IC CAE Colloquium Series: Artificial Intelligence and Machine Learning - Standards for Autonomy in Use

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IC CAE and City College of New York

The IC CAE Colloquium Series on Artificial Intelligence for Intelligence Analysis (AI4IA) is part of the IC CAE Center for Critical Intelligence Studies at Rutgers University. The speakers are active researchers and practitioners in using AI, Data Analytics and Machine Learning techniques in defense, intelligence and security applications. The audience includes students, faculty, researchers and practitioners who are interested in using AI and advanced sensing for data collection and data analysis in intelligence applications. The aim of the colloquium series is for making connections between technologies and applications, and enhance awareness of the importance of and stir the interests of faculty and students in both artificial intelligence (AI) and intelligent analysis (IA). 

Artificial Intelligence and Machine Learning: Standards for Autonomy in Use


Current trends in autonomy, machine learning (ML), and sensor data fusion (SDF) require systems engineering. Systems engineering coordinates instrumentation, modeling, and computational architectures for decision support and domain awareness. Examples are autonomy in motion (AIM) for dynamic signals assessment systems (e.g., data fusion), autonomy at rest (AAR) for static data collection systems (e.g., surveillance), and autonomy in use (AIU) for networked intelligence enterprises (e.g., smart cities). AIU requires pragmatic use of message passing and data flow architectures; contextual and theoretic modeling; and user and machine teaming for Physics-based and Human-derived Information Fusion (PHIF) systems.
Recently, ML has been proposed as the solution to many problems which inherently includes multi-modal data. To meet the objective, there is a need for common standards and evaluation methods for product deployment. The Multisource AI Scorecard Table (MAST) assists in the systems engineering of data analytics (AI/ML/SDF).
The discussion will highlight MAST in relation to ML/SDF scalable and trustable solutions requiring computational efficiency, decision making reliability, experience expansion, and security robustness; while highlighting challenges for multi-domain operations, human-machine teaming, and accountable deployment strategies.


Erik Blasch is a program officer at the United States Air Force Research Laboratory (AFRL) – Air Force Office of Scientific Research (AFOSR). Previously, he was a Principal Scientist at AFRL in Rome, NY (2012-16); Exchange Scientist to Defence Research and Development Canada (DRDC) at Valcartier, Quebec (2010-12); and Information Fusion Evaluation Tech Lead at AFRL in Dayton, OH (2000-09). Dr. Blasch has served multiple academic roles as an Adjunct Associate teaching and research Processor in electrical and biomedical engineering at 12 universities as well as Colonel (Retired) in the USAF reserves. His engineering designs are fielded in many operational systems.

Dr. Blasch was a founder member of the International Society of Information Fusion (ISIF) (ww.isif.org), 2007 President, and Board of Governors (BoG) member (2000-10). He served on the IEEE Aerospace and Electronics Systems Society (AESS) BoG (2011-16), distinguished lecturer (2012-21), and co-chair of 7 conferences. He has focused on information fusion, target tracking, pattern recognition, and robotics research compiling 900 scientific papers, 35 patents, 30+ team-robotics wins, 50+ tutorials, and 19 medals. His co-authored books include High-Level Information Fusion Management and Systems Design (Artech House, 2012), Advances and Applications of DSmT for Information Fusion (American Research Press, 2015), Context-Enhanced information Fusion (Springer, 2016), Multispectral Image Fusion and Colorization (SPIE, 2018), Handbook of Dynamic Data Driven Applications Systems (Springer, 2018), and Deep Learning for Radar and Communications Automatic Target Recognition (Artech, 2020), among others.

Dr. Blasch received his B.S. in Mechanical Engineering from the Massachusetts Institute of Technology in 1992 and M.S. degrees in Mechanical, Health Science, and Industrial Engineering (human factors) from Georgia Tech and attended the Univ. of Wisconsin for a MD/PhD in Neuroscience/ME until being called to military service in 1996 to the United States Air Force. He completed an MBA, MSEE, MS Econ, and PhD in Electrical Engineering from Wright State University and is a graduate of Air War College. He is the recipient of the Military Sensing Symposium (MSS) Mignogna Leadership in Data Fusion Award, AIAA Information Systems Award, Fulbright Scholarship, IEEE AESS Mimno Best Paper Award, and IEEE Russ Bio-Engineering Award. He is an Associate Fellow of American Institute of Aeronautics and Astronautics (AIAA), Fellow of the Society of Photo-Optical and Instrumentation Engineers (SPIE), and Fellow of Institute of Electrical and electronics Engineers (IEEE).

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