Invited Short Courses
Short Course I (SC1) | Deep Learning in Statistics
Instructor(s): Annie Qu (UC Irvine), Xiao Wang (Purdue University), Edgar Dobriban (University of Pennsylvania)
Time: Tuesday, May 26, 2020, 9 AM - 5 PM
This short course is for those new to data science and interested in understanding the cutting-edge deep learning models. It is for those who want to become familiar with the core concepts behind deep learning algorithms and their successful applications. It is for those who want to start thinking about how deep learning might be useful in their business or career. This one-day short course will provide a comprehensive overview of deep learning methods from the statistics perspective. Topics include deep feedforward neural networks, convolutional neural networks, deep Boltzmann machine, variational autoencoders, generative adversarial networks, learning theory fundamentals, generalization error bounds for deep neural networks, deep learning in R, an overview of the Keras package. Various application examples will be discussed in detail as well.
Cost: $100. Seats are limited. One may add this short course to his/her registration at any time via the registration form.
Short Course II (SC2) | Artificial Intelligence, Machine Learning, and Precision Medicine
Instructor(s): Haoda Fu (Eli Lilly)
Time: Tuesday, May 26, 2020, 1 PM - 5 PM
This half-day short course will provide an overview of statistical machine learning, and artificial intelligence techniques with applications to the precision medicine, in particular, to deriving optimal individualized treatment strategies for personalized medicine. This short course will cover both the treatment selection and treatment transition. The treatment selection framework is based on outcome weighted classification. We will cover logistic regression, support vector machine (SVM), ?-learning, robust SVM, and angle based classifiers for multi-category learning, and we will show how to modify these classification methods into outcome weighted learning algorithms for personalized medicine. The second part of this short course will also cover the treatment transition. We will provide an introduction to reinforcement learning techniques. Algorithms, including dynamic programming for Markov Decision Process, temporal difference learning, SARSA, Q-Learning algorithms, actor-critic methods, will be covered. We will discuss how to use these methods for developing optimal treatment transition strategies. The techniques discussed will be demonstrated in R. This course is intended for graduate students who have some knowledge of statistics and want to be introduced to statistical machine learning, or practitioners who would like to apply statistical machine learning techniques to their problems on personalized medicine and other biomedical applications.
Cost: $50. Seats are limited. One may add this short course to his/her registration at any time via the registration form.
Instructor Bios
Annie Qu
Affiliation: University of California at Irvine (aqu2@uci.edu)
Annie Qu is Professor of Statistics at the University of California at Irvine. She received her Ph.D. in Statistics from Penn State in 1998. Her research interests include machine learning, medical imaging, recommender system, natural language processing, personalized medicine, longitudinal/correlated data analysis, missing data, model selection and nonparametric models. Dr. Qu received an NSF Career award, and is a fellow of the Institute of Mathematical Statistics and of the American Statistical Association. She is the past Chair of the Statistics Learning and Data Science Section of the American Statistical Association. Currently, she is associate editor for JASA, Electronic Journal of Statistics and Journal of Nonparametric Statistics. She also served as associate editor for Biometrics and Statistical Science in the past.
Xiao Wang
Affiliation: Purdue University (wangxiao@purdue.edu)
Xiao Wang obtained his B.S. and M.S. in mathematics from University of Science and Technology of China, and Ph.D. in Statistics from University of Michigan. He is now a Professor in Department of Statistics at Purdue University. His research interests are primiarly on machine learning, deep learning, nonparametric statistics, functional data analysis, and reliability, as well as their application in neuroimage, bioinformatics, and industry. He also serves as associate editor of Journal of the American Statistical Association, Technometrics, and Lifetime Data Analysis.
Edgar Dobriban
Affiliation: University of Pennsylvania (dobriban@wharton.upenn.edu)
Edgar Dobriban holds a B.A. in mathematics from Princeton University (with highest honors) and a Ph.D. degree in Statistics from Stanford University. He is currently an Assistant Professor of Statistics in the Wharton School of the University of Pennsylvania. His research interests include the statistical analysis of massive datasets, applications of random matrix theory, distributed statistical learning and deep learning. His work has been published in leading journals in statistics, such as the Annals of Statistics, Annals of Applied Statistics, Journal of the Royal Statistical Society, Biometrika. He has developed publicly available software for processing big data using tools from random matrix theory. He received the TW Anderson Prize for best PhD thesis in the theory of statistics from Stanford University in 2017.
Haoda Fu
Affiliation: Eli Lilly
Dr. Haoda Fu is a research fellow and a enterprise lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association). He is also an adjunct professor of biostatistics department, Indiana university school of medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics methodology research. He has more than 90 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session.