About

I am an AI Researcher at the Center for AI Safety, where my work focuses on developing robust, aligned, and transparent artificial intelligence systems. 

I was previously a Senior Research Scientist at the Institute for Infocomm Research (I²R), specializing in multimodal large language models (LLMs). My research focused on developing localized audio-text LLMs and benchmarks, as part of the National Multimodal LLM Programme.

My work also spans vision-language models, computer vision (particularly robustness and transfer learning), and time series prediction and anomaly detection. My broader research interest lies in advancing generalizable and trustworthy AI models for real-world applications.

I received my PhD in Statistics from Cornell University in 2020, advised by David Matteson, where I focused on anomaly and change detection. Prior to that, I earned my B.A. in Applied Mathematics and Statistics from UC Berkeley. My undergraduate and PhD studies were supported by the A*STAR National Science Scholarship (BS-PhD). I have also gained experience through research and industry internships at Amazon, MERL (supervised by Devesh Jha) and IBM Science of Social Good Fellowship (supervised by Kush Varshney, Lingfei Wu, Karthikeyan Ramamurthy, Jinfeng Yi, Raya Horesh) during my PhD.

For a full list of my publications, please refer to my Google Scholar profile.