Abhijit Chatterjee
Papers
11
Total Citations
69
H-Index
5
About
Abhijit Chatterjee is a researcher whose work sits at the intersection of autonomous systems, control theory, machine learning, and hardware reliability. His research is primarily focused on ensuring the safety, correctness, and resilience of nonlinear control systems — challenges that are increasingly critical as robots, autonomous vehicles, and sensor networks become deeply embedded in society. Chatterjee's most significant contributions center on real-time error detection and recovery in control systems. He has pioneered innovative techniques including analog checksums, encoded check states, and machine learning-assisted state-space encoding to identify and compensate for faults in sensors, actuators, and control algorithms — often without the computational overhead of full redundancy. His 2020 work on Gaussian Control Barrier Functions extends this safety focus into the domain of safe learning under model uncertainty, earning 13 citations and representing his most visible contribution to date. Across his portfolio, Chatterjee has tackled the difficult problem of making autonomous systems dependable under real-world impairments, spanning linear and nonlinear systems alike. His more recent work on error resilience in deep neural networks reflects a timely expansion into AI hardware reliability. With citations spanning foundational and applied work, Chatterjee has established himself as a principled contributor to dependable autonomous systems engineering.
Research Focus
Key Achievements
Top Papers
- 1Gaussian Control Barrier Functions: Safe Learning and Control13 citations · 2020
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- 4Real-time checking of linear control systems using analog checksums9 citations · 2013
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- 9ALERA3 citations · 2019
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