Prof. Polina Golland

Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science

Primary DLC

Department of Electrical Engineering and Computer Science

MIT Room: 32-D470


Sheila Sharbetian

Areas of Interest and Expertise

Computer Vision
Machine Learning
Lossy Data Compression
Developing Novel Techniques for Image Analysis and Understanding
Statistical Modeling, Shape Representation
Medical and Biological Imaging Applications (On Leave Fall Term)
Big Data

Research Summary

Professor Golland studies the shapes and functions of biological structures through the statistical analysis of biomedical images. She builds computational models of the anatomical and functional variability within populations, and develops methods to detect and characterize changes in those distributions under the influence of development or disease. Her models give insight into the functional organization of the brain and into the causes of its variability. Her group releases open-source software packages for wide impact and dissemination. Golland has played a major role in developing three important classes in the EECS curriculum: two very popular graduate classes on inference and information -- Inference and Information and Algorithms for Inference -- and the department’s new undergraduate class 6.008 (Introduction to Inference).

Recent Work

  • Video

    AI in LIfe Science 2018 - Polina Golland

    December 4, 2018Conference Video Duration: 23:3

    Making Images Part of Medical Record

    Currently, medical images require a physician to extract clinically relevant information. This talk will explore current work towards making images part of the quantitative medical history and to enable large-scale image-based studies of disease. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images. Our goal is to enable application of existing algorithms that were originally developed for high resolution research scans to severely undersampled images. Application of the method is illustrated in the context of neurodegeneration and white matter disease studies in stroke patients.

    2018 MIT AI in Life Sciences and Healthcare Conference

    Polina Golland - 2017 Health Conf

    September 26, 2017Conference Video Duration: 27:14

    Making Invisible Obvious: Computational Analysis of Medical Images

    Polina Golland will discuss her group's research in computational analysis of MRI scans that aims to provide accurate measurements of healthy anatomy and physiology, and biomarkers of pathology. Applications range from fetal development to aging brain.

    2017 MIT Health Sensing & Imaging Conference

    Polina Golland - 2016-Digital-Health_Conf-videos

    September 14, 2016Conference Video Duration: 30:55

    Medical Image Analysis

    I will review our work in extracting clinically relevant characterizations of anatomy and pathology from medical images in two domains. First, joint modeling of image, genetic and clinical data is used to gain insight into the patterns of disease in large heterogeneous clinical populations. Examples include studies of white matter disease in stroke patients from brain MRI, of genetically defined patterns of emphysema in COPD patients as observed in chest CT, and others. The second family of applications aims to provide accurate delineations of pathology and make predictions in medical scans of individual patients. Examples include functional imaging of the placenta and cardiac image analysis for surgical planning.

    2016 MIT Digital Health Conference