Research Snippets
Algebraic
Topology 
Optimal
homologous cycles, total unimodularity, and
linear programming (SIAM
J. Computing, 2011, 40(4), 26–40.
Talk in
the Theory
Seminar
at Brown U.



Non
totalunimodularity neutralized simplicial
complexes (Discrete
Applied Mathematics, 240, 11, 2018,
44–62).
Talk
in ISMP 2015.

Synopsis: We define a condition
under which every optimal
homologous chain problem (OHCP) linear
program (LP) has an integer optimal
solution even when the simplicial complex
is not relativetorsion free (RTF),
and hence its boundary matrix
is not totally unimodular (TU). For
2complexes, this condition intuitively
means that every Möbius subcomplex
has a "shortcut that neutralizes" the
nonTU subcomplex.




Geometric Measure Theory 
Flat norm
decomposition of integral currents
(Journal
of Computational Geometry, 7, 1, 2016,
285–307).
Talk
at Simon
Fraser U.



The Maximum Distance Problem and Minimal Spanning Trees
(Intl. J. Analysis and Applications, 2021, 19, 5, 633–659).
Talk
at JMM2021.
Synopsis: We study a fat
generalization of the classical TSP where
we seek a shortest 1D curve (or a 1D set,
more generally) which when fattened by a
radius \(s\) covers a given 2D set. Given
a compact \(E \subset \mathbb{R}^n\) and
\(s>0\), the maximum distance problem
(MDP) seeks a compact and connected subset
of \(\mathbb{R}^n\) of smallest one
dimensional Hausdorff measure whose
\(s\)neighborhood covers \(E\). For \(E
\subset \mathbb{R}^2\), we prove that
minimizing over minimum spanning trees
that connect the centers of balls of
radius \(s\), which cover \(E\), solves
the MDP.




Mathematical Aspects of 3D Printing 
Continuous
Toolpath Planning in a Graphical Framework
for Sparse Infill Additive Manufacturing
(ComputerAided
Design, 127, Oct 2020,
102880).
Talk at SPM 2020.
Synopsis: We develop a new method
to generate continuous toolpaths with no
crossovers in sparse
infill 3D
printing. We define and use
an Euler Transformation of general
polyhedral complexes, which guarantees
that every vertex has an even degree in
the graph of the complex. Hence
an Eulerian
tour that covers every edge exactly
once is guaranteed to exist. Our framework
handles most domains with complex
geometry/topology in layerbylayer
printing. We validate our method on simple
and complex print domains.




SFCDecomp:
Multicriteria optimized tool path planning using
spacefilling curves
(IJCGA,
31, 04, 2021, 193–220).
Talk
at SIAM
DM22.
Synopsis: We explore efficient
optimization of toolpaths based on
multiple criteria for large instances of
dense infill 3D
printing prolems. We develop
SFCDecomp, a space filling curve
based decomposition framework to solve
large instances of 3D printing problems
efficiently by solving these optimization
subproblems independently. Strength
testing of a tensile test specimen printed
with tool paths that maximize or minimize
adjacent layer edge overlaps reveal
significant differences in tensile
strength between the two classes of
prints.




Lattice Problems
and Integer
Optimization 
Column
basis reduction, and decomposable knapsack
problems (Discrete
Optimization, 2009, 6(3), 242–270).
Instances. Talk.
Synopsis: We propose a very simple
preconditioning method called rangespace
reformulation for integer programming
feasibility problems: replacing the
problem \(\{ \mathbf{b}' \leq A \mathbf{x}
\leq \mathbf{b}, \mathbf{x} \in
\mathbb{Z}^n \}\,\) with \(\,\{\mathbf{b}'
\leq AU \mathbf{y} \leq \mathbf{b},
\mathbf{y} \in \mathbb{Z}^n\}\), where
\(U\) is a unimodular matrix computed via
basis reduction, to make the columns of
\(AU\) short and nearly orthogonal. We
also study a family of IP instances called
decomposable knapsack problems (DKPs),
which are knapsack problems with a
constraint vector of the form
\(\mathbf{p}M + \mathbf{r}\) with \(M \gg
0\). We prove (i) hardness results for
branchandbound branching on individual
variables of the DKPs; (ii) that they are
easy, if one branches on the constraint
\(\mathbf{p}^T\mathbf{x}\) instead; and
(iii) that branching on the last few
variables in the rangespace reformulation
is equivalent to branching on
\(\mathbf{p}^T\mathbf{x}\).




Latticebased
Algorithms for Number Partitioning in the Hard
Phase (Discrete
Optimization, 2012, 9(3), 159–171).
Talk
at
the AMS
Spring Western Section Meeting, 2009.
Synopsis: The number partitioning
problem (NPP) is to divide
\(\{a_1,...,a_n\}\) into two disjoint
subsets such that the difference between
the two subset sums  the discrepancy,
\(\Delta\), is minimized. With \(a_j\)s
chosen uniformly from \([1,R]\), \(R >
2^n\) gives the hard phase, when there are
no equal partitions with high
probability. We reduce NPP in the hard
phase to the closest vector problem
(CVP). We can solve the original problems
by making polynomial numbers of calls to a
CVP oracle. In practice, we implement a
heuristic which applies basis reduction
(BR) to several CVP instances. We also
propose a truncated NPP algorithm, which
finds approximate minimum discrepancies
for instances on which the BR approach is
not effective.




