Faculty Directory

Babadi, Behtash

Babadi, Behtash

Associate Professor
Associate Chair for Graduate Studies
Electrical and Computer Engineering
The Institute for Systems Research
Brain and Behavior Institute
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.


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, systems neuroscience

  • 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, April 2018.
  • S. Miran, S. Akram, A. Sheikhattar, J. Z. Simon, T. Zhang, and B. Babadi, Real-Time Tracking of Selective Auditory Attention from M/EEG: A Bayesian Filtering Approach, Frontiers in Neuroscience, Vol. 12, pp. 262, May 2018.
  • N. A. Francis, D. E. Winkowski, A. Sheikhattar, K. Armengol, B. Babadi, and P. O. Kanold, Small Networks Encode Decision-Making in Primary Auditory Cortex, Neuron, Vol. 97, No. 4, Feb. 21, 2018.
  • P. Das and B. Babadi, Dynamic Bayesian Multitaper Spectral Analysis, IEEE Trans. on Signal Processing, Vol. 66, No. 6, pp. 1394-1409, March 2018.
  • A. Kazemipour, J. Liu, K. Solarana, D. A. Nagode, P. O. Kanold, M. Wu, and B. Babadi, Fast and Stable Signal Deconvolution via Compressible State-Space Models, IEEE Trans. on Biomedical Engineering, Vol. 65, No. 1, pp. 74-86, Jan. 2018.
  • S. Miran, P. L. Purdon, E. N. Brown, and B. Babadi, Robust Estimation of Sparse Narrowband Spectra from Neuronal Spiking Data, IEEE Trans. on Biomedical Engineering, Vol. 64, No. 10, pp. 2462-2474, Oct. 2017.
  • A. Sheikhattar, J. B. Fritz, S. A. Shamma, and B. Babadi, Recursive Sparse Point Process Regression with Application to Spectrotemporal Receptive Field Plasticity Analysis, IEEE Trans. on Signal Processing, Vol. 64, No. 8, pp. 2026—2039, April 2016.

Graduate students win ISR and ECE awards

Awards given for outstanding performance, teaching, and dissertation work.

$1.5M in NSF funding secured to improve solar energy conversion systems

The four-year interdisciplinary project could lead to major breakthroughs in control, modeling, sensing, design, and reliability of power electronic interfaces.

Two ECE Graduate Students Win 2023 UMD Three Minute Thesis Competition

Students challenged to present research in just three minutes!

ECE Names 2022-2023 Distinguished Dissertation Fellows

Fellowship recognizes excellence in research

Autism Research Resonates in Hearing-Focused Project

Neurobiologist and Engineer Investigate Neural Networks That Underpin a Common but Little-Understood Disorder

2023 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.

Giving back: New solar panels support a local urban farm

Clark School staff, students and faculty help historic Silver Spring produce farm become more sustainable.

Behtash Babadi Named Associate Chair for Graduate Studies

Will lead Electrical and Computer Engineering Graduate Program

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.