Our next membership meeting is scheduled for Thursday, November 17th and will be held from 11:30 a.m. to 1:00 p.m. at the Headliners Club by invitation only. Adam Klivans, Director of the NSF Institute for the Foundations of Machine Learning and Dr. Ying Ding, Professor in the School of Information and Dell Medical School, will discuss artificial intelligence and machine learning’s effects on healthcare delivery.

Adam Klivans is a Professor of Computer Science and Director of the Machine Learning Lab at UT-Austin, the headquarters for AI/ML research across campus. His research focuses on developing novel algorithms to accelerate training and inference for deployed machine learning system

Ying Ding leads the AI in Health Lab at the School of Information and Dell Medical School, University of Texas at Austin. Her team has developed novel machine learning and deep learning approaches on health risk predication and medical imaging diagnosis with the focus on explainable AI. She has collaborated widely with researchers in healthcare and drug discovery. Together with her colleague at Dell Medical School, the AI Health Lab aims to create innovations about human-centered AI approaches to build trust between AI and patients. They have combined ablation study with the explainable AI methods to generate interpretations with calculated financial concerns to engage low-income patients with chronic diseases to stick to their recommended care management plans. They have applied contrastive learning methods to deal with data imbalance issues in patient cohorts to generate fair and robust health risk predication and detect hidden temporal patterns at temporal interpretations for early signals for sepsis predictions. They have explored a dozen of explainable AI methods on chest x-ray image disease predication and bounding box generation and systematically examined the disparity between the attentions generated by explainable AI methods and the ground truth bounding boxes generated by doctors. She has published 280+ papers, chaired 40+ workshops, and served as a Program Committee member for 240+ international conferences. Her research has been cited over 12,500 times and her h-index is 56. She is specialized in explainable AI, deep learning, knowledge graph, and developing AI algorithms to analyze integrated semantic data, and applying heterogeneous graph mining methods to precision health by using cross-modality health care data.