EE is an innovative and exciting field with unlimited possibilities.
When I was successfullyawarded the first prize out of 23 finalists all over the world in 2017 IEEE ComSoc StudentCompetition: “Communications Technology Changing the World”, I felt immense happinessand satisfaction. Suddenly, I recalled my rewarding journey into the field of wireless networksand mobile computing. This rewarding journey started with my success in the classroom andlanded with three research projects at Peking University. These experiences reinforced mydetermination 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. Afterenrolling in the EECS department at Peking University, which provided the best possibleeducation in China, I had always strived for the best.
This determination resulted in thesatisfaction of obtaining the 4th highest overall GPA and during junior year, 1st overall GPA outof 53 students. Besides obtaining high scores in all my EECS and mathematics courses, I eventook several graduate-level courses like Convex Optimization. My good performance at PekingUniversity won me the National Scholarship—the highest scholarship for top 0.1% Chineseundergraduates granted by the Chinese Ministry of Education.With the accumulation of solid knowledge, I joined Professor Lingyang Song’s lab at PekingUniversity during my sophomore year. Through two years of hard-working, I completed threeindependent research projects on wireless networks and mobile computing, which honed in onmy rigorous attitude, self-motivation and technical skills on research.My first research project on UAV sensing, led to a paper published in IEEE Globecom andanother paper published in IEEE Internet of Things Journal. My work aimed to address thefine-grained air quality index (AQI) distribution in 3D space.
The project was meaningful andchallenging, since the traditional method only provides 2D AQI map, which is also coarsegrainedin both spatial and temporal perspectives. In contrast, profiling real-time AQI maps in3D space gave important instructions and applications for people in urban areas to deal withair pollution problem. I started with designing a mobile sensing system using UAV and wirelesssensors. Under this prototype, I encountered two problems: the missing AQI values ofunmeasured locations and the constrained battery of UAV. To solve the first problem, Imodelled the AQI distribution using the combination of a physical model and the artificialneural networks.
This novel model realized accurate AQI estimation as it utilized key featuresin fine-grained scenarios. As for the battery constraint, I decomposed the monitoring processby two complementary monitoring steps to achieve high efficiency of the system. Our systemdemo 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, practicaldesign and having led a team fueled and continues to fuel my desire to enter the field of wirelessnetworks and mobile computing.The first research project significantly bolstered my ability to find out and solve problemsindependently.
In retrospect of my past works, I soon came up with another problem tocontemplate on how to optimize the UAV’s flight route in 3D space. This was a difficultchallenge due to the modeling and 3D graph analysis. After doing surveys on graph theory, Idefined the “dominating path” based on the concept of dominating set, and established a multilayer3D network grids.
Thus, the problem was formulated mathematically, by finding theoptimal 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 aneffective algorithm to obtain the near-optimal trajectory. My recent paper submitted to IEEEINFOCOM, “Optimal Trajectory Planning of Drones for 3D Mobile Sensing,” witnessed mygreat 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 uniqueintersections between different subjects.
Based on the fine-grained AQI dataset we establishedbefore, I introduced machine learning method to utilize sparse raw data. Our purpose was torecommend the best locations for sensor establishment in a 3D space to provide long-termenergy-efficient monitoring. One of the greatest challenges was dealing with very sparsehistorical data.
Following the intersection between wireless networks and machine learning, Iproposed an entropy-based semi-supervised learning method for effective AQI estimation. Toselect most suitable locations, I proposed an algorithm that aimed to minimize the model’sentropy, in order to achieve the minimum uncertainty. The results look promising, as we areable to find near-optimal locations for sensor deployment. My paper on these results wassubmitted to IEEE ICC. Through this project, I realized the great beneficial intersectionbetween prevailing powerful techniques like machine learning and wireless networks, whichaccounts for better performance while also extends my spectrum of research.My rich experiences in wireless networks and mobile computing have forged me into aconfident and smart researcher. Although I won the Outstanding Undergraduate ResearchAward at the 2017 Peking University Young Scientists Symposium on Informatics, the bestreward for my endeavors has been the knowledge with which I am now better prepared to stepinto 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 andmobile 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 alwaysbeen a pioneer in these areas, tackling seemingly impossible problems and succeeding throughperseverance and ingenuity.
Therefore, Yale offers an unrivaled opportunity for me to worktoward my own vision in a creative and dedicated environment.For my future research, I am interested in mobile wireless networks and systems, together withthe mobile computing and its applications. I am also interested in improving existing wirelessnetworks with emerging techniques such as machine learning. I am applying for a Ph.D. in EEat Yale, because I believe that my research interests align closely with Professor LeandrosTassiulas on wireless communication and networking, Professor Y.
Richard Yang on computernetworks and SDN, and Professor Amin Karbasi on machine learning and optimization.My experience, demonstrated skills, and dedication to the study of wireless networks make mea very good fit for Yale’s program. I am well aware that a career in research calls for personalcommitment and personal sacrifice of time, leisure and immediate reward. If admitted, I willembrace the spirit of Yale Engineering and commit myself fully to succeeding in this rigorousPh.D.
program and going on to shape the future.