Sridha Sridharan
Papers
10
Total Citations
275
H-Index
7
About
Sridha Sridharan is a prominent researcher whose work spans robotics perception, computer vision, and machine learning, with particular expertise in 3D scene understanding, place recognition, and human behavior analysis. His most impactful contributions center on enabling autonomous systems to navigate and understand complex environments. His 2022 paper "LoGG3D-Net," garnering 97 citations, introduced a groundbreaking approach to LiDAR-based place recognition for SLAM applications, while follow-up work including "InCloud" and "Locus" further advanced robust, adaptive point cloud recognition in dynamic real-world settings. Beyond robotics, Sridharan has made significant strides in human-computer interaction, developing multi-modal gesture recognition frameworks and pedestrian motion prediction systems that leverage deep inverse reinforcement learning to anticipate crowd behavior over extended time horizons. His research into generative adversarial imitation learning demonstrates a keen interest in modeling complex human decision-making and strategic reasoning. More recently, his "FactoFormer" work applies transformer architectures to hyperspectral image analysis, reflecting his breadth across sensing modalities. With over 270 cumulative citations, Sridharan's research consistently bridges foundational machine learning advances with practical autonomous systems applications, making his work essential reading for students in robotics, perception, and AI.
Research Focus
Key Achievements
Top Papers
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- 4InCloud: Incremental Learning for Point Cloud Place Recognition26 citations · 2022
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- 9Discovery of facial motions using deep machine perception7 citations · 2016
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