Title: Synergizing Sparse Sequence, Experimental, and AI-Predicted Structures for Protein-Nucleic Acid Interaction Predictions Abstract:  Sequence-specific nucleic acid recognition underlies essential processes in gene regulation, yet experiment-independent methods for simultaneous predictions of genomic DNA recognition sites and their binding affinity remain limited. Our group developed data-driven methods and simulation tools to predict and elucidate protein-nucleic acid…

Title: Behavior-Aware Data Valuation for LLMs at Scale Abstract:  Large Language Models (LLMs) depend on massive datasets whose quality and influence remain largely opaque. Data valuation offers principled methods to quantify how training data contributes to model performance and behavior. Yet, scaling classical approaches such as influence functions to trillion-token corpora continues to be a…

Title: Resource Optimization for ML Inference Serving Abstract:  My research focuses on job scheduling and resource management in Machine Learning (ML) and Large Language Model (LLM) systems. With the growing popularity of deep learning models, minimizing the monetary costs and maximizing the goodput of inference-serving systems have become critical challenges. Addressing these challenges requires efficient…