Dr Wenjie Ruan
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Dr Wenjie Ruan |
Short Bio
Currently, I am a Senior Lecturer in Data Science (~Associate Professor in US system) in the College of Engineering, Mathematics and Physical Sciences at University of Exeter, UK. I am also the Co-Lead of Trustworthy AI Theme, at IDSAI. Before my current position, I was a Lecturer in the School of Computing and Communications, Lancaster University. Before that, funded by the EPSRC Project - Mobile Robotics: Enabling a Pervasive Technology of the Future, I had worked as a Postdoctoral Researcher with Prof. Marta Kwiatkowska in the Department of Computer Science at University of Oxford for two years. I received my Ph.D. degree from the School of Computer Science at University of Adelaide, Australia. During my PhD, I did a six-month research intern in IoT Tech Center (GreenOrbs), Tsinghua University. Before my PhD, I was a Control System Engineer in Institutes of Optics and Electronics, Chinese Academy of Sciences.
My current research interests lie in:
(1) Robustness, Safety and Interpretability of Deep Learning Models;
(2) Interpretable Machine Learning and Its Applications in Health Data Analytics and Human-Centred Computing.
News
Jul/2022: Our paper “Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem” is accepted by Research Track in ECML/PKDD’22
Jul/2022: Our paper “PRoA: A Probabilistic Robustness Assessment against Functional Perturbations” is accepted by Research Track in ECML/PKDD’22
Jul/2022: Our paper “3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models” is accepted with minor revision by Machine Learning Journal
Jun/2022: Our Book “Machine Learning Safety and Security” will appear soon in Springer Nature, which is the part of Book Series in Artificial Intelligence: Foundations, Theory, and Algorithms
Jan/2022: Our Book Chapter “Deep Learning and Its Adversarial Robustness: A Brief Introduction” will appear in HANDBOOK ON COMPUTER LEARNING AND INTELLIGENCE, World Scientific Pulisher
Nov/2021: Invited to give a half-day tutorial “Adversarial Robustness of Deep Learning Theory, Algorithms, and Applications” in CIKM 2021
Nov/2021: Our paper “Sparse adversarial video attacks with spatial transformations” is accepted by BMVC’21
Oct/2021: Have been selected as Co-lead of Trustworthy AI Theme, IDSAI
Aug/2021: Our paper “Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis” is accepted by WWW’21
June/2021: Our paper “Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task” is accepted by IEEE Trans on Artificial Intelligence
June/2021: Congratulations to my PhD students successfully secured multiple CSC-Exeter Studentships this year
April/2021: Invited by Dstl and Liverpool University, I gave a tutorial about our universal adversarial attack tool - GUAP to defence sectors of UK, US and Canada, etc.
March/2021: Invited to be TPC Member and Reviewer by CIKM 2021, NeurlPS 2021, ICML 2021, CVPR 2021, ICCV 2021 and ICLR 2022
April/2021: A paper “Coverage-Guided Testing for Recurrent Neural Networks” was accepted by IEEE Trans on Reliability
March/2021: Invited to give a half-day tutorial “Adversarial Robustness of Deep Learning Theory, Algorithms, and Applications” in ECML/PKDD 2021, with colleagues from Liverpool University
Jan/2021: Welcome Syed Yuns, who graduated his PhD from University of Manchester, to start his Postdoc in our group, working on the ORCA-funded project - AELARS
Jan/2021: Invited to give a half-day tutorial “Towards Robust Deep Learning Models: Verification, Falsification, and Rectification” in IJCAI 2021, with colleagues from Liverpool University and Imperial College London
Oct/2020: A Postdoctoral Research Fellow position now is available, anyone interested is welcome to apply.
Oct/2020: Invited to be a Senior TPC in IJCAI 2021.
Oct/2020: Our paper Memory Augmented Hierarchical Attention Network for Next Point-of-Interest Recommendation is accepted by IEEE Trans. on Computational Social Systems with minor revisions.
