I am a PhD candidate in the Department of Psychology at Yale University (Neuroscience track). I primarily work with Nick Turk-Browne and BJ Casey. I also work with the Krishnaswamy Lab 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 to facillitate more efficient learning. I also develop and apply novel analysis techniques to explore individual differences in neuroimaging phenotypes. I completed my undergraduate degree in Cognitive Science, Computer Science, and Spanish at Dartmouth College in winter of 2020, where I worked with Jim Haxby and Caroline Robertson.
Research
Publications & Preprints
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. Paper Research Briefing Analysis Code T-PHATE Software
Busch, E.L., Rapuano, K.M., Anderson, K.M., Rosenberg, M.D., Watts, R., Casey, BJ, Haxby, J.V., & Feilong, M. (Under review). Dissociation of reliability, predictability, and heritability in fine- and coarse-scale functional connectomes during development. bioRxiv
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] arXiv Code
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. Paper Code
Posters & Presentations (2021-Present)
Posters
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.
Busch, E.L., Letrou, A., Huang, J., Lajoie, G., Wolf, G., Krishnaswamy, S., & Turk-Browne, N.B. (2022). A neural manifold learning framework for real-time fMRI neurofeedback. Society for Neuroscience Annual Meeting. San Diego, California.
Busch, E.L., Letrou, A., Huang, J., Lajoie, G., Wolf, G., Krishnaswamy, S., & Turk-Browne, N.B. (2022). A neural manifold learning framework for real-time fMRI neurofeedback. Real-time Functional Imaging and Neurofeedback Meeting. New Haven, Connecticut.
Busch, E.L., Rapuano, K.M., Anderson, K.M., Rosenberg, M.D., Watts, R., Casey, BJ, Haxby, J.V., & Feilong, M. (2022). Heritable template underlies reliable idiosyncrasies in the developing fine-scale connectome. Organization for Human Brain Mapping Annual Meeting. Glasgow, Scotland
Letrou, A., Busch, E.L., & Turk-Browne, N.B., (2022). Relating neural dynamics and meotion dynamics with nonlinear manifold learning. Poster at the Social and Affective Neurosience Society Annual Meeting (Virtual).
Busch, E.L., Huang, J., Benz, A., Wallenstein, T., Lajoie, G., Wolf, G., Krishnaswamy, S., & Turk-Browne, N.B. (2021). Manifold learning to capture brain-state trajectories in fMRI. Society for Neuroscience Annual Meeting (Virtual).
Walton, A.E., Nizzi, M.C., West, B., Mofe, E., Roth, R.M., Busch, E.L., Holtzheimer, P.E., Roskies A.L. (2021). The impact of anxiety and depression on dimensions of agency. Seventh Annual NIH BRAIN Initiative Annual Meeting (Virtual).
Sivitilli, D.M., Weertman, W.L., Busch, E.L., Ullmann, J.F., Smith, J.R., Gire, D.H. (2021). Strategies of single arm foraging in Octopus rubescens in the absence of visual feedback. Society for Integrative and Comparative Biology (Virtual).
Select talks
Multi-view manifold learning of human brain-state trajectories. (April 2023) Shine Lab Meeting, University of Sydney.
An encoder-decoder framework for cross-modal translation of brain imaging data. (December 2022) MAIN 2022 Conference Lightning Talk.
Enhancing human learning along the neural manifold. (September 2022) Yale FAS Brain Imaging Center Users Meeting.
The LEGO theory of the developing functional connectome. (September 2022) ABCD Imaging Analytics Working Group.
The LEGO theory of the developing functional connectome. (April 2022) Current works in Behavior, Genetics, and Neuroscience, Yale University.
Modern fMRI analysis techniques. Guest Lecture, Yale NSCI 270 November 2021.
Hyperalignment: Foundations, flavors, and functions. (April 2021) FINN Lab Meeting, Dartmouth College.