Recent papers by the TEA lab members (underlined):

• Ziyuan Lin, Hoang Ngoc Nguyen, Jie Xu, Ivan Ruchkin.
  Finite-Sample Analysis of Elimination in Active Hypothesis Testing [Arxiv].
  Preprint, 2026.
• Yuang Geng, Junkai Zhou, Kang Yang, Pan He, Zhuoyang Zhou, Jose C. Principe, Joel Harley, Ivan Ruchkin.
  MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection [Arxiv].
  Preprint, 2026.
• Zhenjiang Mao*, Jiawen Wu*, Gabriel Wagner, and Ivan Ruchkin.
  Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction [Arxiv] [Video].
  Preprint 2026, in submission. * Co-first authors.
• Zhongzheng Zhang, Maxwell Ruyle, Andrew Kappes, Tyler Ruble, William Shaoul, Dana Moreno, Jack Penn, Ivan Ruchkin.
  TEACar: An Open-Source Autonomous Driving Platform [Arxiv] [Github].
  Preprint, 2026.
• Zhenjiang Mao*, Anirudhh Venkat*, Artem Bisliouk, Akshat Kothiyal, Sindhura Kumbakonam Subramanian, Saithej Singhu, Ivan Ruchkin.
  Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning [Arxiv].
  In the Findings of the Association for Computational Linguistics (ACL), San Diego, California, 2026. * Co-first authors.
• Yuang Geng, Zhuoyang Zhou, Zhongzheng Zhang, Siyuan Pan, Hoang-Dung Tran, Ivan Ruchkin.
  Deterministic World Models for Verification of Closed-loop Vision-based Systems [Arxiv] [Github].
  Preprint, 2025.
• Yuang Geng*, Thomas Waite*, Trevor Turnquist, Radoslav Ivanov^†, Ivan Ruchkin^†.
  Statistical-Symbolic Verification of Perception-Based Autonomous Systems using State-Dependent Conformal Prediction. [Arxiv]
  In submission, 2025. * Co-first authors. ^† Co-last authors.
• Chengyu Li, Saleh Faghfoorian, Ivan Ruchkin.
  What Does It Take to Get Guarantees? Systematizing Assumptions in Cyber-Physical Systems [Arxiv].
  Preprint, 2025.
• Christopher Oeltjen*, Carson Sobolewski*, Saleh Faghfoorian*, Lorant Domokos, Giancarlo Vidal, Ivan Ruchkin.
  Online Slip Detection and Friction Coefficient Estimation for Autonomous Racing [Arxiv] [Video].
  Preprint, 2025. * Co-first authors.
• Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin.
  How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy [Arxiv] [Github].
  In submission, 2025.
• Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin.
  Physically Interpretable World Models via Weakly Supervised Representation Learning [Arxiv] [Slides] [Poster] [Demo] [Github].
  In Proceedings of the 17th ACM/IEEE International Conference on Cyber-Physical Systems, Saint Malo, France, 2026.
• Wade Fortney, Dhruv Kushwaha, Zhenjiang Mao, Anuj Papriwal, Ivan Ruchkin, Christophe Bobda, and Zoleikha Biron.
  Attack Resilience of UAVs with Embedded FPGAs [IEEE].
  In Proceedings of the 19th IEEE Dallas Circuits and Systems Conference (DCAS), 2026.
• Jordan Peper, Yan Miao, Sayan Mitra, and Ivan Ruchkin.
  Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy [Arxiv] [Github].
  In Proceedings of the International Symposium on Automated Technology for Verification and Analysis (ATVA), Bangalore, India, 2025.
• Yuang Geng, Yang Zhou, Yuyang Zhang, Zhongzheng Ren Zhang, Kang Yang, Tyler Ruble, Giancarlo Vidal, Ivan Ruchkin.
  Unsupervised Anomaly Detection Improves Imitation Learning for Autonomous Racing [IEEE] [Poster] [Video] [Slides].
  In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2025.
• Carson Sobolewski, Zhenjiang Mao, Kshitij Vejre, Ivan Ruchkin. 
  Generalizable Image Repair for Robust Visual Autonomous Racing [Arxiv] [Poster 1] [Poster 2] [Github] [Video] [Slides].
  In Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Hangzhou, China, 2025.
• Zhenjiang Mao, Artem Bisliouk, Rohith Reddy Nama, Ivan Ruchkin. 
  Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic [Arxiv]. 
  In 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA), 2025.
• Jordan Peper*, Zhenjiang Mao*, Yuang Geng, Siyuan Pan, Ivan Ruchkin.
  Four Principles for Physically Interpretable World Models [Arxiv] [OpenReview] [Github] [Poster] [Slides].
  In Proceedings of the 2nd International Conference on Neuro-symbolic Systems (NeuS), Philadelphia, PA, 2025. * Co-first authors.
• Thomas Waite, Yuang Geng, Trevor Turnquist, Ivan Ruchkin*, and Radoslav Ivanov*. 
  State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems [Arxiv] [Poster] [Slides summary] [Slides talk] [Github].
  In Proceedings of the 2nd International Conference on Neuro-symbolic Systems (NeuS), Philadelphia, PA, 2025. * Co-last authors.
• Pengyuan Lu, Oleg Sokolsky, Insup Lee, and Ivan Ruchkin. 
  Accelerating Neural Policy Repair with Preservation via Stability-Plasticity Interpolation [ACM] [Slides]. 
  In Proceedings of the International Conference on Cyber-Physical Systems (ICCPS), Irvine, CA, 2025.
• Souradeep Dutta, Michele Caprio, Vivian Lin, Matthew Cleaveland, Kuk Jin Jang, Ivan Ruchkin, Oleg Sokolsky, Insup Lee. 
  Distributionally Robust Statistical Verification with Imprecise Neural Networks [Arxiv] [ACM] [Slides]. 
  In Proceedings of the International Conference on Hybrid Systems: Computation and Control (HSCC), Irvine, CA, 2025. 
• Matthew Cleaveland, Pengyuan Lu, Oleg Sokolsky, Insup Lee, Ivan Ruchkin.
  Conservative Perception Models for Probabilistic Model Checking [Arxiv] [UIUC] [Github] [Slides]. 
  In Proceedings of the Allerton Conference on Communication, Control, and Computing, Urbana, Illinois, 2025. Invited paper.
• Zhenjiang Mao, Ivan Ruchkin.
  Towards Physically Interpretable World Models: Meaningful Weakly Supervised Representations for Visual Trajectory Prediction [Arxiv] [Poster].
  Preprint, 2025. 
• Yuang Geng, Jake Brandon Baldauf, Souradeep Dutta, Chao Huang, Ivan Ruchkin.
  Bridging Dimensions: Confident Reachability for High-Dimensional Controllers [Arxiv] [Springer] [Github] [Poster 1] [Poster 2] [Slides] [Demo (w/ subs)] [Demo (w/o subs)] [Talk]. In Proceedings of the International Symposium on Formal Methods (FM), Milan, Italy, 2024.
• Zhenjiang Mao, Carson Sobolewski, Ivan Ruchkin. 
  How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy[PMLR] [Arxiv] [Poster 2023] [Poster 2024] [Github]. In Proceedings of the Annual Learning for Dynamics & Control Conference (L4DC), Oxford, UK, 2024.

An exhaustive list of papers can be found on Ivan’s page.