Cracking Big Data with Small Data

Speaker: Amin Karbasi
Assistant Professor of Electrical Engineering and Computer Science, Yale University

Abstract: For the last several years, we have witnessed the emergence of datasets of an unprecedented scale across different scientific disciplines. The large volume of such datasets presents new computational challenges as the diverse, feature-rich, and usually high-resolution data does not allow for effective data-intensive inference. In this regard, data summarization is a compelling (and sometimes the only) approach that aims at both exploiting the richness of large-scale data and being computationally tractable; Instead of operating on complex and large data directly, carefully constructed summaries not only enable the execution of various data analytics tasks but also improve their efficiency and scalability.

A systematic way for data summarization is to turn the problem into selecting a subset of data elements optimizing a utility function that quantifies “representativeness” of the selected set. Often-times, these objective functions satisfy submodularity, an intuitive notion of diminishing returns stating that selecting any given element earlier helps more than selecting it later. Thus, many problems in data summarization require maximizing submodular set functions subject to cardinality and massive data means we have to solve these problems at scale.

In this talk, I will present an overview of our efforts in developing practical schemes for data summarization.  

Bio: Amin Karbasi is currently an assistant professor of Electrical Engineering and Computer Science. He has been the recipient of AFOSR 2018 Young Investigator Award, Grainger Award 2017 from National Academy of Engineering for interdisciplinary research, Microsoft Azure research award2017, DARPA 2016 Young Faculty Award, Simons-Berkeley fellowship 2016, Google Faculty Award 2015, and ETH fellowship 2013. His work has been recognized with a variety of paper awards, including Medical Image Computing and Computer Assisted Interventions Conference (MICCAI) 2017, International Conference on Artificial Intelligence and Statistics (AISTAT) 2015, IEEE ComSoc Data Storage 2013, International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2011, ACM SIGMETRICS 2010, and IEEE International Symposium on Information Theory (ISIT) 2010 (runner-up). His Ph.D. work received the Patrick Denantes Memorial Prize 2013 for the best doctoral thesis in the School of Computer and Communication Sciences at EPFL, Switzerland.

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