[PAST EVENT] New Nonlinear Machine Learning Algorithms with Applications to Biomedical Data Science
Speaker: Xiaoqian (Joy) Wang, University of Pittsburgh
Recent advances in machine learning have spawned innovation and prosperity in various fields. In machine learning models, nonlinearity facilitates more flexibility and ability to better fit the data. However, the improved model flexibility is often accompanied by challenges such as overfitting, higher computational complexity, and less interpretability. Thus, my research has been focusing on designing new feasible nonlinear machine learning models to address the above different challenges posed by various data scales, and bringing new discoveries in both theory and applications. In this talk, I will introduce my newly designed nonlinear machine learning algorithms, such as additive models and deep learning methods, to address these challenges and validate the new models via the emerging biomedical applications.
First, I introduce new interpretable additive models for regression and classification, and address the overfitting problem of nonlinear models in small and medium scale data. I derive the model convergence rate under mild conditions in the hypothesis space, and uncover new potential biomarkers in Alzheimer's disease study. Second, I propose a deep generative adversarial network to analyze the temporal correlation structure in longitudinal data, and achieve state-of-the-art performance in Alzheimer's early diagnosis. Meanwhile, I design a new interpretable neural network model to improve the interpretability of the results of deep learning methods. Further, to tackle the insufficient labeled data in large-scale data analysis, I design a novel semi-supervised deep learning model and validate the performance in the application of gene expression inference.
Xiaoqian Wang is a Ph.D. candidate in Computer Engineering at the University of Pittsburgh, working with Prof. Heng Huang. She received the B.S. degree in Bioinformatics from Zhejiang University in 2013. Her research interests span across multidisciplinary areas of machine learning, data mining, computational neuroscience, cancer genomics, and precision medicine. She has published 21 papers in top-tier conferences such as NIPS, ICML, KDD, IJCAI, AAAI, RECOMB, and ECCB. She received the Best Research Assistant Award at the University of Pittsburgh in 2017.