Democratizing Research; Stanford Scholars Program

With break, exams and the like keeping me from putting a new post on the blog, I’ve been trying for a few days to get this awesome find down on paper.

If you’re anything like me, you find enjoyment doing research. Even though it might not be the best career path for other industry minded engineers like myself, it offers you the sometimes undeserved opportunity to get into a cutting edge project, and if you’re lucky, play around with really cool technology while learning from some of the best minds in the world. I really accredit research in my undergraduate career for giving me that extra boost to want to learn more, and better myself as a programmer. I also accredit the work I’ve done in research to broaden my view on programming, and has made me see my craft as a truly ubiquitous, incredible skill that can be applied in nearly any field or project imaginable.

In research, I have worked at MGH as a metabolic engineer, where my role was to contextualize metabolite data from liver transfusions to attempt to recreate a human metabolome to allocate where the metabolome was changing as a result of various perfusion techniques (liver viability). This gig got me into my current lab, which is a bioinformatics lab working on protein-protein docking prediction, where I am currently a member of the software team working on PIPER, which is the computational engine behind these protein docking predictions.

If you’re like most people, you’re probably thinking…what are you talking about Josh? This is precisely the reason research frustrates me. You perform all of this incredible work only to find out that a handful of people in the world understand it, and only a handful of that handful can apply it.

That is why I’, here today to write to all of you about the Stanford Scholars program, which is a great initiative I recently joined. Their motivation is to make research accessible to almost anyone, and as a member of the Machine Learning and Data Mining team, I am currently working on creating a talk on an Asynchronous Gibbs Sampling paper (post to come later). This initiative is great in the sense that all of the grueling time spent trying to understand the paper is done for you; you skip the jargon, get the crux of the meaning and get a sizable understanding of the work done.

Such an initiative is essential to merging current entrepreneurs and industry workers with the great work done in academia, and is an initiative I hope to further contribute. Check out the link for more info. and be sure to apply if you have an interest and share the same frustration I had before I joined:

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