Fourth annual UCL-PKU Strategic Partner Funds recipients

22 January 2021

Four projects received up to £10,000 each in a joint seed funding call between UCL and Peking University

In the fourth annual UCL-Peking University (PKU) Strategic Partner Funds, four projects have been awarded funding in areas including mental health and synthetic data generation.

This annual funding call supports collaborative and interdisciplinary research initiatives to UCL academics collaborating with colleagues based at PKU, as part of the deep strategic partnership between the two institutions.

One of the award's recipients, Dr Christina Carlisi (UCL Brain Sciences), said: "The UCL-PKU Strategic Partner Funds award is a fantastic opportunity for us to establish global collaborations and examine cross-cultural differences in psychology research that are often overlooked. The award presents an exciting chance to help shape the direction of clinical psychology in China, as it is still a young research field but rapidly gaining momentum."

The proposals were assessed by a selection panel chaired by UCL’s Pro-Vice-Provost for East Asia, Katharine Carruthers.

Last year, six projects were awarded funding under this call in areas such as ophthalmology, renewable energy and digital humanities.

Congratulations to all the successful 2020-21 funding recipients:

Lead UCL Applicant	Faculty	Project
Dr Christina Carlisi	Brain Sciences	Investigating crosscultural cognitive differences in emotion processing and how they contribute to the development of common mental health difficulties in relation to COVID-19.
Dr Naxi Tian	Medical Sciences	Investigating the connection between neuropathic pain and axon regeneration after peripheral nerve injury (PNI) to facilitate the discovery of novel PNI therapies and achieve better clinical benefit.
Professor Samuel Solomon	Brain Sciences	Creating a bidirectional summer internship scheme between UCL and PKU to develop teaching and research collaborations in cognitive and psychological science.
Dr Hao Ni	MAPS	Innovating state-of-the-art machine learning methods to generate realistic synthetic time series and validate the proposed methodology on electronic health records data as an example.

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