Hejie Cui

Research Abstract:

Health data is inherently multimodal and complicated, which necessitates domain expertise to interpret. Moreover, in the medical field, there is a crucial need for rigorous explanations rooted in domain knowledge. Such insights go beyond target predictions, fostering novel scientific discoveries. Toward these, my research vision focuses on advancing healthcare with multimodal structured knowledge. This involves transforming complicated and large-scale datasets into knowledge, which then serves as the foundation for developing interpretable AI models for downstream applications. Two themes from my previous work, namely brain network analysis (MICCAI'22 Oral, IEEE Transaction on Medical Imaging, KDD'23, NeurIPS'22 Spotlight) and multimodal data analysis (NeurIPS'23, ACL'23, ECIR'22, ECML'21), underscore this vision. Looking forward, I am passionately dedicated to refining the evaluation of large models, advancing data-centric AI, and harmonizing domain knowledge from medical professionals within healthcare. My long-term aspiration is to contribute to a health system that values interpretability, reliability, and equity for everyone.

Bio:

Hejie Cui is a 5th-year PhD student in Computer Science at Emory University. She specializes in developing machine learning and data science methods to extract insights from diverse, real-world multimodal data and create interpretable AI models for clinical outcome predictions. Her research intersects the fields of machine learning, biomedical imaging, natural language processing, geometric deep learning, and data science. Hejie has showcased her findings at esteemed machine learning conferences and biomedical venues, with notable oral and spotlight presentations at NeurIPS, MICCAI, CHIL, KDD, and AAAI. Her work was recognized by the Rising Star in EECS in 2023 and previously by the CRA-WP Grad Cohort for Women. She has interned at Microsoft Research and Amazon Science during her PhD. Beyond research, Hejie is an active member of the Laney-EDGE Graduate School Diverse Scholars in the Sciences at Emory. She co-organized the 1st International Workshop on Neural Network Models for Brain Connectome Analysis (BrainNN) at IEEE BigData, and has conducted tutorials on Brain Network Analysis with Graph Neural Network at the International Conference on Intelligent Biology and Medicine.