I am a PhD candidate in the Department of Psychology at Yale University (Neuroscience track) supported by a NSF GRFP. I primarily work with Dr. Nick Turk-Browne and collaborate with Mechanisms of Disinhibition (MoD) Lab (PI: Dr. Arielle Baskin-Sommers) in the Department of Pychology and the Krishnaswamy Lab (PI: Dr. Smita Krishnaswamy) in the Department of Computer Science and Department of Genetics at Yale. My work lies at the intersection of human and machine learning. I am interested in the computational principles of human brain activity that enable and constrain learning new skills, and manipulating those with neurofeedback to facillitate more efficient learning. I also develop and apply novel analysis techniques to explore individual differences in neuroimaging phenotypes and their relation to perception, behavior, and mental health. I completed my undergraduate degree in Cognitive Science, Computer Science, and Spanish at Dartmouth College in February 2020, where I worked with Dr. Jim Haxby and Dr. Caroline Robertson.
Some of my research highlights can be found below, and an exhaustive list can be found on my CV.
Research
Select papers, proceedings, and preprints
Busch, E.L., Fincke, E.C., Lajoie, G., Krishnaswamy, S., & Turk-Browne, N.B. (2025). Accelerated learning of a noninvasive human brain-computer interface via manifold geometry. doi:10.1101/2025.03.29.646109 Preprint.
Busch, E.L.*, Conley, M.I.*, & Baskin-Sommers, A. (2024) Manifold learning uncovers nonlinear interactions between the adolescent brain and environment that predict psychopathology. In Press, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. doi.org/10.1016/j.bpsc.2024.07.001 | Paper | Analysis Code | E-PHATE Software.
Afrasiyabi, A., Bhaskar, D., Busch, E.L., Caplette, L., Singh, R., Lajoie, G., Turk-Browne, N.B., & Krishnaswamy, S. (2025) Latent representation learning for multimodal brain activity translation. Accepted, IEEE International Conference on Acoustics, Speech, and Signal Processing [ICASSP2025]. doi.org/10.48550/arXiv.2409.18462
Busch, E.L., Fincke, E.C., Lajoie, G., Krishnaswamy, S., & Turk-Browne, N.B. (2024). Learning along the manifold of human brain activity via real-time neurofeedback. Proceedings of the Cognitive Computational Neuroscience Annual Meeting. Paper
Busch, E.L., Rapuano, K.M., Anderson, K.M., Rosenberg, M.D., Watts, R., Casey, BJ, Haxby, J.V., & Feilong, M. (2024). Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development. Journal of Neuroscience, 44 (6). doi.org/10.1523/JNEUROSCI.0735-23.2023 | Paper | Code
Busch, E.L., Yates, T.S., Turk-Browne, N.B. (2023). Tasks constrain the intrinsic dimensionality of activity in non-selective cortex. Proceedings of the Cognitive Computational Neuroscience Annual Meeting. Paper.
Busch, E.L., Huang, J., Benz, A., Wallenstein, T., Lajoie, G., Wolf, G., Krishnaswamy, S.*, & Turk-Browne, N.B.* (2023). Multi-view manifold learning of human brain-state trajectories. Nature Computational Science, 3, 240-253. doi.org/10.1038/s43588-023-00419-0. Paper | Research Briefing | Analysis Code | T-PHATE Software.
Huang, J.*, Busch, E.L.*, Wallenstein, T.*, Gerasimiuk, M., Benz, A., Lajoie, G., Wolf, G., Turk-Browne, N.B., Krishnaswamy, S. (2022). Learning shared neural manifolds from multi-subject FMRI data. 32nd IEEE Machine Learning for Signal Processing [MLSP 2022] doi.org/10.1109/MLSP55214.2022.9943383 | arXiv
Busch, E.L.*, Slipski, L.*, Feilong, M., Guntupalli, J.S., Visconti di Oleggio Castello, M., Huckins, J.F., Nastase, S.A., Gobbini, M.I.,Wager, T.D., Haxby, J.V. (2021). Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity. NeuroImage, 233, 117975. doi.org/10.1016/j.neuroimage.2021.117975 | Paper | Code
Select posters & presentations (2023-Present)
Select talks
Representing youth brain data to highlight neural correlates of psychopathology. (December 2024) Research Topics in Cognitive Neuroscience, Yale University Department of Psychology.
Accelerated learning along the intrinsic manifold of human brain activity. (December 2024) Yale Magnetic Resonance Research Center fMRI Seminar Series. New Haven, CT.
Learning on the manifold of human brain activity via real-time neurofeedback. (September 2024) Kavli Institute 20th Anniversary Symposium. New Haven, CT.
Learning on the manifold of human brain activity via real-time neurofeedback. (August 2024) Contributed talk. Cognitive Computational Neuroscience Annual Meeting. Cambridge, MA.
Dissociable scales reflect reliable, heritable, and behaviorally-relevant individual differences in the developing connectome. (March 2024) ABCD Innovations and Insights Meeting. NIH Campus.
Learning on the manifold of human brain activity via real-time neurofeedback. (November 2023) Nanosymposium on Neural Decoding and Neuroprosthetics. Society for Neuroscience Annual Meeting. Washington D.C.
Multi-view manifold learning of human brain-state trajectories. (April 2023) Shine Lab Meeting, University of Sydney.
Posters
Busch, E.L., Fincke, E.C., Lajoie, G., Krishnaswamy, S., Turk-Browne, N.B., (2024). Learning on the manifold of human brain activity through real-time neurofeedback. 8th Annual Conference on Cognitive Computational Neuroscience. Cambridge, MA.
Busch, E.L., Conley, M.I., Baskin-Sommers, A., (2024). Using manifold learning to uncover the embedded brain and implications for mental health in youth. Organization for Human Brain Mapping Annual Meeting. Seoul, South Korea.
Busch, E.L., Fincke, E.C., Lajoie, G., Krishnaswamy, S., Turk-Browne, N.B., (2024). Learning on the manifold of human brain activity through real-time neurofeedback. Organization for Human Brain Mapping Annual Meeting. Seoul, South Korea.
Busch, E.L., Yates, T.S., Turk-Browne, N.B., (2023). Tasks constrain the intrinsic dimensionality of activity in non-selective cortex. 7th Annual Conference on Cognitive Computational Neuroscience. Oxford, United Kingdom.
Busch, E.L., Bhaskar, D., Letrou, A., Zhang, X., Noah, J.A., Lajoie, G., Hirsch, J., Turk-Browne, N.B., Krishnaswamy, S. (2022). An encoder-decoder framework for cross-modal translation of brain imaging data. Montreal AI-Neuroscience Meeting. Montreal, Quebec, Canada.