The Academy’s 2015 CECI Class2020-10-28T12:15:58-04:00

The Academy's 2015 CECI2 Class

Costas D. Arvanitis, PhDDr. Arvanitis’ research is focused on ultrasound biophysics, and its overarching goal is the discovery and translation of novel therapeutic interventions against human disease. Specific areas of his research include biomedical ultrasonics, linear and nonlinear acoustics, sound propagation in complex media (brain/skull), microbubble dynamics (acoustic cavitation) and control, and image guided therapy. He is particularly active in the field of cancer research, where he conducts fundamental investigations on ultrasound and microbubble-meditated mass transport in brain tumors, and develops computational tools to support the more rational design of focused-ultrasound-based treatment of brain cancer.
James Scott Cordova, MD, PhDDr. Cordova is currently a Radiation Oncology Resident at Washington University in St. Louis. He received his MD from Emory University, as well as his PhD in Molecular and Systems Pharmacology.
Ryne Didier, MDDr. Didier is an attending radiologist with the Department of Radiology at Children’s Hospital of Philadelphia. She was a recipient of the prestigious RSNA Research Scholar Grant for her work in the field of fetal research.
Vinay Duddalwar, MDDr. Duddalwar is a Professor of Radiology, Urology and Biomedical Engineering. His research focus is in the field of radiomics, quantitative and multiomic evaluation of neoplasms. He established and leads the USC Radiomics Lab (www.radiolicslab.usc.edu ) which is an interdisciplinary translational research group interested in the development and use of quantitative methods of evaluating imaging data. His lab is at the intersection of quantifying imaging, artificial intelligence and multiomic analysis. The lab is funded by federal, foundation an industry grants and is currently exploring radio genomics, imaging evaluation of angiogenesis, molecular and immune correlates and biomarkers, as well as treatment response in various cancers. This includes developing human allied explainable decision support systems. He has extensive collaboration with both clinical and translational researchers. Dr. Duddalwar is an active member of a number of national and international radiology societies, a GU editor for Clinical Radiology and a reviewer for a number of radiology and urology journals.
Michael E. Hahn, MD, PhDDr. Hahn diagnostic radiologist with expertise in body imaging and imaging tumors in the abdominal cavity. He uses a range of imaging technologies such as x-rays, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, or nuclear medicine to diagnose disease.Dr. Hahn serves as director of the Body Imaging Fellowship in the Department of Radiology, where he educates fellows at UC San Diego School of Medicine.
He is currently a member of the Genitourinary Oncology Disease Research Team and the Genitourinary Tumor Board, the Liver Cancer Group and Tumor Board, and the Gastrointestinal and Endocrine Tumor Board, all at UC San Diego Health.Dr. Hahn completed fellowship training in body imaging at UC San Diego School of Medicine, where he also completed a combined residency program in clinical radiology and research. He earned his medical degree from Weill Cornell Medical College of Cornell University and his doctoral degree in synthetic protein chemistry at The Rockefeller University in New York. He is board certified in diagnostic radiology.He is a member of many professional organizations, including the Radiological Society of North America, the American College of Radiology, the Association of University Radiologists, and the American Institute of Ultrasound in Medicine.
Shangdong Wu, PhDDr. Wu is an Associate Professor (with tenure) in Radiology and several other computational sciences at Pitt, and he 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 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, which includes more than 90 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 has been growing from focusing 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 medical imaging AI for clinical/translational applications. Dr. Wu received the Pitt Innovator Award in 2019, and his lab received the prestigious “RSNA Trainee Research Award” twice in 2017 and 2019. Dr. Wu’s research is supported by NIH/NCI, RSNA, UPMC Enterprises, Pittsburgh Foundation, Pittsburgh Health Data Alliance, Stanly Marks Research Foundation, University of Pittsburgh Physician (UPP) Foundation, Amazon, and Nvidia. As a PI he has received more than 5 million dollars in research funds over the past 5 years. Dr. Wu has published over 100 papers/abstracts in both computing and clinical fields and has mentored more than 30 students. Dr. Wu is a regular reviewer for many grant agencies/study sections, renowned journals, and conferences.
Valentina Taviani, PhDDr. Taviani is a Senior AI Applications Engineer for GE Healthcare. She works on translating clinical MR application prototypes into product-quality solutions by developing state-of-the-art enabling software technology, with a focus on advanced image reconstruction and artificial intelligence methods.
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