Presented a poster at NRT Annual Meeting Arizona '23

Oct 30, 2023·
Jason Lucas
Jason Lucas
· 1 min read
Image credit: Limeng Cui
Abstract
At the 2023 NSF NRT Annual Meeting, I presented a poster on native language processing impacts on human language technology. My collaborators and I used EEG to evaluate neural processing differences when native Spanish listeners hear matched versus mismatched native accent variants. We found evidence that unfamiliar variants incur cognitive costs. As language ID and speech recognition systems currently struggle with non-native speech, I’m integrating psychological and computational linguistics techniques to explore improvements. This project synthesizes varied disciplines - from psycholinguistics to natural language processing - to address inclusion gaps in speech technology across accent variants. Enhancing system robustness to diverse forms of speech is essential as virtual assistants and other AI applications continue permeating everyday life.
Location

Arizona State University

1151 S Forest Ave, Tempe, Arizona

event
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Jason Lucas
Authors
Jason Lucas
Ph.D. Candidate in Informatics

I am a PhD candidate in Informatics in the College of IST at Penn State University, where I conduct research at the PIKE Research Lab under the guidance of Dr. Dongwon Lee. I specialize in AI/ML research focused on Information Integrity, Safe and Ethical AI, including combating harmful content across multiple languages and modalities. My research spans low-resource multilingual NLP, generative AI, and adversarial machine learning, with work extending across 79 languages. I have published 12 papers with 260+ citations in premier venues including ACL, EMNLP, IEEE, and NAACL.

My doctoral research focuses on bridging the digital language divide through transfer learning, classification (NLU), generation (NLG), adversarial attacks, and developing end-to-end AI pipelines using RAG and Agentic AI workflows for combating multilingual threats. Drawing from my Grenadian background and knowledge of local Creole languages, I bring a global perspective to AI challenges, working to democratize state-of-the-art AI capabilities for underserved linguistic communities worldwide. My mission is to develop robust multilingual multimodal systems and mitigate evolving security vulnerabilities while enhancing access to human language technology through cutting-edge solutions.

As an NSF LinDiv Fellow, I conduct transdisciplinary research advancing human-AI language interaction for social good. I actively mentor 5+ research interns and teach Applied Generative AI courses. Through industry experience at Lawrence Livermore National Lab, Interaction LLC, and Coalfire, I bridge academic research with practical applications in combating evolving security threats and enhancing global AI accessibility. I see multilingual advances and interdisciplinary collaboration as a competitive advantage, not a communication challenge. Beyond research, I stay active through dance, fitness, martial arts, and community service.