Academic Seminar: Research and Application of Regularized Machine Learning Methods on Big Data Analysis
In order to promote academic communication and cooperation, Professor Yong Liang, a researcher from Shenzhen Pengcheng Laboratory, was invited by Centre for Artificial Intelligence Driven Drug Discovery (AIDD Centre) to give a lecture entitled "Research and Application of Regularized Machine Learning Methods on Big Data Analysis" in conference room N47A, Wuichi Building on October 7. The lecture was chaired by Prof. Henry Tong, director of the AIDD Centre. All the teachers and doctoral students at AIDD Centre attended the lecture.
Prof. Liang introduced the research and application of regularized machine learning methods on big data analysis from three aspects: regularization methods for sparse variable selection, regularization methods for meta-learning and self-step, and the current opportunities and challenges. Among them, the structural sparse regularization machine learning method can bring a realistic personalized treatment model, and avoid the model from falling into the trap of overfitting, showing a very broad application prospect. In addition, Prof. Liang's team has successfully constructed a support vector machine model for diagnosing early tuberculosis by analyzing RNAomics data of human peripheral blood cells and screening 13 RNA biomarkers, which provides a powerful scientific tool for the early diagnosis of tuberculosis. After the report, the participating teachers and students actively discussed with Prof. Liang and conducted in-depth discussions and on the related issues. The lecture was rich in cutting-edge content and academic atmosphere, which helped inspire the academic innovation of the participating teachers and students, and provided a new impetus to promote scientific and technological innovation in our university.
About Prof. Liang: Yong Liang, a researcher at Pengcheng Laboratory and a member of the Board of Directors of the Chinese Society for Industrial and Applied Mathematics. He received his Ph.D. degree in computer science from the Chinese University of Hong Kong in 2003, under the supervision of Professor Zongben Xu, an academician of the Chinese Academy of Sciences, and Professor Guangxi Liang, Chair of Computer Science and Engineering at the Chinese University of Hong Kong. His research interests include computational intelligence, machine learning, big data, and bioinformatics. The high dimensionality, high noise, weak annotation, unstructured, and multiple batches of biomedical big data make traditional computer methods face serious challenges. In recent years, Prof. Liang has made several achievements in the research and application of big data analysis based on machine learning methods. He has published more than 140 academic papers in Nature Biomedical Engineering, Nature-Communications, IEEE Transactions on Cybernetics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Internet of Things Journal, IEEE-Transactions on Vehicular Technology, Science of China and other well-known journals at home and abroad, with more than 5,200 citations (Google Scholar). And he has been granted 12 international invention patents. He is also a reviewer for many international journals such as IEEE-Transactions on Cybernetics and Bioinformatics.