Join us for “Robust Vision and Multimodal Learning From Classical to Quantum Machine Learning Approaches” a keynote presentation by Dr. Khoa Luu, College of Engineering – Electrical Engineering & Computer Science, MizzouForward faculty candidate.  Dr. Luu will present on his research for approximately 40-minutes with a 20-minute question and answer session to follow.

Dr. Khoa Luu is an Assistant Professor and the Director of the Computer Vision and Image Understanding (CVIU) Lab in the Department of Electrical Engineering and Computer Science (EECS) at the University of Arkansas (UA), Fayetteville, US. He is affiliated with the Center for Public Health and Technology, UA, and the NSF MonARK Quantum Foundry. He is an Associate Editor of the IEEE Access Journal and the Multimedia Tools and Applications Journal, Springer Nature. He is also the Area Chair in CVPR 2023-2025, NeurIPS 2024-25, ICML 2025, ICLR 2025 and WACV 2025. He received eight patents, three best paper awards, and coauthored 120+ papers in conferences, technical reports, and journals. He has supervised one postdoc, 8 Ph.D. students, 4 Master's students (Master's theses), and 35+ Undergraduate and Honors Thesis students in the EECS Department at the University of Arkansas. His research has been funded by the National Science Foundation (NSF), National Institute of Food and Agriculture (NIFA), Department of Defense (DOD), U.S. Dept of Transportation (DOT), Arkansas Biosciences Institute (ABI), Chancellor's Commercialization and Innovation Funds, Google Research, JB Hunt, and several other industrial companies.  He was the Research Project Director at the Cylab Biometrics Center at Carnegie Mellon University (CMU). He led the team to develop successful AI products and applications, including 2D Quantum Material Identification, Smart Insect Monitoring System, Vision-Brain Reconstruction (ranking 3/102), AI-based Early Autism Detection, Facial Micro-Expression Recognition, Face Recognition (Top Ranking in NIST Face Recognition Vendor Test in 2020), Mutli-camera Multi-Object Tracking, Driver Monitoring System (DMS), Long-range Biometrics and Soft Biometrics Systems.

The self-operation of robots, autonomous vehicles (AV) and unmanned aerial vehicles (UAV) in open-world environments heavily relies on the robustness of the visual perception model. The perception model needs to understand the surrounding scene, track the target objects, and analyze the human behaviors of the surrounding objects. Although applications of AV in on-road or urban scenarios where the objects and environments have been well studied for many years, their deployment in the wild environments still needs further improvement. This presentation will present their recent studies in robust and fair vision-based perception approaches to surveillance cameras, robots, AV, and UAV. To improve self-operation capability in open-world environments, he will introduce their recent studies in vision-learning approaches to fairness continual learning, and cross-domain adaptation. In addition, to advance the tracking ability of the perception model, their recent work in prompted-based multi-camera multi-object tracking via large-language models will be presented. Finally, he will present their recent achievement in some of these applications using quantum machine learning approaches.

You can access Dr. Luu’s CV via OneDrive here:    KLuu_CV - 02032025.pdf (University log in required to access)

After the keynote, please provide candidate feedback with our brief survey.