Lu Feng

Research Overview

Cyber-physical systems (CPS) are increasingly being deployed throughout society and revolutionizing a range of applications domains such as transportation and healthcare. While tremendous progress has been made in advancing CPS technologies over the past decades, the safety assurance of CPS still poses significant challenges, which can lead to catastrophic failures (e.g., self-driving car crashes, robot-caused fatalities). My research focuses on assuring the safety and trustworthiness of CPS. Specifically, my current research tackles challenges in the following themes.

Safety of AI-enabled CPS

The data-rich nature (e.g., enormous sensing data) of CPS has resulted in AI and machine learning technologies increasingly being embedded in CPS. Nevertheless, many existing AI systems lack safety guarantees. My research develops novel approaches that integrate formal methods and AI algorithms to provide mathematically rigorous guarantees about the safety of AI-enabled CPS. Some of my recent papers in this theme include

• Counterfactual Explanations for Continuous Action Reinforcement Learning
  Shuyang Dong, Shangtong Zhang, and Lu Feng.
  International Joint Conference on Artificial Intelligence (IJCAI), 2025
  

• Adaptive Reward Design for Reinforcement Learning
  Minjae Kwon, Ingy Elsayed-Aly, and Lu Feng.
  International Conference on Uncertainty in Artificial Intelligence (UAI), 2025
  

• Safe POMDP Online Planning among Dynamic Agents via Adaptive Conformal Prediction
  Shili Sheng, Pian Yu, David Parker, Marta Kwiatkowska, and Lu Feng.
  IEEE Robotics and Automation Letters, 2024
  

• Safe POMDP Online Planning via Shielding
  Shili Sheng, David Parker, and Lu Feng.
  IEEE International Conference on Robotics and Automation (ICRA), 2024
  

• Safe Multi-Agent Reinforcement Learning via Shielding
  Ingy Elsayed-Aly, Suda Bharadwaj, Christopher Amato, Rüdiger Ehlers, Ufuk Topcu, and Lu Feng.
  International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021
  

• Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems
  Meiyi Ma, John Stankovic, Ezio Bartocci, and Lu Feng.
  ACM Transactions on Embedded Computing Systems, 2021
  

• STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks
  Meiyi Ma, Ji Gao, Lu Feng, and John Stankovic.
  Thirty-fourth Conference on Neural Information Processing Systems (NeurlPS), 2020
  

Trust in Human-CPS

There is a growing trend toward human-CPS, where systems collaborate or interact with humans to harness complementary strengths of humans and autonomy. My research develops novel approaches that model and reason about human behavior, improve human's trust and increase the system transparency by providing explanations about the automation. Some of my recent papers in this theme include

• Quantitative Predictive Monitoring and Control for Safe Human-Machine Interaction
  Shuyang Dong, Meiyi Ma, Josephine Lamp, Sebastian Elbaum, Matthew B. Dwyer, and Lu Feng.
  AAAI Conference on Artificial Intelligence (AAAI), 2025
  

• Trust-Aware Motion Planning for Human-Robot Collaboration under Distribution Temporal Logic Specifications
  Pian Yu, Shuyang Dong, Shili Sheng, Lu Feng, and Marta Kwiatkowska.
  IEEE International Conference on Robotics and Automation (ICRA), 2024
  

• Planning for Automated Vehicles with Human Trust
  Shili Sheng, Erfan Pakdamanian, Kyungtae Han, Ziran Wang, John Lenneman, David Parker, and Lu Feng.
  ACM Transactions on Cyber-Physical Systems, 2022
  

• Explainable Multi-Agent Reinforcement Learning for Temporal Queries
  Kayla Boggess, Sarit Kraus, and Lu Feng.
  International Joint Conference on Artificial Intelligence (IJCAI), 2023
  

• Toward Policy Explanations for Multi-Agent Reinforcement Learning
  Kayla Boggess, Sarit Kraus, and Lu Feng.
  International Joint Conference on Artificial Intelligence (IJCAI), 2022
  

• Multi-Objective Controller Synthesis with Uncertain Human Preferences
  Shenghui Chen, Kayla Boggess, David Parker, and Lu Feng.
  ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), 2022
  

• DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data
  Erfan Pakdamanian, Shili Sheng, Sonia Baee, Seongkook Heo, Sarit Kraus, and Lu Feng.
  ACM Conference on Human Factors in Computing Systems (CHI), 2021
  

Formal Methods for Large-Scale CPS

The prevalence of smart and connected devices has enabled the emergence of large-scale CPS testbeds such as smart cities, which include millions of sensors and actuators. Failure of safety assurance can result in conflicts among smart services or even catastrophic outcomes. My vision is to develop scalable formal methods that can assure real-time operations of smart services at a city scale satisfying safety and performance requirements. Some of my recent papers in this theme include

• Towards Formal Methods for Smart Cities
  Meiyi Ma, John Stankovic, and Lu Feng.
  IEEE Computer, 2021
  

• A Novel Spatial-Temporal Specification-Based Monitoring System for Smart Cities
  Meiyi Ma, Ezio Bartocci, Eli Lifland, John Stankovic, and Lu Feng.
  IEEE Internet of Things Journal, 2021
  

Sponsors

I gratefully acknowledge ongoing and past support from National Science Foundation (CCF-2131511, CCF-1942836, CNS-1739333, CRII CNS-1755784), National Institutes of Health, Office of Naval Research, Air Force Office of Scientific Research,Toyota InfoTech Labs, Assuring Autonomy International Programme, 4-VA Collaborative Research Grant, James S. McDonnell Foundation, Center for Innovative Technology, Northrop Grumman Corporation, and UVa SEAS.