Farzin Ahmadi

I am a data science and healthcare systems engineering researcher with an interest in developing computational approaches that translate into enhanced medical decisions and quality of care. My work involves optimization modeling, machine learning algorithms, simulation studies, and preference elicitation techniques to mimic provider and patient priorities for treatment planning. I leverage modeling, data analytics, and emerging AI methods to personalize care recommendations that align with individual health goals.

 

I have had the opportunity to build my teaching expertise through teaching, guest lecturing, and assistantships at Johns Hopkins University. I recently taught a course on Healthcare System Engineering at the undergraduate level. I have been the principal teaching assistant for the core courses of Artificial Intelligence at the Johns Hopkins Carey Business School and Operations Research at the Johns Hopkins Whiting School of Engineering. In 2022, I received the Teaching Assistant Award from the Whiting School of Engineering.

 

Outside my focus on healthcare applications, I enjoy pursuing impactful collaborations, mentoring students, and disseminating insights to shape discourse at the nexus of technology and policy. I aspire to continue pursuing respected research and thought leadership roles emerging at the forefront of data-driven clinical innovation.

Ongoing Research

  • Inverse Learning to Improve Radiation Therapy Treatment Plans
  • Learning Group-Preferences in Constrained Environments: A Machine Learning and Inverse Optimization Hybrid Approach
  • A Temporal Analysis of COVID-19 Mortality, Healthcare Capacity, Vaccination Rates, and Socioeconomic Factors between 2020 and 2022
  • Smart Surgical Scheduling Tool: An Optimization Model with Integrated Perioperative Information Input
  • Supervised Inverse Optimization

Submitted/ Pre-prints

  • Inverse Learning: A Data-driven Framework to Infer Optimizations Models
    • A data-driven novel methodology for direct inference of optimal solutions and unknown parameters of a linear optimization problem using a set of existing decisions.
  • Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19
    • Providing models for optimal and robust demand and resource transfer between healthcare centers during demand surge periods such as the COVID-19 pandemic.

Completed Projects

  • The Johns Hopkins University Center for Systems Science and Engineering COVID-19 Dashboard: data collection process, challenges faced, and lessons learned