From Wikipedia, the free encyclopedia
Content deleted Content added
| Line 37: | Line 37: | ||
|
[[Category:University of California, Berkeley alumni]] |
[[Category:University of California, Berkeley alumni]] |
||
|
[[Category:University of California, Davis alumni]] |
[[Category:University of California, Davis alumni]] |
||
|
[[Category:Neural network researchers]] |
|||
|
}} |
}} |
||
Revision as of 19:31, 25 January 2026
 |
This is a draft Articles for creation (AfC) submission. It is not currently pending review. While there are no deadlines, abandoned drafts may be deleted after six months. To edit the draft click on the “Edit” tab at the top of the window. To be accepted, a draft should: It is strongly discouraged to write about either yourself or your business or employer. If you do so, you must declare it. Where to get help
Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. |
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]
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.[3]
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.[4][5]
His more recent work has focused on recurrent neural networks, including developing simplified variants of long short-term memory (LSTM) architectures with reduced parameters.[6][7]
References
- ^ “Research”. Michigan State University College of Engineering. Retrieved January 25, 2026.
- ^ a b “Fathi Salem”. Michigan State University College of Natural Science. Retrieved January 25, 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.
- ^ 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. 21 (3). Springer: 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.
