Hi, I'm Anthony, a CS PhD at Georgia Tech working with Polo Chau. My research advances safer, more efficient, and robust AI systems at scale — spanning training, inference, and deployment — by tackling core challenges in safety alignment, inference efficiency, and scalable system design across language, vision, and multimodal models:

• Advancing Safety Alignment Throughout Model Training: LLM safety basin, the first framework explaining how minimal unsafe data can collapse alignment during fine-tuning (NeurIPS'24); robust CNN architectures that achieved SOTA on RobustBench (BMVC'23 with Best Poster Award); dynamic safety shaping framework for LLM finetuning risk mitigation (In Submission).
• Optimizing Inference for Scalability and Throughput: video VLM scaling study for optimal inference (ACL'25); token reduction method that doubles LLM inference throughput (In Submission); a scalable framework that empowers MLLMs to understand composite images (ICCV'25).
• Bridging Research and Deployment for Real-World Impact: UniTable, a modular table parsing system with over 470+ stars (workshops at NeurIPS'23 (oral), AAAI'24 (oral), & NeurIPS'24); distributed systems tutorials on Medium (33K+ readers).

I am fortunate to have collaborated with researchers, developers, scientists, and engineers at Georgia Tech, Meta, IBM Research, Intel Lab, ADP, UCLA.