Dr. Ergun Simsek
Assistant Professor · Tenure-Track
Department of Computer Science and Electrical Engineering
College of Engineering and Information Technology
He/Him/His/Himself
About
Dr. Simsek received a B.S. degree in Electrical and Electronics Engineering from Bilkent University in 2001 and an M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Massachusetts Dartmouth and Duke University in 2003 and 2006, respectively. After working as a postdoctoral research associate at Schlumberger-Doll Research and a faculty member at Bahcesehir University and George Washington University, he joined the University of Maryland Baltimore County in 2018, where he currently is an Assistant Professor.
Research interests
Scientific Computing, Photonics, and Machine Learning.
Teaching interests
Electromagnetics, Photonics, Numerical Methods, Signals and Systems, Data Science, Machine Learning, and Scientific Computing.
Education
-
Ph D, Electrical and Computer Engineering
— Duke University (2006) Electromagnetic Scattering from Inhomogeneous Objects of Arbitrary Shape Embedded in a Layered Medium
-
MS, Electrical and Computer Engineering
— University of Massachusetts (2003) Evaluation of Closed-Form Green's Functions for Multi-layer Micro-strip Antennas and Circuits
- BS, Electrical and Electronics Engineering — Bilkent University (2001)
Publications
- Microresonator Frequency Comb Implementations in Photonic Integrated Circuits for PNT 2025
- Excito-Plasmonic Phototransistors with Improved Thermal Management 2025
- Plasmonically Enhanced 2D Material Based Phototransistor with Efficient Heat Management 2025
- Plasmonically Enhanced 2D Material Based Phototransistor with Efficient Heat Management 2025
- Broadband Substrate Optimization with Adjoint Method and Green's Functions 2024
- Effective Heat Dissipation From Plasmon Enhanced Monolayer WSe2 Phototransistors 2024
- Sources of Nonlinearity in Phase Noise of MUTC Photodetectors at Comb-Line Frequencies 2024
- A compact numerical model for photodetectors made from two-dimensional materials 2024
- Resolution enhancement with machine learning 2024
- Enhancement of Non-Destructive Measurement Resolution with Neural Networks 2024
- Heat Management and Quantum Efficiency Enhancement of Phototransistors Made from Two-Dimensional Materials 2024
- Thermal Analysis of Photodetectors in Steady-State 2024
- Enhancing the Resolution of Local Near-Field Probing Measurements with Machine Learning 2024
- Dual-Objective Numerical Optimization of MUTC Photodetectors for Frequency Comb Applications 2023
- Optimized MUTC Photodetector for Lower Phase Noise at Comb-Line Frequencies Below 40 GHz 2023
- Designing Energy Efficient Neural Networks According to Device Operation Principles 2023
- Designing Nonlinear Optoelectronic Devices with Numerical Optimization: Lessons Learned 2023
- Solving Drift Diffusion Equations on Non-uniform Spatial and Temporal Domains 2023
- Predicting broadband resonator-waveguide coupling for microresonator frequency combs through fully connected and recurrent neural networks and attention mechanism 2023
- Non-Uniform Time-Stepping and Windowing for Fast Simulation of Photodetectors 2023
- Use of Evolutionary Optimization Algorithms for the Design and Analysis of Low Bias, Low Phase Noise Photodetectors
- Design and Analysis of Low Bias, Low Phase Noise Photodetectors for Frequency Comb Applications Using Particle Swarm Optimization
- Efficient and Accurate Calculation of Photodetector RF Output Power
- Non-Uniform Time-Stepping For Fast Simulation of Photodetectors Under High-Peak-Power, Ultra-Short Optical Pulses
- Designing Photodetectors with Machine Learning
- Determining Optical Constants of 2D Materials with Neural Networks from Multi-Angle Reflectometry Data
Presentations
- Design and Analysis of Low Bias, Low Phase Noise Photodetectors for Frequency Comb Applications Using Particle Swarm Optimization 2022
- Non-Uniform Time-Stepping For Fast Simulation of Photodetectors Under High-Peak-Power, Ultra-Short Optical Pulses 2022
- Designing Photodetectors with Machine Learning 2022
- A Robust Drift-Diffusion Equations Solver Enabling Accurate Simulation of Photodetectors 2022
- Calculation of the Phase Noise at Comb-Line Frequencies in a Frequency Comb 2021
- Photodetector Performance Prediction with Machine Learning 2021
- A Robust Drift-Diffusion Equations Solver Enabling Accurate Simulation of Photodetectors 2021
- Thinner and Faster Photodetectors Producing Lower Phase Noise 2021
Grants and Contracts
- Modeling and Characterization of Modulators for Frequency-Comb Generation 2024
- Substrate and Defect Engineering for More Efficient Photovoltaic Systems 2024
- DSC: Empowering Underrepresented Educators: A Data Science Corps Program to Cultivate Diversity and Excellence in Data Science Education
Courses Taught
- Electromag Theory I
- Em Waves Transmission
- Independent Study
- Computer Architecture
- Capstone in Data Science
- Introduction to Data Science
- Computer Architecture