Sponsor: Foothill Section Chapter, C16
Speaker: Dr. Grace Lewis of Carnegie Mellon Software Engineering Institute

Meeting Date: 19 Sep 2024
Time: 12:00 PM to 01:00 PM
Cost:
Location:
Reservations: IEEE
Summary:
Increasing rate of progress in hardware and artificial intelligence (AI) solutions is enabling a range of software systems to be deployed closer to their users, increasing application of edge software system paradigms. Edge systems support scenarios in which computation is placed closer to where data is generated and needed, and provide benefits such as reduced latency, bandwidth optimization, and higher resiliency and availability. AI-enabled systems are software systems that integrate AI components to provide key functionality, and as such need to account for potential non-determinism in AI component outputs. Deploying AI-enabled systems at the tactical edge, to support users who operate in highly-uncertain and resource-constrained environments, such as first responders, law enforcement, and soldiers, can greatly benefit from edge AI-enabled systems to support timelier decision making.

In this talk, Dr. Lewis will share the work that takes place at the Carnegie Mellon Software Engineering Institute’s Tactical and Ai-Enabled Systems (TAS) initiative. We are an applied research and development team creating and transitioning innovative solutions, principles, and best practices for (1) defining and improving the software stack and development tools for systems that operate at the edge, (2) defining, adapting, and improving principles and practices for software engineering of AI/ML-enabled systems; and (3) using AI/ML techniques at the tactical edge for improved capabilities and mission support.

Bio: Dr. Grace Lewis is a Principal Researcher at the Carnegie Mellon Software Engineering Institute (SEI), where she conducts applied research on how software engineering and software architecture principles, practices, and tools need to evolve in the face of emerging technologies. She is the principal investigator for the Automating Mismatch Detection and Testing in Machine Learning Systems project that is developing toolsets to support these two activities, in addition to other projects that are advancing the state of the practice in software engineering for machine learning (SE4ML). She is also the lead for the Tactical and AI-Enabled Systems (TAS) applied research and development team at the SEI that is creating and transitioning innovative solutions, principles, and best practices for defining and improving the software stack and development tools for systems that operate at the edge, (2) defining, adapting, and improving principles and practices for software engineering of AI/ML-enabled systems; and (3) using AI/ML techniques at the tactical edge for improved capabilities and mission support.