Chris Liberatore

Chris received his B.S. in Computer Science from California State University, Sacramento, in 2013, and his Ph.D. in Computer Science from Texas A&M University in 2021, advised by Dr. Ricardo Gutierrez-Osuna. His dissertation, “Developing Sparse Representations for Anchor-Based Voice Conversion,” applied sparse coding techniques to voice conversion to provide a human-interpretable model for voice conversion. This interpretability was leveraged in the Golden Speaker Builder, which allowed an expert accent coach to build accent training models for nonnative speakers of English seeking to gain a native accent. 

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His research interests include deep learning, compact models, model building with limited training data, few-shot learning, and harnessing human perception in evaluation of algorithms and algorithm explainability. He is passionate about injecting good software engineering practices early in the research process to aid the implementation and transition of products to customers and end users.

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Before joining Galois in 2025, Chris held the position of Research Computer Scientist at Air Force Research Laboratory, Sensors Directorate, and focused on the development of novel, explainable computer vision algorithms, especially by leveraging synthetic data. These algorithms were focused on ISR tasks, and focused on the detection of objects and evaluation of scenes. He worked with many cross-functional teams, working with multiple directorates on several AI and Machine Learning projects.

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Chris thoroughly enjoys anything automotive, closely follows auto racing, and currently is actively building a dedicated race car from the vehicle which got him through his undergraduate and graduate studies.