CV

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Education

• B.S. in Computer Science, United States Air Force Academy, 2014
• M.S. in Information Security, Carnegie Mellon University, 2016
• (in progress) M.S. in Machine Learning, Carnegie Mellon University
• (in progress) Ph.D in Electrical and Computer Engineering, Carnegie Mellon University

Highlighted Experience

• Student Researcher at Google AI
  • Summer 2022
  • Worked with Dr Nicholas Carlini on the challenges of evaluating randomized machine learning defenses. Pre-print of paper available here.
• Summer Intern at Massachusetts Institute of Technology Lincoln Laboratory
  • Summer 2021
  • Worked with Dr Ross Allen on an intrinsic augmentation to cooperative reinforcement learning that incentivizes accommodative behavior in agents, resulting in demonstrated zero-shot coordination. Paper published at AAMAS 2022 (click for more details).
• Deep Learning Researcher at Department of Defense (DoD) Research Directorate (RD)
  • Spring 2017 to Fall 2019
  • Customizes dense neural nets, recurrent neural nets, generative adversarial networks, and reinforcement learning networks to aid in solving Intelligence Community (IC) problems. Attended NeurIPS and other AI related conferences.
  • Author on technical paper in final stages. The work has been presented at internal conferences and is being pushed into production as a pathfinding use case to justify deep learning analytics.
• Machine Learning Researcher at Center for Communications Research La Jolla
  • Summer 2018
  • Co-wrote 2 technical papers submitted for publication. Executed 100+ hours of experimentation with top university mathematicians to solve DoD’s hardest problems. Implemented experimental ML process derived from research
• Technical Project Lead at DoD Joint Artificial Intelligence Center
  • Fall 2018 to Spring 2019
  • Focused on applying ML algorithms for the Humanitarian Assistance and Disaster Relief National Mission Initiative. This is a particularly demanding job that requires constant mission justification to external non-technical organizations and rapid objective pivoting in a highly uncertain environment.

Publications

Group-based Robustness: A General Framework for Customized Robustness in the Real World

Weiran Lin, Keane Lucas, Neo Eyal, Lujo Bauer, Michael K. Reiter, Mahmood Sharif. Group-based Robustness: A General Framework for Customized Robustness in the Real World. In Proc. NDSS '24. To appear.

RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion

Zhuoqun Huang, Neil G Marchant, Keane Lucas, Lujo Bauer, Olga Ohrimenko, Benjamin IP Rubinstein. RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion. In Proc. NeurIPS '23.

Adversarial Training for Raw-Binary Malware Classifiers

Keane Lucas, Samruddhi Pai, Weiran Lin, Lujo Bauer, Michael K. Reiter, Mahmood Sharif. Adversarial Training for Raw-Binary Malware Classifiers. In Proc. USENIX Security '23.

Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems

Clement Fung, Shreya Srinarasi, Keane Lucas, Hay Bryan Phee, Lujo Bauer. Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems. In Proc. ESORICS'22.

Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks

Weiran Lin, Keane Lucas, Lujo Bauer, Michael K. Reiter, Mahmood Sharif. Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks. In Proc. ICML'22.

Any-Play: an Intrinsic Augmentation for Zero-Shot Coordination

Keane Lucas, Ross E. Allen. Any-Play: an Intrinsic Augmentation for Zero-Shot Coordination. AAMAS'22.

Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes

Keane Lucas, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, Saurabh Shintre. Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes. In Proc. AsiaCCS'21.

Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery

Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, Matthew Gaston. Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery. In Proc. CVPR Workshops, '19.

Presentations

Workshop Presentation: Adversarial Training with a Surrogate

Keane Lucas, Alec Jasen and Lujo Bauer. Adversarial Training with a Surrogate. NeurIPS NewInML Workshop. December 2020

Conference Presentation: Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes

Keane Lucas, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, Saurabh Shintre. Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes. In Proc. AsiaCCS'21.

Conference Presentation: Any-Play: an Intrinsic Augmentation for Zero-Shot Coordination

Keane Lucas, Ross E. Allen. Any-Play: an Intrinsic Augmentation for Zero-Shot Coordination. AAMAS'22.

Conference Presentation: Adversarial Training for Raw-Binary Malware Classifiers

Keane Lucas, Samruddhi Pai, Weiran Lin, Lujo Bauer, Michael K. Reiter, Mahmood Sharif. Adversarial Training for Raw-Binary Malware Classifiers. In Proc. USENIX Security '23.

Projects

Search Engine

Doom Bot

Cephalometric Landmark Tagging

Top Achievements

• 2019 National Defense Science and Engineering Graduate (NDSEG) Fellowship Awardee (166 awarded from 2928)
• Joint Commendation Medal – awarded August 2019
• 2018 DoD Research Directorate Military Performer of the Year, awarded Research Directorate and Math Research coins
• 2018 JASON Presenter – Conducted hour-long technical DoD AI presentation for the elite government advisory group
• 2016 Carnegie Mellon University Information Networking Institute Leadership Annual Award
• Undergraduate Cyber Training Top Graduate, Honor Graduate, and Major General Robert E. Sadler Award
• Honored by Alaska State Legislature (my home state) for ‘exemplary service with dignity and honor’ (2014)
• USAFA Instructor Letter (rare award given upon instructor agreement that this cadet should come back to teach)
• Honor Lists (7 x Dean’s and 4 x Commandant’s) / Dean’s: academic honor / Commandant’s: military honor
• 2010 Graduation High School Valedictorian in class size of 290

Skills

• Data Analysis/Security
  • Introduction to Statistical Data Mining, Graduate Artificial Intelligence, Introduction to Machine Learning, Machine Learning Large Datasets, Search Engines, Cryptography, Secure Software Systems, Network Security
• Systems
  • Software Reverse Engineering, Distributed Systems, Embedded Systems, Languages and Machines
• Languages
  • Python, Java, LaTeX, C, Go, ARM, x86, Octave, Perl, C#, Objective C, SQL

Service and Leadership

• Deputy Flight Commander, 7th Intelligence Squadron, US Air Force
  • Spring 2017 to Fall 2019
  • Led a Flight consisting of 70+ airmen across 4+ directorates in research, capabilities, and IT support
• President, Graduate Organization Information Networking Institute, Carnegie Mellon University
  • , Fall 2015 to Spring 2016
  • Led graduate organization comprising ~200 students from ~15 countries and served as a dept strategic board member
• Commander, US Air Force Academy Cyber Defense Exercise
  • Spring 2014
  • Led an interdisciplinary team of 40 cadets to operate a secure network for the NSA Cyber Defense Competition

Other Experience

• Rapid Capability Scripter, Air Force Red Team (57th Information Aggressor Squadron), US Air Force
  • Winter 2017/2018
  • Delivered 28 new capabilities to simulate adversary behavior for large scale war games (RED FLAG)
• Cyber Security Intern, NASA Jet Propulsion Laboratory
  • Summer 2015
  • Found security bugs in Key System and built bare-metal to VM process for pen-testing and private cloud transition