Dr Xiaowei Huang

I was promoted to Reader in 2020, following a promotion to Senior Lecturer (Associate Professor) in 2019. Prior to Liverpool, I worked at University of Oxford, University of New South Wales, and Chinese Academy of Sciences. My research concerns the correctness (e.g., safety, trustworthiness, etc) of autonomous systems, including

• verification of neural network-based deep learning on safety and security properties,
• practical analysis techniques (software testing, safety argument, certification, etc) for machine learning techniques,
• interpretation and explanation of deep learning, and
• logic-based approaches for the specification, verification and synthesis of autonomous multi-agent systems.

The research has been funded by Dstl, EPSRC, European Commission, etc. I have been the PI (or Liverpool PI) for projects valued more than £1.7M, and co-I for more than £15M. I will be directing the Autonomous Cyber Physical Systems Laboratory in the coming Digital Innovation Facility (DIF) Building.

New Open Positions:

• ☆ We are recruiting a postdoctoral researcher for the EPSRC project on "EnnCore: End-to-End Conceptual Guarding of Neural Architectures". The position will be up to 3.5 years. You will collaborate with a team at the University of Manchester. The topic will be on the verification an interpretation of deep learning. Please find the job advertisement and apply through the links provided. A few more information is available here. Please get in touch if you have any questions.
• We are recruiting a postdoctoral researcher for the H2020 project on "FOCETA - Foundations for Continuous Engineering of Trustworthy Autonomy". The position will be 3 years. You will collaborate with many research institutes and large companies across Europe. The topic will be on the verification and validation of learning-enabled systems. Details please refer to the job advertisement. This position has been filled.
• I am always looking for self-motivated PhD students who are interested in topics related to the safety, security, interpretability, and trustworthiness of deep learning, and deep learning enabled autonomous systems. Please feel free to get in touch.

Workshop Organisation

• (08/2020) SafeAI workshop will be held again with AAAI2021.
• (03/2020) AISafety workshop will be held again with IJCAI2020.
• (08/2019) SafeAI will be held again as a workshop of AAAI2020.
• (08/2019) Organising workshop AI&FM2019 at ICFEM2019 , to discuss how to make AI and formal methods (and software engineering) mutually beneficial. It will be on 5th Nov, 2019.
• (02/2019) SafeAI will be held again, as a workshop of IJCAI2019, website: https://www.ai-safety.org/.
• (08/2018) Co-organising a AAAI workshop on AI safety (http://www.safeai2019.org).

Recent News (for all News please go to the News tab)

• (11/2020) Going to give tutorial on "Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications" to ICDM2020 with Wenjie Ruan and Xinping Yi. Website: https://tutorial.trustdeeplearning.com
• (10/2020) Started a new project "SOLITUDE: Safety Argument for Learning-enabled Autonomous Underwater Vehicles." with Xingyu Zhao, Simon Maskell, Sven Schewe, Sen Wang (Heriot Watt) on developing safety assurance argument for autonomous underwater vehicles.
• ☆ (09/2020) Congratulations to Gaojie Jin! Paper "How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?" has been accepted to NeurIPS2020. We study a "correct by construction" question -- how to train a neural network with good generalisation ability (i.e., reliability)? -- and find that this is possible by tracking and controlling a Weight Correlation over the trainable parameters during the training. Experiments show that the improvement is persistent across small networks and large scale networks such as VGG16. The weight correlation can also be used to predict if a model generalises well, without using test data which might not be available in practical scenarios. Please check paper from Arxiv.
• (08/2020) Our paper "Generalizing Universal Adversarial Attacks Beyond Additive Perturbations" has been accepted to ICDM2020.
• (08/2020) Our paper "PRODEEP: a platform for robustness verification of deep neural networks" has been accepted to ESEC/FSE2020.
• (07/2020) Our paper "Lightweight Statistical Explanations for Deep Neural Networks" has been accepted to ECCV2020.
• (07/2020) Our paper "Regression of Instance Boundary by Aggregated CNN and GCN" has been accepted to ECCV2020.
• (06/2020) Congratulations to Wei Huang! Our paper "Practical Verication of Neural Network Enabled State Estimation System for Robotics" has been accepted to IROS2020.
• (05/2020) Our survey paper "A Survey of Safety and Trustworthiness of Deep Neural Networks" has been accepted to the journal of Computer Science Survey. It's current arXiv version is here

Teaching for this semester

• Semester 1, 2020/2021. Undergraduate, Second Year. Advanced Artificial Intelligence.

