Joel Ye

Program Year:

University:

Carnegie Mellon University

Field of Study:

Computational Neuroscience

Advisor:

Leila Wehbe

Degree(s):

M.S. Computer Science, and B.S. Computer Science, Georgia Institute of Technology, 2020

Practicum Experience(s)

Lawrence Berkeley National Laboratory (2024)

Practicum Supervisor(s):

Kristofer

Bouchard

Practicum Title:

Patterns of generalization in pretrained neural data models.

Summary of Research

My research aims to develop deep learning systems for sensorimotor brain-computer interfaces. These interfaces require decoding user intended movement from motor cortex, and designing stimulation patterns to provide useful sensory feedback. Deep learning systems promise to advance these aims in both directions by making them more robust and data-efficient.

Annual Program Review Abstracts

Neural Data Transformer 3: A Foundation Model for Motor Cortical Decoding (2024)

Modeling Local Neural Population Responses to Intracortical Microstimulation (2023)

Publications

FALCON: Few-shot Algorithms for Consistent Neural Decoding. NeurIPS, 2024.
Karpowicz, B.*, Ye, J.*, Fan, C., Tostado-Marcos, P., Rizzoglio, F., Washington, C., Scodeler, T., de Lucena, D., Nason-Tomaszewski, S. R., Mender, M. J., Ma, X., Arneodo, E. M., Hochberg, L. R., Chestek, C. A.,Henderson, J. M., Gentner, T. Q., Gilja, V., Miller, L. E., Rouse, A. G., Gaunt, R. A., Collinger, J. L., Pandarinath, C

Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity. Neural Information Processing Systems (NeurIPS), 2023. J. Ye*, J. Collinger, L. Wehbe, R. Gaunt.

Neural Latents Benchmark: Evaluating latent variable models of neural population activity. Neural Information Processing Systems (NeurIPS) Benchmarks and Datasets, 2021. F. Pei*, J. Ye*, D. Zoltowski, A. Wu, R. Chowdhury, H. Sohn, J. O'Doherty, K. Shenoy, M. Kaufman, M. Churchland, M. Jazayeri, L. Miller, J. Pillow, M. Park, E. Dyer, C. Pandarinath.

Auxiliary Tasks and Exploration Enable ObjectNav. International Conference on Computer Vision (ICCV) 2021. J. Ye, D. Batra, A. Das, and E. Wijmans.

Auxiliary Tasks Speed Up Learning PointGoal Navigation. Conference on Robot Learning (CoRL), 2020. J. Ye, D. Batra, E. Wijmans, and A. Das.

Representation learning for neural population activity with Neural Data Transformers. Neurons, Behavior, Data analysis, and Theory (NBDT), 2021. J. Ye, C. Pandarinath.
I have given accompanying talks in the associated conferences, along with informal presentations for journal clubs for the above works.
Representation learning for neural population activity with Neural Data Transformers (an earlier version of the paper above) was presented at Neuromatch 3.0.

Awards

Donald V. Jackson Fellowship. Award for academic excellence and leadership. 1 of 3 awards for 250 eligible MS students in the Georgia Tech College of Computing.