Profiles

Professor Salama received his B.S. (Hons.) degree from Cairo University, Egypt, in 1997. He obtained his M.S. and Ph.D. degrees in electrical engineering from Stanford University, U.S., in 2000 and 2005, respectively.

The principal investigator of the KAUST Sensors Lab, Salama joined the University in 2009. From 2009 to 2011, he served as the founding program chair for Electrical Engineering at KAUST. Before joining KAUST, he worked as an assistant professor at Rensselaer Polytechnic Institute, U.S., from 2005 to 2009.

Dr. Salama—a senior member of the Institute of Electrical and Electronics Engineers (IEEE)—has authored 360 articles and holds 50 patents on low-power mixed-signal circuits for intelligent, fully integrated sensors and nonlinear electronics, particularly memristor devices.

His work on complementary metal-oxide semiconductor (CMOS) sensors for molecular detection has been funded by the National Institutes of Health (NIH) and the Defense Advanced Research Projects Agency (DARPA). He is also the co-founder of Ultrawave Labs, a biomedical imaging company.

Salama received the Stanford-Berkeley Innovators Challenge Award in Biological Science.

Professor Salama’s research interests cover various interdisciplinary aspects of electronic circuit design and semiconductor fabrication. He is actively engaged in developing devices, circuits, systems and algorithms to enable inexpensive analytical platforms for a variety of industrial, environmental and biomedical applications.

Salama’s most recent research has focused on developing neuromorphic circuits for brain emulation.

Abdullah Bukhamsin received his B.Sc. degree in Bioengineering from Rice University (Houston, Texas) in 2018. Following that, he completed his M.Sc. degree in Electrical Engineering from King Abdullah University of Science and Technology (Thuwal, Saudi Arabia) in 2020 under the supervision of Dr. Jurgen Kosel. In 2025, he completed his PhD degree in Bioengineering from King Abdullah University of Science and Technology (Thuwal, Saudi Arabia) under the supervision of Dr. Khaled N. Salama and co-supervision of Dr, Ikram Blilou.

Abdullah is interested in developing scalable micro-fabrication methods for 3D electrodes for electrochemical sensors pertaining to applications centered on physiological monitoring of biomarkers.

• Developing Biosensors to detect biomarkers for cancer and other ailments.
• Studying electrochemical characteristics of various materials, polymers and films.
• Designing wearable, point-of-care, biotech medical devices.
• Utilizing machine learning to aid in sensing and data interpretation.

Li Zhang is a Ph.D. candidate in the Sensors Lab of the Electrical Engineering Department, CEMSE Division, King Abdullah University of Science and Technology (KAUST). He earned his B.S. in Microelectronic Science and Engineering from the University of Electronic Science and Technology of China in 2018, and his M.S. in Electrical Engineering from KAUST in 2019. His research interests include quantized neural networks, neural-network accelerator architectures, and software–hardware co-design. He has technical expertise in optimization algorithms, machine-learning methods, FPGA-based digital circuit design, and experimental measurement techniques.

Li Zhang is interested in quantized neural networks, neural network accelerator and software/hardware co-design.

Mario Soto Martinez earned his Bachelor’s degree in Nanotechnology Engineering from ITESO, Guadalajara, Mexico, in 2020. During his studies, he completed an internship at CIIDEP, ITESM Monterrey (2018), where he worked on smart materials for environmental applications. His undergraduate research focused on the fabrication of nanostructured electrodes for biosensing. He later obtained his M.Sc. in Bioengineering from King Abdullah University of Science and Technology (KAUST), Saudi Arabia, in 2022, where he specialized in the microfabrication of thin-film transistors for biosensing applications. Mario’s interdisciplinary background in nanotechnology, materials engineering, and bioelectronics underpins his current research on multimodal sensing systems for point-of-care diagnostics

Mario’s research focuses on the development of multimodal sensing systems for point-of-care disease screening. His approach combines electrochemical and gas sensors to detect biomarkers that indicate disease presence or stage. He integrates materials engineering, microfabrication, electronics, and prototyping to design and optimize these platforms for practical implementation, aiming to advance accessible and reliable healthcare technologies.

