Babadi, Behtash
Associate Professor
Associate Chair for Graduate Studies
Associate Chair for Graduate Studies
Electrical and Computer Engineering
The Institute for Systems Research
Brain and Behavior Institute
Maryland Applied Graduate Engineering
The Institute for Systems Research
Brain and Behavior Institute
Maryland Applied Graduate Engineering
2113 A.V. Williams Bldg.
Behtash Babadi received the Ph.D. and M.Sc. degrees in Engineering Sciences from Harvard University in 2011 and 2008, respectively, and the B.Sc. degree in Electrical Engineering from Sharif University of Technology, Tehran, Iran in 2006. From 2011 to 2014, he was a post-doctoral fellow at the Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology as well as at the Department of Anesthesia, Critical Care and Pain Medicine at Massachusetts General Hospital. His research interests include statistical and adaptive signal processing, neural signal processing, and systems neuroscience.
Education
Ph.D. in Engineering Sciences, Harvard University, 2011
M.Sc. in Engineering Sciences, Harvard University, 2008
B.Sc. in Electrical Engineering, Sharif University of Technology, Tehran, Iran, 2006
Honors and awards
E. Robert Kent Teaching Award for Junior Faculty (2019)
NSF Faculty Early Career Development (CAREER) Award (2016)
GSAS Merit Fellowship, Harvard University (2010)
Statistical and adaptive signal processing, neural signal processing, machine learning, systems neuroscience
- S. Mukherjee, B. Babadi, and S. A. Shamma, Sparse high-dimensional decomposition of non-primary auditory cortical receptive fields, PLoS Computational Biology, Vol. 21, No. 1, e1012721, 2025.
- S. Mukherjee and B. Babadi, Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach, PLoS Computational Biology, Vol. 20, No. 5, e1011605, 2024.
- P. Das and B. Babadi, Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO, IEEE Trans. on Information Theory, Vol. 69, No. 11, pp. 7439–7460, 2023.
- B. Soleimani, P. Das, I.M. D. Karunathilake, S. E. Kuchinsky, J. Z. Simon, and B. Babadi, NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis, NeuroImage, Vol. 260, 119496, 2022.
- A. Rupasinghe, N. Francis, J. Liu, Z. Bowen, P. O. Kanold, and B. Babadi, Direct Extraction of Signal and Noise Correlations from Two-Photon Calcium Imaging of Ensemble Neuronal Activity, eLife, 10:e68046, 2021.
- A. Rupasinghe and B. Babadi, Multitaper Analysis of Semi-Stationary Spectra from Multivariate Neuronal Spiking Observations, IEEE Trans. on Signal Processing, Vol. 68, pp. 4382–4396, 2020.
- J. Liu, M. R. Whiteway, A. Sheikhattar, D. A. Butts, B. Babadi, and P. O. Kanold, Parallel Processing of Sound Dynamics across Mouse Auditory Cortex via Spatially Patterned Thalamic Inputs and Distinct Areal Intracortical Circuits, Cell Reports, Vol. 27, No. 3, pp. 872–885.E7, 2019.
- A. Sheikhattar, S. Miran, J. Liu, J. B. Fritz, S. A. Shamma, P. O. Kanold, and B. Babadi, Extracting Neuronal Functional Network Dynamics via Adaptive Granger Causality Analysis, Proceedings of the National Academy of Sciences, Vol. 115, No. 17, E3869-E3878, 2018.
- S. Akram, J. Z. Simon, and B. Babadi, Dynamic Estimation of the Auditory Temporal Response Function from MEG in Competing-Speaker Environments, IEEE Trans. on Biomedical Engineering, Vol. 64, No. 8, pp. 1896-1905, 2017.
- S. Akram, A. Presacco, J. Z. Simon, S. A. Shamma, and B. Babadi, Robust Decoding of Selective Auditory Attention from MEG in a Competing-Speaker Environment via State-Space Modeling, NeuroImage, Vol. 124, Part A, pp. 906—917, 2016.
Autism Research Resonates in Hearing-Focused Project
Neurobiologist and Engineer Investigate Neural Networks That Underpin a Common but Little-Understood Disorder2023 BBI Seed Grants Inspire New Interdisciplinary Collaborations
The six interdisciplinary teams will use state-of-the-art neuroscience tools to translate basic science research into real-world impact.New robust and scalable computational methodology developed by UMD researchers helps identify directed connectivity within the brain
‘NLGC’ can be used with magnetoencephalography to better understand the neural mechanisms behind sensory processing.Uncovering the mysteries of networking in the brain
UMD researchers are sorting out how our brains’ neurons organize as we take in, decide and act on information.How does the brain turn heard sounds into comprehensible language?
$2.88M in NIH funding could lead to brain-aware, tunable hearing devices.Cornelia Fermüller is PI for 'NeuroPacNet,' a $1.75M NSF funding award
The resulting "network of networks" will further the scientific grand challenge of developing neuromorphic artificial intelligence.Babadi research to reveal computational principles for the brain's sensory processing and behavior
The research could help improve prosthetics for people with paraplegia and make possible reconfigurable neuromorphic devices.New algorithms for estimating the latent dynamics underlying biological processes
Work by Miran, Presacco, Babadi, Simon, Fu and Marcus published in PLOS Computational Biology.Behtash Babadi promoted to associate professor
Babadi has broad research interests in statistical and adaptive signal processing, neural signal processing, and systems neuroscience.Clark School faculty 'AIM-HI' to address major health challenges
The new UMB-UMCP program has announced its first grants for projects combining AI and medicine.Brain and Behavior Initiative Hosts 3rd Annual Seed Grant Symposium
Researchers from across UMD convene to discuss the latest projects funded by BBI