TensorFlow and PyTorch User Group Talks

Thu, Jul 25, 2019, 4:30 pm to 5:30 pm
138 Lewis Library
PICSciE/Research Computing

There will be one lightning talk and two 20-minute talks.

"GPU Computing with R and Keras"
Danny Simpson, Ph.D. candidate, Lewis-Sigler Institute for Integrative Genomics
Thursday, July 25, 4:30-5:30 pm, 138 Lewis Science Library
[Refreshments will be served. RSVP to alrubins@princeton.edu]

The R interface to Keras combines the high-level approach to designing deep neural networks of Keras with the straightforward data processing capabilities of R. However, despite Keras’s ability to use either CPU or GPU nodes with the same code, GPU computing with this interface requires changes to both scripts and working environment. In this lightning talk, I present the MNIST classification tutorial using the R interface to Keras utilizing a GPU node on Princeton’s Adroit cluster.

"Big Data of Big Tissues: Deep Neural Networks to Accelerate Analysis of Collective Cell Behaviors" 
Julienne LaChance, Ph.D. candidate, Mechanical and Aerospace Engineering

Coordinated cellular motion is crucial for proper tissue organization and function. The analysis of massive, living tissues can provide key insights into these behaviors but requires more versatile feature extraction approaches. Convolutional neural networks (CNNs) have been gaining in popularity among cell biologists for tasks such as object classification (as in the identification of cell phenotypes) and segmentation (for the detection of individual cells or nuclei). I will demonstrate the use of a U-Net style architecture for reconstructing UV-excited labels directly from low-magnification transmitted light images of cells. I will also present preliminary findings on the prediction of complex cell behaviors using deeper CNNs.

"Selene: A PyTorch-based Deep Learning Library for Sequence Data"
Kathleen Chen, Data Scientist, Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY

Selene is a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for any biological sequences [1]. Selene was developed to increase the accessibility of deep learning in biology and facilitate the creation of reproducible workflows and results. The library contains modules for data sampling and training for model development, as well as prediction and visualization for analyses using a trained model. Furthermore, Selene is open-source software that will continue to be updated and expanded based on community feedback. In this talk, I will discuss how we designed Selene to support sequence-based deep learning across a broad range of biological questions and made the library accessible to users at different levels of computational proficiency. I will also explain the motivation for creating Selene and give some examples of how we currently use Selene in our research projects.

[1] K. M. Chen, E. M. Cofer, J. Zhou & O. G. Troyanskaya, Nature Methods 16, 315-318 (2019).

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