Assistant Professor, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County. Building numerical solvers and machine-learning methods for photodetectors, microresonators, and multilayered photonic structures.
Dr. Simsek received a B.S. in Electrical and Electronics Engineering from Bilkent University (2001), an M.S. from the University of Massachusetts Dartmouth (2003), and a Ph.D. in Electrical and Computer Engineering from Duke University (2006), where his dissertation addressed electromagnetic scattering from inhomogeneous objects embedded in layered media.
He subsequently worked as a postdoctoral research associate at Schlumberger-Doll Research, and held faculty positions at Bahçeşehir University and George Washington University before joining UMBC in 2018, where he is now an Assistant Professor in the Department of Computer Science and Electrical Engineering.
His group develops numerical solvers and machine-learning methods for problems where light interacts with multilayered structures from photodetector design to microresonator dispersion. He is a Senior Member of IEEE, a Senior Member of Optica, a life member of ACES, and a licensed Professional Engineer.
"One scientific epoch ended and another began with James Clerk Maxwell."
— Albert Einstein
Coupled electro-opto-thermal drift-diffusion solvers for p-i-n and modified uni-traveling-carrier photodetectors, paired with evolutionary and adjoint optimization to push III-V and Si-Ge devices toward higher bandwidth and lower phase noise for frequency-comb and RF-photonic systems.
Drift-diffusion · PSO · Adjoint methodsEnergy-efficient neural networks built around device physics rather than off-the-shelf architectures — applied to electromagnetic object classification, inverse design, resolution enhancement, and dispersion metrology for microresonators.
Inverse design · Classification · MetrologyMixed-field and finite-difference solvers for dielectric ring resonators and waveguides, coupled with Lugiato–Lefever modeling to characterize dispersion and support low-noise, chip-scale comb generation.
Mode solvers · Ring resonators · DispersionLayered-medium Green's functions and singularity-subtracted integral equation solvers for scattering problems, applied to surface-plasmon sensors and 2D-material (graphene, MoS₂, WSe₂) absorbers and modulators embedded in multilayer stacks.
LMGFs · SPR sensing · 2D materials10 most recent of 46+ peer-reviewed articles.
The complete list of journal articles can be found on this page. The complete list of conference papers can be found on this page.
| Course | Title | Term |
|---|---|---|
| ENEE 684 | Introduction to PhotonicsUpcoming | Fall 2026 · 7 students |
| CMPE 306 | Introductory Circuit Theory | Spring 2026 · 89 students |
| CMPE 330 | Electromagnetic Waves and Transmission | Spring 2026 · 23 students |
| ENEE 680 | Electromagnetic Theory | Fall 2023–2025 |
| CMPE 330 | Electromagnetic Waves and Transmission | Spring 2024–2025 |
| ENEE 691 | Special Topics — Machine Learning and Photonics | Spring 2023 · 14 students |
| CMSC 411 | Computer Architecture | Fall 2022 · 36 students |