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About

Arindam Sengupta is a researcher specializing in sensor fusion, autonomous perception, and millimeter-wave (mmWave) radar systems — fields that sit at the cutting edge of autonomous driving and intelligent sensing technologies. His most influential contribution, "Robust Multiobject Tracking Using mmWave Radar-Camera Sensor Fusion" (2022, 57 citations), advances the state of multiobject classification and tracking by leveraging the complementary strengths of radar and camera modalities, addressing a critical challenge in real-world autonomous systems. Building on this foundation, his work on 3D radar-camera co-calibration (2023, 24 citations) provides a flexible and accurate extrinsic calibration framework that is essential for reliable heterogeneous sensor fusion pipelines. Sengupta has also extended his expertise into human-centric sensing, developing methods to stabilize skeletal pose estimation using mmWave radar through dynamic modeling and filtering techniques (2022, 11 citations), demonstrating the technology's potential beyond automotive applications. With a growing citation record across multiple research directions, Sengupta is establishing himself as a versatile and impactful contributor to the sensor fusion community, with relevance to both industry practitioners and academic researchers working on next-generation perception systems.