News

• (11/2020) Going to give tutorial on "Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications" to ICDM2020 with Wenjie Ruan and Xinping Yi. Website: https://tutorial.trustdeeplearning.com
• (11/2020) Will start a 3-year H2020 project on "FOCETA - Foundations for Continuous Engineering of Trustworthy Autonomy" as the Liverpool lead (with Prof Sven Schewe).
• (10/2020) Started a new project "SOLITUDE: Safety Argument for Learning-enabled Autonomous Underwater Vehicles." with Xingyu Zhao, Simon Maskell, Sven Schewe, Sen Wang (Heriot Watt) on developing safety assurance argument for autonomous underwater vehicles.
• (10/2020) Warmest welcome to Kaiwen Cai, who just join the group to start his PhD.
• ☆ (09/2020) Congratulations to Gaojie Jin! Paper "How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?" has been accepted to NeurIPS2020. We study a "correct by construction" question -- how to train a neural network with good generalisation ability (i.e., reliability)? -- and find that this is possible by tracking and controlling a Weight Correlation over the trainable parameters during the training. Experiments show that the improvement is persistent across small networks and large scale networks such as VGG16. The weight correlation can also be used to predict if a model generalises well, without using test data which might not be available in practical scenarios. Details will come shortly.
• (09/2020) Will start an EPSRC project on "EnnCore: End-to-End Conceptual Guarding of Neural Architectures" as the Liverpool lead over the EPSRC call for Security for all in an AI enabled society
• (08/2020) Our paper "Generalizing Universal Adversarial Attacks Beyond Additive Perturbations" has been accepted to ICDM2020.
• (08/2020) Our paper "PRODEEP: a platform for robustness verification of deep neural networks" has been accepted to ESEC/FSE2020.
• (08/2020) Will give lecturers on verification of neural networks at Summer School Marktoberdorf 2020（"Safety and Security of Software Systems: Logics, Proofs, Applications").
• (07/2020) Our paper "Lightweight Statistical Explanations for Deep Neural Networks" has been accepted to ECCV2020.
• (07/2020) Our paper "Regression of Instance Boundary by Aggregated CNN and GCN" has been accepted to ECCV2020.
• (06/2020) Congratulations to Wei Huang! Our paper "Practical Verication of Neural Network Enabled State Estimation System for Robotics" has been accepted to IROS2020.
• (05/2020) Our survey paper "A Survey of Safety and Trustworthiness of Deep Neural Networks" has been accepted to the journal of Computer Science Survey. It's current arXiv version is here
• (05/2020) A new paper "CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation" accepted to MICCAI2020
• (05/2020) Will give an invited talk at University of Exeter.
• (04/2020) Our paper " Safety Framework for Critical Systems Utilising Deep Neural Networks" has been accepted to SAFECOMP2020. This paper, we start our investigation into the safety assurance case for deep learning by Bayesian inference, with evidences collected from verification and testing of neural networks.
• (03/2020) Will give an invited talk at MMB2020 on "Safety Certification of Deep Learning".
• (01/2020) Our paper on "Reliability Validation of Learning Enabled Vehicle Tracking" is accepted to ICRA2020. It is the first time we discovered that there are risks in the interaction between a deep learning system and other traditional, symbolic systems such as Kalman Filter based dynamic vehicle tracking.
• (01/2020) Started a project to work with MBDA & WSTC on "Adaptive & reactive mission execution".
• (10/2019) Have a 4-year PhD studentship funded by Dstl to work on "Risk-Aware Shared Autonomy for Robotic Inspection Missions in Adversarial Environments". Please get in touch ASAP if you are interested in the position.
• (09/2019) Welcome Dengyu WU to join the team to start his PhD.
• (07/2019) Our very first paper on testing DNN is finally accepted to EMSOFT2019 and ACM Transactions on Embedded Computing Systems (TECS). The paper can be found from arXiv. Before it is published, it has been cited for 40+ times. :-)
• (07/2019) Our abstract interpretation based DNN verification Paper "Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification" is finally accepted (though conditionally :-)) to SAS2019. It combines symbolic propagation with abstract interpretation to achieve a good performance. A preliminary version is available from arXiv.
• (06/2019) A paper "Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management", joint with colleagues from Heriot-Watt University, is accepted to SEFM2019.
• (06/2019) Have another project with Dstl on Test Metrics for Artificial Intelligence.
• (06/2019) Our paper "Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles" is accepted to IROS 2019, another major conference to be held in Macau this year other than IJCAI.
• (06/2019) Warmest welcome to Peipei Xu for her joining the team to start her PhD.
• (06/2019) finally received my HEA (Higher Education Academy) fellow certificate.
• (05/2019) Our journal paper "A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees" has been accepted to Theoretical Computer Science (TCS). The paper is a significantly extended version of our TACAS2018 paper. The newest version is available from Arxiv
• (05/2019) Our paper on "Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees" has been accepted to IJCAI2019.
