Research Interests
Design, analysis and implemenation of algorithms and
data structures;
Computational Geometry, Graph Theory.
I am a Ph.D. Candidate in the
UCI Center for Algorithms and Theory of Computation
I am co-advised by
David Eppstein
and
Michael T. Goodrich
| Email: |
jsimons at uci dot edu |
| Office: |
DBH 4219 |
Bibliography on
DBLP.
Preprints on arXiv.
We show that a transitively reduced digraph has a confluent upward drawing if
and only if its reachability relation has order dimension at most two. In this
case, we construct a confluent upward drawing with $O(n^2)$ features, in an
$O(n) \times O(n)$ grid in $O(n^2)$ time. For the digraphs representing series-parallel partial orders we show how to construct a drawing with $O(n)$ features in an $O(n) \times O(n)$ grid in $O(n)$ time from a series-parallel decomposition of the partial order. Our drawings are optimal in the number of confluent junctions they use.
We describe fully retroactive dynamic data structures
for approximate range reporting and approximate nearest
neighbor reporting. We show how to maintain, for any
positive constant $d$, a set of $n$ points in $\mathbb{R}^d$
indexed by time such that we can perform insertions or
deletions at any point in the timeline in $O(\log n)$
amortized time. We support, for any small constant
$\epsilon>0$, $(1+\epsilon)$-approximate range
reporting queries at any point in the timeline in
$O(\log n + k)$ time, where $k$ is the output size. We
also show how to answer $(1+\epsilon)$-approximate
nearest neighbor queries for any point in the past or
present in $O(\log n)$ time.
We show that several problems of compacting orthogonal graph drawings to use the minimum number of rows, area, length of longest edge or total edge length cannot be approximated to within better than a polynomial factor in polynomial time unless it's the case that $\mathsf{P} = \mathsf{NP}$. We also provide a fixed-parameter-tractable algorithm for testing whether a drawing can be compacted to a small number of rows.
We give a new efficient data-oblivious PRAM simulation and several new data-oblivious
graph-drawing algorithms with application to privacy-preserving graph-drawing in a cloud
computing context.