Transcriptomic cell type structures in vivo neuronal activity across multiple time scales.
Cell Reports, April 2023.
Mehdi Azabou - ML PhD Student @ Georgia Tech
I am actively working on developing methods for self-supervised representation learning for different modalities, and researching new models to learn from neural activity and behavior.
Transcriptomic cell type structures in vivo neuronal activity across multiple time scales.
Cell Reports, April 2023.
I am a third-year Machine Learning Ph.D. student advised by Dr. Eva L. Dyer. My main areas of interest are 🤖 Deep Learning and 🧠 Computational Neuroscience.
My research focuses on the development of new methods for representation learning. I am particularly interested in expanding the use of these tools to novel domains where the structure of the data can be complex and obscured. Through the development of new approaches for analyzing and interpreting complex modalities, I aim to make an impact in our understanding of the brain, and biological intelligence, and to contribute new tools that facilitate new scientific discoveries. I am currently working on developing methods for learning representations of neural activity, behavior, and graphs, with the goal of improving our understanding of the brain.
May 2023 | I will be interning at IBM Thomas J. Watson Research Center, this summer. | |
Apr 2023 | Half-Hop is accepted at ICML 2023 🎉. More details coming soon. | |
Apr 2023 | Our paper on identifying cell type from in vivo neuronal activity was published in Cell Reports [Link]. | |
Mar 2023 | Check out our latest behavior representation learning model BAMS which ranks first 🥇 on the MABe 2022 benchmark [Project page]. |