Recent papers by the TEA lab members (underlined):

• 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.
• 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] [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.
  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.