Data Champions Search

My current work combines neurobehavioral observation, fMRI and EEG in patients with Disorders of Consciousness. In particular, my research investigates which factors might predict patient performance, which technique (or combination of techniques), might be most sensitive in detecting residual conscious awareness, and which pattern of performance best classifies conscious state. 
I am also interested in the role of attentional mechanisms in conscious perception; specifically, I have been studying which temporal and spatial factors influence divided attention when stimuli in different sensory modalities compete for conscious access, leading to integration or interference. 
I strongly believe in the importance of open access and data sharing. I joined the Data Champions initiative at the University of Cambridge, aiming to increase awareness of the importance and benefits of sharing data, and to providing support within my department on how to share data through the university repository.

Additional areas of expertise:

Attentional cognitive processes, disorder of consciousness.

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.

Qi received her PhD at the University of California, Los Angeles, where she developed computational methods to study alternative splicing and the evolution of HIV. Later she joined the UCLA Division of Dermatology to help identify the resistance mechanisms of targeted cancer therapies using computational tools. 
    
In 2011 she was awarded the Humboldt Research Fellowship to work in the Division of Theoretical Bioinformatics, and later the Division of Applied Bioinformatics, at the German Cancer Research Centre. There she worked on diverse bioinformatics projects with NGS data, such as studying genome-wide epigenetic regulation of cell differentiation, developing sequencing protocols and detecting somatic mutations from whole-genome sequencing data, as well as data integration and predictive modelling. 
    
Currently, she provides bioinformatics training and support to PhD students and postdoctoral researchers in the lab of Professor Beverley Glover.

My research lies at the intersection of psychology, education, and computer science. I am interested in the philosophical underpinnings of different statistical paradigms and reproducible science. I was previously trained in Bayesian cognitive modeling at the JAGS and WinBUGS workshop (by Prof. Michael Lee and Prof. Eric-Jan Wagenmakers) and Bayesian item response modeling at the University of Twente (by Prof. Jean-Paul Fox).