ABCP Online Research Training Summer School 2020-Computer Science
Date: 2020-07-20 Course duration: 8 weeks

I.  Programme Description

The ABCP (Association of British Chinese Professors)Online Research Training Summer School (ABCP-ORTSS) programme will allow UG students from overseas universities to be trained by UK academics on how to conduct research. 

The programme will make participants more prepared for future applications for a postgraduate research degree (i.e., a PhD or a research master’s degree) or their future research career in general (e.g., doing research in a company or a research institute).

The programme will see students working on cutting-edge research projects within a small group over the summer for 8 weeks, supervised by a senior academic who has experience in successfully supervising PhD students at a UK university. 

Research training seminars will be arranged about various aspects of doing research in an international scientific environment. Students will gain hands-on experiences by working on a research project with their supervisor and also be supported by a peer researcher (PhD or postdoctoral researcher). 

At the end of the programme, students will be assessed by their supervisor and be awarded a certificate issued by the ABCP.

Students will also have access to the following benefits after the end of the programme:

  • the opportunity to get a recommendation letter from their supervisor;

  • the opportunity to continue their research collaboration with their supervisor, eventually converting their work into (part of) a co-authored research paper;

  • the opportunity to pursue a PhD degree in the UK with their supervisor or other academics working at a UK university (e.g., via applying for PhD studentships made possible by the China Scholarship Council and/or UK universities, whose deadlines are normally in winter or spring).

II. Project Introduction

Project A: Can AI replace clinical doctors? Can AI help us to find treatment for Covid-19 or other disease? Exploring AI for clinical data analysis

We have seen an AI boost for clinical trials, to name a few: predictive models can be built with Deep Neural Network, Markov Models and etc. It is hypothesised that big data and artificial intelligence could help to accelerate clinical workflow. An interesting reading from Nature article by Marcus Woo can be found via this link:


In order to answer these questions, we will explore this topic and use data collected from a real clinical environment. It broadly covers a variety of data processing and machine learning methods (including AI) used in clinical settings. As part of the project, students will be expected to:

  • Familiarise themselves with the pipeline from formulating the research question, data curation, data pre-processing up to complicated machine learning models and model evaluation.

  • Familiarise themselves with some relevant existing work in statistics, AI and clinical data analysis.

  • Design, implement, test and evaluate models using appropriate methods.

Project B: Wearable devices, Human Machine Interaction and AI

Wearable sensors are becoming more and more popular and they are developed to measure heart rate, breathing frequency, brain and muscle signals. These can be used to control robots, just as you have seen in the film (The Matrix). For example, Myoelectric prostheses allow users to control a robotic device。 


“AI-powered shoes”

In order to understand more about this topic, we will use data collected from a real world environment and explore a variety of Deep Neural Network models. Students will be expected to:

  • Familiarise themselves with wearable devices (such as muscle signals, ink-jet printed smart insoles) and models to understand the human physical state (i.e. hand gestures, gait patterns, and fatigue). 

  • Design, implement, test and evaluate models using appropriate methods.

III. Supervisor

Dr Caroline Li

Deputy Director, Institute for Creative and Cultural Industries (ICCI) Senior Lecturer (Associate Professor) & Director of Internationalisation;

School of Computing Medway Campus,University of Kent

Bio: Dr Li has been leading the multidisciplinary BC2 Lab focusing on understanding human health and wellbeing, developing advanced data analytics methods for domain-specific applications. She worked under large scale project including the £6 million EPSRC project “ESPRIT with Pervasive Sensing”. She also works closely with industry and organisations to deliver research impact, including winning of Samsung GRO Award, the DASA funding awards, EIRA funding supported by Research England, Charity funded project (i.e. the £1.3 million project funded by the LifeArc). She is actively developing cutting-edge technologies in how human interact with the culture and creative domain. She won the British Council “Showcase Your Innovation” bid with the project of the brain composed modern art, Best Paper Award at the Flagship conference ICED 19 (International Conference on Engineering Design). Prior to joining the University of Kent, she gained her PhD and research experiences at the Dept of EEE and Dept of Computing at Imperial College London. She also worked at an investment bank (Goldman Sachs) and as a Manager at Forwessun Ltd. and set up the factory in Shanghai as the Asian Headquarter. She now serves at the editorial board of Brain Informatics and the secretary of IEEE Computing Society in UK and Ireland.


UG students who are currently studying an UG course in Year 2 or above  are eligible to apply.

Project A: Good programming skills required. Knowledge in Python is a bonus. Ability to engage with clinical research preferred.

Project B: Good programming skills required. Knowledge in Python is a bonus. Interest in wearable devices preferred.

Applicants will be considered by the academic supervisor and an interview will be organised to decide if the application will be made an offer. The maximum vacancies for each project is 4-5 students.

V. How to Apply

In 2020, the programme is expected to last for 8 weeks, starting on Monday 20th July 2020 and end on Friday 11th September 2020.

The deadline for applications is 1st July 2020.

Interested students in China should contact the following person on how to apply to participate:

•   Pei (Patch) Huang : 

This course includes
8 weeks of quality courses