Papers
8
Total Citations
61
H-Index
5
About
Indar Sugiarto is a researcher at the intersection of neuromorphic computing, robotics, and embedded intelligence. His work focuses on developing energy-efficient, brain-inspired computing systems and probabilistic inference engines for autonomous robots. Sugiarto’s most impactful contribution is a 2016 study demonstrating high-performance, low-power image processing on the SpiNNaker neuromorphic platform (25 citations), showcasing how neural-inspired architectures can achieve fast response with minimal energy consumption. He has also pioneered the use of factor graphs—a unified probabilistic graphical model—for robot kinematics and control, with several papers (8–9 citations each) applying belief propagation and population coding to enable reasoning under uncertainty. His research extends to hardware acceleration, including FPGA-based implementations of factor graph inference engines and fuzzy logic controllers for mobile robot navigation. More recently, Sugiarto has applied his expertise to socially relevant problems, such as autonomous eldercare monitoring systems that detect abnormal human poses and vital signs. With a consistent focus on bridging theoretical probabilistic models with practical, real-time embedded systems, Sugiarto’s work offers a compelling model for building intelligent, efficient, and autonomous robotic platforms.
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