{"id":18038,"date":"2025-06-27T03:43:20","date_gmt":"2025-06-27T03:43:20","guid":{"rendered":"https:\/\/www.europesays.com\/us\/18038\/"},"modified":"2025-06-27T03:43:20","modified_gmt":"2025-06-27T03:43:20","slug":"cutting-edge-talks-from-around-campus-and-beyond-theu","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/18038\/","title":{"rendered":"Cutting-edge talks from around campus and beyond \u2013 @theU"},"content":{"rendered":"

Reposted from College of Engineering<\/a>.<\/p>\n

The University of Utah\u2019s John and Marcia Price College of Engineering hosted its first AI Summit for Utah on June 18, bringing artificial intelligence researchers from every department together for an all-day symposium. They connected with more than 400 faculty, students, industry partners and policymakers from across the region.\u00a0<\/p>\n

\"\"<\/a>Charles Musgrave, dean of the Price College of Engineering, kicked off the sold-out proceedings held at the S. J. Quinney College of Law. As a chemical engineer, Musgrave has been using machine learning techniques for more than a decade in the pursuit of developing new materials. But from the vantage of the dean, and at the precipice of a new technological revolution, the prospects for artificial intelligence are limitless.\u00a0<\/p>\n

\u201cThose who lead in AI will lead in science, economics, national security and innovation,\u201d Musgrave said. \u201cBut if we do it right, we\u2019ll also lead in art, entertainment and personal fulfillment.\u201d\u00a0<\/p>\n

U trustee Steven Price detailed the ways university research redounded to the public\u2019s benefit over the past decades. \u201cThese are the ingredients that fertilized Utah\u2019s growth,\u201d he said. \u201cWe\u2019re in a movement and a moment. The movement is AI, and the moment is now. AI is moving fast and we have to move faster.\u201d\u00a0<\/p>\n

In addition to a series of poster presentations by more than 60 students from across the state, the symposium was organized into four panels, each bookended by a series of one-minute \u201clightning talks\u201d from a selection of the student poster presenters, as well as Q&A sessions with the audience.\u00a0<\/p>\n

AI in sensing, seeing and securing the world<\/strong><\/p>\n

Weilu Gao<\/a>, Department of Electrical & Computer Engineering, \u201cMachine learning with optics\u201d
Ziad Al-Halah<\/a>, Kahlert School of Computing, \u201cMultimodal Embodied AI\u201d
Guanhong Tao<\/a>, Kahlert School of Computing, \u201cTowards Safe and Secure Large Language Models\u201d<\/p>\n

Moderator\u00a0Varun Shankar<\/a>, assistant professor in the Kahlert School of Computing<\/p>\n

\"\"<\/a>Panelists engaged with the ways AI systems are increasingly integrated with physical ones. As such, these systems need new ways of sensing features of their environments and making sure they are interacting in ways that won\u2019t harm the people in them. Weilu Gao\u2019s work adds another dimension to this field; he and his colleagues recently published research on an \u201coptical neural engine<\/a>\u201d that can potentially accelerate the computation involved in these applications.\u00a0<\/p>\n

Next-Gen AI: From supervision to autonomy<\/strong><\/p>\n

Jacob Hochhalter<\/a>, Department of Mechanical Engineering, \u201cReducing Training Costs with Derivative Informed Data from Hypercomplex Automatic Differentiation\u201d
Daniel Brown<\/a>, Kahlert School of Computing, \u201cToward Robust, Interactive, and Human-Aligned AI Systems\u201d
Vivek Srikumar<\/a>, Kahlert School of Computing, \u201cHuman language technology: Can we do better than bigger?\u201d<\/p>\n

Moderator\u00a0Tucker Hermans<\/a>, associate professor in the Kahlert School of Computing<\/p>\n

This panel explored the tradeoffs AI systems must make when it comes to the data they\u2019re trained on. Many machine learning techniques rely on massive data sets, but not all applications have access to them, for practical, economic, or even legal reasons. Beyond reducing the upfront costs of training, new strategies for building up these models will have applications in low-resource and off-the-grid environments.\u00a0\u00a0<\/p>\n

The nexus of health, humans and machines<\/strong><\/p>\n

Tolga Tasdizen<\/a>, Department of Electrical & Computer Engineering, \u201cTowards interpretable AI models in radiology\u201d
Neda Netagh<\/a>, Department of Electrical & Computer Engineering, \u201cToward less artificial intelligence\u201d
Alan Kuntz<\/a>, Kahlert School of Computing, \u201cAutonomous Surgical Robots that Learn from Human Surgeons\u201d
Amir Arzani<\/a>, Department of Mechanical Engineering, \u201cScientific machine learning: From no data to large data\u201d
Ashley Dalrymple<\/a>, Department of Biomedical Engineering, \u201cReinforcement Learning for Predicting Walking-Related Events\u201d<\/p>\n

Moderator\u00a0Laura Hallock<\/a>, assistant professor in the Department of Mechanical Engineering<\/p>\n

This panel continued the theme of AI\u2019s integration into physical systems, in this case, biological ones. Healthcare applications hold some of the highest promise of AI systems, but technical and ethical issues abound.\u00a0 Panelists discussed automated analysis of medical images, the neuroscience of vision, physics-based models of blood flow and of tumor tissue, and the biomechanics of walking.<\/p>\n

\u201cBridging\u201d AI and infrastructure<\/strong><\/p>\n

Ryan Johnson<\/a>, Department of Civil & Environmental Engineering, \u201cHigh-Resolution Snow Mapping with Machine Learning: Pioneering Products to Enhance Season-to-Season Water Supply Forecasting\u201d
Masood Parvania<\/a>, Department of Electrical & Computer Engineering, \u201cAI for Autonomous Power Grid Operation\u201d
Chenxi (Dylan) Liu<\/a>, Department of Civil & Environmental Engineering, \u201cLeveraging AI for intelligent transportation systems\u201d
Taylor Sparks<\/a>, Department of Materials Science Engineering, \u201cThe materials of tomorrow, today\u201d<\/p>\n

Moderator\u00a0Cathy Liu<\/a>, associate professor in the Department of Civil & Environmental Engineering<\/p>\n

Participants zoomed out to even larger physical systems: transportation networks and other pieces of civic infrastructure. Here, the multiplicity of inputs and complexity of their interactions are a natural fit for machine learning techniques. Panelists discussed digital twins of the electric grid for training against cyberattacks, the connection between winter snowfall and summer water levels, preventing chain-reaction crashes on highways and how the materials that make up everyday life are now being invented with these technologies.<\/p>\n

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