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SCI Seminar: Principled Learning for Medical AI: Structure, Reliability, and Interpretability
Xiaoling Hu
Principled Learning for Medical AI: Structure, Reliability, and Interpretability, Abstract: The widespread deployment of AI in medicine demands not only predictive accuracy but also structural awareness, reliability under uncertainty, and interpretability for clinical trust. In this talk, I will present a unified research agenda toward principled learning for medical AI, grounded in these core pillars.
First, I will discuss how incorporating explicit structure, such as topology and spatial priors, into neural networks enhances the model's ability to reason about fine-grained anatomical and pathological features, which are critical for tasks like brain and tumor segmentation. Second, I will focus on reliability, exploring how we can quantify and mitigate uncertainty arising from imperfect labels, limited data, and domain shifts, using methods such as distributional modeling, hyperparameter learning, and probabilistic inference. Third, I will show how these approaches naturally support…
Campus Locations: Warnock Engineering Building - John and Marva (WEB). Campus Wide Event: Yes.
Monday, March 30, 2026, 10:00 AM – 11:00 AM.
SCI Seminar: Advancing GeoAI and Earth observation for environmental monitoring
Dr. He Yin
Advancing GeoAI and Earth observation for environmental monitoring
Abstract: Landscapes around the world are changing rapidly, with important consequences for sustainability, climate resilience, and society. Yet monitoring these changes across regions and scales remains difficult. In this talk, I present a research program that combines multi-sensor Earth observation, geospatial artificial intelligence (GeoAI), and land system science to better understand how land systems are changing, what drives those changes, and why they matter.
I begin by presenting my studies using satellite image time series to map land use change and, in collaboration with environmental scientists and ecologists, to examine its implications for carbon sequestration and biodiversity. These studies also reveal key limitations of conventional remote sensing approaches, including sensor constraints, limited transferability, and scarce training data. I then show how these challenges motivate my more recent work in sensor…
Campus Locations: Warnock Engineering Building - John and Marva (WEB). Alternate Location: https://utah.zoom.us/j/89458834009 Passcode: 453613. Campus Wide Event: Yes.
Tuesday, March 31, 2026, 10:30 AM – 11:30 AM.
SCI Seminar: Efficient and Reliable AI for Real-World Healthcare Deployment
Md Mostafijur Rahman
Efficient and Reliable AI for Real-World Healthcare Deployment, Abstract: Healthcare is one of the highest-impact domains for AI, yet reliable deployment at scale remains difficult. To truly improve patient care and clinical workflows, AI must operate under real clinical constraints, not just in ideal lab settings. In practice, deployment is limited by high compute and memory costs, scarce labeled data, and distribution shifts across sites and time. Many clinically important findings are also rare and long-tailed, which makes generalization especially challenging. My research makes deployability a design objective by developing methods that stay accurate under strict resource and data constraints. In this talk, I will first discuss high-performance lightweight deep learning architectures built by redesigning core building blocks. I will then present training-time generative supervision strategies that improve data efficiency and generalization to rare and long-tailed cases with no…
Campus Locations: Warnock Engineering Building - John and Marva (WEB). Campus Wide Event: Yes.
Wednesday, April 1, 2026, 10:30 AM – 11:30 AM.
SCI Seminar: Towards Data-Efficient, Trustworthy, and Generalizable AI for Visual Understanding
Dr. Qiang Ji: Towards Data-Efficient, Trustworthy, and Generalizable AI for Visual Understanding, 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…
Campus Locations: Warnock Engineering Building - John and Marva (WEB). Campus Wide Event: Yes.
Monday, April 6, 2026, 10:30 AM – 11:30 AM.
PSC Monthly Workshop: Machine Learning and BIG DATA (day 1 of 2)
The Center for High Performance Computing is hosting this event as a satellite site. This is an in-person event and registration is required. A description from the PSC website follows.
The Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop.
This workshop will focus on topics including big data analytics and machine learning with Spark, as well as deep learning.
This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event.
This event is sponsored by the National Science Foundation, there is NO REGISTRATION FEE to attend.
Campus Locations: Intermountain Network Scientific CC (INSCC). Contact Name: Wim Cardoen. Contact Email: wim.cardoen@utah.edu. Campus Wide Event: Yes.
Wednesday, April 8, 2026, 9:00 AM – 3:00 PM.
