40 learning hours spread over 3 weeks covering live lectures, workshops, tutorials, project work and self-study time.

Project work will be done through team-based learning with supervision. Final project will be presented in groups to a panel of experts on the last day of the programme. A prize will be awarded to the team presented with the best project.

The programme will be delivered online. Online project channels will be allocated to each team for project work and tutorials. Students will be able to use the channel at any time to work on their project.

Live classes of between 1.5 to 2 hours duration will be delivered on weekdays over a three-week period. Some days will have an additional one-hour live tutorial session with a project supervisor. All classes will be delivered between 08:00 to 11:30 UK time / 16:00 to 19:30 China time.

The entire programme will be taught in English.

The winter school is directed by Prof. Yike Guo and taught by a multi-disciplinary teaching faculty from the Data Science Institute and other departments.

Yike Guo, is a Professor of Computing Science in the Department of Computing at Imperial College London. He is the founding Director of the Data Science Institute at Imperial College. 

He is a Fellow of the Royal Academy of Engineering (FREng), Member of Academia Europaea (MAE), Fellow of British Computer Society and a Trustee of The Royal Institution of Great Britain. Professor Guo received a first-class honours degree in Computing Science from Tsinghua University, China, in 1985 and received his PhD in Computational Logic from Imperial College in 1993 under the supervision of Professor John Darlington.

He founded InforSense, a software company specialized in big data analysis for life science and medicine, and served as CEO for several years before the company's merger with IDBS, a global advanced R&D software provider, in 2009.

He was then the Chief Innovation Officer of the IDBS until 2018. He also served as the Chief Technical Officer of the tranSMART foundation, a global alliance in building open source big data platform for translational medicine research.

He has been working on technology and platforms for scientific data analysis since the mid-1990s, where his research focuses on data mining, machine learning and large-scale data management. He has contributed to numerous major research projects including: the UK EPSRC platform project, Discovery Net; the Wellcome Trust-funded Biological Atlas of Insulin Resistance (BAIR); and the European Commission U-BIOPRED project. He was the Principal Investigator of the European Innovative Medicines Initiative (IMI) eTRIKS project, a €23M project building a cloud-based informatics platform, in which tranSMART is a core component for clinico-genomic medical research, and co-Investigator of Digital City Exchange, a £5.9M research programme exploring ways to digitally link utilities and services within smart cities.

Professor Guo has published over 250 articles, papers and reports. Projects he has contributed to have been internationally recognised, including winning the “Most Innovative Data Intensive Application Award” at the Supercomputing 2002 conference for Discovery Net, the Bio-IT World "Best Practices Award" for U-BIOPRED in 2014 and the "Best Open Source Software Award" from ACM SIGMM in 2017.

Students will receive an Imperial College London digital certificate on successful completion of the winter school and a prize will be awarded to the best project team. Each student will also receive a transcript for their project mark.

All students are expected to be studying an undergraduate degree in any engineering discipline, IT or computing degree at a well-recognised university in China.

English requirements:

All students are required to have a good command of English, and if it is not their first language, they will need to satisfy the College requirement as follows:

• a minimum score of IELTS (Academic Test) 6.5 overall (with no less than 6.0 in any element) or equivalent.
• TOEFL (iBT) 92 overall (minimum 20 in all elements)
• CET- 4 (China) minimum score of 550
• CET- 6 (China) minimum score of 520

Technical knowledge requirements:

As the project has a strong technical element, students are expected to have the following technical knowledge and interest:

• Interested in computer visualisation / natural language processing;
• Have at least intermediate level at one of the common programming language (Python, Java, C ++, etc.);
• Have mathematical foundation (probability theory, linear algebra, etc.);
• Have understanding of the Linux environment;
• Knowledge of Machine Learning knowledge with experience in using PyTorch / Tensorflow / Keras.

Students will need to have access to a computer pre-installed with python, have a webcam, microphone and good internet connection to attend the live classes.