讲座内容：Navigating Uncertainty in Machine Learning: Approaches and Challenges in Building Robust AI Systems （应对机器学习中的不确定性：构建鲁棒AI系统的方法与挑战）
讲座人：Hamido Fujita教授
讲座时间：11月26日 14:00-16:00
腾讯会议：409-8117-7679
Abstract：
In the field of machine learning, uncertainty is a fundamental challenge that impacts model reliability and performance, especially when dealing with unknown or noisy data. Despite the remarkable success of deep learning models in tasks such as classification, prediction, and pattern recognition, these models often struggle to quantify and handle uncertainty effectively. Uncertainty in machine learning can arise from various sources, including data noise, model ambiguity, and lack of prior knowledge. Addressing this issue, techniques such as Bayesian inference, Monte Carlo Dropout, and uncertainty propagation in neural networks have emerged. These methods allow models to estimate not only predictions but also the confidence level in those predictions, making them more robust in uncertain environments. By incorporating uncertainty quantification, machine learning models can improve their generalization capabilities, provide more reliable decision-making, and enhance their ability to manage ambiguous or incomplete data. However, challenges remain, such as balancing model complexity with computational efficiency and addressing the limitations of current uncertainty models.
Short Bio：
He is Executive Chairman of i-SOMET Incorporated Association, Japan, and Distinguished Professor at Iwate Prefectural University, Japan, he is also Research Professor at University of Granada, Spain. He is Highly Cited Researcher in CrossField for the year 2019 and in Computer Science for the year 2020, 2021 and 2022, by Clarivate Analytics. He received Doctor Honoris Causa from Óbuda University, Budapest, Hungary, in 2013 and received Doctor Honoris Causa from Timisoara Technical University, Timisoara, Romania, in 2018, and a title of Honorary Professor from Óbuda University, in 2011. He is Distinguished Research Professor at the University of Granada, and Adjunct Professor with Taipei Technical University, Taiwan, Harbin Engineering University, China and others. He supervised Ph.D. students jointly with the University of Laval, Quebec City, QC, Canada; University of Technology Sydney; Oregon State University, Corvallis, OR, USA; University of Paris 1 Pantheon-Sorbonne, Paris, France; and University of Genoa, Italy. Dr. Fujita is the recipient of the Honorary Scholar Award from the University of Technology Sydney, in 2012. He was the Editor-in-Chief for Knowledge-Based Systems (Elsevier) (2005- 2019) and then Emeritus Editor of Knowledge-Based Systems in 2020~. Since 2020 he is currently the Editor-in-Chief of Applied Intelligence (Springer) and the Editor-inChief of International Journal of Healthcare Management (Taylor & Francis). He headed a number of projects including intelligent HCI, a project related to mental cloning for healthcare systems as an intelligent user interface between human-users and computers, and SCOPE project on virtual doctor systems for medical applications. He collaborated with several research projects in Europe, and recently he is collaborating in OLIMPIA project supported by Tuscany region on Therapeutic monitoring of Parkison disease. He has published more than 400 articles mainly in high impact factor journals.
