Data Science & AI Lecture Series
Towards Data-Efficient, Trustworthy, and Generalizable AI for Visual Understanding
Qiang Ji
Towards Data-Efficient, Trustworthy, and Generalizable AI for Visual Understanding
| When | Monday, April 6, 2026, 10:30 AM – 11:30 AM (MT) |
|---|---|
| Where | WEB 3780 |
Abstract
Artificial Intelligence (AI) has achieved remarkable progress and is increasingly integrated across a wide range of fields, fueling what many describe as the fourth industrial revolution. However, behind this widespread enthusiasm lie fundamental limitations. Today’s AI systems face three major challenges: (1) an insatiable demand for large-scale labeled data, (2) limited trustworthiness due to inadequate uncertainty quantification, and (3) poor generalization across domains. These challenges cannot be addressed simply by scaling data and computation; instead, they require foundational advances in theory and methodology.
In this talk, I will present recent research from my lab that addresses these challenges in a variety of computer vision tasks. To improve data efficiency and generalization, I will introduce our work on knowledge-augmented deep learning, where prior knowledge from diverse sources is systematically identified, encoded, and integrated with data-driven neural networks. This approach leads to hybrid neural-symbolic models that are both more data-efficient and more generalizable. To enhance model trustworthiness and explainability, I will discuss our advances in Bayesian deep learning. First, I will present our work on a Bayesian Transformer framework for accurate and robust human activity recognition. I will then introduce our work on uncertainty attribution, which identifies the sources of uncertainty in deep models and leverages this information for uncertainty mitigation and improved model performance. Finally, I will highlight our recent work on causal deep learning for addressing domain generalization. I will introduce a neural causal model that learns domain-invariant representations by identifying and eliminating spurious correlations arising from data biases.
Together, these efforts aim to advance a new generation of AI systems that are more data-efficient, trustworthy, and robust, enabling reliable deployment across a range of domains including human behavior understanding, medical imaging, scientific discovery, and human–robot interaction.
Speaker
Qiang Ji
Dr. Qiang Ji received his Ph.D. in Electrical Engineering from the University of Washington. He is currently a Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI). From 2009 to 2010, he served as a Program Director at the National Science Foundation (NSF), where he managed NSF’s research programs in computer vision and machine learning. He has also held teaching and research positions at the University of Illinois at Urbana–Champaign, Carnegie Mellon University, the University of Nevada, and the Air Force Research Laboratory.
Prof. Ji’s research focuses on computer vision, probabilistic graphical models, Bayesian deep learning, and causal machine learning, with applications across a wide range of domains including human behavior analysis, medical imaging, intelligent transportation, and human–robot interaction. He has published over 400 papers in leading journals and conferences and has received multiple awards recognizing his contributions to the field.
Prof. Ji has served the research community extensively as an editor for several IEEE and international journals and as a General Chair, Program Chair, Area Chair, and Program Committee member for numerous major international conferences and workshops. He is a Fellow of both the IEEE and the International Association for Pattern Recognition (IAPR).
Tags: causal inference computer vision deep learning machine learning
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