Data Champions Search

I primarily work with large-scale language datasets, using methods from quantitative and computational social sciences, as well as the digital humanities. I also teach associated research methods, like statistics, data wrangling, and visualization. 

Outside my research, I am an active science communicator, primarily via two websites that I author: Effectiviology (https://effectiviology.com/), where I write about psychology and philosophy that have practical applications, and Solving Procrastination (https://solvingprocrastination.com/), where I write about what procrastination is, why we do it, and how to overcome it. 

As a Data Champion, I focus on the use of quantitative and computational methods for working with data (e.g., for data wrangling and statistical analyses). I also deal with associated topics, like maximizing the impact of research through open research and science communication. 

Dr. Pingfan Song is a senior research associate at the Machine Learning group in University of Cambridge. He is endorsed under the Global Talent by UK Research and Innovation (UKRI) in recognition of his critical contributions.  
   He has been working on trustworthy machine learning which aims to advance work on key technical underpinnings of interpretability/transparency, fairness, and robustness of machine learning systems, and to develop timely key applications in real world healthcare settings, such as medical imaging diagnosis systems. In addition to machine learning, he is also a professional in image processing, sparse modeling and sampling theory. His research has been applied to multi-disciplinary fields such as medical imaging, biological imaging, and other computational imaging tasks and inverse problems.  
   He is a STEM ambassador in UK and a mentor of in2scienceUK where he provides free teaching, career guidance and advice for A-level students in order to empower students from disadvantaged backgrounds to achieve their potential and progress to STEM and research careers. He is also Co-president of London PhD Network (LPN), a non-profit academic association promoting interdisciplinary communication.

I am bioinformatician who has been working in Cancer Research UK Cambridge Institute since 2017. I have a background in biology and have experience with HPC, version control, R and python, and confidential data. I work with big volumes of cancer patient data on a daily basis.

Additional areas of expertise:

High Performance Computing (HPC) - cluster.

I joined the Office of Scholarly Communication in November 2018 as Research Data Coordinator for the Research Data Management Facility. I manage the Data Champion Programme as part of my role. I have a PhD in Biological Anthropology from the University of Cambridge and was a Post-doctoral Research Associate at the McDonald Institute for Archaeological Research from 2009–2017, specialising in human evolution and Palaeolithic archaeology. 

Additional areas of expertise:

Archaeology data