Xiyue Zhang (Pronounced: Shee-you-eh Jahng - or, feel free to call me Ciel/Cielle) is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Bristol. Her current research mainly focuses on trustworthy deep learning, formal modelling and verification. Previously, she was a postdoctoral research associate working with Prof. Marta Kwiatkowska in the Department of Computer Science at the University of Oxford (2022-2024). She received her Ph.D. (2022) in Applied Mathematics, advised by Prof. Meng Sun, and her B.Sc. (2017) in Information and Computing Science from the Peking University.

Recruitment

I am looking for motivated Ph.D. students with a strong interest in formal verification or empirical analysis of data-driven models and systems. Ideal candidates will have a background in computer science or a closely related field. Scholarships are available for both home and international students, including those at the University-level, Faculty-level, CDTs, CSC, and others. Feel free to email me if you’d like to discuss further!

News

• [November 2024 - Talk]: I am honoured to present an inaugural talk on Neural Network Certification to kick off the Trustworthy Systems Lab (TSL) seminar series at the University of Bristol. Explore the seminar series here and join our Teams channel for more details. (Location: In-Person: Queens Building, Room 1.07 | Online: click the link here; Time: Wednesday 6th November, 12:00-13:00). This is an open group, so feel free to spread the word! To observe, participate in debates, or present, please reach out. Join our mailing list for weekly invitations and updates on future talks.
• [August 2024 - Paper]: Our paper “FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection” is accpeted by ASE 2024.
• [July 2024 - Event]: I give a talk at SAIV 2024 speaking on preimage approximation-based neural network analysis.
• [May 2024 - Paper]: Our paper “Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks” is accpeted by ECML PKDD 2024.
• [May 2024 - Event]: I present my work on neural network certification at Oxbridge Women in Computer Science Conference 2024.
• [April 2024 - Event]: I give a talk at TACAS 2024 speaking on Preimage Approximation (Backward Analysis) for Neural Networks.
• [December 2023 - Paper]: Our paper “Provable Preimage Under-Approximation for Neural Networks” is accpeted by TACAS 2024.
• [October 2023 - Award]: I am selected as a DAAD AInet fellow for the Postdoc-NeT-AI 11/2023.
• [August 2023 - Paper]: Our paper “Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks” is accpeted by Journal of Logical and Algebraic Methods in Programming.
• [August 2023 - Paper]: Our paper “When to Trust AI: Advances and Challenges for Certification of Neural Networks” is accepted by FedCSIS 2023.
• [August 2023 - Award]: I am selected as Future Digileader ‘23 by Digital Futures.
• [April 2023 - Paper]: Our paper “Using Z3 for Formal Modeling and Verification of FNN Global Robustness” is accpeted by SEKE 2023.
• [December 2022 - Paper]: Our paper “kProp: Multi-Neuron Relaxation Method for Neural Network Robustness Verification” is accpeted by FSEN 2023.
• [October 2022 - News]: I will serve on the Program Committee of TASE’23, to be held in Bristol, UK, on 04-06 July 2023. Welcome submissions!
• [September 2022 - Event]: I give a talk at the ICTAC 2022 conference speaking on A Unifying Logical Framework for Neural Networks.
• [August 2022 - Paper]: Our paper “Towards a Unifying Logical Framework for Neural Networks” is accepted by ICTAC 2022.
• [June 2022 - Paper]: Our paper “Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages” is accpeted by ICFEM 2022.