PhD, Electrical Engineering
California Institute of Technology (Caltech)

m_elkhamy at ieee.org


My research lies at the intersection of information theory, digital communications, networking, machine learning, and signal processing.
In broad terms, my research contributes to the reliable and efficient processing, transmission, storage, and recovery of information.

My research includes the design and implementation of deep machine learning algorithms for applications in computer vision and speech processing. My research also includes wireless communication systems as modem algorithm design, wireless cellular communication systems, heterogeneous wireless networks, sensor networks, cognitive radio as well as optical and deep space communications. My research also includes the design and implementation of coding systems for the reliable transmission and  storage of data, for communication systems or storage devices as CDs, DVD, flash memories and disk drives. 
My research has contributed to the theory and techniques of the physical layer (PHY) and the media access control (MAC) layer of communication systems.  Contributions of my research include the analysis and design of novel error correction codes, such as Turbo codes, LDPC codes, spatially coupled codes, and polar codes, as well as their decoding algorithms.
Such contributions include novel iterative and algebraic soft-decision decoding of Reed-Solomon codes as well as robust iterative decoding of Turbo codes. Other contributions are the analysis of maximum likelihood performance of binary images of Reed-Solomon codes, list sphere decoding of block codes, and the performance of multi-user Reed-Solomon codes. Other contributions include the design of rate compatible Low Density Parity Check (LDPC) codes for Hybrid Automatic Repeat Request (HARQ) applications, as well as the design of network codes for joint networking and routing.  Contributions also include the design and analysis of spatially coupled and quasi-cyclic LDPC codes to show state of art finite length performance. For polar codes, my research has contributed to the design and analysis of polar codes and their decoding algorithms, concatenated polar codes, improving performance of polar codes on bursty erasure channels, designing polar codes and their decoding algorithms for multiple access channels, bit interleaved coded modulation channels, and HARQ wireless channels. 

My research has contributed to the PHY layer techniques for wireless interference management such as adaptive multi-user detection in Code Division Multiple Access (CDMA) cellular systems, novel MIMO techniques such as low complexity sphere decoding and interference alignment, MAC layer techniques to improve capacity and QoS in heterogeneous wireless networks such as novel interference management techniques in heterogeneous macro-cell femto-cell (home Node B)  cellular systems, resource allocation in HSPA and OFDMA cellular relay networks, coexistence between ZigBee wireless sensor networks and Wireless LANs, and adaptive channel allocation in WiFi mesh networks. My research has also contributed to novel spectrum sensing techniques for cognitive radio systems to alleviate wireless spectrum scarcity problems. My research contributions also include efficient encoding, compression and error concealment of H.264 3D multi-view video for efficient transmission of multimedia over noisy networks.
My teaching experience at multiple universities includes teaching numerous undergraduate and graduate level courses in the field of electronics, signal processing, and communications engineering. I have also been a key founding faculty member of  School of Electronics, Communications, and Computing at Egypt-Japan University of Science and Technology, a graduate research university supported by Egyptian and Japanese governments, and a consortium of 12 top Japanese universities. My industry experience includes leading the design and implementation of PHY and MAC algorithms for modem chips for the 3GPP cellular wireless systems, as HSPA, LTE and, LTE-A. My industry experience also includes leading the design of machine learning and deep learning algorithms for application and multimedia processors for applications such as computer vision and speech recognition.