Curriculum Vitae

Curriculum Vitae#

Overview Computational physicist and quantum-AI researcher with expertise bridging quantum technologies and machine learning. Skilled in high-performance simulation, quantum hardware control, and hybrid quantum-classical algorithm design. Quick learner, systems thinker, and cross-disciplinary communicator with strong scientific integrity and a deep curiosity for emerging research.

Job Experience#

  • 2023–2025 — Postdoctoral Researcher — Leiden University (Own Grant) NWO Quantum Delta Initiative – MaZelTof-Q project

• Developed digital twins for superconducting quantum devices using physics-aware neural networks. • Automated tune-up and calibration using deep RL and Bayesian optimization. • Addressed flux crosstalk calibration via AI-driven methods.

  • 2021–2023 — Postdoctoral Research Fellow — Leiden University Applied Quantum Algorithms Center

• Led projects regarding quantum advantage demonstrations on real hardware. • Designed GPU-based quantum simulators using JAX. • Designed and implemented a reinforcement learning-based optimization algorithm to improve performance of variational quantum circuits under hardware-induced noise, improving con- vergence and robustness in simulation benchmarks. • Led projects in Hamiltonian & non-Markovian noise learning.

  • 2021 — IBM-HBCU Quantum Center Research Fellow — Howard University, University of Illinois Chicago

• Worked on quantum optimal control and tomography with IBM Quantum hardware. • Developed a quantum game based on Fock state interactions in coupled waveguide systems. • Designed a volume-law entangled quantum optical system based on the spatial geometry of a cavity, and specific placements of qubits inside this cavity. • Simulated the classical modes within these quantum optical cavities using FDTD methods. Combined these classical results with full quantum modeling via Green’s dyad formalism. • Applied surrogate modeling and RL for photonic structure optimization.

  • 2014–2020 — Graduate Research Assistant — Tulane University

• Designed ML and numerical methods to model the generation, propagation and control of spatial modes of quantum light for quantum information processing in non-linear optical systems. In particular generation multi-mode squeezed coherent states, and their propagation under atmospheric turbulence & other obstructions.

  • 2019 — Data Science Intern — Jackson Laboratory

• Optimized demultiplexing software (libraries in C++. R, Python) for single-cell omics data (mRNA-Seq) using Bayesian ML. Used deep convolutional neural networks to classify bioinformatic data. This internship brought me upto speed both in modern practices of data science & machine learning engineering, and bio-statistics/bio-informatics.

  • 2011–2013 — Graduate Research Assistant — Koc University • Modeled quantum optical and plasmonic systems using numerical tools (Qutip & Lumeruical FDTD) and Green’s dyad formalism (to fully model quantum dynamics using classical mode solutions).

Skills#

  • Languages: Python, C/C++, E, Bash, LaTeX, Mathematica

  • Frameworks: JAX, TensorFlow, Qiskit, Cirq, Pennylane, QuTiP, Qulacs, Tensorcircuit

  • Scientific Computing: NumPy, SciPy, CUDA, MPI, OpenMP, PetSc, Lumerical FDTD, Marlab

  • ML & Optimization: Deep neural networks, Reinforcement Learning, Bayesian Optimiza- tion, Surrogate Modeling

  • Tools & Platforms: Docker, Git, SLURM, Linux (Ubuntu), Windows

Education#

  • PhD, Physics – Tulane University Thesis: Adaptive Feedback Control for Noise-Reduced Vacuum Squeezed States

  • MS, Physics – Koç University Thesis: Quantum Control of Hyperbolic Metamaterials

  • BS, Physics – Middle East Technical University Thesis: Tunneling of Relativistic Particles Through Magnetic Fields

Honors & Rewards#

  • Quantum Delta Initiative Grant (NGF.1582.22.031), 2023 MAZeLTof-Q, Machine Assisted Zero-Knowledge Tune-up of Superconducting Qubits

  • NWO SURF Compute Access Grant, 2022 GPU Simulation of Noisy Quantum Circuits in Scale

  • Best Industrial Project, Workshop: NWO Physics for Industry, 2022 Developed an industrially relevant method for the company Sense 360

  • Best Presenter (3rd), Photonics 2018 Conference on photonics & Quantum Optics, Bilkent University, Ankara

Additional Coursework#

  • Quantum/ML: TU Delft ML4Quantum, Stanford Statistical Learning (Lagunita), Coursera Deep Learning Specialization

  • Computational Science MSc Curriculum: Scientific Computing I–III, Optimization, Bayesian ML, Large Scale Computation (CUDA, MPI, OpenMP), Linear Models, Data Analysis, C++

Contents