Human written language can be very nuanced. This is especially true when the written text is twisted to avoid detection, which is the case when applying NLP to detect hate speeches. In order to detect hate speech with high performance, we have to apply adversarial learning techniques to find words that sounds the same as hateful ones, but spelled differently, or are scrambled and manipulated to avoid detection by typical ML algorithms, but can still be recognized by human. On the other end of the spectrum, I found human names are very well formed and we can easily detect the gender and nationality/ethnicity from names alone.

Visualization can be considered unsupervised machine learning, because the goal of visualization is to discover patterns in the data without labels, exactly the same as the goal for unsupervised machine learning. This seamless connection between machine learning and visualization means that techniques from one area can help to solve problems in another. For instance, we can use SVD and convex optimization to improve graph visualization, or use the multilevel approach successful in visualization to DeepWalk, a graph dimension reduction algorithm in machine learning. This alleviates the tendency of DeepWalk to be trapped in a local minimum on very large graphs, and improves its performance overall.

Can we understand and characterize the human notion of beauty? This is the ultimate goal of a joint project with Ohio State University (Prof. Hanwei Shn and his student XIaoqi Wang). We are using generative adversarial networks (GAN) to generate visual representations of graphs that are predicted to be aesthetically appealing to humans, and the results are very promising. Long term, I would like to create a generator that takes data as input, and automatically generates a visualization that not only reveals the intrinsic structure and anomalies of the data, but is also pleasing to look at and easy for our end users to understand.

A common task of recommender systems is to improve customer experience
through personalized recommendations based on prior implicit
feedback. These systems passively track different sorts of user
behavior, such as purchase history, watching habits and browsing
activity, in order to model user preferences. Unlike the much more
extensively researched explicit feedback, in our application we do not have any direct
input from the users regarding their preferences. In particular, we
lack substantial evidence on which products consumer dislike.
We proposed an algorithm treating the data as indication of positive and negative
preference associated with vastly varying confidence levels. This
leads to a factor model which is especially tailored for implicit
feedback recommenders. We also suggest a scalable optimization
procedure, which scales linearly with the data size.
In addition, we offer a novel
way to give explanations to recommendations given by this factor
model. A paper describing this work
is
here . The algorithm is
used successfully within a recommender system for an IPTV service for
millions of users.
Here is a visualization of
the Map
of TV based on the recommender
system.

The many endless rivers of text now available present a serious challenge in
the task of gleaning, analyzing and discovering useful information. I developed a system, TwitterScope, that
streams, stores, clusters and visualizes tweets related to any keywords of interest. This
search engine is available internally to AT&T users. A public version
which monitors a set number of topics are at http://tibesti.research.att.com/twitterscope/.
A paper is here.

Networks or graphs encapsulate complex relations in many application areas. Algorithms to analysis these networks, for example, in finding out community structures/clusters, calculating PageRank, and visualizing the networks, play an important role in discovering structures and gaining knowledge from complex data. A novel multilevel force directed algorithm with fast force calculation using octree data structure and supernode force approximation was proposed and implemented, resulted in an efficient graph drawing algorithm with a complexity of O(|V| log(|V|) + |E|). This algorithm can handle graphs of millions of nodes, as well as large tree like graphs, such as the Mathematics Genealogy Trees.

Sometimes, the conventional node-link diagram is not sufficient, not cognatively appealing, for representing information contained in the data. GMap is a technique that visualize relational data as maps. A paper is here. Some maps..

One interesting problem is node overlap removal. Often nodes contain information that need to be displayed. Traditional graph drawing algorithm ignore the node size. An efficient and effective overlap removal algorithm, PRISM, has been developed.

Figure: An illustration of the PRISM overlap removal algorithm.

Edge bundling is a technique that can help in reduce clutters of large graph visualization.

I have been doing research and
developing software in
AMG, iterative algorithms and geometric
multigrid. A parallel AMG code SAM (Scalable Algebraic
Multigrid) has been developed.

