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I joined MSR Cambridge initially as a Bright Minds intern, with little clue as to what to expect, and I came away with a newfound appreciation of the field, after having learned a tremendous amount about research processes, from the people and culture at MSR, and having become a better practitioner in the process. "There are many good ideas around us," co-researcher and mentor Filip Radlinski said to me during the OneWeek Hackathon. "But the key is choosing the right opportunity, and having the right people and tools to make a difference."

I am excited at the opportunity to be part of MSR again, this time for a much longer term as a resident. After graduation, I've had the chance to work in different industries, and managed to gain some insight into domain-specific challenges, for instance, in modelling risk, preventing trajectory singularities, accounting for missing data, and so on. In between work, I have been pursuing Stanford's Graduate Certificate in AI in order to keep up with the literature over the years.

The project I'm most proud of is Bounding Out-of-Sample Objects (PDF, presentation), a semi-supervised Resnet aimed at addressing the need to be able to bound novel object categories which lack sufficient annotation, much like how the quality of machine translation depends on the number of available corpus pairs. Having worked in this domain, it's really inspiring to see how far researchers have taken tasks like localization over the years (e.g. Mask R-CNN), not to mention more difficult ones like caption-to-image (ChatPainter, AttnGAN etc.)

As for my background, I'm familiar with clinical settings and bioinformatics due to my stay in medical school. At the other end of the spectrum, apparently my combination of advanced music theory and machine learning is a rare one, as remarked by co-founder of Jukedeck, Patrick Stobbs, when he aproached me about a role. On the language side of things, I am natively multilingual, but I'm especially interested in Japanese as it has language features that challenge traditional methods like word alignment during machine translation, due to omitted subjects, formality shifts, how nuance is expressed, having differences between spoken/written, male/female, encoded social status, just to name a few. I find this interesting because thinking in such a language (or a similarly foreign grammar) often means thinking with a different set of priorities that, to some extent, reflects different cultural norms, which of course, can be really difficult to translate. Last but not least, I am familiar with with the accomplishments by OpenAI, DeepMind, and MSR in the DotA, Starcraft II, and Minecraft scene, as I happen to have played all three!

Right now I'm waist-deep in Stanford's CS234 Reinforcement Learning, working my way through deep Q-learning to implement a pong-playing agent. It's tough-going sometimes, but at the same time, it is also really encouraging to think that I could be applying these methods with fellow researchers and practitioners in September, to solve engineering problems that really matter.

I look forward to a favorable response.

Yours faithfully,