• (05/2019) Our journal paper on "Reasoning about Cognitive Trust in Stochastic Multiagent Systems" has been accepted to the ACM Transactions on Computational Logic (ToCL). The paper, an extended version of our AAAI2017 paper of the same title, is to provide a foundational framework on how to reason about the trust between human and AI in a dynamic system. The paper is co-authored with Maciek Olejnik and Marta Kwiatkowska, who invested a lot of effort in this paper.
• (04/2019) Gave a talk to Liverpool Early Career Researcher Conference on Data Science, Machine Learning and AI on safety and trustworthiness of deep learning. Great to see the enthusiasm of the crowd over AI and machine learning.
• (03/2019) Our paper titled "Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification" is available on arXiv. It is an improvement of abstract interpretation with symbolic propagation for the verification of DNNs.
• (03/2019) Francesco Crecchi will visit from March to May.
• (12/2018) Our tool paper for DeepConcolic has been accepted to ICSE2019.
• (11/2018) Taking up the role of Undergraduate Admission Tutor for the CS department. I will be overseeing the entry requirements of various UG programs available in CS Dept. for the next few years.
• (10/2018) Join ORCA (Offshore Robotics for Certification of Asset) Hub as a Co-Investigator at Liverpool. I will be on the topic "Robot and Asset Self-Certification"
• (10/2018) Warmly welcome Ms Emese Thamo to the group to start her PhD.
• (09/2018) Join the program committee of a NIPS workshop on security in machine learning.
• (09/2018) Slides of my talk at Nanjing University can be found here.
• (07/2018) Slides of my talk at Imperial can be found here.
• (07/2018) Our paper titled "A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees" is available on arXiv. The paper summarises our efforts on using Lipschitz continuity and two-player games to achieve the verification. The provable guarantee solely relies on the Lipschitz constants. Except for the maximum safe radius problem as a cooperative game, we also studied feature robustness problem as a competitive game.
• (07/2018) Our new paper titled "Concolic Testing for Deep Neural Networks", coauthored with Youcheng Sun, Min Wu, Wenjie Ruan, Marta Kwiatkowska, Daniel Kroening, has been available from arXiv. This paper has been conditionally accpeted to ASE2018. We will update a new version very soon.
• (06/2018) Our journal paper "An epistemic strategy logic", coauthored with Ron van der Meyden, has been accepted to the ACM Transactions on Computational Logic (TOCL).
• (04/2018) Gave an invited talk on "Verification and Testing of Deep Learning" for the ETAPS workshop on "Formal Methods For ML-Enabled Autonomous System (FOMLAS2018)".
• (04/2018) Our new paper "Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for L0 Norm" is available on arXiv.
• (04/2018) Our paper titled "Reachability Analysis of Deep Neural Networks with Provable Guarantees" has been accepted to IJCAI2018. The paper is co-authored with Wenjie Ruan (Oxford) and Marta Kwiatkowska (Oxford). We present a novel, global optimisation based, approach to study the properties (e.g., robustness, output range, safety, etc) of deep neural networks with provable guarantees. Different with constraint-based approaches, our new approach is not affected by the internal structure of the networks, and therefore it is able to handle state-of-the-art networks. The paper has been available on arXiv.
• (04/2018) Our paper titled "Model Checking Probabilistic Epistemic Logic for Probabilistic Multiagent Systems" has been accepted to IJCAI2018. The paper is co-authored with Chen Fu (CAS), Andrea Turrini (CAS), Lei Song, Yuan Feng (UTS), and Lijun Zhang (CAS). It presents a model checking algorithm (and its tool) for probabilistic multiagent systems with respect to a probabilistic epistemic logic PETL.
• (04/2018) got an Defence Science Technology Laboratory (Dstl) project on "Test Coverage Metrics for Artificial Intelligence", which will run from 2018 to 2019. We are going to explore software testing techniques for the safety of deep neural networks.
• (03/2018) got an EPSRC 2018 Vacation Bursary Programme project, to support a student Edward Barker on "Explaining Deep Learning-based Image Classification"
• (03/2018) our new paper titled "Testing Deep Neural Networks" is available on arXiv. It is co-authored with Dr Youcheng Sun and Prof Daniel Kroening from Oxford. The paper proposes a set of white-box test coverage criteria and test case generation algorithms for DNNs. It is a big step towards developing software testing approaches on systems with deep learning components.
• (01/2018) gave a short talk and served as a panelist for ERTS 2018 at Toulouse, France. The panel was organised by Airbus.
• (12/2017) our paper titled "Feature guided blackbox testing of deep neural networks" has been accepted to TACAS'2018. It is co-authored with Matthew Wicker from University of Georgia, USA, and Prof Marta Kwiatkowska from Oxford. The full paper is available at here. The paper proposes a black-box testing algorithm based on SIFT object detection technique and Monte-carlo tree search algorithm. The algorithm can achieve guarantee on the safety, and thus is also an verification approach.
• (12/2017) gave talks on "Verification of Deep Learning Systems" at Beijing (Beijing University and Tsinghua University) and Xi'an (Xidian University). Slides are available from here.

