Allen Goodman
Genentech
149 5th Ave
New York, NY
10010
goodmaa3@gene.com
Allen Goodman
Genentech
149 5th Ave
New York, NY
10010
goodmaa3@gene.com
I’m currently a member of Genentech’s Research and Early Development organization (gRED) team. Prior to joining Prescient, I was a member of Meta’s Fundamental AI Research (FAIR) team and the Broad Institute of MIT and Harvard’s Imaging Platform. While I’m best known for my work in microscopy, my primary research interest is differentiable programming. I was an active member of the demoscene and, as far as I know, the only American programmer to have won multiple Scene.org and Meteoriks awards.
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.