Combinatorial Scientific Computing

We focus on discrete optimization problems on large-scale graphs which are often used to accelerate the performance of scientific computing algorithms. Typical combinatorial scientific computing tasks include such problems as (hyper)graph partitioning, reordering, and coloring to improve load-balancing, task mapping, and data locality on modern high-performance computing systems.

Multiscale Methods

We are interested in fundamental modeling and computational principles underlying various multiscale methods. A broad range of scientific problems involve multiple scales. Traditional monoscale approaches have proven to be inadequate, even with the largest supercomputers, because of the prohibitively large number of variables involved. Thus, there is a growing need to develop multiscale approaches in which a hierarchy of coarse scale approximations is used to solve large-scale problems efficiently.

Machine Learning and Data Mining

Many standard machine learning and data mining algorithms are prohibitive for large-scale number of variables. For example, this can happen because of the slow convergence or NP-hardness of underlying optimization problems (such as in support vector machines and cut-based clustering). We are interested in algorithms that cope with such problems.

Hypothesis Generation and Text Mining

Hypothesis generation is becoming a crucial time-saving family of techniques which allow researchers to quickly discover implicit connections between important concepts. We are interested in such techniques and complex text mining problems, in general.

Network Science

We are interested in computational, modeling, theory and data problems related to complex networks in social/natural/information sciences, and engineering. The types of analysis often include frequent pattern discovery, outliers detection, quantitative methods for importance ranking of network elements, time-dependent data analysis, network evolution modeling, visualization, and community detection.

Your research?

We are always looking for talented graduate and undergraduate students. Please feel free to apply and suggest exciting projects.


  • Ruslan Shaydulin, Ilya Safro "Aggregative Coarsening for Multilevel Hypergraph Partitioning"

    Mar 27th, 2018|link

    Paper is accepted at International Symposium on Experimental Algorithms (SEA) 2018.

  • Justin Sybrandt, Angelo Carrabba, Alexander Herzog, Ilya Safro "Are Abstracts Enough for Hypothesis Generation?"

    Apr 12th, 2018|link

    Paper is submitted

  • H. Ushijima-Mwesigwa, MD Z. Khan, M. Chowdhury, I. Safro "Optimal Installation for Electric Vehicle Wireless Charging Lanes"

    Jan 17th, 2018|link

    Submitted paper is under revision.

  • William Hager, James Hungerford, Ilya Safro "A Multilevel Bilinear Programming Algorithm for the Vertex Separator Problem"

    Jan 17th, 2018|link

    Computational Optimization and Applications, 2018