Keshav Pingali

Papers

Total Citations

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About

Keshav Pingali is a leading researcher in high-performance computing, programming systems, and approximate computing. His work focuses on developing foundational principles and methodologies to improve the efficiency, performance, and energy usage of complex computational systems. A major contribution is his pioneering approach to principled approximation in critical algorithms, such as those used in Simultaneous Localization and Mapping (SLAM) for robotics and autonomous driving. His 2020 paper on this topic, which has garnered 6 citations, proposes a systematic methodology to reduce time and energy requirements without catastrophic failure. Pingali is also known for his accessible and influential work on Kalman filtering, a classic state estimation technique; his 2017 introduction to the topic (19 citations) bridges signal processing and modern computer systems, including dynamic voltage and frequency scaling for processors. Through his research, Pingali has shaped how autonomous systems and high-performance architectures can be made both faster and more reliable, making him a key figure in the intersection of algorithms, systems, and energy-aware computing.