Ba Tuong Vo

Professor Ba Tuong Vo is a faculty member in the Department of Electrical and Computer Engineering at Curtin University, Perth, Australia. He holds the academic rank of Professor within the School of Electrical Engineering, Computing, and Mathematical Sciences. His primary research interests lie in statistical signal processing, Bayesian methods, multi-target tracking, and random finite set theory, with applications to sensor fusion, data-driven decision making, and stochastic control systems. He has contributed significantly to developing advanced filtering techniques such as the Generalized Labeled Multi-Bernoulli (GLMB) filter and multi-sensor fusion algorithms for large-scale tracking problems.

His work bridges theoretical advancements in random finite set theory and practical implementations for real-world applications like autonomous robotics, medical imaging, and environmental monitoring. Notably, he has pioneered methodologies for handling unknown clutter rates, sensor management, and occlusion challenges in multi-object systems. Professor Vo’s research also extends to multi-modal data integration (e.g., audio-visual tracking) and has been applied to diverse domains including space situational awareness and cell lineage tracking.

His academic profile includes over 150 peer-reviewed publications, with a focus on IEEE Transactions journals and leading conferences in signal processing and robotics. Beyond technical contributions, he actively mentors students and collaborates internationally on projects funded by industry and government grants. A key focus is fostering innovation in scalable multi-target tracking solutions for emerging technologies like autonomous systems and smart infrastructure.