For many practical applications, such as
adaptive mesh
based calculations, the load changes during
the course of parallel computation,
making it necessary to balance the load dynamicly, and in parallel.
Most of the existing parallel
dynamic load balancing
algorithms
involve two steps: flow calculation and migration.
The traditional algorithm for flow calculation is the diffusion
algorithms. We have proved that
this type of algorithms
are "optimal" in terms of
the amount of load migration. However we have proposed
a new algorithm
which is also "optimal", but is much more efficient.

the numerical solution of partial differential equations
usually involves dividing up the
physical domain into small elements or volumes to form a mesh.
To solve the problem
on a distributed memory parallel computer,
the mesh should be decomposed into
subdomains, the number of which usually equals the number
of processors, with each subdomain assigned to a unique processor.
The static
load balancing problem
is that of how to
decompose the mesh into
subdomains so that each processor has about the same
amount of computation and so
that the communication cost between processors is minimized,
with the overall aim of
minimizing the runtime of the calculation.

Figure: mesh around a car body partitioned into 16 subdomains.

solving general nonsymmetric sparse systems in parallel efficiently
is a challenging problem. One way of achieve that is to
order the system into bordered block-diagonal form, and
factorize the diagonal blocks in parallel.
A multilevel algorithm
MONET has been develop for this purpose. Other ordering
algorithms are also being investigated, for example,
for minimizing frontal size/bandwidth
in the context of frontal/multifrontal
solvers.

Figure: A sparse matrix ordered into BBD form with two diagonal blocks.

sparse matrices ordering II

The problem of reordering a matrix to minimize its
front size has many applications. A good reordering
helps to reduce the memory usage as well as floating
point operations of a frontal solver, it also
helps when forming incomplete a LU factorization as
a preconditioner for Krylov subspace iterative algorithms.
We have proposed a multilevel algorithm for
reordering sparse symmetric matrices to
reduce the wavefront and profile. The algorithm uses a maximal
independent vertex set for coarsening and the Sloan
algorithm on the coarsest graph. It is shown to produce orderings
that are of a similar quality to those obtained using the best existing
combinatorial algorithm (the hybrid Sloan algorithm), yet
does not require any spectral information and is
significantly faster, requiring on average half the CPU time.
A paper entitled Multilevel algorithms for wavefront reduction is available for download.

Figure: mesh around the skirt of a rocket partitioned into 16 subdomains.

research and development
in solvers for CFD, particularly for finite volume/finite
element based calculations. These include FAS nonlinear
multigrid for SIMPLE based finite volume imcompressible
calculations, block correction based parallel
multigrid linear solver and AMG solvers for DNS.

Figure: agglomeration based multigrid -- mesh coarsening.

FLITE3D
is a multigrid Euler code for external aero-dynamics. It
is used extensively by British Aerospace in aircraft design and
simulation. This code has been efficiently parallelised in the
FLITE3D project.
It has been used for production since and has been proved
highly robust and efficient.

density distribution around an F18 as iterations evolve,
calculated using FLITE3D on a Cray T3E/900.

chemical processes are frequently simulated using a large
set of differential algebraic equations (DAEs). Computer simulation
of such chemical processes involves integration of DAEs over the
time interval of interest. Implicit integration schemes are
normally used for stability.
The nonlinear equations resulted from the implicit integration scheme
can be solved using Newton's method, with the
sparse Jacobian systems solved using a (direct) sparse solver,
such as the MA48 in the Harwell subroutine library.
This can be extremely computing intensive.
Increasingly, efforts are being made to utilise the vector
and parallel computers.
Effective use of parallel computers for the solution of
DAEs presents a great challenge, because
the initial value problems are intrinsically sequential, and
the components of the solution process, such as the direct
sparse solver, are not easily parallelised either.
One way of introducing parallelism into the sparse solver
is through reordering the matrices into special for
for parallel factorization. To this end we have developed
the MONET software .
Alternative approaches have also been explored, including
iterative algorithms with suitable preconditioners.
On a more coarse grain level,
parallel extrapolation method
has been investigated. It has been demonstrated that
some speedups can be achieved.