Dr. Yijun Zhao is an assistant professor of Computer and Information Science at Fordham University. She received her MS and BS degrees in Computer Science from the University of Kansas and Tianjin University, respectively. Dr. Zhao earned her PhD degree in Computer Science from Tufts University for her research in addressing bias and subjectivity in Machine Learning.
Prior to embarking on her academic career, Dr. Zhao worked in the financial industry for over 10 years in a wide variety of roles, including a consultant at PricewaterCoopers, a senior quantitative analyst at Wells Fargo Advisors, and a quantitative trader at hedge funds Millennium Partners and Ronin Capital
The success of supervised machine learning algorithms rests on the assumption that data are drawn from the same underlying distribution. However, this assumption is often violated in real world applications where collected data involves human judgement. Dr. Zhao’s research focuses on investigating machine learning methods that can be used to enhance performance while learning with datasets containing bias and human subjectivity. In collaboration with leading experts from Harvard Medical School and NYU's Langone Medical Center, Dr. Zhao has applied Machine Learning methods to predict disease course in Multiple Sclerosis patients and detect brain abnormalities occurring in neurological disorders such as Epilepsy.
Y. Zhao, B. Healy, D. Rotstein, C. Guttmann, R. Bakshi, H. Weiner, C. Brodley, T. Chitnis "Exploration of Ma- chine Learning Techniques in Predicting Multiple Sclerosis Disease Course," PLOS ONE, 2017
Y. Zhao, B. Ahmed, T. Thesen, K. E. Blackmon, J. Dy, C. Brodley "A Non-parametric Approach to Detect Epileptogeic Lesions using Restricted Boltzmann Machines," 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016
Y. Zhao, T. Chitnis, B. Healy, J. Dy, C. Brodley "Domain Induced Dirichlet Mixture of Gaussian Processes: An Application to Predicting Disease Progression in Multiple Sclerosis Patients," The IEEE International Conference on Data Mining Series (ICDM), 2015
Y. Zhao, C. Brodley, T. Chitnis, B. Healy. "Addressing Human Subjectivity via Transfer Learning: An Application to Predicting Disease Outcome in Multiple Sclerosis Patients," 2014 SIAM International Conference on Data Mining, 2014
B. Ahmed, T. Thesen, K. Blackmon, Y. Zhao, O. Devinsky, R. Kuzniercky, C. Brodley, "HierarchicalConditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations," The 31st International Conference on Machine Learning (ICML), 2014
M. Kong, Y. Zhao, "Computing k-independent sets for regular bipartite graphs," Congressus Numerantium Vol. 143(2000), pp. 65-80
CISC 6930 Data Mining