Gates Scholar will use data skills honed in Silicon Valley to explore depression
Richard Dear, Gates Scholar, joins Trinity Hall in Michaelmas Term 2021
What are you looking forward to most about studying at Trinity Hall?
I’m looking forward to watching people from the Jerwood Library windows while immersed in reading about how their brains work! Then I’ll wander out by the river and imagine Stephen Hawking’s spirit encouraging me in the same gardens where he did his PhD. I chose Trinity Hall because I imagine it to be filled with the rarefied air of an old college, yet smaller and friendlier, a place I can come and feel comfortable and bump into friends for those spontaneous wide-ranging conversations that we’ve so missed this last year. Also, I just found out recently that my supervisor Petra Vértes also studied at Trinity Hall!
What difference does the recognition of the Gates Scholarship mean to you?
The intention of the Gates scholarship resonates with me. Bill Gates left Microsoft to work on global health, and I’ve similarly left my data science job in Silicon Valley to contribute to mental health research. My life has been incredibly privileged, so it is an honour to be supported by a scholarship specifically aimed towards improving the lives of others.
What motivated you to pursue your research topic?
In addition to the problem of mental health, psychiatric neuroscience excites me because it’s a very holistic way of thinking about the brain — we have to consider everything from neurotransmitters and neuronal circuitry, to whole brain network models, to clinical treatments. I feel we have barely scratched the surface of the brain’s mysteries and psychiatry helps me keep an open mind about what the brain research of the 21st century may look like — while also not losing sight of why this research matters.
How will the skills honed working in Silicon Valley help you shed light on the brain’s complexities?
My PhD project hopes to identify biological subtypes of depression through analyzing large, complex datasets from gene expression from several thousand post-mortem brains of psychiatric patients and a randomized trial of a new antidepressant treatment where we will also collect gene expression and neuroimaging data.
Finding meaningful patterns in these multimodal data require similar analytical skills to what I learnt as a data scientist in Silicon Valley: for example, dimension reduction and clustering to find subtypes in the expression of 20,000 genes from depression patients, followed by text analysis of patient records to understand how these patterns relate to clinical symptoms, and network analysis of neuroimaging data to relate these subtypes to patients in the randomized trial.