics topics and given national and international talks based on his informatics work.

Liang Zhan, PhD, is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering (Primary) and Department of Bioengineering at the University of Pittsburgh (Pitt). He obtained his Ph.D. in Biomedical Engineering from the University of California, Los Angeles (UCLA) in 2011. Dr. Zhan’s research grows from brain MRI signal modeling and now covers much clinical/translational research on brain diseases, such as Alzheimer’s disease, Parkinson’s disease, bipolar disorder, depression, and Traumatic Brain Injury, etc.  Since 2018, Dr. Zhan leads the Biomedical Computing lab at Pitt, in which the main research areas include computational neuroimaging analysis, artificial intelligence in clinical applications, and deep learning on big data. Dr. Zhan has published over 100 peer-reviewed manuscripts and his research is supported by NIH/NIA R21, R01, U01, NSF IIS, and OIS. As a PI, he has received more than 2 million dollars in research funds over the past 3 years.

Kayhan Nematollah Batmanghelich, PhD, is an Assistant Professor of the Department of Biomedical Informatics (DBMI) and Intelligent Systems Program at the University of Pittsburgh and an adjunct faculty in the Machine Learning Department at the Carnegie Mellon University. His research lies at the intersection of biomedical image analysis, machine learning, and computational medicine. The main themes of his research are Learning from Limited and Noisy data, Explainable AI (XAI) for clinicians, and Incorporating Causal Relations between clinical measurements. His group develops efficient machine learning algorithms for those problems  for applications with large-scale datasets, and  statistical models to understand how changes at the cellular level (e.g. transcriptome, genetics) affect intermediate phenotypes measured by imaging and ultimately lead to changes in disease progression and outcome. His lab contributes to foundational and applied research for projects motivated by real-world applications in the clinical domain. His research is supported by NIH (R01), NSF, and industry. His long-standing research goal is to understand underlying mechanisms of diseases by developing computational tools to analyze image and -omic data.

Priyanga Gunarathne, PhD, MBA, is an Assistant Professor of Business Administration (Information Systems and Technology Management) at the Katz Graduate School of Business, of the University of Pittsburgh. She received her Ph.D. in Business Administration (Computer Information Systems) from the Simon Business School of the University of Rochester. Her research interests lie in how information technology enables firms to leverage big data and artificial intelligence (AI) for better operational and customer outcomes. Her current research portfolio mostly focuses on the development of theory-driven empirical studies to provide new insights into consumer and firm behavior in the digital age. Her scholarship is largely grounded on an interdisciplinary multi-method approach that includes econometric analysis, survey data analysis, and experiments, along with machine learning, deep learning, and natural language processing techniques to leverage largely unstructured data in the digital age. She has particular research interests in social media, digital discrimination, electronic privacy, and the economics of AI in healthcare. Dr. Gunarathne holds a B.Sc. in Computer Science and Engineering and an MBA in Information Technology from the University of Moratuwa.