Monday, May 1, 2023

12:30 PM to 3:00 PM

 

506 Dolorosa St | San Pedro I | Room 430

San Antonio, Texas 78204

 

This event is complimentary with lunch provided.

 

Please arrive 30min early for lunch & registration. 

 

12:00 PM - 12:30 PM

- Registration and lunch provided by Rohde & Schwarz

 

12:30 PM - 1:30 PM

- DL Part 1: Cognitive EW: Assuring In-Mission Learning for EW

 

1:30 PM - 2:00 PM

- Break/Demo: R&S®IRAPS™: Integrated, Record, Analysis, Playback System

 

2:00 PM - 3:00 PM

- DL Part 2: Cognitive EW: Assuring In-Mission Learning for EW

 

Cognitive EW: Assuring In-Mission Learning for EW
This presentation will discuss the challenges of assuring the performance of a system that can learn from novel experiences in the field. EW systems operate at a timescale that means they cannot afford to learn post-mission, or with human supervision. EW systems must learn from a single observation, using self-supervised reinforcement feedback. The validation infrastructure must therefore support automated closed-loop, multi-resolution testing, and ways to test the effectiveness of actions. We must validate the learning process, rather than validating the learned model.

 

Dr. Karen Haigh is an expert and consultant in Cognitive EW and embedded AI. Her focus is on physical systems with limited communications and limited computation resources that must perform under fast hard-real-time requirements. She recently wrote the book "Cognitive EW: An AI Approach" with Julia Andrusenko. She was a pioneer in three fields now common across the globe:

(1) closed-loop planning and machine learning for autonomous robots

(2) smart homes for elder care

(3) cognitive RF systems

 

Dr. Haigh is a regular presenter to both academic and military communities around the world, and has created a variety of online content discussing embedded AI for mission-critical systems, supporting rapid real-time in-mission learning, and assuring AI in the field. Her course on Cognitive EW is available through the Association of Old Crows. She received her PhD from Carnegie Mellon University in Computer Science with a focus on AI and Robotics, and her undergraduate from the University of Ottawa in Honours Computer Science (summa cum laude). She is a Fellow of IEEE and of the AsiaPacific AI Association (AAIA), and a Distinguished Lecturer for the IEEE Aerospace and Electronic Systems Society.