1. 'Target-Aware Language Modeling via Granular Data Sampling' accepted to EMNLP 2024 Industry Track.
2. 'Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning' accepted to ACL 2024 SRW.
3. 'Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin' accepted to LREC-COLING 2024.
4. 'Projecting Annotations for Discourse Relations' accepted to CODI @ EACL 2024.
Research Experience
Contributed to NLP research on historical archives at Academia Sinica; selected as a Google CSRMP Fellow in 2023.
Education
Ph.D. in Computer Science at Virginia Tech, advised by Tu Vu; M.S. in Language Science and Technology from Saarland University, where he worked with Dietrich Klakow and Vera Demberg.
Background
Research interests: Developing efficient, modular LLMs for multilingual, multitask, and multimodal deployments through collaborative and communal machine learning. Focuses on efficient model development, parameter-efficient transfer learning, advanced reasoning, instruction tuning, and data-centric methods.