For more info visit www.psc.edu. PSC Monthly Workshop: Machine Learning and BIG DATA (day 2 of 2)
The Center for High Performance Computing is hosting this event as a satellite site. This is an in-person event and registration is required. A description from the PSC website follows.
The Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop.
This workshop will focus on topics including big data analytics and machine learning with Spark, as well as deep learning.
This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event.
This event is sponsored by the National Science Foundation, there is NO REGISTRATION FEE to attend.
Campus Locations: Intermountain Network Scientific CC (INSCC). Contact Name: Wim Cardoen. Contact Email: wim.cardoen@utah.edu. Campus Wide Event: Yes.
Thursday, April 9, 2026, 9:00 AM – 3:30 PM.
For more info visit www.psc.edu. SCI Seminar: Supporting responsible use of AI in visual-based teaching and learning
Dr. Jihyun Rho
Supporting responsible use of AI in visual-based teaching and learning, Abstract: Artificial intelligence (AI) is increasingly integrated into educational contexts, reshaping how teachers and students generate and interpret visual representations such as diagrams, illustrations, and data visualizations. While AI offers efficiency and flexibility, it also introduces inaccuracies and misleading interpretations that can undermine learning. In this talk, I present a research program centered on supporting the responsible use of AI in visual-based teaching and learning by promoting visual literacy. Grounded in design-based research, I design and evaluate AI-augmented learning environment where educators and students engage with AI-generated outputs as critical inquirers. This work shows that these approaches improved interpretive accuracy, deepened reasoning about visual representations, and reduce over-reliance on AI. Together, this work contributes design principles for fostering responsible AI…
Campus Locations: Warnock Engineering Building - John and Marva (WEB). Alternate Location: https://utah.zoom.us/j/85083856693?pwd=SqFW2CLa6SPAqfTTJ9efwvzdC7Ck7X.1. Campus Wide Event: Yes.
Monday, April 13, 2026, 1:00 PM – 2:00 PM.
AI & the Planet: Sustainable Innovation in Higher Education
Join us for an Earth Month event hosted by the University of Utah College of Nursing and the Scientific Computing and Imaging Institute, home of the One-U Responsible Artificial Intelligence Initiative.
AI isn’t “coming” to academia—it’s already in the group chat! As we adopt these tools, we also need to ask: what are their environmental costs? Join us for an afternoon panel discussion focused on the sustainability of AI, from data centers and energy use to campus-level decision-making. Hear from experts unpacking the environmental dimensions of AI infrastructure, explore tradeoffs between innovation and resource consumption, and help shape thoughtful, climate-aligned use of AI in academic work. This session will be highly interactive, so bring your questions, curiosities, and honest feedback.
We will host an hour-long panel discussion with experts from across the U followed by unstructured discussion and networking.
Registration via Luma is required.
Speakers Moderator: Penny Atkins, Director of…
Event Categories: Sustainability. Campus Locations: Health Sciences Education Building - Spencer F. and Cleone P. Eccles (HSEB). Campus Wide Event: Yes.
Wednesday, April 15, 2026, 10:00 AM – 11:30 AM.
For more info visit luma.com. AI and Cybersecurity: Building Self-Defending Systems through Biological Blueprints
Royal Hansen
AI and Cybersecurity: Building Self-Defending Systems through Biological Blueprints, Abstract: By applying biological concepts like evolutionary arms races to cybersecurity, this presentation argues for the creation of autonomous, self-defending AI systems capable of automatically discovering and patching vulnerabilities, provided these agents are strictly constrained by safety frameworks, sector-specific guardrails, and broad industry collaboration.
Bio: Royal Hansen is a Vice President of Engineering at Google. Most recently, he led the Privacy, Safety and Security team (the CISO for Alphabet), which is the central engineering function that builds and scales the foundational technology that keeps billions of people safe online.
Over the last several years, Royal and his team have focused on cybersecurity threat detection, analysis and counterabuse, building advanced AI/ML security technologies, and protecting privacy, identity and data. He continues to work on safety innovations across…
Campus Locations: Warnock Engineering Building - John and Marva (WEB). Campus Wide Event: Yes.
Friday, April 24, 2026, 2:00 PM – 3:00 PM.
Memorial Day
University Observed Holidays
Campus Wide Event: Yes.
Monday, May 25, 2026.
Juneteenth
University Observed Holidays
Campus Wide Event: Yes.
Monday, June 15, 2026.