About

Rob Johnson received his Ph.D. in Computer Science from University of California, Berkeley. He performs research on software security, system and network security, cryptography, digital rights management, operating systems, networks, and algorithm design and analysis. He is a Research Assistant Professor at the Department of Computer Science at Stony Brook University, where he has been recognized with awards including the 2012 Department Award for Graduate Teaching and Research and undergraduate education in 2010.

Research topics

• Computer Science
• Artificial Intelligence
• Nuclear physics
• Particle physics
• Physics
• Database
• Combinatorics
• Theoretical computer science
• Mathematics

Selected publications

• Search for heavy neutral leptons decaying into muon-pion pairs in the MicroBooNE detector

  Physical review. D/Physical review. D. · 2020 · 51 citations

  • Physics
  • Nuclear physics
  • Particle physics

  We present upper limits on the production of heavy neutral leptons (HNLs) decaying to $\ensuremath{\mu}\ensuremath{\pi}$ pairs using data collected with the MicroBooNE liquid-argon time projection chamber (TPC) operating at Fermilab. This search is the first of its kind performed in a liquid-argon TPC. We use data collected in 2017 and 2018 corresponding to an exposure of $2.0\ifmmode\times\else\texttimes\fi{}{10}^{20}$ protons on target from the Fermilab Booster Neutrino Beam, which produces mainly muon neutrinos with an average energy of $\ensuremath{\approx}800\text{ }\text{ }\mathrm{MeV}$. HNLs with higher mass are expected to have a longer time of flight to the liquid-argon TPC than Standard Model neutrinos. The data are therefore recorded with a dedicated trigger configured to detect HNL decays that occur after the neutrino spill reaches the detector. We set upper limits at the 90% confidence level on the element $|{U}_{\ensuremath{\mu}4}{|}^{2}$ of the extended PMNS mixing matrix in the range $|{U}_{\ensuremath{\mu}4}{|}^{2}<(6.6--0.9)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}7}$ for Dirac HNLs and $|{U}_{\ensuremath{\mu}4}{|}^{2}<(4.7--0.7)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}7}$ for Majorana HNLs, assuming HNL masses between 260 and 385 MeV and $|{U}_{e4}{|}^{2}=|{U}_{\ensuremath{\tau}4}{|}^{2}=0$.

  DOI

• An Efficient, Scalable, and Exact Representation of High-Dimensional Color Information Enabled Using de Bruijn Graph Search

  Journal of Computational Biology · 2020 · 41 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  The colored de Bruijn graph (cdbg) and its variants have become an important combinatorial structure used in numerous areas in genomics, such as population-level variation detection in metagenomic samples, large-scale sequence search, and cdbg-based reference sequence indices. As samples or genomes are added to the cdbg, the color information comes to dominate the space required to represent this data structure. In this article, we show how to represent the color information efficiently by adopting a hierarchical encoding that exploits correlations among color classes-patterns of color occurrence-present in the de Bruijn graph (dbg). A major challenge in deriving an efficient encoding of the color information that takes advantage of such correlations is determining which color classes are close to each other in the high-dimensional space of possible color patterns. We demonstrate that the dbg itself can be used as an efficient mechanism to search for approximate nearest neighbors in this space. While our approach reduces the encoding size of the color information even for relatively small cdbgs (hundreds of experiments), the gains are particularly consequential as the number of potential colors (i.e., samples or references) grows into thousands. We apply this encoding in the context of two different applications; the implicit cdbg used for a large-scale sequence search index, Mantis, as well as the encoding of color information used in population-level variation detection by tools such as Vari and Rainbowfish. Our results show significant improvements in the overall size and scalability of representation of the color information. In our experiment on 10,000 samples, we achieved >11 × better compression compared to Ramen, Ramen, Rao (RRR).

  PublisherOA PDFDOI

Recent grants

• CT: Authenticating Reality

  NSF · $350k · 2006–2010

• Neutrino Physics at Fermilab

  NSF · $330k · 2004–2011

Frequent coauthors

• Andrea Chiarelli

  33 shared

• D. Naples

  University of Pittsburgh

  29 shared

• G. P. Zeller

  21 shared

• W. Van De Pontseele

  Massachusetts Institute of Technology

  20 shared

• M. Söderberg

  Syracuse University

  20 shared

• V. Wu

  China Pharmaceutical University

  20 shared

• A. M. Szelc

  20 shared

• W. C. Louis

  Los Alamos National Laboratory

  20 shared

Education

• MSc Management and Leadership, Business School

  Loughborough University

  2013

• BA (Hons) English Studies, English

  University of Nottingham

  2001

Awards & honors

• 2012 Department Award winner for Graduate Teaching and Resea…

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