Keynote: Dr. Maya Gupta



Maya Gupta is a Principal Scientist at Google where she leads the Glassbox Machine Learning R&D team that focuses, among other things, on end-to-end machine learning interpretability and trusting machine learning classifiers.  Prior to Google, Maya was an Associate Professor of Electrical Engineering at the University of Washington in Seattle.  There, she received numerous awards, including the presidential early career award for scientists and engineers and the Office of Naval Research Young Investigator Award.  She received her PhD in Electrical Engineering at Stanford under the direction of Robert Gray, and joint degrees in Electrical Engineering and Economics from Rice University.  She is also the CEO of the wooden jigsaw puzzle company Artifact Puzzles.

Keynote: Dr. Alexander Cott


Dr. Alexander Kott serves as the ARL’s Chief Scientist. In this role he provides leadership in development of Army Research Laboratory technical strategy, maintaining technical quality of ARL research, and representing ARL to external technical community. Between 2009 and 2016, he was the Chief, Network Science Division, Computational and Information Sciences Directorate, US Army Research Laboratory headquartered in Adelphi MD. He was responsible for a diverse portfolio of fundamental research and applied development in network science and science for cyber defense. In particular, he played a key role in initiating the Network Science Collaborative Technology Alliance, among the world-largest efforts to study interactions between networks of different types. His efforts helped start Cyber Security Collaborative Research Alliance, a unique program of creating basic science of cyber warfare. In 2013, Dr. Kott served as the Acting Associate Director for Science and Technology of the ARL’s Computational and Information Sciences Directorate; in 2015 he also served as the Acting Director of the Computational and Information Sciences Directorate.

Beginning his Government career, between 2003 and 2008, Dr. Kott served as a Defense Advanced Research Programs Agency (DARPA) Program Manager responsible for a number of large-scale advanced technology research programs. Technologies developed in programs under his management ranged from adversarial reasoning, to prediction of social and security phenomena, to command and control of robotic forces. His earlier positions included Director of R&D at Carnegie Group, Pittsburgh, PA; and Information Technology Research Department Manager at AlliedSignal, Inc., Morristown, NJ. There, his work focused on novel information technology approaches, such as Artificial Intelligence, to complex problems in engineering design, and planning and control in manufacturing, telecommunications and aviation industries. Dr Kott received the Secretary of Defense Exceptional Public Service Award and accompanying Exceptional Public Service Medal, in October 2008.

He earned his PhD from the University of Pittsburgh, Pittsburgh PA in 1989, where his research proposed AI approaches to innovative design of complex systems. He published over 80 technical papers and served as the initiator, co-author and primary editor of over ten books, including Advanced Technology Concepts for Command and Control, 2004; Information Warfare and Organizational Decision Process, 2006; Adversarial Reasoning: Computational Approaches to Reading the Opponent's Mind, 2006; The Battle of Cognition: the Future Information-Rich Warfare and the Mind of the Commander, 2007; Estimating Impact: A Handbook of Computational Methods and Models for Anticipating Economic, Social, Political and Security Effects in International Interventions, 2010; Cyber Defense and Situational Awareness, 2015; Cyber Security of SCADA and other Industrial Control Systems, 2016; and Cyber Resilience, (to appear, 2018).

Accepted  talks

Kyle Gwinnup, "Serverless Data Processing Architecture for Binary Analysis"

Rebecca Bilbro,"Inferring Model Families from Deployed Black Boxes"

Shanchieh (Jay) Yang, "Anticipatory Cyber Defense via Predictive Analytics, Machines Learning and Simulation"

Malachi Jones, "Automated in-memory malware/rootkit detection via binary analysis and machine learning"

Matthew Berninger, "APTinder: An optimized approach for finding that perfect APT match"

Scott Coull, "Activation Analysis of a Byte-based Deep Neural Network for Malware Classification"

Awalin Nabila Sopan, "Interpretation of Threat Prediction Model for SOC Analysts"

Bobby Filar, "TreeHuggr: Discovering where tree-based classifiers are vulnerable to adversarial attack"

Nahid Farhady Ghalaty, "An Effective Framework for Malware Detection and Classification using Feature Prioritization"

Bronwyn Woods, "Point process modeling of temporal patterns in user authentication behavior"

Richard Harang, "Estimating uncertainty for binary classifiers"

Frances Zlotnick and Will Fitzgerald, "Using Anomaly Detection on User Demographic Distributions to Identify Fake Account Bursts"

David Krisiloff, "Measure Twice, Quarantine Once: A Tale of Malware Labeling over Time"

Cody Wild, "Some Mistakes are More Mistaken Than Others: Using Cost-Matrix Clustering to Address Misclassification Cost Asymmetries in Website Content Classification"

Lindsey Lack, "Improved Multi-Stage Classification for Information Security Applications"

Hyrum Anderson, "A feature presentation: semi-supervised learning of file representations"

Brian Genz, "Labeling Red: Harvesting Labeled Data from Adversary Simulations"

C. Bayan Bruss, "Worm2Vec: Embedding Malicious Code for Efficient Clustering & Classification"

Ryan Kovar, "Datasets for the Everyman"