ORNL AI Core Universities Workshop 2026

Posters

We look forward to featuring your work during the poster session, which will be held on September 15, 2026, 11:15 AM–1:00 PM. See the workshop agenda for more details.

Please review the following guidelines for your poster:

  • Poster size: Posters should be formatted at 24 × 36 inches and should not exceed these dimensions. 
  • Poster printing: Presenters are responsible for having their posters printed in advance and bringing the finished poster with them. We are unable to provide poster printing services either ahead of the conference or on site.
  • Poster drop-off: Printed posters may be dropped off at the event venue registration desk:
    • September 14: Throughout the day 
    • September 15: Until 11:00 a.m.
  • Mounting: Materials needed to display posters will be provided on site. 
  • Poster session: Presenters should plan to be available at their posters during the designated poster session to discuss their work and answer questions. 

Presenters traveling to Charlottesville are encouraged to arrange for printing before traveling to avoid last-minute printing or logistical issues

Poster Details

  1. A Model-data integrated framework for learning and…, Phuoc Toan Huynh
  2. Memory-Centric KV Cache Servers, Chiplet-DRAM-PIM, Khyati Kiyawat
  3. Controllable Generative Diffusion Models, Dongsheng Ding
  4. Energy-Guided Diffusion Models for High-Resolution…, Mathews Jacob
  5. Algorithms for Large-Scale Machine Learning, Aditya Devarakonda
  6. Automatic Layer Selection for Hallucination Detect…, Xinpeng Wang
  7. Generative Data Assimilation for Real-Time State Estimation in Complex Dynamical Systems, Siming Liang (ORNL)
  8. From Generation to Assurance: Scalable Agentic AI for Scientific Workflows, Dongkuan Xu
  9. Simulating population compliance with pandemic interventions using large language models, Runzhou Liu (UVA)
  10. BeamFormer: Transformer-based Beam Management for 6G Networks, Shunqiang Feng
  11. Chiplet-DRAM PiM, Zhenxing Fan
  12. Scalable, Adaptive & Transparent AI for Education, Bita Akram
  13. Multi-Fidelity UQ for Saturation Spectra, Jonas Katona
  14. AI-Driven Runtime Network Modeling for Fast and Accurate HPC Performance Analysis, Yuchen Liu (NCSU)
  15. Theory-Informed Generative Agents for Human Mobility Modeling, Haoyang Li (UVA)
  16. Entropy-Guided LLM Decoding via HMMs, Zhizhen Chen
  17. Sim2real environments for legged locomotion, Anessh Chavan
  18. Digital Organoid: A Biologically Inspired Neural Network for Image Classification, Ethan Nelson (UVA)
  19. HydraGNN: Towards Principled Fine-Tuning of Graph…, Linda Ungerboeck
  20. RaggedTuner: An Autotuning Framework for Ragged GP…, Sai Krishna Teja Varma Manthena
  21. SABLE: Sparse Adaptive Black Box Learning, Konstantin Pieper
  22. Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks, Devin Seay (UVA)
  23. StellFoundry AI, Jong Youl Choi
  24. Accelerating Spiking Neural Network Training: From Full Integer Learning to Fully Local Online Learning, Xiaoxuan Yang
  25. Big Enough to Break Out: Open LLM Pentesting, Victoria Lovelace
  26. Tiny Enough to Break In: Agentic RAT, Yuhan You
  27. LooseLeaf: Enabling Serverless GPUs for Computatio…, Yusen Wu
  28. Uncertainty Quantification for Named Entity Recognition via Full-Sequence and Subsequence Conformal Prediction, Srijan Sengupta (NCSU)
  29. Addressing the Reasoning Gap: Mechanistic Circuit-Based Knowledge Editing in Large Language Models, Chen Chen
  30. Bridging Dynamics and Data: A Unified Diffusion Framework for Mechanistically-Informed Epidemic Forecasting, Guanghui Min
  31. Proof of Knowledge vs. Agentic Collusion, Yuhan Shao
  32. HDLouvain: High-Throughput Incremental Clustering for Hyperdimensional Data, Beenish Gul
  33. A Domain-Specific Language for High-Performance Wildfire Spread Simulation, Srikar Mutnuri
  34. A Generic Framework for Creating GeoAI-Ready Multi…, Ranga Raju Vatsavai (NCSU)
  35. Assessing the Learning Efficiency of Including Subsurface Stormwater Infrastructure in a Deep Learning Surrogate of a Hydrodynamic Model for Urban Compound Flooding, Aashutosh Aryal (UVA)
  36. [Title to be provided], Michael Martinez
  37. Position: Temporal Measurement Interval Determines Computational and Model Complexity in Single-Cell Perturbation Analysis, Alireza Jafari (UVA)
  38. Auditable Sparse Active Learning for Expensive Scientific Simulations, Paul Soh (ORNL)
  39. Differentiable Probabilistic Routing for MoE, Heng Zhao