AI Research Symposium
Overview
Join us for a full-day AI Research Symposium on Friday, April 17, 2026, at the Watt Family Innovation Center. The event will bring together faculty, students, researchers, staff, industry participants and other attendees interested in artificial intelligence for a day of plenary talks, poster presentations, parallel technical sessions and networking.
The symposium is open to the public. Planned programming includes morning and afternoon plenary sessions, a poster session, industry or career booths and parallel session talks in Watt classrooms. Participants will have the opportunity to talk to representatives from industry, from companies like NVidia, OpenAI, Michelin, Siemens, and more. Registration is free, and includes lunch.
This event is for faculty, students, researchers, staff, industry participants and community members interested in artificial intelligence research and applications. Contact Carl Ehrett for more information. Registration is free; use the links below to register and view the agenda.
AI Symposium Registration Events Page Agenda
Event Partners
Speakers
Bart Knijnenburg
School of Computing, Clemson Univeresity
9:15-9:45 a.m.
Understanding human-centered AI through user experiments
Bart Knijnenburg is an associate professor in the Human-Centered Computing division of Clemson’s School of Computing and co-director of the HATLab. His research focuses on privacy decision-making and recommender systems. He holds degrees from Eindhoven University of Technology, Carnegie Mellon University, and the University of California, Irvine.
Xiaoyong (Brian) Yuan
Department of Electrical and Computer Engineering, Clemson University
9:45-10:15 a.m.
Can We Trust AI? Security, Privacy, and Safety in Modern AI Systems
Xiaoyong (Brian) Yuan is an assistant professor in Clemson’s Holcombe Department of Electrical and Computer Engineering. Before joining Clemson, he was an assistant professor at Michigan Technological University. His research spans machine learning, security and privacy, edge computing, and efficient and distributed machine learning.
F. Alex Feltus
Genetics and Biochemistry, Clemson University
10:15-10:45 a.m.
Leveraging generative AI to discover complex molecular explanations of tumor formation and small molecules interventions
F. Alex Feltus is a professor in Genetics and Biochemistry at Clemson and core faculty in Biomedical Data Science and Informatics. His work spans artificial intelligence, bioinformatics, cyberinfrastructure, high-performance and cloud computing, systems biology, tumor genomics, and medical genetics. He trained at Auburn University, Vanderbilt, and Emory.
April Heyward
Department of Public Health Sciences, Medical University of South Carolina
1-1:30 p.m.
A Natural Language Processing (NLP) Method to Analyze Responses to Public Health Interventions
April Heyward is a staff scientist in MUSC’s Department of Public Health Sciences and one of the leads of the Clemson-MUSC AI Hub. She focuses on artificial intelligence and public health integration and teaches in the Master of Public Health program. Her interests include data science, digital twins for health, deep learning, large language models, public health, and epidemiology.
Zhen Li
Mechanical Engineering, Clemson University
1:30-2 p.m.
Neural Operator Learning for Multi-fidelity Data Fusion and Inverse Design of Manufacturing Processes
Zhen Li is an associate professor of mechanical engineering at Clemson. He joined Clemson in 2019 after appointments at Brown University and the University of California, Merced. His research centers on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics, using machine learning, high-performance computing, and physics-based methods.
Sessions
Parallel Sessions
AI Methods for Science, Engineering and Health
Parallel Session 1 | Watt 308 | 2:10-4:40 p.m.
| Time | Description |
|---|---|
| 2:10-2:35 p.m. |
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information Andrew Brown School of Mathematical and Statistical Sciences, Clemson University Gaussian processes (GPs) are ubiquitous tools for modeling and predicting continuous processes in physical and engineering sciences. |
| 2:35-3 p.m. |
Spatial Attention Noise Making for Casual Interpretability Benjamin Formby Electrical and Computer Engineering, Clemson University We present a novel causal approach to interpretability for computer vision models that dynamically masks the input image. |
| 3-3:25 p.m. |
On the Foundation of Spatial Intelligence Chaoyi Zhou School of Computing, Clemson University This work explores the foundation of spatial intelligence, aiming to enable AI systems to understand and reason about the 3D real world. |
| 3:25-3:50 p.m. |
Bézier Splatting for Fast and Differentiable Vector Graphics Rendering Xi Liu School of Computing, Clemson University Differentiable vector graphics (VGs) are widely used in image vectorization and vector synthesis, while existing representations are costly to optimize and... |
| 3:50-4:15 p.m. |
Reconstructing 3D Anatomy from 2D Ultrasound for Real-Time Guidance Hudson Smith School of Mathematical and Statistical Sciences, Clemson University Ultrasound is fast, inexpensive, and widely used, but interpreting it remains difficult because operators must mentally reconstruct three-dimensional anatomy... |
| 4:15-4:40 p.m. |
Estimating Site-Specific Seismic Amplification Functions for South Carolina Using Machine Learning Technique Ravi Ravichandran Civil Engineering, Clemson University Accurate estimation of seismic design parameters, particularly the site amplification function, is essential for seismic hazard assessment and for ensuring... |
Trustworthy, Human-Centered and Societal AI
| Time | Description |
|---|---|
| 2:10-2:35 p.m. |
The Participation Paradox: How Study Design Features Differently Shape Who Engages with LLM Research Han Alzughbi School of Computing, Clemson University This talk examines demographic disparities in LLM research participation, asking why participation gaps arise and what research design can do about them. |
| 2:35-3 p.m. |
How Generative AI Reshapes Worker Competition in Ideation Contests Jiahui Mo Management, Clemson University As generative AI (GenAI) increasingly permeates open innovation contests, it creates a mixed competitive landscape in which some workers use GenAI while... |
| 3-3:25 p.m. |
Rise of Intelligent Machines: Transforming Work, Talent, and Purpose Sabbir Salek Civil Engineering, Clemson University The future of the job market will be shaped by the continued advancement of Artificial Intelligence (AI) and robotics, as intelligent machines take on an... |
| 3:25-3:50 p.m. |
An LLM-Based Approach to Extracting Tax Footnote Data from 10-K Filings Jesse Gardner Accounting, Clemson University This paper describes and implements an LLM-based approach to extract data on disallowed interest deductions under Section 163(j) from firms' financial... |
| 3:50-4:15 p.m. |
Augmenting Crisis Communication: Integrating AI into Strategic Response Frameworks Timothy Whims AI development for the Division of Marketing Communication, Clemson University This research evaluates the application of artificial intelligence (AI), specifically large language models, in crisis communication, demonstrating how AI... |
| 4:15-4:40 p.m. |
Safety Verification for Embedded AI Models Aditya Parameshwaran Mechanical Engineering, Clemson University To ensure the reliability of autonomous systems, we must address the significant computational challenge posed by high-dimensional visual data. |
Poster Session
AI Research Poster Session
Poster presentations will run during the midday block, followed by lunch and networking in the Atrium.
- Time: 11 AM - 1 PM
- Location: Atrium
