Allen Goodman
Meta
100 Binney St.
Cambridge, MA
02142
agoodman@broadinstitute.org
(617) 714-8160
Allen Goodman
Meta
100 Binney St.
Cambridge, MA
02142
agoodman@broadinstitute.org
(617) 714-8160
My primary interests are computer vision and machine learning. I’m also interested in computer architecture, microbiology, and software engineering. My recent research interests are differentiability as a first-class programming language feature and optical computing.
I’m a member of the Anne Carpenter Laboratory, the Broad Institute of MIT and Harvard’s Imaging Platform, and a Chan Zuckerberg Initiative Engineering Fellow.
I co-maintain, alongside David Stirling, CellProfiler, an application for processing and analyzing microscopy images. Likewise, I co-maintain, alongside Shantanu Singh, Cytominer, a library for mining patterns in perturbation data. Most recently, I've been writing, alongside Alice Lucas, Piximi, an image understanding application.
Caicedo, J., McQuin, C., Goodman, A., Singh, S., & Carpenter, A. (2018). Weakly supervised learning of single-cell feature embeddings. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9309–9318).
Caicedo, J., Roth, J., Goodman, A., Becker, T., Karhohs, K., Broisin, M., Molnar, C., McQuin, C., Singh, S., Theis, F., & others (2019). Evaluation of deep learning strategies for nucleus segmentation in fluorescence images. Cytometry Part A, 95(9), 952–965.
McQuin, C., Goodman, A., Chernyshev, V., Kamentsky, L., Cimini, B., Karhohs, K., Doan, M., Ding, L., Rafelski, S., Thirstrup, D., & others (2018). CellProfiler 3.0: Next-generation image processing for biology. PLoS Biology, 16(7), e2005970.
Caicedo, J., Goodman, A., Karhohs, K., Cimini, B., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., McQuin, C., & others (2019). Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nature Methods, 16(12), 1247–1253.
Hung, J., Goodman, A., Ravel, D., Lopes, S., Rangel, G., Nery, O., Malleret, B., Nosten, F., Lacerda, M., Ferreira, M., & others (2020). Keras R-CNN: library for cell detection in biological images using deep neural networks. BMC Bioinformatics, 21(1), 1–7.