Morteza Mardani


Postdoctoral Research Fellow
Stanford University

Address: 350 Serra Mall, Stanford, CA, 94305
Email: morteza at stanford dot edu

Research Statement
Teaching Statement
Google Scholar Profile

Short Bio

I am a postdoctoral research fellow in the Electrical Engineering Dept. and Medical Physics Dept. at Stanford University. Prior to that I was a Visiting Scholar with the EECS Dept. and the International Computer Science Institute (ICSI) at UC Berkeley. I received my Ph.D. in Electrical Engineering and a Ph.D. minor in Mathematics from the University of Minnesota, Twin Cities.

The overarching theme of my research is on algorithms, analysis, and applications of (deep) machine learning and statistical signal processing tools for data science and artificial intelligence (AI). Armed with a multidisciplinary training at the intersecion of electrical engineering, computer science, and medicine, my vision is to harness the power of artificial intelligence and big data for developing computational imaging analytics that positively impact patient care.

Recent News

  • Dec 2017: Presenting our GANCS work at NIPS, Long Beach, LA

  • Dec 2017: Received the Young Author Best Paper Award of the IEEE Signal Processing Society for the paper “subspace learning and imputation for streaming big data matrices and tensors” published in Volume: 63, Issue: 10, May 2015

  • Nov 2017: ISMRM papers submitted for automated MRI reconstruction and prediction on “DeepSPIRiT”, “Deep GANCS”, and “Deep predictive encoding of image dynamics using RNNs”

  • Sep 2017: Attended the Stanford Medical Imaging Symposium

  • Aug 2017: My summer student, Jordan Harrod, recieved the best research presentation award among all Amgen Scholars at Stanford

  • July 2017: Submitted as the PI a DoD grant for the prostate cancer program about “Precision Radiotherapy using Deep Convolutional Neural Networks”

  • June 2017: Attended and presented at the “Big Data in Biomedicine Conference”, Stanford, LKSC

  • May 2017: our TMI submission “Learning Subspaces of Large-Scale Tensors with Informative Sampling for Real-Time MR Imaging” appears at