Director of Data Science and Advanced Analytics
Program Director, SmarterHealth Artificial Intelligence Program
Dr. Callcut is double board certified in General Surgery and Critical Care and is an Associate Professor of Surgery at UCSF. She maintains an active clinical practice as an ICU Intensivist and Trauma/General Surgeon. Dr. Callcut’s efforts with the Center for Digital Health Innovation are focused on leading a multi-disciplinary team working at the intersection of artificial intelligence to drive change in the delivery of healthcare for both patients and providers. Dr. Callcut also directs a multidisciplinary NIH and DOD funded translational research lab focused on optimizing outcomes following severe injury and illness.
Expert in Residence for AI
Bob Rogers, PhD, is Expert in Residence for Artificial Intelligence (AI) at UCSF/CDHI Smarter Health, where he applies his experience solving problems with advanced analytics and AI to help build world-class medical AI technologies. Prior to CDHI, Bob was Chief Data Scientist in the Data Center Group at Intel. Bob was also co-founder and Chief Scientist at Apixio, a big data analytics company for healthcare. Bob’s mission is to put powerful analytics and AI tools in the hands of all decision makers. To achieve that, he believes that the secrets of unstructured data must be unlocked through the application of broadly accessible analytical tools.
UCSF Email: Robert.Rogers@ucsf.edu
Lu Chen is a Data Scientist at CDHI. She works on applying computer vision and natural language processing to the medical field at CDHI. Prior to CDHI, she analyzed millions of points of user behavioral data and worked in search engine optimization, where she gained a deep understanding of relational and non-relational database structure, data reporting, containerization, and machine learning. Her background is in economics and statistics, and she is very passionate about public health and AI. She believes in using data for good and is striving for advancing medical practice efficiency with deep learning.
UCSF Email: Lu.Chen@ucsf.edu
UCSF Directory: https://directory.ucsf.edu/people/search/id/158598
Clinical Research Coordinator
Haley McCalpin assists in the translational research efforts of CDHI’s SmarterHealth data science team. In addition to her role at CDHI, she also supports the clinical research studies relating to trauma patients led by Dr. Callcut at SFGH. Bridging both the clinical and computational environments, she is committed to improving the integration of technology in health services and enhancing the power of bioinformatics to predict outcomes following trauma. Following her undergraduate studies in Psychology and Computer Science at Wake Forest University, Haley completed a post-baccalaureate pre-medical program at the University of Michigan.
UCSF Email: Haley.McCalpin@ucsf.edu
UCSF Directory: https://directory.ucsf.edu/people/search/id/172964
UCSF Profile: https://profiles.ucsf.edu/haley.mccalpin
Matt O'Brien works to improve the healthcare delivery landscape by developing AI in the medical imaging domain. He received his training in the second cohort of University of San Francisco's masters of data science program. Prior to UCSF, Matt worked in movement science, taught mathematics, and trained amateur athletes.
UCSF Email: email@example.com
UCSF Directory: https://directory.ucsf.edu/people/search/id/103156
Michael is a Data Scientist on the SmarterHealth team in CDHI. He has several fields of interest in the medical AI domain but mainly focuses on applying cutting edge computer vision techniques to a variety of medical imaging modalities. He also spends time creating and developing code for repeatable and reliable machine learning experiments. Some of his research includes innovative machine learning pipelines in medical AI and transfer learning in MRI based vision algorithms.
UCSF Email: Michael.Girard@ucsf.edu
UCSF Directory: https://directory.ucsf.edu/people/search/id/146332
Data Systems Analyst
Pavan Gupta is a University of Virginia trained Biomedical Engineer focused on cutting edge CDHI research and cloud computing problems.
UCSF Email: Pavan.Gupta@ucsf.edu
UCSF Directory: https://directory.ucsf.edu/people/search/id/148399
Cosmo originally trained in Astrophysics at the University of Arizona, working in radio astronomy and extrasolar planet detection. For his PhD work he studied the metabolic syndrome at ASU and the Mayo Clinic, studying the insulin signaling pathway in skeletal muscle. Cosmo specializes in the bioinformatics of protein biophysics and the characterization of genetic variants. In the Rankin lab, Cosmo used his data science training to characterize the whole collection of neuroimaging data collected by the memory and aging center.
In his spare time, Cosmo works on a range of side projects, including Project Infinome, an open genomics experiment focusing on obesity research.
UUCSF Email: Clinton.Mielke@ucsf.edu
UCSF Directory: https://directory.ucsf.edu/people/search/id/107966
Valentina Pedoia, PhD, is an Assistant Professor in the Musculoskeletal and Imaging Research Group.
She is a data scientist with a primary interest in developing algorithms for advanced computer vision and machine learning for improving the usage of non-invasive imaging as diagnostic and prognostic tools.
Dr. Pedoia obtained her doctoral degree in computer science working on features extraction from functional and structural brain MRI in subjects with glial tumors. After graduation, in 2013, she joined the Musculoskeletal and Imaging Research Group at UCSF as post-doctoral fellow. Her role was in providing support and expertise in medical computer vision, with a focus to reduce human effort and to extract semantic features from MRI to study degenerative joint disease.
Her current main research focus is on exploring the role of machine learning in the extraction of contributors to osteoarthritis (OA). She is studying analytics to model the complex interactions between morphological, biochemical and biomechanical aspects of the knee joint as a whole; deep learning convolutional neural network for musculoskeletal tissue segmentation and for the extraction of silent features from quantitative relaxation maps for a comprehensive study of the biochemical articular cartilage composition; with ultimate goal of developing a completely data-driven model that is able to extract imaging features and use them to identify risk factors and predict outcomes.
Dr. Pedoia’s recent work on machine learning applied to OA was awarded as annual scientific highlights of the 25th conference of the International Society of Magnetic Resonance In Medicine (ISMRM 2017) and selected as best paper presented at the MRI drug discovery study group.