Eva Dyer’s research combines machine learning and neuroscience to understand the brain, its function, and how neural circuits are shaped by disease. Her lab, the Neural Data Science (NerDS) Lab, develops new tools and frameworks for interpreting complex neuroscience datasets and building machine intelligence architectures inspired by the brain. Through a synergistic combination of methods and insights from both fields, Dr. Dyer aims to advance the understanding of neural computation and develop new abstractions of biological organization and function that can be used to create more flexible AI systems.
- Technological Innovations in Neuroscience Award, McKnight Foundation, 2021
- Sloan Research Fellowship, Neuroscience, Alfred P. Sloan Foundation, 2019
- Next Generation Leader Award, Allen Institute for Brain Science, 2018
- Research Initiation Initiative Award (CRII), National Science Foundation, 2018
- Liu, R., Azabou, M., Dabagia, M., Lin, C. H., Gheshlaghi Azar, M., Hengen, K., Valko, M., & Dyer, E. (2021). Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. Advances in Neural Information Processing Systems (NeurIPS), 34.
- Lin, C. H., Azabou, M., & Dyer, E. L. (2021). Making transport more robust and interpretable by moving data through a small number of anchor points. Proceedings of Machine Learning Research (ICML), 139, 6631.
- Dyer, E. L., Sankaranarayanan, A. C., & Baraniuk, R. G. (2013). Greedy feature selection for subspace clustering. The Journal of Machine Learning Research, 14(1), 2487-2517.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.