Computational
Biology 
Fourbody
scoring function for mutagenesis
(Bioinformatics,
2007, 23(22), 3009–3015).
Overview
colloquium
at Portland
State U. Mathematics.
Synopsis: We develop a Delaunay
tessellationbased fourbody scoring function to
predict the effects of single and multiple residue
mutations on the stability and reactivity of
proteins. We also develop an efficient method for
screening huge numbers of mutants of a protein, called
combinatorial mutagenesis. In one study, 64 million
mutants of the protein 1CSQ, with six of its residues
being changed to all possible (20) amino acids, were
screened within a few hours on a PC, and all five
stabilizing mutants reported were correctly identified
as stabilizing by combinatorial mutagenesis. 



Topological
features in cancer gene expression data
(in PSB 2015, 20,
108–119).
Talk
in SIAM
AG 2017.
Synopsis: We develop a new method
using topological
persistence to analyze cancer gene
expression datasets. We circumvent the
problem of high dimensionality by
dualizing the data, i.e., by
studying genes as points in the sample
space. We identify a small relevant subset
from tens of thousands of genes while
simultaneously identifying nontrivial
higher order topological features, i.e.,
holes, in the data.




Biomedical
Applications 
Neck
muscle paths and moment arms are significantly
affected by wrapping surface parameters
(Comp. Met. Biomech. Biomed. Engg,
2012, 15(7), 735–744).
Synopsis: We studied the effects of
wrapping surfaces on muscle paths and
moment arms of the neck muscle,
semispinalis capitis. Sensitivities to
wrapping surface size and the kinematic
linkage to vertebral segments were
evaluated. Kinematic linkage, but not
radius, significantly affected the
accuracy of model muscle paths compared to
centroid paths from images. Both radius
and linkage affected the moment arm
significantly. This study highlights the
sensitivity of moment arms to wrapping
surface parameters and the importance of
including multiple postures when
evaluating muscle paths and moment
arm.




A visual
analytics framework for analysis of patient
trajectories (In ACM BCB, 2019).
Synopsis: A new visual analytics
approach to analyze hospital patient
trajectories in a scalable manner. We
view the problem as one of structure
discovery and tracking how structure
evolves with time over the course of
patients' stay at the hospital(s). Our
approach helps to delineate subpopulations
(i.e., subgroups of patients) that show
divergent behavior. Initial results on a
large data set from the Duke Antimicrobial
Stewardship Outreach Network
(DASON)
database.




Publications (in PDF)
CV
Events (Conferences, Workshops,
etc.)
SIAM
Conference on Mathematics of Data Science (MDS22),
San Diego, Sep 26–30, 2022.
SIAM
Conference on Discrete Mathematics (DM22), CMU, Jun
14–16, 2022.
SIAM PNW
21, WSU Vancouver, May 20–22, 2022.
JMM
2022, Online, Apr 6–9, 2022.
IMSI
Workshop on The Mathematics of Soft Matter, Online,
Feb 28–Mar 4, 2022.
Computational
Persistence Workshop, Online, Nov 1–5,
2021.
JMM
2021, Online, Jan 2021.
SPM 2020, Online, June 2020.
WCOM Fall 2019, UBC, Vancouver, Sep 28, 2019.
CG Week and
SoCG, Portland, OR, June 2019.
Optimization
Methods in Vision and Image Processing,
ICERM, Apr 29–May 3, 2019.
CGWeek
and SoCG, Budapest, Hungary, June 2018.
Joint
Mathematics Meetings, 2018, San Diego, CA.
SIAM PNW
Biennial Meeting, Corvallis, OR, Oct 2729,
2017.
SIAM
Conference on Applied Algebraic Geometry, Atlanta,
Jul 31Aug 4, 2017.
AMS
Spring Western Sectional Meeting (Special
sessions: 1, 2, 3),
Pullman, Apr 2223, 2017.
Workshop
on Topological Data Analysis in
Biomedicine in ACMBCB 2016.
Joint
Mathematics Meetings (Special
sessions 1
and 2),
Seattle, Jan 69, 2016.
Workshop on Abstract
Algebra and Algebraic Topology in
Biomedicine in PSB
2016, Hawaii, Jan 4, 2016.
Mathematics
in Data Science, ICERM, Jul 2830, 2015.
International
Symposium on Mathematical programming (ISMP),
July 1217, 2015.
Topology
and Geometry of Networks and Discrete Metric
Spaces, IMA, Apr 28May 2, 2014.
Topological
Systems: Communication, Sensing, and Actuation,
IMA, Mar 37, 2014.
Topological
Structures in Computational Biology, IMA, Dec
913, 2013.
Modern
Applications of Homology and Cohomology, IMA, Oct
28Nov 1, 2013.
Topological
Data Analysis, IMA, Oct 711, 2013.
Modern
Trends in Optimization Reunion II, IPAM, June
2013.
ASCR/BES
Data Workshop, Bethesda, MD, Oct 2425, 2011.
Applied
Mathematics and Image Processing Summer Workshop
at UTPA, May 31Jun 1, 2011.
Modern
Trends in Optimization and Its Application at
IPAM, Sep 13Dec 17, 2010.
STOC
2010
IMA
short course on Applied Algebraic Topology, June
1526, 2009.
AMS
Spring Western Section Meeting 09
