Title:

Deep Adversarial Learning

Abstract:
Adversarial machine learning in a broad sense is the study of machine learning theory and algorithms in environments with multiple agents that have different goals. This broad definition of adversarial machine learning includes narrow-sense adversarial machine learning which studies the vulnerability of learning algorithms in the presence of adversarial data perturbation during the training or the testing phases. However, there are many learning problems that involve multiple agents and objectives in different contexts. For example, generative adversarial nets, domain adaptation, data sanitization, attacking/defending deep neural net, and hyperparameter optimization can all be formulated as adversarial learning problems. In this talk, I will introduce several applications of adversarial learning focusing on privacy preservation, and then discuss the challenges of adversarial optimization and propose new solutions. I will also present ongoing and future research in this direction.

Biography:
Dr. Hamm is a Research Scientist at the Department of CSE, the Ohio State University. He received his Ph.D. from the University of Pennsylvania in 2008 with a focus on nonlinear dimensionality reduction and kernel methods, and was a post-doctoral researcher at the Penn medical school working on machine learning applied to medical data analysis.
Dr. Hamm's recent research is focused on machine learning theory and algorithms in adversarial settings and in the field of security and privacy.

He has received the best paper award from MedIA-MICCAI (2010), was a finalist for MICCAI Young Scientist Publication Impact Award (2013), and is a recipient of the Google Faculty Research Award (2015). He has served as a reviewer for JMLR, IEEE TPAMI/TNN/TIP/TIFS, NN, PR, IJPR and others, and also as a program committee member for NIPS, ICML, AAAI, and AISTATS.