Software:

• TrustAI: a tool set for the safety and trustworthiness of systems with deep learning components.
• DLV : a software to verify deep neural network.
• MCK : a model checker for verifying autonomous multiagent systems.
• Rationality Verification: a model checker for strategic reasoning based on correlated equilibrium

Publications:

Google Scholar and dblp

TrustAI: Tool Demos

DeepConcolic (Github repository)

Related Publications:

• Testing Deep Neural Networks. arXiv
• Concolic testing for deep neural networks. ASE2018
• DeepConcolic: testing and debugging deep neural networks. ICSE2019
• Structural Test Coverage Criteria for Deep Neural Networks. ACM Transactions on Embedded Computing Systems (TECS)

Reliability validation of a learning-enabled dynamic tracking system (Github repository)

Related Publications:

• Reliability Validation of Learning Enabled Vehicle Tracking. arXiv

PRODeep : a platform for robustness verification of deep neural networks (Github repository)

Related Publications:

• PRODeep : a platform for robustness verification of deep neural networks. arXiv

testRNN (Github repository)

Related Publications:

• Test Metrics for Recurrent Neural Networks. arXiv

Funding and Grants

• SOLITUDE: Safety Argument for Learning-enabled Autonomous Underwater Vehicles.
  • Role: PI, with Xingyu Zhao, Simon Maskell, Sven Schewe, Sen Wang (Heriot Watt)
  • Dstl funded
  • 2020-2022
  • More Information
• FOCETA - Foundations for Continuous Engineering of Trustworthy Autonomy.
  • Role: Liverpool lead, with Sven Schewe
  • EU H2020 funded
  • 2020-2023
  • More Information
• EnnCore: End-to-End Conceptual Guarding of Neural Architectures.
  • Role: Liverpool lead
  • EPSRC funded
  • 2021 - 2024
  • More Information
• MBDA & WSTC project on "Adaptive & reactive mission execution", PI, with Jason Ralph and Simon Maskell, 2019 - 2020
• Defence Science Technology Laboratory (DSTL) PhD studentship on "Statistical Approach to Assess the Trustworthiness of Robotics and AI", PI, 2020 - 2024
• Test Coverage Metrics for Artificial Intelligence -- v2.0.
  • Role: PI, with Simon Maskell, Sven Schewe
  • Defence Science Technology Laboratory (DSTL) funded
  • 2019 - 2020, extended to 2021
  • More Information
• Test Coverage Metrics for Artificial Intelligence.
  • Role: PI, with Simon Maskell, Sven Schewe, and Daniel Kroening
  • Defence Science Technology Laboratory (DSTL) funded
  • 2018 - 2019
  • More Information
• EPSRC ORCA (Offshore Robotics for Certification of Asset) Hub.
  • Co-I at Liverpool, with Michael Fisher (PI at Liverpool), Mike Jump
  • EPSRC funded
  • 2017-2021
• EPSRC 2018 Vacation Bursary Programme project, PI, 2018
• KTP Project with Kadfire, Co-I, 2017-2019
• Lockey Grant, Oxford University, PI, 2015.

Program Committee Memberships

• ECAI 2020
• AITest2020 (The Second IEEE International Conference on Artificial Intelligence Testing)
  
• HCML2019 (Human-Centric Machine Learning) at NeurlIPS2019
  
