This project will center around building a pre-trained model for shapes and processes related to Developmental Biology and Neurobiology and extracted from image data. Our organization’s Machine Learning interest group (DevoWormML) has published a blog post  on the advantages and need for pre-trained models in this area. In short, biological development is characterized by characteristic shapes, movements, changes in shape, and temporal processes that define important features. Pre-trained models are used in NLP and Deep Learning for the domains of sequence discovery in language processing (GPT-2) and bounding box methods for segmenting complex images (DeepLabv3). Models specialized for biology, however, do not exist. A suitable pre-trained model would greatly reduce the need for input data without sacrificing the ability to generalize to different contexts.
Our main interest is in extracting spatiotemporal features from image data. We will focus on microscopy data such as that found in the DevoZoo or from more specialized sources . For a typical pre-trained model, the network is pre-trained with non-random weights that approximate the generalized versions of the features we would like to discover. However, we are also interested in a semantic component, particularly the ability to incorporate elements such as meaning assigned to static knowledge (semantics) and multiple meanings for a single feature (polysemy). This will enable relational modeling and the mapping of segmented image data to lineage trees and taxonomies. This will enable relational modeling and the mapping of segmented image data to lineage trees and taxonomies. Our model, tentatively called DevLearningv1, should be applicable to a wide range of neural network and deep learning techniques.
As a student, you will become a contributor at the OpenWorm Foundation, where we are attempting to build a virtual organism. You will learn about developmental neurobiology, and join the DevoWorm group. OpenWorm has an active interest in data science, and DevoWorm in particular has an active interest in machine learning research and education. We seek someone with experience with programming languages C++ and Python, and a machine learning platform such as TensorFlow or Keras.
 Blogpost on pre-trained models: https://thenode.biologists.com/pre-trained-machine-learning-models-for-developmental-biology/uncategorized/
 Crawford-Young, S.J., Dittapongpitch, S., Gordon, R., and Harrington, K.I.S. (2018). Acquisition and reconstruction of 4D surfaces of axolotl embryos with the flipping stage robotic microscope. Biosystems, 173, 214-220. doi:10.1016/j.biosystems. 2018.10.006.