COMPASS talk by Jane Hung (MIT): The Challenges and Promises of Large-Scale Biological Imaging or A picture is worth more than a million pixels

19.04.2018 11:00

COMPASS: Computing Platforms Seminar Series

CAB E 72

Speaker: Jane Hung (MIT)

Title: The Challenges and Promises of Large-Scale Biological Imaging








Microscopy images contain rich information about the state of cells, tissues, and organisms and are an important part of experiments to address a multitude of basic biological questions and health problems. The Broad Institute of MIT and Harvard’s Imaging Platform works with dozens of collaborators around the world to design and execute large-scale microscopy-based experiments in order to identify the causes and potential cures of disease. These experiments, though carried out in a non-profit environment, have led to the discovery of drugs effective in animal models of disease, and the uncovering of mechanisms underlying other diseases and biological processes.

Most recently, we have been working on software to support the increased physiological complexity of modern screening systems, for example, using whole organisms and co-cultured cell types. As well, our machine learning tools allow a biologists’ intuition to guide the computer to measure subtle phenotypes. We are also working to use patterns of morphological features to group samples by similarity, in order to identify drug targets and gene function. Ultimately, we aim to make microscopy images as computable as other sources of genomic and chemical information.

Short Bio:

Jane received her Ph.D. in the Department of Chemical Engineering at MIT and is interested in how accessible software can make processes more efficient. She had her first computer vision experience at an internship at Novartis in Basel working on automated drug manufacturing monitoring. From there, she joined Anne Carpenter's biological imaging analysis lab at the Broad Institute. She has worked on machine learning-based software application CellProfiler Analyst in collaboration with David Dao as well as deep learning-based object detection software Keras R-CNN in collaboration with Allen Goodman.