Dual background (MS, MBA) with 13 years of experience in imaging data science and analytics

I work at the Scientific Computing and Imaging Institute (SCI), as director of the Biomedical imaging and data science core (BIDAC), a University of Utah health science core facility. By leveraging the expertise of the SCI Institute, BIDAC offers application-oriented consulting services in the areas of deep learning, medical computing, visualization, data science and data engineering. My role entails both business administration and scientific expertise, helping partners turn data into insights.


Technical skills

  • Data science
  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Data analytics
  • Medical imaging
  • Neuroscience

Business skills

  • Consulting
  • Account management
  • Project management
  • Product management
  • Budget management

Areas of Interests

  • AI
  • Data science
  • Business intelligence / analytics

Project collaborations

I have been managing and executing various data science projects, working in close interdisciplinary collaborations with researchers in the field of medicine and computer science. I have been a member of the National Alliance for Medical Image Computing (NA-MIC), and an Autism Center of Excellence Network (ACE-IBIS).

Artificial intelligence applied to nuclear forensics via deep learning

Nuclear forensics aims to investigate the origin and history of nuclear or radioactive materials via analytical techniques. Using microscopy images, we have performed deep learning analysis to assess multiple calcination conditions and processing routes of nuclear samples. We have applied, compared and fine-tuned via transfer learning state-of-the-art convolutional neural networks, performing various classification and regression tasks. In addition to applying standard neural network architectures, we have investigated the use of parallel networks for multi-magnification acquisitions, and quantified model uncertainty during inference.

Data engineering workflow for AI in radiology imaging

We aimed to investigate the use of machine learning on large retrospective radiology imaging studies, including chest x-rays and related clinical text reports. In partnership with researchers from the Radiology Department, the Enterprise Data Warehouse and the Center for High Performance Computing, we have leveraged various software and hardware infrastructure to enable secured data transfer from the hospital PACS system, HIPAA-compliant data storage via a database, and data management of large radiological datasets.

Data management and data analytics for radiology studies

We aimed to predict the severity of pulmonary function tests in patients with chronic obstructive lung disease from chest radiographs using deep convolutional neural networks. For this clinical project, we transferred and managed a multi-modal dataset including unstructured chest x-ray acquisitions, text reports and metrics from pulmonary function tests. We also performed some data analytics, gaining insights into demographic and clinical information. These initial stages provided a clean dataset of 14,000+ studies from 7,000+ patients available for deep learning.

Data science for neuroimaging clinical studies

Over the years, I have managed and executed parallel data science projects to support various national clinical research studies. These entailed automated image registration, atlas generation, tissue classification and structural segmentation from brain MRIs, focusing on human, rodent and pig imaging. Driving clinical applications have included brain development in autism spectrum disorder, Down syndrome, Huntington's disease, deep brain stimulation, and obsessive-compulsive disorder.