NAIAD, the EXPAND AI Institute, is a multidisciplinary partnership between researchers at the University of Texas at San Antonio’s MATRIX AI Consortium and the NSF AI Institute ATHENA. NAIAD team will conduct collaborative research that advances core AI capabilities for the edge. The team will create novel AI foundation models that draw inspiration from neural mechanisms and will design neuromorphic computing systems that offer new functionality, robustness, and energy efficiency for the edge. The team will further perform novel use-inspired research on edge platforms that benefit from neuro-inspired design elements.
NAIAD will also offer immersive research programming, that will strengthen AI competencies at both organizations as well as the broader professional community. Through this project, a roadmap for strategic partnerships that support inclusive AI workforce training will be established.
NAIAD will also offer immersive research programming, that will strengthen AI competencies at both organizations as well as the broader professional community. Through this project, a roadmap for strategic partnerships that support inclusive AI workforce training will be established.
Meet Our Team
Principal Investigators
Senior Personnel
Postdocs & Students
Press Releases
Events
Relevant Publications
-
J. A. Sanchez Viloria, D. Stripelis, P. P. Markopoulos, G. Sklivanitis, and D. A. Pados, “Adaptive Federated Learning for Automatic Modulation Classification Under Class and Noise Imbalance,” in Proc. AAAI 2024 Spring Symp. Series, Stanford University, Stanford, CA, March 2024, p. 309.
-
S. Singh, E. Saber, P. P. Markopoulos, and J. Heard, “Regulating Modality Utilization within Multimodal Fusion Networks”.
-
M. Krol, R. Hyder, A. Prater-Bennette, S. Asif, and P. P. Markopoulos, “Continual Learning in Convolutional Neural Networks with Tensor Rank Updates,” in Proc. IEEE Sensor Array and Multichannel Signal Processing Workshop (IEEE SAM), Corvallis, OR, July 2024.
-
M. Sharma, J. Heard, E. Saber, and P. P. Markopoulos, “Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning,” journal under review; available at arXiv:2401.08014, January 2024.
-
Y. Xiu, T. Scargill, M. Gorlatova, Poster: LOBSTAR: Language Model-based Obstruction Detection for Augmented Reality to appear in Proc. IEEE ISMAR, Seattle, WA, Oct. 2024.
-
T. Hu, F. Yang, T. Scargill, M. Gorlatova, Apple v.s. Meta: A Comparative Study on Spatial Tracking in SOTA XR Headsets. To Appear in Proc. ACM ImmerCom (co-located with ACM MobiCom), Washington, DC, Oct. 2024.
-
A. K. Bozkurt, Y. Wang, and M. Pajic, “Learning Optimal Strategies for Temporal Tasks in Stochastic Games”, IEEE Transactions on Automatic Control, 2024.
-
A. Khazraei, H. Pfister, and M. Pajic, “Attacks on Perception-Based Control Systems: Modeling and Fundamental Limits”, IEEE Transactions on Automatic Control, 2024.
-
X. Yang, Z. Wang, X. S. Hu, C. H. Kim, S. Yu, M. Pajic, R. Manohar, Y. Chen, and H. Li, “Neuro-Symbolic Computing: Advancements and Challenges in Hardware-Software Co-Design”, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 3, pp. 1683 – 1689, March 2024.
-
A. Khazraei, H. Meng, and M. Pajic, “Black-box Stealthy GPS Attacks on Unmanned Aerial Vehicles”, 63rd IEEE Conference on Decision and Control (CDC), Dec. 2024.
-
R. S. Hallyburton and M. Pajic, “Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy”, 63rd IEEE Conference on Decision and Control (CDC), Dec. 2024.
-
H. L. Hsu, A. K. Bozkurt, J. Dong, Q. Gao, V. Tarokh, and M. Pajic, “Steering Decision Transformers via Temporal Difference Learning”, 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2024.
-
H. L. Hsu and M. Pajic, “Robust Exploration with Adversary via Langevin Monte Carlo”, 6th Annual Learning for Dynamics and Control Conference (L4DC), pp. 1592-1605, July 2024.
-
Z. Xuan, A. Bozkurt, M. Pajic and Yu Wan, “On the Uniqueness of Solution for the Bellman Equation of LTL Objectives”, 6th Annual Learning for Dynamics and Control Conference (L4DC), pp. 428-439, July 2024.
-
H. L. Hsu, H. Meng, S. Luo, J. Dong, V. Tarokh, and Miroslav Pajic, “REFORMA: Robust REinFORceMent Learning via Adaptive Adversary for Drones Flying under Disturbances”, IEEE International Conference on Robotics and Automation (ICRA), May 2024.
-
D. Hunt, S. Luo, A. Khazraei, X. Zhang, R.S. Hallyburton, T. Chen, and M. Pajic, “RadCloud: Real-Time High-Resolution Point Cloud Generation Using Low-Cost Radars for Aerial and Ground Vehicles”, IEEE International Conference on Robotics and Automation (ICRA), May 2024.
-
H. L. Hsu, Q. Gao, and M. Pajic, “ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment”, 15th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), pp. 224-234, May 2024.
-
D. Hunt, K. Angell, Z. Qi, T. Chen, and M. Pajic, “MadRadar A Black-Box Physical Layer Attack Framework on mmWave Automotive FMCW Radars”, The Network and Distributed System Security Symposium (NDSS), 2024.
-
G. Gao, Q. Gao, X. Yang, S. Ju, M. Pajic, and M. Chi, “On Trajectory Augmentations for Off-Policy Evaluation”, 12th International Conference on Learning Representations (ICLR), April 2024.
-
Q. Gao, G. Gao, J. Dong, V. Tarokh, M. Chi, and M. Pajic, “Off-Policy Evaluation for Human Feedback”, 37th Conference on Neural Information Processing Systems (NeurIPS), 9065-9091, Dec. 2023.
-
Xueying Wu, Edward Hanson, Nansu Wang, Qilin Zhang, Xiaoxuan Yang, Huanrui Yang, Shiyu Li, Feng Cheng, Partha Pratim Pande, Janardhan Rao Doppa, Krishnendu Chakrabarty, Hai Helen Li, “Block-Wise Mixed-Precision Quantization: Enabling High Efficiency for Practical ReRAM-based DNN Accelerators,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD).
-
Brady Taylor, and Hai Helen Li, “Weight Update Scheme for 1T1R Memristor Array Based Equilibrium Propagation,” IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), Abu Dhabi, April 2023.
-
Karia, Vedant, Abdullah Zyarah, and Dhireesha Kudithipudi. “PositCL: Compact Continual Learning with Posit Aware Quantization.” Proceedings of the Great Lakes Symposium on VLSI 2024.
-
Soures, Nicholas, Vedant Karia, and Dhireesha Kudithipudi. “Advancing Neuro-Inspired Lifelong Learning for Edge with Co-Design.” Proceedings of the AAAI Symposium Series. Vol. 3. No. 1. 2024.
-
Zyarah, Abdullah M., and Dhireesha Kudithipudi. “Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System.” arXiv preprint arXiv:2405.13347 (2024).
-
Hickok, Truman, and Dhireesha Kudithipudi. “Watch Your Step: Optimal Retrieval for Continual Learning at Scale.” arXiv preprint arXiv:2404.10758 (2024).
-
Zohora, Fatima Tuz, et al. “Probabilistic Metaplasticity for Continual Learning with Memristors.” arXiv preprint arXiv:2403.08718 (2024).