I am a Director and AI Lead in the Digital Sciences & Translational Imaging group within Pfizer’s Discovery & Early Development organization. I am interested in medical imaging, machine learning, and drug R&D.

I received a Bachelor’s degree in Chemistry from Nankai University, a Master’s degree in Computer Science from Georgia Institute of Technology, and a PhD from Washington University in St. Louis. My PhD thesis focused on cancer imaging:

MRI in Cancer: Improving Methodology for Measuring Vascular Properties and Assessing Radiation Treatment Effects in Brain

During my PhD, I did an internship at Schlumberger, where my research focused on spin physics simulation and virtual prototyping for oil well logging.

Following PhD, I did my postdoctoral research on cardiovascular imaging at Beth Israel Deaconess Medical Center and Harvard Medical School. Before I joined Pfizer in 2019, I had a stint at Invicro, working as a Study Director to support imaging programs for pharma/biotech clients.

I currently lead the development, validation, and deployment of novel AI-based solutions to support drug development programs across therapeutic areas at Pfizer. Some recent projects are:

• A fully automated AI system for analyzing echocardiography videos. This project won Pfizer’s Breakthrough Science & Innovation award in 2021, the highest honor that can be bestowed to an R&D colleague.
  • Paper
  • GitHub
  • AWS Blog
• Using AI to assess solid tumor treatment response in clinical trials and unlock novel endpoints for early decision making that can accelerate drug discovery and development, in collaboration with Vysioneer
  • ESMO 2023
  • Press Release
• Predicting PD-(L)1 response using a deep learning biomarker with routine baseline CT scans, in collaboration with Onc.AI
  • ASCO 2024
  • Paper
  • Press Release

I am also representing Pfizer in multiple public-private parternships/consortiums, including:

• FNIH Mucosal Healing in Ulcerative Colitis, a Biomarker Consortium lead by the The Foundation for the National Institutes of Health to define the best practice for measuring mucosal healing, including a machine learning-based method for scoring of mucosal healing that can be used in clinical trials, regulatory approvals, and clinical practice.
• IHI SYNTHIA, a public-private partnership funded by the Innovative Health Initiative in the EU to revolutionize the field of personalized medicine by harnessing the power of synthetic data.