Quantum optimization scientist
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Quantum scientist specializing in optimization, quantum computing, and artificial intelligence. Experienced in designing and implementing hybrid quantum–classical algorithms for real-world optimization, scheduling, and routing problems. Strong background in optimization theory, variational quantum algorithms, and machine learning. Passionate about translating cutting-edge quantum research into deployable applications that can advance the next generation of intelligent optimization technologies.
[Present] Senior AI Engineer/Researcher at K3Y labs. Leading the Quantum–AI development efforts within EU-funded programs (Horizon Europe).
[Past] Quantum Optimization Scientist. Developed a Quantum Optimization-as-a-Service (QOaaS) framework enabling generalizable, problem-independent optimization through proprietary hybrid algorithms. Investigated qubit-efficient variational quantum algorithms for large-scale combinatorial optimization, focusing on the Vehicle Routing Problem with Time Windows (VRPTW).
Collaborated with scientists at Centre for Quantum Technologies (Singapore) as part of my MSc research thesis. Contributed to the Horizon EU Quantum Computational Fluid Dynamics (QCFD) project, developing and benchmarking hybrid algorithms such as VQE, QAOA, and qubit-efficient heuristics for PDE-based optimization in fluid dynamics.
[MSc, ECE] Qubit efficient quantum optimization in routing and scheduling
[BSc + MEng, ECE] Quantum approximate optimization algorithms and applications