Mufeeda  earned her Bachelor’s degree in Chemistry from the University of Calicut, Kerala, India, in 2020. During her studies, she completed an internship at the Indian Institute of Science (IISc), Bengaluru, where she worked on electrocatalysts for sodium hybrid air batteries. She went on to obtain her M.Sc. in Analytical Chemistry from the National Institute of Technology (NIT), Warangal, India, where she specialized in electrochemical analysis and sensor development. Following her master’s, she worked as a Research Assistant, focusing on electrochemical sensors for biomedical applications with an emphasis on novel two-dimensional materials such as MXenes, and published her work in leading Q1 journals. In 2024, she joined King Abdullah University of Science and Technology (KAUST), Saudi Arabia, as a Ph.D. student in Bioengineering. Her current research focuses on the development of wearable sensors for stress monitoring in plants, integrating interdisciplinary approaches across nanotechnology, bioelectronics, and materials science.

Mufeeda’s research focuses on the development of wearable and implantable electrochemical sensors for real-time stress monitoring in plants. She is exploring various electrode systems, including laser scribed graphene and microneedle arrays integrated platforms, for the highly sensitive and selective detection of reactive oxygen species and other stress biomarkers. By combining Materials engineering and bioelectronics, she aims to create innovative sensing systems that provide direct, in vivo insights into plant physiological status, contributing to sustainable agriculture. Beyond plant monitoring, Mufeeda is also interested in extending her research toward the development of advanced sensing platforms for biomedical applications, leveraging similar interdisciplinary strategies to address challenges in healthcare diagnostics.

Olga Krestinskaya (Graduate Student Member, IEEE) is a Ph.D. candidate at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Her research focuses on software–hardware co-design for in-memory computing (IMC) architectures and AI hardware, with a particular interest in hardware-aware neural architecture search (NAS) algorithms, memristor-based systems, neuromorphic computing, and mixed-signal IMC implementations. She has authored several high-impact works on analog memristive neural networks, mixed-signal circuit-level implementations of in-memory computing architectures, quantized neural networks, and brain-inspired algorithms, with a focus on developing energy-efficient and scalable IMC hardware for AI applications.

Olga is the recipient of the 2019 IEEE CASS Predoctoral Award, the 2025 Web of Talents STEM Award (1st place), and multiple KAUST Dean’s Awards. Her work was recognized with the Best Poster Award at the 2nd Nature Conference on Neuromorphic Computing (2024), and she was shortlisted for the prestigious Rising Stars Women in Engineering Workshop (Asian Deans’ Forum 2024).

Olga`s research area is neuromorphic and brain-inspired algorithms, circuits, and architectures. In particular, she is interested in memristor-based architectures for neural networks and neuro-inspired systems. Currently, Olga is focusing on analog circuit-level implementations of reconfigurable memristive neural network architecture and optimization of hyperparameters.

• Developing Biosensors to detect biomarkers for cancer and other ailments.
• Studying electrochemical characteristics of various materials, polymers and films.
• Designing wearable, point-of-care, biotech medical devices.
• Utilizing machine learning to aid in sensing and data interpretation.

Sebastian Celis Sierra is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), where he develops advanced integral-equation solvers for the accurate simulation of electromagnetic metasurfaces. He holds an M.S. in Electrical Engineering (2018–2020) from KAUST and dual B.Eng. degrees in Electrical/Power Engineering and Electronics Engineering (2014–2018) from Universidad de los Andes, Bogotá.

Sebastian's research focuses on formulating and implementing surface and volume integral-equation methods in both the time and frequency domains to capture complex electromagnetic behavior in arbitrarily shaped metasurfaces. By coupling these formulations with generalized sheet transition conditions (GSTCs), he aims to achieve both numerical efficiency and physical fidelity. His work advances the theoretical foundations and practical computational tools necessary for the next generation of communication systems.