Writing quickly

Every blog I read eventually contains a post about i) navigating the blog, and ii) the author’s policy on writing. Consider this my attempt at the latter.

I have mentioned before that I find that writing benefits my long-term thought processes. It is meditative. I am forced to formalise my thoughts and chase-up loose ends. I have never considered myself to be good at writing – I failed English in school – but I find my confidence growing as I get older.

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The era of the solo contributor is dead

I have been reconnecting with some of my academic friends. We all belong more or less to the same age cohort. In recent weeks, I have been watching them interacting with one another on Twitter and through various other media. They each have achieved considerable degrees of success in their chosen fields – all have tenure at global top-50 ranked institutions. Through my observations, I have come to the realisation that the era of the solo contributor is dead.

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Data Science in Biomedical Industry

I am asked quite often how I see Data Science in the biomedical industry. I have, of course, many answers each of which is context dependent. However one theme which I find frequently recurring is a sort of straw-man debate which seems to inherently attract technical practitioners.

The debate is usually structured as follows:
How do you see the validation of medical AI products working in practice?
Answer: clinical trials, test-validation sets, blah, blah
But doesn’t this lead to enormous overheads?
Answer: yes, but there are shortcuts
But if you take these shortcuts then don’t you run the risk of running into costly failures when you finally run the clinical trials?
It goes on….

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Preprint Announcement – AI in Medicine Product Development Framework

Working in industrial research is usually very motivating but occasionally it is also frustrating. You’ve just done something really cool but you’re not allowed to tell anybody outside the company about it. Indeed, in a small company there might not be anybody inside of the company who can even appreciate it!

I have worked on roughly 4 really cool projects since leaving academia at the end of 2017. And apart from some basic mentions in my blog (e.g. here and here) most of what I have done has been known only to a few key stakeholders.

Since leaving Fosanis last September I have had a visiting researcher affiliation at the Digital Health Accelerator of the Berlin Institute of Health. I have used my time to mentor a cohort of teams attempting to spin out their ideas; to work on a causal inference project; and, to write a paper about the structural aspects of medical AI products. This week, along with my co-author Vince Madai, we submitted that paper.

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ML Embeddings and the Neuronal Code

I had the opportunity to talk recently with a relatively advanced researcher in machine learning methods. The conversation turned briefly to the study of embeddings when he mentioned that most of his work involves things that can be embedded in Euclidean space. Since I’ve been spending a bit of time thinking about embeddings recently, I asked him some questions to get the official ML take on the subject. I was resonably gratified to learn that – although most ML engineers don’t think much about embeddings – the research on this topic considers the embedding to be tightly bound to the network architecture. It is not possible to study abstract embeddings, divorced from applications. I fully agree with this point-of-view.

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RCTs vs Real-World Evidence for medical AI

Randomised controlled trials (RCTs) have been the gold standard for statistical evidence, of treatment effect, for over 100 years. Their strength is in their attempt to avoid major sources of bias in a comparison of the evidence. However, they are costly to run, particularly in the domain of personalised medicine, to which medical AI products typically belong.

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