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
- A Diffusion-Based Model-Data Integrated Framework for Learning and Predicting Complex Dynamical Systems, Phuoc Toan Huynh (FSU)
- Reimagining LLM Inference Infrastructure with Memory-Centric KV Cache Servers, Khyati Kiyawat (UVA)
- A Constrained Optimization Framework for Controllable Diffusion Models, Dongsheng Ding (UTK)
- ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts, Heng Zhao (UVA)
- Energy-Guided Diffusion Models for High-Resolution…, Mathews Jacob (UVA)
- Algorithms for Large-Scale Machine Learning, Aditya Devarakonda (WFU)
- Automatic Layer Selection for Hallucination Detection, Xinpeng Wang (UVA)
- Generative Data Assimilation for Real-Time State Estimation in Complex Dynamical Systems, Siming Liang (ORNL)
- From Generation to Assurance: Scalable Agentic AI for Scientific Workflows, Dongkuan Xu (NCSU)
- Simulating population compliance with pandemic interventions using large language models, Runzhou Liu (UVA)
- BeamFormer: Transformer-based Beam Management for 6G Networks, Shunqiang Feng (UVA)
- Sangam: A Chiplet-Based DRAM-PIM Accelerator with CXL Integration for LLM Inferencing, Zhenxing Fan (UVA)
- Scalable, Adaptive & Transparent AI for Education, Bita Akram (NCSU)
- Uncertainty-Informed Latent-Space Residual Learning for Saturation Models with Limited Data, Jonas Katona (ORNL)
- AI-Driven Runtime Network Digital Twins, Yuchen Liu (NCSU)
- Theory-Informed Generative Agents for Human Mobility Modeling, Haoyang Li (UVA)
- Entropy-Guided LLM Decoding via HMMs, Zhizhen Chen (UVA)
- Sim2real environments for legged locomotion, Anessh Chavan (UVA)
- Digital Organoid: A Biologically Inspired Neural Network for Image Classification, Ethan Nelson (UVA)
- HydraGNN: Towards Principled Fine-Tuning of Graph…, Linda Ungerboeck (ORNL)
- RaggedTuner: An Autotuning Framework for Ragged GPU Operators, Sai Krishna Teja Varma Manthena (NCSU)
- SABLE: Sparse Adaptive Black Box Learning, Konstantin Pieper (ORNL)
- Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks, Devin Seay (UVA)
- StellFoundry AI, Jong Youl Choi (ORNL)
- Accelerating Spiking Neural Network Training: From Full-Integer Learning to Fully Local Online Learning, Xiaoxuan Yang (UVA)
- Big Enough to Break Out: Open LLM Pentesting, Victoria Lovelace
- Tiny Enough to Break In: Agentic RAT, Yuhan You (UVA)
- LooseLeaf: Enabling Serverless GPUs for Computation, Yusen Wu (UVA)
- Uncertainty Quantification for NER, Srijan Sengupta (NCSU)
- Addressing the Reasoning Gap: Mechanistic Circuit-Based Knowledge Editing in Large Language Models, Chen Chen (UVA)
- Bridging Dynamics and Data: A Unified Diffusion Framework for Mechanistically-Informed Epidemic Forecasting, Guanghui Min (UVA)
- Proof of Knowledge vs. Agentic Collusion, Yuhan Shao (UVA)
- HDLouvain: High-Throughput Incremental Clustering for Hyperdimensional Data, Beenish Gul (UVA)
- A Domain-Specific Language for High-Performance Wildfire Spread Simulation, Srikar Mutnuri (UVA)
- Computational Frameworks for Synthesizing and Deploying Digital Twins for Integrated Energy Systems, Aparna Kishore (UVA)
- A Conceptual Framework for Creating GeoAI-Ready Multisensor Remote Sensing Data, Ranga Raju (NCSU)
- 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)
- Structural Relaxation via Flow Matching for Inorganic Materials, Michael Martinez (UVA)
- Position: Temporal Measurement Interval Determines Computational and Model Complexity in Single-Cell Perturbation Analysis, Alireza Jafari (UVA)
- Auditable Sparse Active Learning for Expensive Scientific Simulation, Paul Soh (ORNL)
- LLM-Guided Supply Chain Optimization and Narrative Generation, Ritwick Mishra
- Agentic AI Workflows for Project-Directed Literature Review and Mining, Seung-Hwan Lim (ORNL)
- Learning Higher-Order Latent Structure from Hypergraphs at Scale: Tree-Free Symmetric Sparse Tucker Decomposition, Paul Soh (ORNL)
- Learning Discrete Decisions for MIPs with Constraint Aware Diffusion, Vincenzo Di Vito Francesco (UVA)
- Using Networked Models to Improve Length of Stay Predictions, Jack Heavey (UVA)
- Uncertainty-aware Multi-modality Spatio-temporal Wildfire and Smoke Prediction, Chenguang Yang (UCR)
- Characterizing GPU Memory Oversubscription Techniques for LLM Inference on CPU–GPU Superchips, Mohammad Sonji
- Private LLM Inference with Confidential GPUs, Yang Yang
- The Geography of AI Exposure in the US Labor Market, Amro Alabsi Alhundi
- Tailoring LLM Weight Compression for PIM Architectures, Sabiha Tajdari, Shekar Akhil
- Preventing Collusion via Counterfactual Auditing of Black-Box Pricing Algorithms, Gustavo Moreira
- PCL-Test Bed: ATHENA: Advanced Testbed for High-throughput Experimentation in Nano- and Atomic Science, Xionye Xiao
- Task-Aware Fusion for Multimodal Materials Language Models, Binh Huu Ta