LLM empowered Financial News Topic Extraction & Market Prediction: Developed LLM-driven pipeline to extract financial themes from 40+ years of news data; Created attention scores that improved stock market prediction (15.2% annual return)
A Probability Contrastive Learning Framework for Graph Representation Learning: NeurIPS 2024; Solved False Pairs problem in graph contrastive learning via Bayesian modeling; Achieved state-of-the-art results on MoleculeNet and QM9 benchmarks
A Unified Biomedical Knowledge Model for Molecule-Protein Interaction Prediction: Integrated multi-modal biological data using optimal transport; Improved accuracy and zero-shot generalization for interaction prediction
KidSpeak: A General Multi-Purpose LLM for Kids' Speech Recognition and Screening: Arxiv
SE-3 Equivariant Mamba for Molecular Representation Learning: Arxiv
Prompt tuning based adapter for vision-language model adaptation: Ongoing Work
Background
Motivated Ph.D. student in Computer Science and Engineering specializing in deep learning, large language models, and multimodal representation learning. Experienced in designing and scaling machine learning pipelines on large, noisy datasets for financial prediction and biomedical discovery.
Miscellany
Personal interests and other information not provided