SANJUKTA BHOWMICK


Assistant Professor
Department of Computer Science
University of Nebraska at Omaha
Phone: (402)-554-2081, E-mail: sbhowmick@mail.unomaha.edu

 

RESEARCH

COURSES

PUBLICATIONS

RESUME

RESEARCH


    My core research area is in high performance computing with a focus on the application of combinatorial and machine learning methods in solving sparse linear systems, large scale data analysis and bioinformatics. Current research projects include:
  • Multimethod Solvers
      Design of multimethod solvers involves the development of robust and scalable algorithms for sparse linear systems by leveraging the strength of existing linear solvers. We developed the composite multimethod solver, where the linear system is applied to a sequence of solvers, and the adaptive multimethod solver, where linear solvers are dynamically selected to match evolving linear system properties. Related Publications
      (With: Dinesh Kaushik, Lois Curfman McInnes, Boyana Norris, Padma Raghavan)
  • Machine Learning for Linear Solvers
      Linear solvers are designed to cater to different solution requirements, and their performance depend on the properties of the linear systems being solved. Matching linear solvers to problem characteristics can produce near optimal performance. However, given the wide variety of linear solvers, the algorithmic options explode combinatorially. We use machine learning techniques to select "good" solvers according to linear system characteristics. Related Publications
      (With: Victor Eijkhout ,Yoav Freund, Erika Fuentes, David Keyes, Brice Toth)
  • Automatic Differentiation
      Automatic differentiation is based on evaluating the exact derivatives of functions by using the chain rule, in order to avoid the inevitable numerical errors of Taylor's method. Function sequences in the chain rule are represented as directed acyclic graphs. Therefore, algorithms for automatic differentiation can be derived through combinatorial techniques. Our work involves the optimization of Hessian (second order derivative) calculation through detecting symmetry in the computational graph. Related Publications
      (With: Paul Hovland)
  • Applications of Graph Embedding
      Graph embedding is used to arrange vertices and edges in an aesthetically pleasing pattern on a two dimensional space. Extending this concept to placement in an n-dimensional space, we use embedding algorithms to optimize data layout in multicore architectures and in improving the accuracy of supervised and unsupervised clustering.
      Related Publications
      (With: Anirban Chaterjee, Padma Raghavan, Archana Vishwanathan)
  • Protein Mediated DNA Folding
      The propensity of DNA folding due to protein binding at different sites of the chromatin is regulated by the density of solvent molecules. Depending on the region of binding, the folding gives rise to different loop patterns such as cycles, figures of eight, etc. This project involves calculating solvent density through clustering and enumerating a set of unique designs that will enable biologists to quantitatively describe the structural modications.
      (With: Wilma Olson, Frank Pugh, Padma Raghavan)
  • Large Scale Dynamic Networks
      An important challenge in large scale dynamic networks lie in designing effcient algorithms to calculate properties of the graph. This project involves development of fast algorithms to recompute graph properties based only on the updates to the network at each time step, and techniques for comparing different large-scale networks.
      (With: Shweta Bansal, Kelly Fermoyle, Padma Raghavan)

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