When I was a child, I was fascinated by the detectives in novels and movies:
intelligently finding important clues, recovering the truth, and at the same time help
people in need. After growing up, I realized that they are only in stories. However, I
suddenly found that I feel like being a detective when I face math or algorithm design
problems. I discover the intrinsic structure of the problems, grab the key points, and
then manipulate them to design suitable solutions or algorithms; whether successful
or not, I always enjoy the process. This enthusiasm triggered me to join many
problem-solving competitions and dedicate myself in many research projects. In my
undergraduate and master's research I focused on machine learning
and optimization projects. Being a IOI(International Olympiad in Informatics)
participant and double majoring in CS and Mathematics, I acquired the outstanding
ability to perform high quality research, and papers published in ICML, KDD, ACL and
JMLR proves my ability. The experiences of developing training methods and
conversing with other researchers in conferences are really enjoyable for me.
Nevertheless, to help more people in the world, I want to conquer more important
problems in a more active environment. Thus I decided to apply the PhD program at
University of Texas at Austin.
My studies in Bachelor's and Master's degree mainly focused on optimization
methods for linear support vector machines (SVMs). My colleagues and I successfully
applied coordinate descent methods to solve the primal and dual form of SVM. The
results are published in JMLR 2008, ICML 2008, and KDD 2008. We further embedded
these methods into liblinear, which has become a widely used linear SVM software.
Realizing that training speed is important for SVM, we also released the code which
uses OPENMP, a multi-core parallel computing library, to speed up libsvm and
liblinear. Additionally, I have also tried new methods to parallel the training of
nonlinear-SVM with hundreds of computers during an internship in Google. We
further applied coordinate descent methods to train L1-regularized problems
(submitted to JMLR) and Maximum Entropy Model (ACL 2009). In addition to new
training algorithms, we proposed frameworks to discuss previous methods for these
problems. This gave me deep knowledge for various optimization methods. Based on
these results and experiences in machine learning/data mining, we won the first
prize for SVM-track at the ICML 2008 PASCAL workshop, and third prize for slow-track
at KDDCUP-2009.
My math background gave me strong ability in developing new training algorithms
and proving their convergence rates, and programming skills helped me to do the
experiments in a fast and systematic way. Each time I conquered a key point of a
proof or developed a trick to reduce the training time, I was so excited that I could
not stopped myself from doing more research. With these abilities, I also have many
inspirations for other problems. For example, after a meeting introducing ``learning
to rank'' problem, I wrote formulas for the task and suddenly found it could be
approximately reduced to the classical SVM formula. Implementation was done in
one day, and the performance was competitive with state-of-the-art methods. After
that I further extended it to reduce multi-label problems to binary classifications. At
KDD, I discussed these reductions with other students, and they told me about some
related papers. I am really happy to discuss with people from different regions, and
this has become one of my motivation to study abroad.
Besides my abilities with optimization methods, I also have plenty of knowledge at
the application level. Experiences of maintaining LIBLINEAR and LIBSVM taught me
how SVM is used in applications, and how to find research topics from users'
requirements. In college, my friend and I built a web photo ranking system called
``Hotter or Notter''(http://hotter.csie.org). With more than 3 million pair-comparing
votes, we use the Bradley-Terry model to generate a global ranking. From this
experience I learned how to manage a large web site. I also have a great deal of
experiences in NLP software. We embedded our training method for maximum
entropy model into opennlp, a well-known NLP package. Besides, during our
internship at Google, we noticed that ``dependency parsing'' is another important
NLP task. MaltParser, one of the most famous parsing software, uses SVM as the core
classification method. Based on the experience using SVM, we transformed the
polynomial kernel to a linear kernel with spanned features. With LIBLINEAR, the
transformation significantly improved the training/testing speed.
The curiosity to facing new problems forms my diverse ability in many different areas,
which may be useful in future investigations. Before college I already read some
advanced physic books and got top 24 in the process of selecting Taiwan's participant
to International Olympiad in Physics. In college I also choose courses in various topics,
including robotics, bioinformatics, graphics, and financial algorithms. It's always not
easy at first when I put myself into another area, but after becoming familiar with it, I
usually can further apply my machine learning knowledge into these areas. For
example, in a bioinformatics course, I built a classifier to predict
protein-phosphorylation (one of protein-protein interactions) which achieved 92%
accuracy by gene ontology and microarray data. In one robotics course, I applied
object detection methods based on machine learning to detect bricks in photos and
automatically construct the map by robot. This earned me the second prize of the
final competition in the class. Moreover, during the research assistantship in
Academia Sinica for biology, I use my knowledge in computer science to analysis
biology data in an efficient way. And working as a research assistant in Mathematics
department in Academia Sinica, I not only theoretically studied the behavior of chaos
systems but also wrote programs to simulate them. These experiences show my
potential in using my knowledge in machine learning to conduct interdisciplinary
researches.
In the future, I want to focus on large-scale statistics learning methods and its
applications. I believe with suitable models and efficient optimization algorithms, we
can exploit the information in large data to improve people's lives. For this purpose, I
am interested in the machine learning and data mining groups in University of
Texas at Austin. Moreover, I am also interested in applying machine learning methods
to natural language processing and bio-informatic research in University of Texas at
Austin. With my enthusiasm for thinking and rich experiences, I believe I can make
contributions in the future.