Morteza Mardani

300 

Research Scientist
Stanford University

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

I am currently in the job market

Resume
CV
Google Scholar Profile
GitHub Profile

Short Bio

I am a research scientist in the Information Systems Lab (ISL), Electrical Engineering Dept., Stanford University, working with John Pauly, David Donoho, and Shreyas Vasanawala, where I was a postdoc Jun 2015-Dec 2017.

Prior to that I was a visiting research scholar (Jan-Jun 2015) with the RISELab (in the past AMPLab), Electrical Engineering and Computer Science Dept., UC Berkeley working with Michael Mahoney.

I received my Ph.D. in Electrical Engineering and Mathematics (minor) from the University of Minnesota (May 2015), Twin Cities, under the supervision of Georgios Giannakis.

The overarching theme of my research is on algorithm, analysis, and application of machine learning (ML) and statistical signal processing tools for data science and artificial intelligence (AI). My current research develops algorithm and theory for deep learning that solves challenging image recovery, analysis, and recognition problems. The application of interest that I am focusing on these days is medicine, but the tools and techniques are applicable to a wide range of applications.

For contributions to large-scale dimensionality reduction of streaming incomplete data, I received a Young Author Best Paper Award from the IEEE Signal Processing Society at 2017.

Recent Selected News

  • Feb 2019: I will be giving a talk at the Biomedical Eng. dept., Johns Hopkins University

  • Feb 2019: I will be giving a talk at the EECS dept., University of Michigan, An Arbor

  • Feb 2019: I will be giving a talk at the EECS dept., University of California, Berkeley

  • Feb 2019: I will be attending the ITA conference at San Diego, CA

  • Jan 2019: Submitted a paper to ICML conference entitled “Shared Weights Yield Accurate Compressive Image Recovery for Scarce Label Training: SURE analysis”

  • Jan 2019: Submitted a paper to ICML conference entitled “VAE-GANs for Probabilisitc Compressive Image Recovery: Uncertainty Analysis”

  • Dec 2018: Gave a talk at the Winter Seminar Series on Computer Science at the Sharif University of Technology about “Recurrent GANs for Compressive Imaging,”

  • Dec 2018: Presented our paper “Neural Proximal Gradient Descent for Compressive Imaging” at NeurIPS, Montreal, Canada

  • Nov 2018: Submitted a paper to CVPR conference entitled “‘Robust Detection of Subtle Pathologies in Medcial Images using Recurrent Variational Networks”

  • Jun 2018: Delivered a talk at the ETH Computer Science Department, Zurich, Switzerland, about “Neural proximal gradient descent using GANs for compressive imaging,”

  • Jun 2018: Delivered a talk at Google Brain Research, Zurich, Switzerland, about “Neural proximal gradient descent using GANs”

  • May 2018: Delivered a talk at Facebook AI Research, Menlo Park about “GANs for medical image recovery”

  • Mar 2018: Delivered a talk at the NVIDIA GPU technology conference, San Jose, about “Recurrent Generative Adversarial Neural Networks for Compressive Imaging”

  • Mar 2018: Panelist for “AI Barriers in Healthcare” organized by Intel at UCSF (video link)

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

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