I mostly run up hills but occasionally I do other things; here is a list of some worth mentioning


Amazon Scout, 2017

Lead Machine Learning scientist Shop by look produced by amazon. Paper in progress

Adverserial PCA, 2017

Abstract: This paper studies the following question: where should an adversary place an outlier of a given magnitude in order to maximize the error of the subspace estimated by PCA? We give the exact location of this worst possible outlier, and the exact expression of the maximum possible error. Equivalently, we determine the information-theoretic bounds on how much an outlier can tilt a subspace in its direction. This in turn provides universal (worst-case) error bounds for PCA under arbitrary noisy settings. Our results also have several implications on adaptive PCA, online PCA, and rank-one updates. We illustrate our results with a subspace tracking experiment.Link

Authors: Daniel Pimentel-Alcaron, Ari Biswas, Claudia R Solis-Lemus

Hands on Introduction to Computer Science at the Freshman level, 2014.

Abstract: This paper details the creation of a hands-on introduction course that reflects the dramatic growth and diversity in computer science. Our aim was to enable students to get an end-to-end perspective on computer system design by building one. We report on a two-year exercise in using the Arduino platform to build a series of hands-on projects. We have used these projects in two course instances, and have obtained detailed student feedback, which we analyze and present in this paper. The instructions, code and videos developed are available open-source. Link

Authors: Raghuman Balasubramanian, Zachary York, Matthew Doran, Ari Biswas, Timur Girgin and Professor Karu Sankaralingam


Random Projects


Former contributer. Next is a real time distributed system for launching and analysing active learning algorithms. Link

Durian, 2016

In our quest to understand how people behave and think and to somehow quantify this understanding, we come upon the problem of modeling the relationships between objects within a person’s mind. For example, there are many aspects to how peoplethink about different fruits, but one popular comic has suggested a 2-dimensional model in which one axis represents the difficulty of preparation and the other represents tastiness (where bananas might end up at the top right, being both very easy and very tasty, while durian might be at the opposite corner). It is an interesting problem to try to experimentally learn such a model (with or without prescribed axes) for any given person or population. We present Durian, an experimental framework to allow a scientist to directly query subjects’ 2D mental models of a collection of objects using a variety of algorithms to perform the queries and to generalise the model to further data.

Link Needs clean up (code to be uploaded soon)


Set 1-6 complete. Cleanup and 7 pending. Code to be posted soon