EE the field of wireless networks and mobile


EE is an innovative and exciting field with unlimited possibilities. When I was successfully
awarded the first prize out of 23 finalists all over the world in 2017 IEEE ComSoc Student
Competition: “Communications Technology Changing the World”, I felt immense happiness
and satisfaction. Suddenly, I recalled my rewarding journey into the field of wireless networks
and mobile computing. This rewarding journey started with my success in the classroom and
landed with three research projects at Peking University. These experiences reinforced my
determination to pursue an EE Ph.D. degree at Yale.
Why should Yale choose me?
My excellent academic performance has provided a cornerstone for my research. After
enrolling in the EECS department at Peking University, which provided the best possible
education in China, I had always strived for the best. This determination resulted in the
satisfaction of obtaining the 4th highest overall GPA and during junior year, 1
st overall GPA out
of 53 students. Besides obtaining high scores in all my EECS and mathematics courses, I even
took several graduate-level courses like Convex Optimization. My good performance at Peking
University won me the National Scholarship—the highest scholarship for top 0.1% Chinese
undergraduates granted by the Chinese Ministry of Education.
With the accumulation of solid knowledge, I joined Professor Lingyang Song’s lab at Peking
University during my sophomore year. Through two years of hard-working, I completed three
independent research projects on wireless networks and mobile computing, which honed in on
my rigorous attitude, self-motivation and technical skills on research.
My first research project on UAV sensing, led to a paper published in IEEE Globecom and
another paper published in IEEE Internet of Things Journal. My work aimed to address the
fine-grained air quality index (AQI) distribution in 3D space. The project was meaningful and
challenging, since the traditional method only provides 2D AQI map, which is also coarsegrained
in both spatial and temporal perspectives. In contrast, profiling real-time AQI maps in
3D space gave important instructions and applications for people in urban areas to deal with
air pollution problem. I started with designing a mobile sensing system using UAV and wireless
sensors. Under this prototype, I encountered two problems: the missing AQI values of
unmeasured locations and the constrained battery of UAV. To solve the first problem, I
modelled the AQI distribution using the combination of a physical model and the artificial
neural networks. This novel model realized accurate AQI estimation as it utilized key features
in fine-grained scenarios. As for the battery constraint, I decomposed the monitoring process
by two complementary monitoring steps to achieve high efficiency of the system. Our system
demo successfully won the first prize out of 23 finalists in IEEE ComSoc Student Competition,
where I served as the team leader. The knowledge I gained in theoretical analysis, practical
design and having led a team fueled and continues to fuel my desire to enter the field of wireless
networks and mobile computing.
The first research project significantly bolstered my ability to find out and solve problems
independently. In retrospect of my past works, I soon came up with another problem to
contemplate on how to optimize the UAV’s flight route in 3D space. This was a difficult
challenge due to the modeling and 3D graph analysis. After doing surveys on graph theory, I
defined the “dominating path” based on the concept of dominating set, and established a multilayer
3D network grids. Thus, the problem was formulated mathematically, by finding the
optimal dominating path. I spent two weeks deriving the function of the shortest path, and found it was related to the length (mod 3) and width (mod 3) of the grid. Thus, I proposed an
effective algorithm to obtain the near-optimal trajectory. My recent paper submitted to IEEE
INFOCOM, “Optimal Trajectory Planning of Drones for 3D Mobile Sensing,” witnessed my
great progress on the mathematical ability to do modelling and analysis from reality.
My past research projects allowed me to explore deeper into one area, and find unique
intersections between different subjects. Based on the fine-grained AQI dataset we established
before, I introduced machine learning method to utilize sparse raw data. Our purpose was to
recommend the best locations for sensor establishment in a 3D space to provide long-term
energy-efficient monitoring. One of the greatest challenges was dealing with very sparse
historical data. Following the intersection between wireless networks and machine learning, I
proposed an entropy-based semi-supervised learning method for effective AQI estimation. To
select most suitable locations, I proposed an algorithm that aimed to minimize the model’s
entropy, in order to achieve the minimum uncertainty. The results look promising, as we are
able to find near-optimal locations for sensor deployment. My paper on these results was
submitted to IEEE ICC. Through this project, I realized the great beneficial intersection
between prevailing powerful techniques like machine learning and wireless networks, which
accounts for better performance while also extends my spectrum of research.
My rich experiences in wireless networks and mobile computing have forged me into a
confident and smart researcher. Although I won the Outstanding Undergraduate Research
Award at the 2017 Peking University Young Scientists Symposium on Informatics, the best
reward for my endeavors has been the knowledge with which I am now better prepared to step
into further and to pursue the Ph.D. career.
Why am I choosing Yale?
In the past decades, we have witnessed the tremendous development of wireless networks and
mobile computing. When envisioning the world of tomorrow, advanced wireless technologies,
such as IoT, mm-wave and machine learning, are going to be further utilized. Yale has always
been a pioneer in these areas, tackling seemingly impossible problems and succeeding through
perseverance and ingenuity. Therefore, Yale offers an unrivaled opportunity for me to work
toward my own vision in a creative and dedicated environment.
For my future research, I am interested in mobile wireless networks and systems, together with
the mobile computing and its applications. I am also interested in improving existing wireless
networks with emerging techniques such as machine learning. I am applying for a Ph.D. in EE
at Yale, because I believe that my research interests align closely with Professor Leandros
Tassiulas on wireless communication and networking, Professor Y. Richard Yang on computer
networks and SDN, and Professor Amin Karbasi on machine learning and optimization.
My experience, demonstrated skills, and dedication to the study of wireless networks make me
a very good fit for Yale’s program. I am well aware that a career in research calls for personal
commitment and personal sacrifice of time, leisure and immediate reward. If admitted, I will
embrace the spirit of Yale Engineering and commit myself fully to succeeding in this rigorous
Ph.D. program and going on to shape the future.

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