CV
Downloadable PDF available here!
Education
- B.S. in Computer Science, United States Air Force Academy, 2014
- M.S. in Information Security, Carnegie Mellon University, 2016
- M.S. in Machine Learning, Carnegie Mellon University
- Ph.D in Electrical and Computer Engineering, Carnegie Mellon University
Highlighted Experience
- Student Researcher at Google AI
- Summer Intern at Massachusetts Institute of Technology Lincoln Laboratory
- 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
- Brian Singer, Keane Lucas, Lakshmi Adiga, Meghna Jain, Lujo Bauer, and Vyas Sekar. On the Feasibility of Using LLMs to Autonomously Execute Multi-host Network Attacks. arXiv preprint 
- Keane Lucas, Weiran Lin, Lujo Bauer, Michael K. Reiter, Mahmood Sharif. Training Robust ML-based Raw-Binary Malware Detectors in Hours, not Months. In Proc. CCS '24. 
- 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. 
- 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. 
- 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. 
- 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. 
- 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. 
- Keane Lucas, Ross E. Allen. Any-Play: an Intrinsic Augmentation for Zero-Shot Coordination. AAMAS'22. 
- 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. 
- 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
- Keane Lucas, Alec Jasen and Lujo Bauer. Adversarial Training with a Surrogate. NeurIPS NewInML Workshop. December 2020 
- 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. 
- Keane Lucas, Ross E. Allen. Any-Play: an Intrinsic Augmentation for Zero-Shot Coordination. AAMAS'22. 
- 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
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