Deep Tech Defense
Delivering third-order strategies.
A strategic approach that operates beyond immediate objectives (first-order) and tactical adaptations (second-order), emphasizing network, long-term, or paradigm-shifting effects within highly dynamic market-competitive, geopolitical, or technological environments.
third-order strategy
ˈthərd-ˌȯr-dər ˈstra-tə-jē • noun
Collaboration
Areas
Maximized impact at the intersection of business growth and deep tech.
Winning Business
Capture Strategy
Proposal Development
Business Development
New Market Expansion
New Customer Expansion
Corporate Strategy
Business Model Innovation
Legislative Strategies
Technology Roadmapping
AI Product Enhancement
Make vs. Buy Decisions
Technology Transition
IP Strategy
Internal R&D Prioritization
Mergers & Acquisition
Technical Due Diligence
Strategic Investments
Often first in her role
Julia kicked off her career working full-time in the defense industry, serving in the Army National Guard, and attending graduate school concurrently for 8 years. Prior to founding Deep Tech Defense, she served as CTO of the US subsidiary of Saab - innovating across a diverse portfolio spanning aerospace, land, and maritime systems.
Through her military experience, she gained a deep empathy for manual processing of data and challenges it imposed on time-critical decisionmaking. She also learned how to lead, in all its joys and hardships.
Autonomous Systems
In her military career, she orchestrated autonomous systems in support of the intelligence preparation of the battlefield. In academia, she learned rigorous design principles behind differing autonomy frameworks. In industry, Julia invented autonomous systems that swam underwater and flew in space.
Julia Allen
A multi - lens view
Julia's career in the defense ecosystem spans military service, government test & evaluation, not-for-profit advisory, for-profit industry, research labs, and academia. These multiple lenses help her capture and anticipate changes from the battlefield to procurement.
She doesn't just follow the network effects on the defense ecosystem, she predicts them based on multi-faceted experience and a trustworthy forward model.
Trust & Transparency
Her dissertation research in Interpretable AI focused on increasing trust in and transparency of “black box” machine learning systems through neural network activation pattern analysis.
Nevertheless, her most lasting impacts have been in the inspiration of people and championing of ideas. She earns trust through unquestionable impact.
Julia is a graduate of the University of Maryland (Ph.D., Mechanical Eng), The Johns Hopkins University (M.S., Applied and Computational Math), and Dickinson College (B.S., Mathematics). She is an Adjunct Professor at Purdue University in the Elmore School of Electrical and Computer Engineering.