Summary
Daniel Lengyel is a quantitative researcher with eight years of experience bridging academic research and industry applications in optimization, interacting systems, and data-efficient function approximation. He completed a PhD at Imperial College London focused on derivative-free and distributed optimization and has taught advanced courses in computational optimisation, machine learning, and computational finance. Daniel has applied his research to real-world energy and HVAC control projects—deploying algorithms across commercial buildings—and interned in quantitative research at GSA Capital before joining GSA as a researcher. Equally comfortable with theory and deployment, he brings hands-on experience in control, IoT, and quantum computing education from his Berkeley years, and a proven track record of turning complex models into tested, operational systems.
8 years of coding experience
3 years of employment as a software developer
Bachelor's degree Applied Mathematics, Bachelor's degree Applied Mathematics at University of California, Berkeley
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Imperial College London
English, German, Hungarian, Norwegian