Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division's Research

View all CEMSE Research outputs currently in KAUST Repository

Research in the CEMSE Division clusters into four main areas:

  • Electrical engineering, including the development of communication networks; CMOS integrated circuits; electronic and optics/photonics devices; micro-electro-mechanical systems (MEMS); various types of sensors, measurement and detection devices; as well as functional- and nano-materials.
  • Mathematical analysis, including modeling and simulations with applications to physical, chemical, biological and environmental processes; materials science; oil exploration and reservoir management.
  • Computer science and big data, including bioinformatics; and visual and extreme computing.
  • Statistics and data science, including climate science, environmental statistics, and biostatistics.

Research in the CEMSE Division is driven by independent faculty labs and three Research Centers with which Faculty can affiliate to perform applied, goal-oriented research. Centers affiliated with the Division include:​

  • Computational Bioscience Research Center (CBRC)
  • Extreme Computing Research Center (ECRC)
  • Visual Computing Center (VCC)

In addition, from time to time, the Division undertakes special exploratory and collaborative research initiatives, currently in Sensors, Uncertainty Quantification, TIC Solid State Lighting at KAUST, and Center of Excellence for NEOM Research at KAUST.


Katsaounis, T., Kotsovos, K., Gereige, I., Basaheeh, A., Abdullah, M., Khayat, A., Al Habshi, E., Al-Saggaf, A., & Tzavaras, A.E (2019). Performance Assessment of Various PV Module Types under Desert Conditions through Device Simulations and Outdoor Measurements. EUPVSEC 2019 Proceedings, 874 - 879. doi:10.4229/EUPVSEC20192019-4CO.2.1
Christoforou, C., Galanopoulou, M., & Tzavaras A.E. (2019). Measure-valued solutions for the equations of polyconvex adiabatic thermoelasticity. Discrete & Continuous Dynamical Systems - A, 39(11), 6175-6206. doi:10.3934/dcds.2019269
Dethise, Canini, M. & Kandula, S. (2019). Cracking Open the Black Box: What Observations Can Tell Us About Reinforcement Learning Agents, NetAI '19
Huo, X., Jüngel, A., & Tzavaras, A.E. (2019). High-friction limits of Euler flows for multicomponent systems. Nonlinearity, 32(8), 2875–2913. doi:10.1088/1361-6544/ab12a6
Schmode, P., Ohayon, D., Reichstein, P. M., Savva, A., Inal, S., & Thelakkat, M. (2019). High-Performance Organic Electrochemical Transistors Based on Conjugated Polyelectrolyte Copolymers. Chemistry of Materials, 31(14), 5286–5295. doi:10.1021/acs.chemmater.9b01722
Katsaounis, T., Kotsovos, K., Gereige, I., Basaheeh, A., Abdullah, M., Khayat, A., Al Habshi, E., Al-Saggaf, A., & Tzavaras, A.E. (2019). Performance assessment of bifacial c-Si PV modules through device simulations and outdoor measurements. Renewable Energy, 143, 1285-1298. doi:10.1016/j.renene.2019.05.057
Waldin, N., Waldner, M., Le Muzic, M., Gröller, E., Goodsell, D. S., Autin, L., … Viola, I. (2019). Cuttlefish: Color Mapping for Dynamic Multi‐Scale Visualizations. Computer Graphics Forum.
Lee, M.-G., Katsaounis, T., & Tzavaras, A.E. (2019). Localization in Adiabatic Shear Flow Via Geometric Theory of Singular Perturbations. Journal of Nonlinear Science, 29, 2055–2101. doi:10.1007/s00332-019-09538-3
A. Alshehri, M. Al-Qadasi, A. S. Almansouri, T. Al-Attar and H. Fariborzi, "StrongARM Latch Comparator Performance Enhancement by Implementing Clocked Forward Body Biasing," 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Bordeaux, 2018, pp. 229-232, doi: 10.1109/ICECS.2018.8617903.