Mazdak Abulnaga

Mazdak Abulnaga

PhD Candidate in Computer Science

Massachusetts Institute of Technology


I am a fourth year PhD student supervised by Polina Golland and Justin Solomon. My research interests are in machine learning, computer vision, and computational geometry applied to medical images. I am excited about developing computational tools to improve healthcare.

Outside of research, I am a communication advisor with the MIT EECS Communication Lab, where I provide coaching on technical communication. From 2017-2019, I was a co-president of the EECS Graduate Students Association, where I worked with the department and graduate students to develop the academic and social environment.

Prior to joining MIT, I received my Bachelor’s Degree in Electrical Engineering from the University of British Columbia (UBC). During my undergraduate studies, I did research in biomedical signal processing and childhood exercise psychology with Guy Dumont (UBC), particle physics with Ruediger Picker (TRIUMF), and medical image analysis with Jerry Prince (JHU).


  • Medical image analysis
  • Computer vision
  • Computational geometry
  • Machine learning


  • PhD in Electrical Engineering and Computer Science, 2018-Present

    Massachusetts Institute of Technology

  • SM in Electrical Engineering and Computer Science, 2018

    Massachusetts Institute of Technology

  • BASc in Electrical Engineering, 2016

    University of British Columbia

Recent Publications

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Placental MRI: Effect of maternal position and uterine contractions on placental BOLD MRI measurements

Placenta, 2020

Placental Flattening via Volumetric Parameterization


Ischemic Stroke Lesion Segmentation in CT Perfusion Scans Using Pyramid Pooling and Focal Loss

MICCAI 2018 Brainlesion Workshop

Volumetric Mesh Parameterization to a Canonical Template

MIT SM Thesis 2018

Recent Posts

Presenting your results in a paper (EECS Comm Lab's CommKit)

As part of the EECS Communication Lab’s CommKit article series, we present a post on writing the Results section of a paper. We cover how to structure and organize your results, how to analyze your audience and communicate effectively, and how to deliver a strong take-home message.


  • abulnaga AT mit DOT edu
  • MIT Stata Center 32-D474, Cambridge, MA