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Statement of Purpose

Pengxiang Cheng

It has long been my dream to understand what intelligence is and how to make in-

telligent machines. As a senior majoring in Automation at Tsinghua University, I

have had several undergraduate research experiences that motivated me to pursue

a doctoral degree in machine learning. The choice arose from a long p eriod of in-

trinsic reflection on my interest and capability, yet the chief motivation is that I do

enjoy research, especially the sense of fulfillment from turning an imaginary idea into

implementation.

At the beginning of sophomore year, I took part in Tsinghua University Electronic

Design Contest, which concerned designing and building a small intelligent car toaccomplish tasks of chasing and evading each other. I led my team enter the final

round, and ranked top 20 among over 100 teams. However, the poor performance

of infrared and photoelectric sensors to recognize surrounding objects of the intelli-

gent cars encouraged me to investigate more efficient recognition methods involving

vision techniques. Therefore I joined a student technology team, and led a research

group on computer vision topics. Our first goal was to implement a human tracking

system with overhead cameras. Considering the performance of hardware, I decided

to use simple Hough Transform to detect human head followed by CamShift object

tracking algorithm, and eventually accomplished the task perfectly under non-dense

circumstances. This whole-year experience of independent exploration in intelligent

systems greatly enhanced my technical skills and scientific ambitions.

With a strong interest in computer vision and solid foundation in statistics and pro-

gramming, I joined Prof. Qionghai Dai’s lab in my junior year, collaborating with a

postdoc on two projects. The first project aims at building highly efficient refocusing

cameras. Previous research used programmable aperture to capture light field, caus-

ing the time cost proportional to angle resolution. We proposed to accelerate light

field capturing by reducing the number of shots and reconstructing missing views via

optical flow estimation. I implemented the algorithm after four months of repetitive

testing and modifying on real datasets, which significantly improved efficiency. Later

I moved to another project, still using programmable aperture, but focusing on image

relighting. The idea was to use coincident patterns on both multiplexing viewpoints

and directional illuminations to capture sequential images, and then applied sparse

decomposition to separate illumination components using the method of augmented

Lagrange multipliers. I designed and implemented the optimization algorithm all by

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my own, now the project is still undergoing. My efforts throughout the year greatly

deepened my understanding of machine learning, also made me realize the learning

process itself, especially the optimization technique, is what truly appeals to me.

In the summer of 2012, I obtained the opportunity of internship in the Virtual Reality

Lab at The University of Texas at Austin, under the guidance of Prof. Dana Ballard. I

conducted an independent research project that focused on mathematically modeling

muscle movements in human gaits. I proposed to use multi-channel sparse decom-

position to substitute state-of-the-art coding techniques by simultaneously learning

different muscle systems. This novel learning method significantly improved the cod-

ing efficiency and satisfied the inner muscle synergies including left-right alternation

and flexor-extensor alternation. Now we are making the final revision of the paper

and preparing to submit it to ICML 2013.

Nevertheless, what impressed me most at UT Austin was not just the research but

what Prof. Ballard’s attitude about research, “You should enjoy research, don’t give

yourself any pressure.” His words reminded me of my high school days, when I spent

more than 30 hours every week to advanced learning in mathematics and physics

without assistance on topics such as number theory, combinatorics and quantum

physics. I loved the latitude of learning and discovering intriguing areas without any

reliance, and the satisfaction from solving problems independently. I was enjoying the

latitude and satisfaction again in Austin, just as Prof. Ballard said, which confirmed

my resolution in pursuing a PhD, a long period of independent research, and a way

to professional in the area of machine learning.

The most exciting part of machine learning to me is its possibility to help us under-

stand human learning and the way brain works. Some relevant progresses have been

made, for example, the sparse representation of images with visual cortex features,

and more recently, the incredible “Google Brain” project. However, there is still much

work to be done to figure out the computational approach under intelligence, which

shall be the goal of my life. For now, I am interested in two specific topics. The first

one is using online learning to simulate human visual attention. A possible solution

is to use eye tracking equipment to record visual attention in real circumstances, and

then use this record together with visual information as auxiliary guidance for statis-tical learning, just like teaching a child to see the world. The second one is about deep

learning, which has been drawing much attention in past few years, but only a few

successful implementations exist. I am eager to explore its applications in traditional

machine learning topics.