Multilingual NLP
Language technologies often fail beyond a handful of well-resourced languages, leaving billions of speakers vulnerable to disinformation and harmful content. This project develops multilingual approaches for detecting fake news, false claims, and machine-generated text across diverse linguistic landscapes—spanning 70+ languages. By building large-scale benchmarks and leveraging transfer learning techniques, this work directly addresses the Digital Language Divide, ensuring that information integrity tools are not limited to English or other high-resource languages but extend protection to the communities most susceptible to unchecked disinformation.
Related Publications:
- BLUFF (2026) — Benchmarking falsehoods/fake news in low-resource languages
- Beyond Speculation (2026, IEEE) — LLM-generated texts in multilingual disinformation
- MULTITuDE (2023, EMNLP) — Multilingual machine-generated text detection benchmark
- Fighting Fire with Fire (F3) (2023, EMNLP) — LLMs’ dual role in crafting/detecting disinformation
- Detecting False Claims in Low-Resource Regions (2022, ACL) — Caribbean false claim detection
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.