Fathi M. Salem
| Fathi M. Salem | |
|---|---|
| Born | |
| 🏫 Education | University of California, Berkeley (PhD) University of California, Davis (MS) |
| 💼 Occupation | |
| Known for | Recurrent neural networks Blind signal separation |
Fathi M. Salem is an American electrical engineer and academic who is a professor of electrical and computer engineering at Michigan State University, where he leads the Circuits, Systems and Artificial Neural Networks research group.[1] He is also affiliated with the MSU Neuroscience Program.[2] In 1996, he became an IEEE Life Member and Fellow "for contributions to the development of tools for the analysis and design of nonlinear and chaotic circuits and systems".[3]
Education
Salem received a PhD in electrical engineering and computer sciences from the University of California, Berkeley in 1983. He earned a Master of Science degree in electrical engineering from the University of California, Davis in 1979.[4]
Research
Salem's research focuses on neural networks and learning systems, blind signal deconvolution and extraction, dynamical systems and chaos, and integrated CMOS sensing and processing.[2] His work on blind source recovery established a state-space framework for the problem using Kullback–Leibler divergence as a performance functional.[5][6]
His more recent work has focused on recurrent neural networks, including developing simplified variants of long short-term memory (LSTM) architectures with reduced parameters.[7][8]
References
- ↑ "Research". Michigan State University College of Engineering. Retrieved January 25, 2026.
- ↑ 2.0 2.1 "Fathi Salem". Michigan State University College of Natural Science. Retrieved January 25, 2026.
- ↑ "IEEE Fellow Directory: Fathi Salem". IEEE Fellows Directory. Retrieved 25 January 2026.
- ↑ "Fathi Salem". ResearchGate. Retrieved January 25, 2026.
- ↑ Waheed, Khurram; Salem, Fathi M. (2003). "Blind Source Recovery: A Framework in the State Space". Journal of Machine Learning Research. 4: 1411–1446. doi:10.1162/jmlr.2003.4.7-8.1411.
- ↑ Salem, F.M.; Waheed, K.; Erten, G. (2005). "Blind Source Recovery in a State-Space Framework: Algorithms for Static and Dynamic Environments". Neural Processing Letters. Springer. 21 (3): 153–173. doi:10.1007/s11063-004-5484-9.
- ↑ Heck, Joel C.; Salem, Fathi M. (2017). Simplified minimal gated unit variations for recurrent neural networks. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). pp. 1593–1596. doi:10.1109/MWSCAS.2017.8053225.
- ↑ Dey, Rahul; Salem, Fathi M. (2017). Gate-variants of Gated Recurrent Unit (GRU) neural networks. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). pp. 1597–1600. doi:10.1109/MWSCAS.2017.8053226.
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