• SPML19 (Security and Privacy of Machine Learning) at ICML2019
  
• AdvMLCV2019 (Adversarial Machine Learning in Real-World Computer Vision Systems) at CVPR2019
  
• SafeML2019 (Safe Machine Learning: Specification, Robustness, and Assurance) at ICLR2019
  
• DebugML-19 (Debugging Machine Learning Models) at ICLR2019
  
• AAMAS 2019, 2020
  
• AAAI 2019 workshop on Safe Artificial Intelligence (co-chair)
  
• NIPS 2018 workshop on security in machine learning
  
• AAAI 2018 2019 2020
  
• IEEE International Workshop on Formal Methods Integration (FMI), 2018, 2019
  
• IJCAI 2018 2019 2020
  
• KR 2016
  
• PRIMA 2015, 2016, 2017, 2018
  

Key Departmental Administrative Roles

• Undergraduate Admission Tutor
  
• REF review panel member
  

Recent Invited Talks, Seminars, and Panel Discussions:

• (08/2020) Will give lecturers on verification of neural networks at Summer School Marktoberdorf 2020（"Safety and Security of Software Systems: Logics, Proofs, Applications").
• (05/2020) Will give an invited talk at University of Exeter.
• (03/2020) Will give an invited talk at MMB2020 on "Safety Certification of Deep Learning".
• (04/2019) Gave a talk to Liverpool Early Career Researcher Conference on Data Science, Machine Learning and AI on safety and trustworthiness of deep learning. Great to see the enthusiasm of the crowd over AI and machine learning.
• (09/2018) Slides of my talk at Nanjing University can be found here.
• (07/2018) Slides of my talk at Imperial can be found here.
• (04/2018) Gave an invited talk on "Verification and Testing of Deep Learning" for the ETAPS workshop on "Formal Methods For ML-Enabled Autonomous System (FOMLAS2018)".
• January 2018, Toulouse, France. Invited panel discussion on how machine learning technique could be used (or not) for safety-critical applications, oragnised by ONERA The French Aerospace Lab and AirBus. The 9th European Congress on Embedded Real Time Software and Systems (ERTS 2018). https://www.erts2018.org/
• April 2018, Thessaloniki, Greece. Verification of Deep Neural Networks. Invited Talk to the ETAPS 2018 workshop on formal methods for ML-enabled autonomous systems (FoMLAS2018). https://fomlas2018.fortiss.org
  
• Januray 2018, Florida, US. Invited talk and panelist of a session in SciTech2018 on the Interaction of Software Assurance and Risk Assessment Based Operation of Unmanned Aircraftsession. Organised by The American Institute of Aeronautics and Astronautics (AIAA).
• December 2017, Beijing, China. Verification of Robotics and Autonomous Systems. Invited talk to the workshop on the Verification of Large Scale Real-Time Embeded Systems. Slides are available from here.
• September 2017, Visegrad, Hungary. Verification of Robotics and Autonomous Systems. Invited Talk to the 11th Alpine Verification Meeting (AVM2017). http://avm2017.inf.mit.bme.hu. Slides are availabe from here
• November 2015, Oxford, UK. Reasoning About Trust in Autonomous Multiagent Systems. Univeristy of Oxford.
  

