Invited Speakers
Invited Speakers
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Title of the talk: "Intelligent Mapping Solutions"
Dr. Aleksandr Petiushko is the Sr. Director of Artificial Intelligence Research at Autonomous Driving company Gatik (Mountain View, California), an Adjunct Professor at Sofia University (Palo Alto, California) teaching courses on ML and AI, and a lecturer at various universities on the Theory of Deep Learning. Before Gatik, Dr. Petiushko worked as Director, Head of ML Research at Nuro, as a Team Lead / Scientific Expert, Chief Scientist at Huawei, and as a Managing Director / Leading Scientific Researcher at Artificial Intelligence Research Institute. In addition to his 20 years of experience as a Principal R&D Researcher, he’s contributed to more than 45 publications and 40 patents.
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Wei Zhan serves as a co-director of Berkeley DeepDrive, one of the leading research centers in the field of AI for autonomy and mobility, which involves many Berkeley faculty and industrial partners. He is an assistant professional researcher at UC Berkeley, leading a team of Ph.D. students and postdocs conducting research. His research is focused on AI for autonomous systems leveraging control, robotics, computer vision, and machine learning techniques to tackle challenges with sophisticated dynamics, interactive human behavior, and complex scenes in a scalable way. He also teaches AI for Autonomy at UC Berkeley. He is also the Chief Scientist of Applied Intuition, leading AI research efforts toward next-generation autonomy and its development toolchain. He is actively hiring Research Scientists, Research Engineers, and Research Interns. He received his Ph.D. degree from UC Berkeley. His publications received the Best Student Paper Award in IV’18, the Best Paper Award – Honorable Mention of IEEE Robotics and Automation Letters, and the Best Paper Award of ICRA’24.
Title of the talk: "The Concept Misalignment between Experts and AI, from Data Labeling to Data Versioning"
Shu Kong is an Assistant Professor in the Faculty of Science and Technology at the University of Macau. He was an Assistant Professor in the Department of Computer Science and Engineering at Texas A\&M University. He was a postdoc fellow and Project Scientist at the Robotics Institute at Carnegie Mellon University. He earned a PhD in Computer Science at the University of California, Irvine. His research lies in Computer Vision and its interactions with other fields (e.g., ML, NLP, HCI, robotics, and graphics), broad applications (e.g., AR/VR, autonomous driving, etc.), and diverse disciplines (e.g., biology, paleoecology, psychology, special education, etc.). His current research focuses on Visual Perception via Learning in the Open World (VPLOW). He has organized workshops at CVPR and WACV from 2021 through 2024. His previous paper on this topic was recognized for the Best Paper / Marr Prize at the International Conference on Computer Vision (ICCV) 2021. His previous work that applied AI to interdisciplinary research built a high-throughput pollen analysis system featured by the National Science Foundation that "opens a new era of fossil pollen research."
Title of the talk: "Reasoning for Anomaly Detection with Large Language Models"
Dr. Shao-Yuan Lo is a Research Scientist at Honda Research Institute USA. He received his Ph.D. from Johns Hopkins University in 2023, and M.S. and B.S. degrees from National Chiao Tung University in 2019 and 2017, respectively. His recent research focuses on Multimodal LLMs and AI Safety. He has first/corresponding-authored nearly 20 publications, such as IEEE T-PAMI, IEEE T-IP, CVPR and ECCV. He won the Best Paper Award at ACM Multimedia Asia 2019.
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Dr. Daniel Kondermann received his PhD (2009) and Habilitation (2016) at the Heidelberg Collaboratory for Image Processing. His research revolves around performance analysis of computer vision and machine learning methods, with a focus on dataset architecture, ranging from sensor array design to data annotation and performance metric definition. In 2013, he founded Pallas Ludens, a visual data annotation company. He and his team joined Apple in 2016, where he headed a small team researching dataset quality metrics for large-scale annotation projects. Daniel left Apple in 2019 to create a new company. Quality Match is a visual dataset quality assurance company that is based on the hypothesis that the amount of data needed for machine learning is less important than its quality.
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Dr. Maged Shoman is a Research Assistant Professor in Intelligent Transportation Systems with the University of Tennessee-Oak Ridge Innovation Institute. His research interests are Deep Learning, Computer Vision, Transportation and Traffic Safety Research, Autonomous and Connected Vehicles, Digital Twins and Smart Cities and Intelligent Transportation Systems (ITS). Dr. Shoman’s research has been supported by various funding agencies, including the National Science Foundation (NSF), the U.S. Department of Transportation (DOT), as well as state DOTs and municipalities. He has published over 10 peer-reviewed papers in prestigious journals and conferences, including IEEE Computer Vision and Pattern Recognition (CVPR), ASCE Journal of Transportation Engineering, and the Transportation Research Record. His research has been featured on St Louis National Public Radio, and he has received multiple accolades, including placements in the NVIDIA AI City Challenge and the 2021 Outstanding PhD Student Award.
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