Toyota Technological Institute at Chicago
Sepideh is Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC). She received her PhD in Computer Science, at the Theory of Computation Group at CSAIL, MIT, where she had Prof. Piotr Indyk as her advisor. For a year, she was a Postdoctoral Research Scientist at Simons Collaboration on Algorithms and Geometry based at Columbia University. Prior to MIT, She received her B.Sc. in Computer Engineering from Sharif University of Technology, Iran.
My research lies in theoretical aspects of big data, with a focus on algorithms for massive data problems. The recent availability of massive data sets has had a significant impact on the design of algorithms. This has led to the emergence of new computational models that capture various aspects of massive data computation. Examples include streaming algorithms (where one does not afford to store the whole input), sublinear time algorithms (where one does not afford to even read the whole input), or just algorithms for computing small summaries for large data sets. My research is centered on designing algorithms and proving lower bounds for basic computational problems in these models of computation.
More specifically, my research interests include high dimensional computational geometry, streaming algorithms, sub-linear time algorithms, sketching, metric embeddings, and graph algorithms. More broadly, I have a
deep interest in problems that are interesting from the theoretical point of view and at the same time have real world applications. For instance, searching among a large data set is a fundamental and practical task that is often formalized as the approximate
nearest neighbor problem. In a series of work, I have addressed major shortcomings of the standard algorithms for this problem, and for example, shown how we can incorporate diversity, or fairness in the results of a search query.