Jitendra K Tugnait
James B Davis Professor Contact313 Broun Hall, Auburn AL 36849-5201 |
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Opening for a Post-Doctoral Research Position
A Post-Doc Research Position in Machine Learning for Signal Processing is available immediately. Will be supported by NSF Grant CCF-2308473, CIF: Small: Learning Sparse Vector and Matrix Graphs from Time-Dependent Data, PI: J.K. Tugnait. The position requires a strong background in (or willingness and ability to learn) statistical signal processing, optimization, (high-dimensional) statistics and graph signal processing.Interested persons should contact Prof. Tugnait at tugnajk@auburn.edu AND apply through https://www.auemployment.com/postings/40746 (college of engineering postdoc pool 2023-2024: HR requirement).
Current Research Interests
Statistical Signal Processing Conditional independence graphs of random vectors and matrices, and multivariate time series; Improper/non-circular complex-valued random signal processing; Robust signal processingMachine Learning for Signal Processing Graphical modeling of random vectors and matrices, and multivariate time series; Differential (dependency) graphical modeling
Past Research Interests
Wireless Physical & Secure Communications Physical layer security; Massive MIMO; Cognitive radioMultisensor Multitarget Tracking
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Forthcoming
J.K. Tugnait, "Delay embedding for matrix graphical model learning from dependent data," to be presented at the 2024 IEEE Intern. Conf. Acoustics, Speech & Signal Processing (ICASSP 2024), Seoul, South Korea, April 14-19, 2024.
Some recent papers
J.K. Tugnait, "Learning high-dimensional differential graphs from multi-attribute data," IEEE Trans. Signal Processing, vol. 72, pp. 415-431, 2024. [pdf]
J.K. Tugnait, "Estimation of differential graphs from time-dependent data" in 2023 Proc. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2023), pp. 261-265, Los Suenos, Costa Rica, Dec. 10-13, 2023.
J.K. Tugnait, "Estimation of differential graphs via log-sum penalized D-trace loss," in Proc. 22nd IEEE Statistical Signal Processing Workshop (SSP-2023), pp. 240-244, Hanoi, Vietnam, July 2-5, 2023.
J.K. Tugnait, "Sparse high-dimensional matrix-valued graphical model learning from dependent data," in Proc. 22nd IEEE Statistical Signal Processing Workshop (SSP-2023), pp. 344-348, Hanoi, Vietnam, July 2-5, 2023.
J.K. Tugnait, "Estimation of high-dimensional differential graphs from multi-attribute data," in Proc. 2023 IEEE Intern. Conf. Acoustics, Speech & Signal Processing (ICASSP 2023), Rhodes Island, Greece, June 5-9, 2023.
J.K. Tugnait, "Graph learning from multivariate dependent time series via a multi-attribute formulation," in Proc. 2022 IEEE Intern. Conf. Acoustics, Speech & Signal Processing (ICASSP 2022), pp. 4508-4512, Singapore, May 22-27, 2022. [pdf]
J.K. Tugnait, "Sparse-group log-sum penalized graphical model learning for time series," in Proc. 2022 IEEE Intern. Conf. Acoustics, Speech & Signal Processing (ICASSP 2022), pp. 5822-5826, Singapore, May 22-27, 2022. [pdf]
J.K. Tugnait, "On sparse high-dimensional graphical model learning for dependent time series," Signal Processing, vol. 197, Aug. 2022, Article 108539. [pdf:arXiv] [pdf:journal]
J.K. Tugnait, "Sparse graph learning under Laplacian-related constraints," IEEE Access, vol. 9, pp. 151067-151079, 2021. [pdf]
J.K. Tugnait, "Corrections to 'Sparse-group lasso for graph learning from multi-attribute data'," IEEE Trans. Signal Processing, vol. 69, p. 4758, 2021. [pdf]
J.K. Tugnait, "Sparse-group lasso for graph learning from multi-attribute data," IEEE Trans. Signal Processing, vol. 69, pp. 1771-1786, 2021. [pdf]
Link to our graduate program.
