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EEC 265C
(205) 934-8444

Research and Teaching Interests: biomedical signals processing, dynamical network models, neuroengineering, mathematical modeling, machine learning, brain computer interface

Office Hours: By appointment


B.S. in Biomedical Engineering, University of Tennessee, Knoxville 

M.S. and Ph.D. in Biomedical Engineering, University of California, Irvine

Rachel June Smith joined the Department of Electrical and Computer Engineering and the Neuroengineering Program as an assistant professor in August 2022. She completed her Ph.D. with Dr. Beth Lopour at UC Irvine in 2019. Her doctoral work focused on the development of computational metrics in scalp EEG data that reflected disease burden and predicted response to treatment in patients with infantile spasms (IS), a potentially devastating epileptic encephalopathy. Rachel completed her postdoctoral training at Johns Hopkins University with Dr. Sridevi Sarma, where she used dynamical systems and control theory techniques to localize the onset of seizures in the epileptic brain from intracranial EEG recordings during single-pulse electrical stimulation. As an assistant professor at UAB, Rachel will continue to collaborate with expert neurologists and use her computational skills to develop novel measures of therapeutic response in patients with neurodegenerative disorders. 

  • Research Interests
    Epilepsy is a chronic neurological disease that affects nearly 60 million people worldwide and is defined by recurrent and unprovoked seizures. If the patient’s seizures are not controlled with anti-epileptic medications, they often lose their independence, and can experience profound behavioral, psychological, cognitive, social, and financial burdens. Currently, diagnosis and treatment of epilepsy involves visual inspection, channel by channel, of electroencephalographic (EEG) signals recorded noninvasively from the scalp or invasively through intracranial monitoring. These methods of EEG assessment are extremely time-consuming, subjective, and often disregard the network phenomena that drive seizure genesis. The Neural Signal Processing and Modeling Lab focuses on the development of novel computational tools that investigate epilepsy with quantitative frameworks to characterize the disease more objectively and accurately. Incorporation of such computational tools into clinical practice will advance current clinical decision support systems, and has the potential to significantly improve treatment outcomes.
  • Recent Courses
    • EE638/738: Neural Time Series Analysis (coming Spring 2023)
    • EE210: Digital Logic (Fall 2023)
  • Select Publications
    • Hays MA, Smith RJ, Wang Y, Coogan C, Sarma SV, Crone NE, Kang JY: Cortico-cortical evoked potentials in response to varying stimulation intensity improves seizure localization. Clinical Neurophysiology. (in press, accepted Aug 2022).
    • Gunnarsdottir K, Li A, Smith RJ, Kang J, Korzeniewska A, Crone N, Rouse A, Cheng J, Kinsman M, Landazuri P, Uysal U, Ulloa C, Cameron N, Cajigas I, Jagid J, Kanner A, Elarjani T, Bicchi M, Inati S, Zaghloul K, Boerwinkle V, Wyckoff S, Barot N, Gonzalez-Martinez J, Sarma SV: Source-sink connectivity: A novel interictal EEG marker for seizure localization. Brain. (in press, accepted Aug 2022).
    • Zhai SR*, Ehrens D, Li A, Assaf F, Schiller Y, Sarma SV, Smith RJ: Temporal and morphological characteristics of high-frequency oscillations in an acute in vivo model of epilepsy. IEEE Engineering in Medicine and Biology Society. July 2022.
    • Smith RJ, Kamali G, Hays M, Coogan C, Crone NE, Kang JY, Sarma SV: Stimulating native seizures with neural resonance: a new approach to localize the seizure onset zone. Brain. (in press, accepted May 2022).
    • Hays MA, Smith RJ, Haridas B, Coogan CG, Crone NE, Kang JY: Effect of increasing stimulation intensity on intracranial cortico-cortical evoked potentials: a titration study. Clinical Neurophysiology. 132:11. Pg. 2766-2777. Nov 2021.
    • Smith RJ, Shrey DW, Rajaraman R, Hussain SA, Lopour BA: Computational characteristics of interictal EEG as objective markers of epileptic spasms. Epilepsy Research. 176: 106704. Jun 2021.
    • Smith RJ, Alipourjeddi E, Garner C, Maser AL, Shrey DW, Lopour BA: Infant functional networks are modulated by state of consciousness and circadian rhythm. Network Neuroscience: 1-17. Mar 2021.
    • Kamali G, Smith RJ, Hays M, Coogan CG, Crone NE, Sarma SV, Kang JY: Transfer Function Models for the Localization of Seizure Onset Zone from Cortico-Cortical Evoked Potentials. Frontiers in Neurology: Epilepsy. 11: 579961. Dec 2020.
    • Smith RJ, Kamali G, Hays M, Coogan CG, Crone NE, Sarma SV, Kang JY: State-space Models of Evoked Potentials to Localize the Seizure Onset Zone. IEEE Engineering in Medicine and Biology Society. July 2020.
    • Stacey W, Kramer M, Gunnarsdottir K, Gonzalez-Martinez J, Zaghloul K, Inati S, Sarma S, Stiso J, Khambhati A, Bassett D, Smith RJ, Liu VB, Lopour BA, Staba R: Emerging roles of network analysis for epilepsy. Epilepsy Research. 159(1). 106255. Jan 2020.
    • Smith RJ, Shrey DW, Ombao H, Lopour BA: Inference on long-range temporal correlations in human EEG data. Journal of Biomedical and Health Informatics. 24(4), pp 1070-1079. Apr 2020.
    • Smith RJ, Shrey DW, Hussain SA, Lopour BA: Quantitative Characteristics of Hypsarrhythmia in Infantile Spasms. 40th International Conference of the IEEE Engineering in Medicine and Biology Society. July 2018.
    • Smith RJ, Sugijoto A, Rismanchi N, Hussain SA, Shrey DW, Lopour BA: Long-range temporal correlations reflect treatment response in the electroencephalogram of patients with infantile spasms. Brain Topography. 30(6), pp 810-821. Nov 2017.
  • Academic Honors and Societies
    • American Epilepsy Society Young Investigator Award, 2020
    • American Epilepsy Society 2020 Fellow, 2020
    • Most Promising Future Faculty Award, 2018
    • BME Graduate Student of the Year Award, 2016 & 2017
    • IEEE Society Member
    • American Epilepsy Society Member