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AIC Seminar Series

Advancing the Design and Utility of Adversarial Machine Learning Methods

Nathan InkawhichDuke University

Notice:  Job Talk

Date:  Monday, August 9th 2021 at 10:30am

Location:  Zoom: https://sri.zoomgov.com/j/16028867898  (Directions)

   Abstract

Despite achieving remarkable accuracy, Deep Neural Networks (DNNs) have many vulnerabilities with the potential to render them useless (and even dangerous) in complex and extended operating conditions. Motivated by this reality, in this talk I will discuss two distinct threads of research that are intrinsically linked by the use of Adversarial Machine Learning (AdvML) methods, and will ultimately make up my PhD dissertation. The first thread investigates ways in which DNNs are vulnerable to adversarial attacks through the design of advanced blackbox methods for use in both standard and non-standard threat models. The second thread leverages robust training techniques to build more safe and reliable ATR systems that are equipped for operation in “open-world” environments. Together, these works show the power and utility of AdvML techniques in multiple contexts.

   Bio for Nathan Inkawhich

Nathan Inkawhich is a PhD candidate in the Electrical and Computer Engineering Department at Duke University, advised by Dr. Yiran Chen of the Computation Evolutionary Intelligence (CEI) Lab. Nathan’s main areas of research are in adversarial machine learning, deep learning robustness and interpretability, automatic target recognition for remote sensing applications, and out-of-distribution detection. Previously, Nathan received a M.S. in Electrical and Computer Engineering from Duke University, a B.S. in Computer Engineering from Clarkson University, and worked for the Air Force Research Laboratory Information Directorate (AFRL/RI) in Rome, NY.

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