I do research in the areas of machine learning, artificial intelligence, cognition, and neuroscience.

My work in applied machine learning focuses mainly on tools for animal vocalizations and bioacoustics.

• TweetyNet
  Most recently, I collaborated with Yarden Cohen and Tim Gardner to develop a neural network that learns how to annotate birdsong from spectrograms:
  https://github.com/yardencsGitHub/tweetynet

• vak
  https://github.com/NickleDave/vak
  neural network toolbox for animal vocalizations and bioacoustics

• Crowsetta
  https://github.com/NickleDave/crowsetta
  a tool to work with any format for annotating animal vocalizations

• hybrid-vocal-classifier: a Python machine learning library for animal vocalizations and bioacoustics https://hybrid-vocal-classifier.readthedocs.io/en/latest/
  https://github.com/NickleDave/hybrid-vocal-classifier
  combines scikit-learn with functionality for researchers studying animal vocalizations

• evfuncs
  https://github.com/NickleDave/evfuncs
  functions for working with files created by the EvTAF program and the evsonganaly GUI.
  The functions can be used to work with Bengalese finch song in this data repository:
  https://figshare.com/articles/Bengalese_Finch_song_repository/4805749

• birdsong-recognition-dataset
  https://github.com/NickleDave/birdsong-recognition-dataset
  functions for working with this data repository:
  https://figshare.com/articles/BirdsongRecognition/3470165

• ML-comparison-birdsong
  https://github.com/NickleDave/ML-comparison-birdsong
  code to accompany the SciPy 2016 Proceedings Paper "Comparison of machine learning methods applied to birdsong element classification"
  http://conference.scipy.org/proceedings/scipy2016/david_nicholson.html

• songdkl
  https://github.com/NickleDave/songdkl
  a measure of similarity between birdsongs (and other things)
  As described in:
  Mets, David G., and Michael S. Brainard. "An automated approach to the quantitation of vocalizations and vocal learning in the songbird." PLoS computational biology 14.8 (2018): e1006437.
  https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006437&rev=2
  in collaboration with David Mets

During my post-doctoral fellowship at Emory University in Atlanta, Georgia, I worked with Astrid Prinz in the Biology department, on brain-inspired algorithms for continual machine learning, as part of a DARPA program. The Prinz lab provided neuroscience expertise for members of our team working on algorithms for goal-driven perception. My goal for this project was to understand visual search: how does our brain solve the problem of finding an object we're looking for?

• untangling-search https://github.com/NickleDave/untangling-visual-search
  experiments to test whether the untangling mechanism proposed for object recognition can also account for behavior measured in visual search tasks, using deep neural network models of the primate ventral visual stream.

Preprint: https://www.biorxiv.org/content/10.1101/2020.10.26.354258v1

• visual-search-nets
  https://github.com/NickleDave/visual-search-nets
  library for modeling visual search behavior with neural networks.

• searchstims
  https://github.com/NickleDave/searchstims
  Python package to make stimuli like those used in classic visual search experiments.

• Recurrent Models of Visual Attention
  https://github.com/NickleDave/thrillington
  replication of "Recurrent models of visual attention", Mnih et al. 2014

• aver: active vision models in Nengo
  https://github.com/NickleDave/aver
  framework for visual search models that incorporate eye movements.
  proposal for Nengo summer school 2019.

• learning-curves
  https://github.com/NickleDave/learning-curves
  study of Cortes et al. 1994 "Learning Curves" paper with scikit-learn and scipy for first Atlanta Jupyter Day.

• Data Science for Scientists ATL
  https://data-science-for-scientists-atl.github.io/
  https://github.com/Data-Science-for-Scientists-ATL

• BEST data science short course
  https://github.com/NickleDave/BEST-data-science-short-course
  short course in scientific computing and data science sponsored by the Atlanta BEST program.

• EWIN Coding Bootcamp
  https://github.com/NickleDave/EWIN-coding-bootcamp
  coding bootcamp sponsored by Emory Women In Neuroscience