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<h1 class="post-title">
<span class="font-weight-bold">Ben</span> Cobb
</h1>
<p class="desc"><a href="https://cse.gatech.edu/people/benjamin-myers-cobb" target="_blank"> Graduate PhD Student </a>, <a href="https://cse.gatech.edu/" target="_blank"> School of Computational Science and Engineering </a>, <a href="https://www.gatech.edu/" target="_blank"> Georgia Institute of Technology</a></p>
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<h2 id="welcome">Welcome</h2>
<p>Hi! I am currently a PhD student at the Georgia Institute of Technology under the supervision of
<a href="https://faculty.cc.gatech.edu/~hpark/experiences.html" target="\_blank">Haesun Park</a>
and <a href="https://vuduc.org/v2/" target="\_blank">Richard Vuduc</a>. Previous to attending Georgia Tech, I attended Wake Forest University, where I received my Bachelor of Sciences’ in Computer Science and Mathematical Business. To get in contact with me, feel free to send me an email using the link below.</p>
<h2 id="research">Research</h2>
<p> My current research focuses on developing distributed Non-negative Matrix Factorization (<a
href="https://en.wikipedia.org/wiki/Non-negative_matrix_factorization" target="\_blank">NMF</a>)
algorithms within the Parallel Low-rank Approximations with Non-negativity Constraints (<a
href="https://github.com/ramkikannan/planc" target="\_blank">PLANC</a>) framework for use in
large-scale datasets. This work is in close collaboration with Oak Ridge National Lab (<a
href="https://www.ornl.gov/publication/planc-parallel-low-rank-approximation-nonnegativity-constraints"
target="\_blank">ORNL</a>). </p>
<p>Previous to this, I worked on the <a href="https://gitlab.com/tensors/genten"
target="\_blank">GenTen</a> project in collaboration with Sandia National Laboratories (<a
href="https://www.sandia.gov/" target="\_blank">SNL</a>) as part of the Department of Energy's (<a
href="https://www.energy.gov/" target="\_blank">DOE</a>) larger <a
href="https://www.exascaleproject.org/" target="\_blank">Exascale Computing project</a>. On this
project I worked to optimize portable dense tensor kernels based upon the <a
href="https://github.com/kokkos" target="\_blank">Kokkos</a> programming model for use in
compressing large combustion simulation datasets. As part of this, I developed a novel in-place
variant of the Sequentially Truncated Singular Value Decomposition (<a
href="https://people.cs.kuleuven.be/~nick.vannieuwenhoven/papers/01-STHOSVD.pdf"
target="\_blank">ST-HOSVD</a>) for computing the <a
href="https://en.wikipedia.org/wiki/Higher-order_singular_value_decomposition"
target="\_blank">Tucker Decomposition</a>. The resulting algorithm, referred to as the Fused
In-place Sequentially Truncated Singular Value Decomposition (FIST-HOSVD), was shown to reduce the
memory consumption of computing the Tucker Decomposition by ~135x with comparable runtime
performance compared to the current state-of-the-art ST-HOSVD implementation within the <a
href="https://gitlab.com/tensors/TuckerMPI" target="\_blank">TuckerMPI </a> framework. </p>
<p>
My undergraduate research primarily focused applying wedge-sampling heuristics to speeding up the
coarsening phase hypergraph partitioners and applying bipartite matching algorithms to computing
least-squares cosine differences of ktensors. This work was performed under the supervision of my
undergraduate advisor and mentor, <a href="http://users.wfu.edu/ballard/index.html"
target="\_blank">Grey Ballard</a>. </p>
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