Recent and featured news:

All news:

  • 07/2022: Lead by Dr. Yi Dong, our paper "Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking" got accepted by IROS'22.

  • 06/2022: Our 1st year PhD student Yi Qi got a paper "A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems" accepted by AISafety'22 co-located with IJCAI’22.

  • 04/2022: It is my privilege to join WAISE2022 as a PC member.

  • 03/2022: Organised together with our colleagues from DSTL and Heriot-Watt Uni a Demo Workshop for SOLITUDE. The 3-hours recorded video is avalible here.

  • 09/2021: Our paper on “Embedding and Extraction of Knowledge in Tree Ensemble Classifiers” is accepted by the Springer journal of Machine Learning.

  • 08/2021: Our AISafety’21 paper won the best paper award, while we are working hard on its journal version! Hopefully it will come out soon.

  • 07/2021: A huge congrats to our PhD student Wei Huang (co-supervised by Xiaowei and me) who is the winner of Siemens AI-Dependability-Assessment-Student-Challenge. News on the school website.

  • 06/2021: Our paper “Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles” is accepted at the workshop AISafety’21 co-located with IJCAI’21. Here is the accepted version.

  • 05/2021: A first XAI paper of mine “BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations” is accepted by UAI2021, thanks to the great efforts of my co-authors. BayLIME (the accepted preprint) is a Bayesian modification of LIME that exploits prior knowledge and Bayesian reasoning to improve the consistency in repeated explanations, the robustness to kernel settings and explanation fidelity.

  • 05/2021: Our journal paper “Coverage Guided Testing for Recurrent Neural Networks” (preprint) got accepted by IEEE Tran. on Reliability. Congratulations to our star PhD student Wei Huang and all co-authors!

  • 04/2021: An abstract paper is accepted by DSN2021 – “Detecting Operational Adversarial Examples for Reliable Deep Learning”, here is a preprint version. Hopefully the more exciting parts, results to the research questions articulated, will come soon.

  • 03/2021: Glad that our paper “Conservative Confidence Bounds in Safety, from Generalised Claims of Improvement & Statistical Evidence” is accepted by DSN2021 (48 accepted out of 295 submissions – 16.3%). Great work by my CSR colleagues Kizito and Lorenzo! Here is a preprint version.

  • 02/2021: It is my great pleasure to be part of the UAI2021 this year as a PC member. Looking forward to reviewing exciting papers from the prestigious community.

  • 01/2021: Give a talk at the FOCETA WP3 workshop on behalf of our Liverpool side. The topic is on reliability assessment for Deep Learning software considering evidence of robustness verification and operational testing.

  • 10/2020: About to start a new PDRA post at the Computer Science Department, University of Liverpool in Jan. 2021, working on projects with Prof. Sven Schewe and Dr. Xiaowei Huang. Looking forward to it!

  • 08/2020: Our journal paper “Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles” (an extension of our ISSRE2019 paper) is accepted by Information and Software Technology. Our friends from CSR, City UoL made a significant contribution. Here is a preprint version.

  • 07/2020: Our paper “Interval Change-Point Detection for Runtime Probabilistic Model Checking” is accepted by ASE2020. A first joint paper with our York friends at TASP – hopefully many more to come! Here is the accepted manuscript.

  • 04/2020: Our paper “A Safety Framework for Critical Systems Utilising Deep Neural Networks” is accepted by SafeComp2020. Many thanks to my co-authors for their great efforts. Here is a preprint.

  • 02/2020: Give a talk at the TASP: Trustworthy Adaptive and Autonomous Systems & Processes research group, University of York. The topic is “Robust Bayesian Estimators for Transition Parameters in Probabilistic Model Checking”, here is the slides.