Directory

Matthew Singh's directory photo.

Matthew Singh

(he/him/his)

Assistant Professor

Primary Affiliation

Brain Connectivity and Networks

Affiliations

Status Affiliate Faculty

Home Department of Statistics

Phone

Email mfsingh@illinois.edu

Address

  • Biography

    Matthew Singh is an assistant professor in the Department of Statistics, Neuroscience Program, and (by courtesy) Department of Psychology. He will be directing the COmputation and NEurodynamics (CONE) lab in the Brain Connectivity and Networks Working Group.

    He received his Ph.D. in Neuroscience from Washington University in St. Louis in which he developed new machine-learning algorithms for individualized brain modeling. He completed his postdoctoral fellowship at Washington University in St. Louis (joint Psychology and Electrical & Systems Engineering) during which he developed methods for high-dimensional estimation of latent brain activity and designed new neurostimulation algorithms and hardware.

    Education

    • B.A., psychology, University of Tennessee, Knoxville, 2014

    • Ph.D., neuroscience, Washington University in St. Louis, 2020

  • Honors

    • 2021-2022: NIN/NIDA Fellowship, Biomedical Research Training in Drug Abuse (T32)

    • 2016-2019: NSF Graduate Research Fellowship

    • 2014: Summa Cum Laude, University of Tennessee

    • 2013-2014: Undergraduate of the Year: Psychology, University of Tennessee

    • 2014: Chancellor’s Honors Thesis Grant, University of Tennessee

    • 2012-2013: Tutor of the Year, University of Tennessee, Student Success Center

  • Research

    Research areas:

    • Brain Connectomics

    • Dynamical Systems

    Research interests:

    • Machine Learning & Optimization

    • Control Engineering

    • Cognitive Control

    His neuroscience research focuses upon the computational significance of brain dynamics in higher cognitive abilities and the application of person-specific brain models to precision medicine and neural-engineering. This endeavor is intimately tied to methodological research and development in machine-learning and control-systems engineering.

  • 2023

    • On the Role of Theory and Modeling in Neuroscience. Levenstein, D., Alvarez, V. A., Amarasingham, A., Azab, H., Chen, Z. S., Gerkin, R. C., Hasenstaub, A., Iyer, R., Jolivet, R. B., Marzen, S., Monaco, J. D., Prinz, A. A., Quraishi, S., Santamaria, F., Shivkumar, S., Singh, M. F., Traub, R., Nadim, F., Rotstein, H. G. and Redish, A. D., Feb 15 2023, In: Journal of Neuroscience. 43, 7, p. 1074-1088 15 p.

    2022

    • Control-theoretic integration of stimulation and electrophysiology for cognitive enhancement Singh, M. F., Cole, M. W., Braver, T. S. and Ching, S., 2022, In: Frontiers in Neuroimaging. 1
    • Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement Singh, M. F., Cole, M. W., Braver, T. S. and Ching, S. N., Jan 2022, In: Annual Reviews in Control. 54, p. 363-376 14 p.
    • Efficient identification for modeling high-dimensional brain dynamics Singh, M. F., Wang, M., Cole, M. W. and Ching, S. N., 2022, 2022 American Control Conference, ACC 2022. Institute of Electrical and Electronics Engineers Inc., p. 1353-1358 6 p. (Proceedings of the American Control Conference; vol. 2022-June).
    • Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics Singh, M. F., Wang, A., Cole, M., Ching, S. N. and Braver, T. S., Feb 15 2022, In: NeuroImage. 247, 118836.
    • The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery. Etzel, J. A., Brough, R. E., Freund, M. C., Kizhner, A., Lin, Y., Singh, M. F., Tang, R., Tay, A., Wang, A. and Braver, T. S., Dec 2022, In: Scientific Data. 9, 1, 114.

    2020

    • Computing and optimizing over all fixed-points of discrete systems on large networks Riehl, J. R., Zimmerman, M. I., Singh, M. F., Bowman, G. R. and Ching, S., Sep 2020, In: Journal of the Royal Society Interface. 17, 170, 20200126.
    • Estimation and validation of individualized dynamic brain models with resting state fMRI Singh, M. F., Braver, T. S., Cole, M. W. and Ching, S. N., Nov 1 2020, In: NeuroImage. 221, 117046.
    • Scalable surrogate deconvolution for identification of partially-observable systems and brain modeling Singh, M. F., Wang, A., Braver, T. S. and Ching, S. N., Aug 2020, In: Journal of Neural Engineering. 17, 4, 046025.

    2018

    • Geometric classification of brain network dynamics via conic derivative discriminants Singh, M. F., Braver, T. S. and Ching, S. N., Oct 1 2018, In: Journal of Neuroscience Methods. 308, p. 88-105 18 p.
    • Network Restructuring Control for Conic Invariance with Application to Neural Networks Singh, M. F. and Ching, S., Jul 2 2018, 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., p. 2704-2709 6 p. 8618729. (Proceedings of the IEEE Conference on Decision and Control; vol. 2018-December).

    2015

    • A simple transfer function for nonlinear dendritic integration. Singh, M. F. and Zald, D. H., Aug 10 2015, In: Frontiers in Computational Neuroscience. 9, AUGUST, 98.