Ziyan An 安紫嫣

PhD student, Vanderbilt University, Department of Computer Science.

Contact: 863-303-2399

ziyan.an@vanderbilt.edu

1025 16th Ave S, Nashville, TN

I am a Ph.D. student (August 2022 start) in Computer Science at Vanderbilt University and a recipient of the Dean’s Graduate Fellowship. I am very grateful for the guidance and mentorship of my advisor, Dr. Meiyi Ma. I am also fortunate to collaborate with Dr. Taylor Johnson, Dr. Jonathan Sprinkle, and Dr. Abhishek Dubey at Vanderbilt. During my Ph.D., I have also interned at UiPath and Uber as an AI and machine learning software engineer.

Prior to Vanderbilt, I earned my bachelor’s degree in Computer Science from New York University (2018–2022), where I served as an undergraduate research assistant at the AI4CE Lab, working on computer vision and autonomous driving in close collaboration with Dr. Yiming Li and PI Dr. Chen Feng.

Research Overview

My research integrates formal methods with deep learning and artificial intelligence (AI) to build explainable and trustworthy systems. In particular, I work on enabling AI-based cyber-physical systems (CPS) to reason about symbolic properties, satisfy formal specifications, and explain their behavior to human stakeholders.

I aim to develop AI-enabled systems that combine strong empirical performance with reliability, interpretability, and formal guarantees. This research direction is motivated by the increasing deployment of AI in safety-critical CPS, including transportation and smart infrastructure, where predictive accuracy alone is insufficient. Toward this goal, I investigate how symbolic reasoning and formal methods can guide learning algorithms and provide rigorous explanations for sequential decision-making systems, contributing to the development of trustworthy AI.

Interests & Contributions

My work sits at the intersection of formal methods, explainable AI, deep learning, CPS, and safe AI. Representative directions include:

  • Formal logic-guided AI and deep learning
  • Logic-based runtime monitoring for AI-enabled systems
  • Trustworthy AI through logic-based explanations and large language models

These have appeared in top AI and CPS venues including AAAI, AAMAS, ICCPS, IJCAI, as well as journals such as IEEE Transactions on Intelligent Transportation Systems (T-ITS) and ACM Transactions on Cyber-Physical Systems.

Selected Publications

  1. LogiEx: Integrating Formal Logic and LLMs for Explainable Transit Planning
    Ziyan An, Xia Wang, Hendrik Baier, Zirong Chen, Abhishek Dubey, Taylor T Johnson, Jonathan Sprinkle, and Meiyi Ma
    In Proceedings of the 17th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) 2026
  2. Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications
    Jiangwei Wang, Shuo Yang, Ziyan An, Songyang Han, Zhili Zhang, Rahul Mangharam, Meiyi Ma, and Fei Miao
    In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025
  3. Formal Logic-Guided Harnessing Heterogeneous Fairness Rules in Smart Cities
    Ziyan An, Yiqi Zhao, Xuqing Gao, Ayan Mukhopadhyay, and Meiyi Ma
    ACM Transactions on Cyber-Physical Systems 2025
  4. ISL: Monitoring Image Segmentation Logic in Medical Imaging Analysis
    Ziyan An, Daniel Moyer, Ipek Oguz, Taylor T Johnson, and Meiyi Ma
    In International Conference on Runtime Verification 2025
  5. Formal Logic Enabled Personalized Federated Learning through Property Inference
    Ziyan An, Taylor T Johnson, and Meiyi Ma
    In Proceedings of the AAAI Conference on Artificial Intelligence 2024
  6. V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving
    Yiming Li, Dekun Ma, Ziyan An, Zixun Wang, Yiqi Zhong, Siheng Chen, and Chen Feng
    IEEE Robotics and Automation Letters 2022

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