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Biography
Dr. Le is currently an Assistant Professor and Director of the Artificial Intelligence & Computer Vision https://uark-aicv.github.io in the Department of Electrical Engineering & Computer Science (EECS) at the University of Arkansas. Previously, she held a Postdoctoral position at Carnegie Mellon University (CMU). Dr. Le obtained her Ph.D. and Master's degrees in Electrical & Computer Engineering from CMU in 2018 and 2015, respectively. She earned her Master's and Bachelor's degrees in Computer Science from Vietnam, in 2009 and 2005, respectively.
Dr. Le is internationally recognized for her significant contributions in Robotics, Machine Learning, Computer Vision, and Medical Analysis. Her research addresses diverse real-world challenges, including trusted decision-making, imperfect data (limited labeled data, noisy data, biased data, unseen data, small objects) and real-time applications on edge devices. Proficient across multiple modalities, she excels in working with image, video, point cloud, volumetric data, time series, and remote sensing data. Notably, her expertise spans image processing, scene understanding, multiple object tracking, behavior analysis, medical image analysis, 3D reconstruction, and real-time robotics perception. Dr. Le's work is considered state-of-the-art, with many of her research endeavors successfully deployed in real-world applications, including cutting-edge implementations on edge devices. Her research portfolio includes ownership of three patents and co-authorship of over 100 papers and articles across various prestigious conferences, book chapters, and top-tier journals. She holds an h-index of 27 and an i10-index of 69 on Google Scholar, with 3,103 citations as of August 15, 2024 (8ck0k_UAAAAJ)
Dr. Le has been an Associate Editor for ScienceDirect's Machine Learning with Applications (MLWA) journal since 2021. She has chaired prominent conferences including Asilomar and MICAD, and has served as a Guest Editor for several journals with Frontier and MDPI. Dr. Le has organized tutorials and workshops at prestigious conferences such as MICCAI and ACCV. For examples, the Tutorial on Deep Reinforcement Learning for Medical Imaging at MICCAI 2018, the Workshop on Medical Image Learning with Less Labels and Imperfect Data at MICCAI 2019, 2020, the Workshop on Robust, Trustworthy and Cost-Optimized Learning Across Multiple Modalities: Theory, Algorithms, and Applications (LAMM).
Dr. Le is also committed to supporting women in research. She has organized events such as WiM (Women in MICCAI) in conjunction with MICCAI 2019, 2020, and 2021, and will be organizing WiCV (Women in Computer Vision) in conjunction with ECCV 2024.