A Unified, Scalable Framework for Neural Population Decoding
NeurIPS, Dec 2023.
Mehdi Azabou - Postdoc @ Columbia University
My areas of interest are Representation Learning, Data-Centric AI, and Computational Neuroscience. I am actively working on developing methods for self-supervised representation learning for time-series and graphs.
| Oct 2024 | I have started as an NSF AI Institute for Artificial and Natural Intelligence (ARNI) Postdoc at Columbia University in 🍎 New York City. | |
| Aug 2024 | I have successfully defended my 🎓 PhD thesis titled "Building a foundation model for neuroscience". | |
| Feb 2024 | I will at COSYNE 2024 in Lisbon, Portugal to present our latest poster titled "Large-scale pretraining on neural data allows for transfer across subjects, tasks and species". | |
| Oct 2023 | Our work on large-scale brain decoders is out 🧠🕹️🐒 [Project page]. We will present it at NeurIPS this Dec! | |
| Sep 2023 | Two papers accepted at NeurIPS 2023 🎉. More details coming soon. | |
| Jul 2023 | Half-Hop is the 🏝️ ICML'23 featured research in ML@GT. Read the article here: New Research from Georgia Tech and DeepMind Shows How to Slow Down Graph-Based Networks to Boost Their Performance. | |
| May 2023 | I am interning at IBM Research this summer. I will be at the IBM Thomas J. Watson Research Center in New York. | |
| 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]. |
I am an ARNI Postdoc at Columbia University working with Dr. Liam Paninski and Dr. Blake Richards. I did my Ph.D. at Georgia Tech and was advised by Dr. Eva L. Dyer. My main areas of interest are Representation Learning, Generative AI, Data-Centric AI, and Computational Neuroscience. I am actively working on developing methods for self-supervised representation learning for time-series and graphs, and developing new frameworks to build large-scale multimodal foundation models to advance scientific discovery.
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 a large-scale multimodal foundation models for neuroscience with the goal of improving brain-computer interfaces, along with our understanding of the brain. If you are interested in collaborating, feel free to reach out!
- Ph.D. in Machine Learning, Georgia Tech 🇺🇸, 2024
- M.S. in Computer Science, Georgia Tech 🇺🇸, 2020
- M.S. in Engineering, CentraleSupélec 🇫🇷, 2019
- AI Research Scientist Intern @ IBM Research, 2023
- Deep Learning Intern @ Parrot Drones, 2019
- Machine Learning & Computer Vision Intern @ Cleed, 2018
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Co-Instructor for the representation learning hands-on session during the Caltech/Chen Institute’s Data Science and AI for Neuroscience Summer School, Pasadena, California, 2022.
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Content Developer for the Python bootcamp session for the DL@MBL: Deep Learning for Microscopy Image Analysis course at Marine Biological Laboratory, Woods Hole, Massachusetts, 2021.Materials
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Content Developer for BMED 6517 Machine Learning in Biosciences at Georgia Tech, 2021.
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Teaching Assistant for CS 4261 Mobile applications and Services at Georgia Tech, Spring 2019.
- I have served as a reviewer for notable conferences and journals:
- Neural Information Processing Systems, NeurIPS 2021, 2022, 2023 and 2024
- International Conference on Learning Representations, ICLR 2023
- International Conference on Machine Learning, ICML 2023 and 2024
- Computer Vision and Pattern Recognition, CVPR 2023 and 2024
- Neural Information Processing Systems Datasets and Benchmarks track, NeurIPS 2022, 2023 and 2024.
- Learning on Graphs Conference, LOG 2022, 2023
- Artificial Intelligence and Statistics, AISTATS 2021
- NeurIPS 2024 NeuroAI Workshop
- IEEE Transactions on Knowledge and Data Engineering
- Cell Patterns, 2022
- Sub-reviewer for Neuron, 2021
- I was privileged to work with and mentor a group of outstanding students at Georgia Tech:
- Venkataramana Ganesh, Master's in CS, 2022-2024
- Vinam Arora, Master's in ECE, 2023
- Puru Malhotra, Master's in CS, 2023
- Ian Knight, Undergrad in CS, 2024
- Michael Mendelson, Undergrad in BME, 2021-2023
- Santosh Nachimuthu, Undergrad in BME, 2023-2024
- Daniel Leite, Undergrad in CS / Math, 2023-2024
- Carolina Urzay, Undergrad in BME, 2021-2022
- Zijing Wu, Undergrad in CS / Math, 2020-2021
- Main Programming Language: Python.
- ML frameworks: PyTorch, PyG, jax.
- Favorite tools: Bokeh, Flask, Docker, TensorBoard, raytune.
- Favorite tools: Illustrator, After Effects
- Designed a logo (with colorful variants) for the Neural Data Science (NeRDS) Lab at Georgia Tech.
- Designed this website using Bulma elements, icons from Font Awesome, and netlify for hosting. Javascript written with the help of ChatGPT and art generated using DALL·E 2. Code can be found here.
