**Anindya De.**** **Prof. De’s research interests lie in computational complexity theory, analysis of Boolean functions and the interplay of these two areas with theoretical computer science. In analysis of Boolean functions, his main interest lies in the interaction of probability theory and Fourier analysis towards structural understanding of specific classes of Boolean functions (like halfspaces) and how such structural information can be helpful in the design of algorithms. In complexity theory, his main interest is in the area of pseudorandomness which seeks to understand the power of randomness in computation.
**Jason Hartline. ** Prof. Hartline’s research introduces design and analysis methodologies from computer science to understand and improve outcomes of economic systems. Optimal behavior and outcomes in complex environments are complex and, therefore, should not be expected; instead, the theory of approximation can show that simple and natural behaviors are approximately optimal in complex environments. This approach is applied to auction theory and mechanism design in his graduate textbook Mechanism Design and Approximation which is under preparation.

**Ming-Yang Kao.**

Prof. Kao studies the design, analysis and implementation of algorithms. His work spans a broad range of applications including bioinformatics, computational finance, electronic commerce, and nanotechnology. Kao’s most recent research includes work on DNA self-assembly, variants of the traveling salesman problem, and graph labeling problems. Kao heads the EECS Computing, Algorithms & Applications Division and is the editor-in-chief of Algorithmica.

** Konstantin Makarychev. **Prof. Makarychev is an Associate Professor of Computer Science at Northwestern University. He is interested in designing efficient algorithms for computationally hard problems, and introducing new core techniques and design general principles for developing and analyzing algorithms that work in theory and practice. His research interests include approximation algorithms, beyond worst-case analysis, and applications of high-dimension geometry in computer science.

**Aravindan Vijayaraghavan.**** ** Prof. Vijayaraghavan’s research interests are broadly in the field of Theoretical Computer Science, particularly, in designing efficient algorithms for computationally hard problems in Combinatorial Optimization and in Machine Learning. His research often uses paradigms that go Beyond Worst-Case Analysis like average-case analysis, instance stability and smoothed analysis to obtain much better algorithmic guarantees than we can obtain in the worst-case.
**Affiliated Faculty:**

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