Sep/2020: Our paper Enabling Cost-Effective Population Health Monitoring By Exploiting Spatiotemporal Correlation: An Empirical Study is accepted by ACM Trans. on Computing for Healthcare. We conducted an in-depth study based on 10-year's spatiotemporal morbidity rates of chronic diseases in more than 500 regions of London to verify if spatiotemporal correlations can be leveraged for data inference using only a small number of samples.
Sep/2020: Our paper Generalizing Universal Adversarial Attacks Beyond Additive Perturbations is accepted by ICDM 2020, we released our Universal Adversarial Attack tool - GUAP.
Sep/2020: Our tutorial proposal on “Adversarial Robustness of Deep Learning Theory, Algorithms, and Applications” is one of four tutorials that are accepted by ICDM 2020.
July/2020: Our paper A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability is accepted by Computer Science Review. We conducted a comprehensive survey of the current research effort into making DNNs safe and trustworthy from four aspects: verification, testing, adversarial attack and defence, and interpretability.
Feb/2020: Our research was covered by multiple media: Lancaster University News, EurekAlert, ScienceBlog, eScience News, Science X, and Heriot-Watt University News.
Feb/2020: Invited by Prof Michael Sheng from Macquarie University, I give a talk about Robustness of Deep Neural Networks in his group.
Dec/2019: Invited by Prof Zhi Jin from Peking University, I give a talk about Robustness of Deep Neural Networks at Key Lab of High Confidence Software Technologies, Ministry of Education.
Nov/2019: Got a Partnership Resource Fund from ORCA Hub for the project “Towards Accountable and Explainable Learning-enabled Autonomous Robotic Systems”, the overall amount is £180K.
Nov/2019: Our paper AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration, is accepted by AAAI 2020 with Oral Presentation, our code is released.
Nov/2019: Our paper ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context is accepted by AAAI 2020, our code is released.
Oct/2019: Got a subcontract from Liverpool University, funded by Defence Science and Technology Laboratory, to work on the project “Test Coverage Metrics for Artificial Intelligence V2”, the overall amount is £15K.
June/2019: Our paper Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles is accepted by IROS 2019.
... More News
Selected Publications
H. Wu, W. Ruan, J. Wang, D. Zheng, B. Liu, Y. Gen, X. Chai, J. Chen, K. Li, S. Li, S. Helal, Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task, IEEE Transactions on Artificial Intelligence, 2021, to appear (Github)
W. Huang, Y. Sun, X. Zhao, J. Sharp, W. Ruan, J. Meng, X. Huang, Coverage Guided Testing for Recurrent Neural Networks, IEEE Transactions on Reliability, 2021, to appear (Github)
D. Chen, T. Jiang, W. Ruan, Q. Ni, S. Helal, Enabling Cost-Effective Population Health Monitoring By Exploiting Spatiotemporal Correlation: An Empirical Study, ACM Trans. on Computing for Healthcare, 2 (2), 1-19, 2021
L. Ma, X. Ma, J. Gao, X. Jiao, Z. Yu, C. Zhang, W. Ruan, Y. Wang, W. Tang, et al., Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis, The Web Conference 2021 (WWW’21), 3558-3568
Y. Zhang, W. Ruan, F. Wang, X. Huang, Generalizing Universal Adversarial Attacks Beyond Additive Perturbations, The IEEE International Conference on Data Mining (ICDM’20), Sorrento, Italy, November 17 - 20, 2020. (Github)
X. Huang, D. Kroening, W. Ruan, J. Sharp, Y. Sun, E. Thamo, M. Wu, X. Yi, A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability, Computer Science Review. 37 (2020): 100270.
M. Wu, M. Wicker, W. Ruan, X. Huang, M. Kwiatkowska, A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees, Theoretical Computer Science. 807 (2020): 298-329. Github
L. Ma, C. Zhang, Y. Wang, W. Ruan, J. Wang, et al., ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context, The 34th AAAI Conference on Artifical Intelligence (AAAI’20), New York, USA, February 7-12, 2020. (Github)
L. Ma, J. Gao, Y. Wang, C. Zhang, J. Wang, W. Ruan, et al., AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration, The 34th AAAI Conference on Artifical Intelligence (AAAI’20), New York, USA, February 7-12, 2020. (Github, Oral Presentation)
W. Ruan, M. Wu, Y. Sun, X. Huang, D. Kroening and M. Kwiatkowska, Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance, The 28th International Joint Conference on Artificial Intelligence (IJCAI’19), Macao, China, August 10-16, 2019. (Github)
M. Wu, T. Louw, M. Lahijanian, W. Ruan, X. Huang, N. Merat, and M. Kwiatkowska, Gaze-based Intention Anticipation over Driving Manoeuvresin Semi-Autonomous Vehicles, The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’19), Macao, China, November 4-8, 2019.
