Daniel Haziza
Papers
2
Total Citations
157
H-Index
2
About
Daniel Haziza is a researcher at the intersection of artificial intelligence and intelligent transportation systems, with a particular focus on applying deep reinforcement learning (RL) to real-world cyberphysical engineering challenges. His most recognized contribution is pioneering the application of multi-task deep reinforcement learning to ramp metering control — a complex traffic management problem traditionally requiring extensive human expertise. By drawing on the same RL breakthroughs that enabled machines to master arcade video games and robotic locomotion, Haziza demonstrated that autonomous systems could achieve expert-level performance in highway on-ramp traffic regulation, a milestone with significant implications for smart infrastructure and autonomous traffic management. His 2017 work on this topic has garnered over 155 citations, reflecting its substantial influence on both the transportation engineering and applied machine learning communities. The research stands out for bridging the gap between cutting-edge AI research and practical engineering deployment, showing that reinforcement learning is not confined to games or simulations but can meaningfully tackle safety-critical, real-world systems. For students and researchers exploring autonomous traffic control, adaptive infrastructure, or applied deep learning, Haziza's work represents a foundational reference point in the evolving field of AI-driven smart mobility.
Research Focus
Key Achievements
Top Papers
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- 2