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MY NAME IS DAN GRAHN.

AND I RESEARCH AI.




RESEARCH

MLSEC

LLMs are increasingly capable of generating code, but this code is far from safe
to deploy. How can we use AI to secure software that is written by human and
mahcines?

AI ETHICS

We are experiencing an epochal change in humanity's technological abilities. As
we approach this new industrial revolution, how can we ethical and responsibly
deploy AI to ensure we maximize benefits while minimizing costs?

EXPLAINABLE AI & TRUST

It's hard to understand the applied statistics of deep learning. It's even
harder to trust those statistics with driving a car or flying a plane. How do we
allow AI users to build trust in these systems?

GRAPH-BASED LEARNING

Graphs are beautifully general data structures which can store any type of
information, from binary call graphs to social networks. Traditional AI
techniques aren't designed to operate on graphs. So how do we overcome this
limitation?

BIG DATA MANAGEMENT

When you work with AI long enough, you'll encounter a data set that's too big
for traditional tools. How we store, manage, and analyze that data is critical.
So what is the best and most cost effective way to do that?




PUBLICATIONS

2023
 * Code Execution Capability as a Metric for Machine Learning&en;Assisted
   Software Vulnerability Detection. Daniel Grahn, Lingwei Chen, Junjie Zhang.
   International Symposium on Intelligent and Trustworthy Computing,
   Communications, and Networking. Conference Invited
 * Assessing the Overlap of the DoD RAI Intiatives with the NIST AI RMF. Daniel
   Grahn. Military Operations Research Society 91st Symposium. Conference
 * Panel Discussion on Data and Information Fusion Techniques to Enhance Digital
   Twin Applications. Daniel Grahn, et. al. SPIE Defense + Commercial Sensing,
   Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII.
   Conference Invited

2022
 * The Prediction Management Framework: Ethical, Governable, and Interpretable
   Deployment of Artificial Intelligence / Machine Learning Systems. Daniel
   Grahn, Melonie Richey. SPIE Defense + Commercial Sensing (2022). Conference
   (+ best paper)
   
   * Military Operations Research Society 90th Symposium, Data Science &
     Analytics Working Group Conference
   * Military Operations Research Society 90th Symposium, AI & Autonomy Systems
     Working Group Conference

2021
 * An Analysis of C/C++ Datasets for Machine Learning-Assisted Software
   Vulnerability Detection. Daniel Grahn. Conference on Applied Machine Learning
   for Information Security (2021). Conference
   * Military Operations Research Society 90th Symposium, Data Science &
     Analytics Working Group. Conference
   * Military Operations Research Society 90th Symposium, Cyber Operations
     Working Group. Conference
 * Satellite Dish Detection as a Semi-Supervised Small-Object Localization
   Problem. Daniel Grahn. Military Operations Research Society 89th Symposium
   (2021). Conference (+ best paper)
 * mil-benchmarks: Standardized Evaluation of Deep Multiple-Instance Learning
   Techniques. Daniel Grahn. arXiv preprint arXiv:2105.01443 (2021). Preprint

2020 & Earlier
 * Potential impacts of elevated aerosol layers on high energy laser aerial
   defense engagements. Steven Fiorino, Stephen Shirey, Michelle Via, Daniel
   Grahn, Matthew Krizo. Atmospheric Propagation IX, vol. 8380, p. 83800T.
   International Society for Optics and Photonics (2012). Journal


EDUCATION

Ph.D. Wright State University Computer Science 2023 M.S. University of Southern
California Computer Science 2018 B.S. Cedarville University Computer Science,
Minor in Mathematics 2013


EXPERIENCE

Altamira Technologies Principal AI Scientist 2020–0000 Northrop Grumman AI
Researcher, Principal Software Engineer 2013–2019 Wright State University
Adjunct Instructor 2019–2019 Air Force Institute of Technology Adjunct
Instructor 2018–2018 Air Force Institute of Technology Research Assistant
2011–2013 Escape Plan Design Web Applications Lead 2009–2011 Main-1-Media Web
Team Lead 2006–2009


TEACHING EXPERIENCE

CS-1160 - Introduction to Computer Programming Wright State University Fa21,
Sp22, Fa22, Sp23 CEG-2170 - Introduction to C for Scientist and Engineers Wright
State University Fa19 CS-589 - Operating Systems Air Force Institute of
Technology Su18

© 2023