Grace Lewis

Grace Lewis is a Principal Researcher and lead of the Tactical and AI-Enabled Systems (TAS) initiative at the Software Engineering Institute (SEI) at Carnegie Mellon University. She serves as principal investigator for multiple research projects including 'Continuum: Establishing the Practice of Integrated T&E for ML Capabilities' and is President-Elect for the IEEE Computer Society in 2025. Her work bridges academic research with practical applications in defense and tactical environments.

Her educational background includes:

• B.Sc. in Software Systems Engineering and Specialization in Administration from Icesi University in Colombia
• Master in Software Engineering from Carnegie Mellon University
• Ph.D. in Computer Science from Vrije Universiteit Amsterdam

Dr. Lewis specializes in software engineering for AI/ML systems, with particular focus on architectural practices for systems integrating emerging technologies. Her research addresses critical challenges in deploying ML systems in operational environments, including test and evaluation of ML capabilities, mismatch detection between development and operational contexts, and edge computing for tactical applications. She has made significant contributions to understanding how software architecture principles must evolve to accommodate AI/ML components within larger systems.

Analysis of her recent publications reveals a strong focus on the practical challenges of implementing ML systems in real-world contexts. Her work spans ML testing methodologies, architectural patterns for edge computing, green computing for ML systems, and addressing collaboration challenges in ML-enabled system development. A consistent theme is the need for better integration between data science, software engineering, and operational perspectives when building ML systems.

Her notable recognitions include:

• BEST PAPER AWARD at WICSA 2016
• DISTINGUISHED PAPER AWARD at ICSE 2022

Dr. Lewis leads the Tactical and AI-Enabled Systems initiative which develops tools like MLTE (ML Test and Evaluation), TEC (ML Mismatch Detection), and UnitML. Her projects address critical gaps in ML system deployment, particularly in defense contexts where testing failures can cause significant delays. She actively collaborates with the Army AI Integration Center and other defense organizations to transition research into practice.