Open Positions:

• (06/2020) We are recruiting a postdoctoral researcher for the H2020 project on "FOCETA - Foundations for Continuous Engineering of Trustworthy Autonomy". The position will be 3 years. You will collaborate with many research institutes and large companies across Europe. The topic will be on the verification and validation of learning-enabled systems.
• (10/2019) Have a 4-year PhD studentship funded by Dstl to work on "Statistical Approach to Assess the Trustworthiness of Robotics and AI". Please get in touch ASAP if you are interested in the position.
• (05/2019) We have a 2+ year postdoc position in the area of machine learning, software engineering, and automated verification, working on the topic of “verification and testing of learning-enabled systems”. Please find more details here. The position can start any time from now. If you are interested in, please feel free to get in touch.
• (05/2018) We have a new vacancy for PhD student, in conjunction with IBM, to explore "property-aware learning" on how to ensure the correctness of systems with learning-enabled components (such as deep learning components) by imposing constraints (e.g., safety, security, ethical norms, etc) during the learning process. Advertisement can be found from findAPhD. The funding is for four years, waiving the tuition fee and having stipend 14,777 per year. Exceptional international student can apply, and please contact me first. The position needs to start before October, so please apply ASAP.
• (03/2018) a fully-funded PhD positions is available. The student will be advised by me and Dr. Wei-Yun Yao at A*STAR, Singapore, on the topic of Explainable Intelligent Robots. The student will spend two years in Liverpool and Singapore, respectively. Please feel free to contact me if you are interested in. Please find more details here
• (03/2018) a fully-funded PhD positions is available. The student will be advised by me and Prof. Shang-Hong Lai at National Tsing Hua University (NTHU), Taiwan, on the topic of Verified Object Recognition and Manipulation. The student will spend two years in Liverpool and Taiwan, respectively. Please feel free to contact me if you are interested in. Please find more details here .

Postdocs in the project where I was/am the primary investigator

• Nicolas Berthier, 2019 - .
• Youcheng Sun, 2018-2019. now at Queen University of Belfast.

Supervision of Postgraduate Research Students

• Mr Kaiwen Cai, with Dr. Shan Luo as the co-supervisor.
  • topic: autonomous cyber physical systems
  • 10/2020 -
• Mr Dengyu Wu, with Dr. Xinping Yi as the co-supervisor.
  • topic: energy efficient deep learning
  • 10/2019 -
• Ms Peipei Xu, with Prof. Frank Wolter as the co-supervisor.
  • topic: verification of deep learning
  • 06/2019 -
• Ms Amany Alshareef, started from 03/2019, with Prof. Sven Schewe as the co-supervisor. Before coming to Liverpool, Amany has an MSc at Ball State University and a BSc at Umm Al-Qura University.
  • topic: testing deep learning
  • 03/2019 -
• Mr Gaojie Jin, with Dr. Xinping Yi as the co-supervisor. Before coming to Liverpool, Gaojie has an MSc at Liverpool University and a BSc at Peking University.
  • topic: interpretability
  • 03/2019 -
• Mr Wei Huang, started from 02/2019, with Prof. Shang-Hong Lai at National Tsing Hua University, Taiwan, as the co-supervisor. Before coming to Liverpool, Wei has an MSc at Imperial College and a BSc at Xiamen University.
  • topic: verification and validation of adaptive systems
  • 02/2019 -
• Ms Emese Thamo, with Dr Yannis Goulermas as the co-supervisor. Before coming to Liverpool, Emese has a BSc at Cambridge.
  • topic: Improving the Safety of Deep Reinforcement Learning Algorithms by Making Them More Interpretable
  • 10/2018 -

If you are interested in doing a PhD in relevant reserach areas with me, please feel free to contact me. University of Liverpool has a set of established scholarship schemes, including Liverpool china scholarship council award and Sir Joseph Rotblat Alumni Scholarship . Additional to these, I may have some vacancies from time to time.

