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Selected Publications

For the most recent works, the pre-print can be found on arXiv, the code are hosted on GitHub.

• Ren H., Deng J. and Xie X., GRNN: Generative Regression Neural Network - A Data Leakage Attack for Federated Learning, ACM Transactions on Intelligent Systems and Technology (TIST), 2022. [ACM | Pre-Print | Code-PyTorch]

• Deng J. and Xie X., 3D Interactive Segmentation with Semi-Implicit Representation and Active Learning, IEEE Transactions on Image Processing (TIP), 2021. [IEEE | Code-Xcode]

Research Students & Visiting Scholars

I am very fortunate to work with the following colleagues and students.

• 2022, Research Student, Mr. Chen Hu (Swansea University, Ext. Co- with Prof. Xianghua Xie), partially funded by Faculty of Science and Engineering, Swansea University.

• 2021, Research Student, Mr. Yiming Li (Swansea University, Ext. Co- with Prof. Xianghua Xie).

• 2020, Research Student, Ms. Yi Hu (Swansea University, Ext. Co- with Prof. Xianghua Xie).

• 2019, Research Student, Mr. Hanchi Ren (Swansea University, Co- with Prof. Xianghua Xie), partially funded by Engineering and Physical Sciences Research Council (EPSRC), Academic Tutor at Swansea University.

• 2018, Research Student, Ms. Katarzyna Szymaniak, M.Res awarded by Swansea University in Oct 2019, Ph.D Student at University of Edinburgh.




• 2019, Visiting Scholars, Dr. Di Lu, XiDian University, funded by National Natural Science Foundation of China (NSFC)

• 2018, Visiting Scholars, Dr. Liumei Zhang (Co- with Prof. Xianghua Xie), Xi'an Shiyou University, funded by China Scholarship Council (CSC)

For prospective students who are interested in reading a Ph.D with me on medical image analysis and artificial intelligence, please email me with your CV and research statement. I am expecting you have a solid background in computer science or applied mathematics or related subjects, (i.e. pure mathematics, physics, EEE), professional programming skills, a deep sense of curiosity, and persistence. A recommendation letter directly from your current supervisor is essential. If there is no explicit evidence to demonstrate your research skills (i.e. publications on recognisable venues, open-source project experience), another recommendation letter from an academic referee is also required. You need to meet the entry requirements of University.

There are a number of EPSRC/university/faculty/departmentally funded Ph.D studentships at Durham every year which (most-likely-only) cover a tuition fee at the UK-Home level and a living stipend. The deadline for applying is typically in early Jan with a decision being made in Feb/Mar and a non-negotiable start date of 1st Oct. You need to secure an (unconditional) offer before submitting your studentship application. The studentship is very competitive, and the applicants are assessed by an award committee mainly based on applicants' performance, potential, track record and academic background. If you are interested, please get in touch with me by 15th Nov for the coming round.

国家公派出国留学项目: For Chinese students, I am more than happy to support your CSC applications. More information can be found here or there. The scholarship will cover the full cost of tuition fees, annual allowance etc. to successful Chinese national candidates.

Grants and Projects

My works have been funded by the following research councils and industry partners in various ways. I am grateful for the supports.

• 2022, Intel® FPGA Academic Program, 2x PAC D5005 (Swansea)

• 2021, The Higher Education Funding Council for Wales (HEFCW) Capital Funds, £35K (Swansea, Internal Sub-Project, Co-I)

• 2018, NVIDIA® Academic Hardware Grant Program, 1x Quadro P6000 (Swansea)

Our team support open-source and open-science initiatives in a greatest possible manner under the permission of ethics, privacy, confidentiality and commercial agreements. We are expecting the potential collaborators are also in favor of the openness .

Professional Activities and Awards

• Website Chair of British Machine Vision Conference (BMVC) 2015.
  

• Local-Organiser of BMVA Computer Vision Summer School (CVSS): 2014, 2015, 2016.
  

• Local-Organiser of Medical Image Understanding and Analysis (MIUA) 2012.
  

• Grant Reviewer: UKRI, NIHR, Rosetrees Trust etc.

• Journal Reviewer: TPAMI, IJCV, TIP, TNNLS, TKDE, TMM, TETCI, PR, TIST, TVT, NN, JBHI, COGN, IET-CV, etc.

• Editorial Board: Frontiers in Robotics and AI (2022-).

• Conference PCM: BMVC 2017-22, CVPR 2019-23, ICCV/ECCV 2019-23, CGVC 2019-22, etc.

Teaching and Mentoring

I am a Fellow of the Higher Education Academy (FHEA). I led the assessment office of CS department at Swansea University between 2019 to 2022. I am teaching or had taught the following modules:

• COMP52715: Deep Learning for Computer Vision and Robotics (Lead), Durham University, 2023-

• COMP42415: Text Mining and Language Analytics (Support), Durham University, 2023-

• CSCM35: Big Data and Data Mining (Lead), Swansea University, 2018-2022

• CSCM38: Artificial Intelligence and Cyber Security (Lead), Swansea University, 2018-2022

• CS-150: Concepts of Computer Science (Support), Swansea University, 2018-2020

Office Hour: Wednesday 3pm-5pm (Book an appointment via Calendly).

Email Response: I will try my best to respond you ASAP, but it does not guarantee my response can meet your expectation. A gentle reminder is always welcome. (A) For module related queries, I will answer them in a batch manner on Monday & Wednesday & Friday. (B) For any other urgent queries, I will answer them on a daily basis given that I have time to do so.

Academic Reference: For University students, please note that I am NOT able to provide you academic references UNLESS you are working with me on your dissertation projects. You should ALWAYS ask permission to pass my contact to your employer or University admission office for every reference. Please be mindful of how many references you are requesting.

Rand²AI Lab

We are a group of enthusiastic researchers from VIViD group at Durham University, and SwanseaVision group at Swansea University, with common interests in general machine learning and artificial intelligence ranging from theory through methodology to application in biomedical science. We focus on studying robustness of machine learning models including privacy and security properties, generative learning using probabilistic graphical models with minimum supervision, and learning theories facilitated from stochastics analysis, differential geometry and statistical physics.

Updated: Feb 2023