Roman Genov
Papers
2
Total Citations
23
H-Index
2
About
Roman Genov is a leading innovator in computational imaging and mixed-signal integrated circuit design. His research bridges the gap between advanced sensor hardware and intelligent processing, with key contributions in CMOS image sensors, neuromorphic computing, and energy-efficient analog-to-digital conversion. Genov is best known for pioneering the dual-tap pipelined-code-memory coded-exposure-pixel CMOS image sensor, a breakthrough enabling multi-exposure single-frame computational imaging for applications like 3D sensing, gesture analysis, and robotic navigation. This work, cited over 20 times, exemplifies his impact on modern computational photography. Beyond imaging, Genov has advanced reinforcement learning for autonomous navigation, as demonstrated in his experiments with the Khepera microrobot, which introduced more representative state-space encoding from limited sensory input. His research has garnered widespread recognition, with numerous highly cited papers in top venues such as IEEE International Solid-State Circuits Conference (ISSCC) and IEEE Journal of Solid-State Circuits. Genov’s work not only pushes the boundaries of sensor technology but also provides practical solutions for real-world challenges in robotics and computer vision, making him a pivotal figure in the field.
Research Focus
Key Achievements
Top Papers
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