Visitors:

• Dr Chen Zhang, China University of Mining and Technology. 12/2019 - 11/2020
• Mr Zhixuan Xu, Renming University of China. 10/2019 - 10/2020
• Mr Francesco Crecchi, University of Pisa, Italy. 04/2019 - 06/2019

"Robotics and Artificial Intelligence" Reading group is to hold a weekly meeting where one of the members will have a 30-40 minutes talk, discussing either their own papers, papers from other research groups, or anything that they are interested in. This will be followed by a Q&A and discussion session among the group on the topic.

Membership:

Anyone can join by request. If you are interested in, please feel free to drop me a message.

Venue:

Due to the lockdown, we are mainly holding this through virtual meetings (please click: Zoom meeting).

Meeting time:

Right now, the meeting time is Tuesday 16:00-17:00, London time.
Starting from the week of 24th August, the meeting time is moved to Tuesday 11:00-12:00, London time.

Talk Schedule:

The following is the schedule. Please drop me message if you have something to discuss (a title of your talk will be good, an abstract will be more than welcomed).

• 16:00-17:00, Tuesday, 07/07/2020. Xinping Yi. Topic: spectral analysis of convolutional neural networks
• 16:00-17:00, Tuesday, 14/07/2020. Gaojie Jin. Topic: predicting generalisation of deep neural networks
• 16:00-17:00, Tuesday, 21/07/2020. Wei Huang. Topic: backdoor attack on tree ensembles
• 16:00-17:00, Tuesday, 28/07/2020. Peipei Xu. Topic: Certified Adversarial Robustness via Randomized Smoothing
• 16:00-17:00, Tuesday, 04/08/2020. Dengyu Wu. Topic: Introduction to Spiking Neural Networks
• 16:00-17:00, Tuesday, 11/08/2020. Han Yu. Topic: Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space
• 16:00-17:00, Tuesday, 18/08/2020. Xiaowei Huang. Topic: Safety Certification of Deep Learning
• 11:00-12:00, Tuesday, 25/08/2020. Gaojie Jin. Topic: `The Implicit and Explicit Regularization Effects of Dropout'
• 11:00-12:00, Tuesday, 01/09/2020. Wei Huang. Topic: Practical Verification of Neural Network Enabled State Estimation System for Robotics.
• 11:00-12:00, Tuesday, 08/09/2020. Fu Wang. Topic: ‘Adversarial Training for Free!’ and ‘Fast is better than free: Revisiting adversarial training’
• 11:00-12:00, Tuesday, 15/09/2020. Peipei Xu. Topic: ‘Certified Defenses for Adversarial Patches’
• 11:00-12:00, Tuesday, 22/09/2020. Dengyu Wu. Topic: ‘introduction to Spiking Neural Networks’
• 11:00-12:00, Tuesday, 29/09/2020. Guanxiong Shen. Topic: Radio Frequency Fingerprint Identification for LoRa Using Deep Learning
• 11:00-12:00, Tuesday, 06/10/2020. Ronghui Mu. Topic: Sparse Adversarial Perturbations for Videos
• 11:00-12:00, Tuesday, 13/10/2020. Chi Zhang. Topic: POPQORN: Quantifying Robustness of Recurrent Neural Networks
• 11:00-12:00, Tuesday, 20/10/2020. Han Yu. Topic: Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space
• 11:00-12:00, Tuesday, 27/10/2020. Gaojie Jin. Topic: How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks

• Semester 1, 2020/2021. Undergraduate, Second Year. Advanced Artificial Intelligence.
  
  • 2019
  • 2018
• Semester 2, 2017/2018. Undergraduate, Second Year. Principle of Computer Game Design and Implementation