Kui Tang

Machine learning aims to develop automated systems to analyze, infer from, predict, and make sense of data. It is one of the most exciting and rapidly advancing engineering fields today, and its relevance will only grow as the quantity and complexity of data, and our ambitions to make use thereof, increase without bound.

With this view, Kui develops fast, principled optimization algorithms for computationally challenging machine learning problems. He works in the Columbia Machine Learning Lab where he is advised by Prof. Tony Jebara. Previously, he has worked with Prof. Martha Kim to characterize and automatically tune the performance of massively parallel computer programs. In industry, he has worked at Hunch, a start-up which delivered recommendations using large-scale collaborative filtering algorithms.

Kui has published in top-tier journals and conferences, including Neural Information Processing Systems (NIPS) and International Symposium for Computer Architecture (ISCA). He was runner-up for the 2014 Outstanding Undergraduate Researcher by the Computer Research Association, after receiving Honorable Mention in 2013. He has been funded by multiple NSF Research Opportunity for Undergraduates (REU) fellowships.

He is currently on the Organizing Committee of the International Conference for Machine Learning (ICML) 2014 as workflow chair. He also organizes the Machine Learning Reading Group and is treasurer for the Society for Industrial and Applied Mathematics (SIAM), where he previously served as president. In addition, he was the treasurer for Beta Theta Pi and a committee member of the Association for Computing Machinery.

Kui is currently a senior and will start graduate school in Fall 2014, after which he intends to seek employment as a professor. When he needs a break from machine learning, he reads philosophy for fun, particularly Schopenhauer.

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