W. Ruan, X. Huang, and M. Kwiatkowska, Reachability Analysis of Deep Neural Networks with Provable Guarantees, The 27th International Joint Conference on Artificial Intelligence (IJCAI’18), Stockholm, Sweden, July 13-19, 2018. (Github, The citation number is ranked 5th among 710 papers accepted by IJCAI’18 based on AMiner)
Y. Sun, M. Wu, W. Ruan, X. Huang, M. Kwiatkowska and D. Kroening, Concolic Testing for Deep Neural Networks, The 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE’18), Montpellier, France, September 3-7, 2018. (Github, The citation number is ranked 2nd among all the papers accepted by ASE’18 based on AMiner)
W. Ruan, Q. Z. Sheng, P. Xu, L. Yang, T. Gu, and L. Shangguan, Making Sense of Doppler Effect for Multi-Modal Hand Motion Detection, IEEE Trans. on Mobile Computing (TMC), Vol 17, No 9, pp 2087-2100, 2018. (Demo)
L. Yao, Q. Z. Sheng, X. Li, T. Gu, M. Tan, X. Wang, S. Wang, and W. Ruan, Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength, IEEE Trans. on Mobile Computing (TMC), Vol 17, No 2, pp 293-306, 2018. (Demo)
W. Ruan, Q. Z. Sheng, L. Yang, T. Gu, P. Xu and L. Shangguan. AudioGest: Enabling Fine-Grained Hand Gesture Detection by Decoding Echo Signals, The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’16), Heidelberg, Germany, 12-16 September, 2016. (Demo)
W. Ruan, Q. Z. Sheng, L. Yao, T. Gu, M. Ruta and L. Shangguan. Device-free Indoor Localization and Tracking through Human-Object Interactions, The IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM’16), Coimbra, Portugal, June 21-24, 2016.
L. Yao, Q. Z. Sheng, X. Li, S. Wang, T. Gu, W. Ruan and W. Zou. Freedom: Online Activity Recognition via Dictionary-based Sparse Representation of RFID Sensing Data, The IEEE International Conference on Data Mining (ICDM’15), Atlantic City, NJ, USA, November 14 - 17, 2015.
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Grants/Awards
PRF Project “Towards Accountable and Explainable Learning-enabled Autonomous Robotic Systems”, Funded through Offshore Robotics for Certification of Assets (ORCA) Hub and EPSRC; Role: PI; Time: Nov 2019 - Aug 2021 (One PostDoc and one PhD position are available for this project, please contact me if you are interested)
ARC DECRA Project “When Robust Deep Learning Meets Health IoT: Towards a Reliable and Explainable Health Monitoring and Caring System”, Funded by Australian Research Council, Role: PI; Time: 2020 - 2023
Subcontract from Project “Test Coverage Metrics for Artificial Intelligence V2”, Funded by Defence Science and Technology Laboratory (DSTL); Role: PI of the subcontract; Time: Oct 2019 - March 2021
Dean's Commendation for Doctoral Thesis Excellence, The University of Adelaide, 2017
Scholarship for Outstanding Ph.D. Students Study Abroad, Awarded by China Government, 2017 (3 students majored in CS awarded in Australia, overall select 500 PhD students globally from all majors)
Best Student Paper, ADMA 2016, Gold Coast, Australia
SIGIR Travel Grant for attending CIKM 2016, Indianapolis, USA
Best Poster Award, The 9th ACM International Workshop on IOT and Cloud Computing, Wuxi, China
Highly Commended Research Poster Award, The 25th Australia Database Conference (ADC 2014) PhD School in Big Data, Brisbane, Australia