[文书] 用于申请所有美国院校的个人简历，恳请大家提出修改意见 [复制链接]

People always say students from universities of 985 and 211 program are more successful. I don't believe this. I also have international awards, I also have research papers, I also have national scholarships; I'm not inept as they think, though I belong to neither of those programs. In the following sections, I'll illustrate my past experience and futural plans, and show you my ability and potential in pursuing the Ph.D. degree.

Outsiders see our fame, only we know the sweat behind it.

Although harsh as it may seem, the experience in ACM-ICPC training team served as the first watershed of my college life. During semester 2, I devoted all my extracurricular time to it. Learning, coding, problem-solving, and discussing, these were already the daily routine. Days and nights spent in the training team rescued me from code phobia, and cultivate my logical thinking ability. Those two abilities are like the seed of academic, planted deeply in my heart, waiting for a proper chance to break out the soil.

If you're a true warrior, competition doesn't scare you. It makes you better.

Two years ago, creativity and efficient learning helped me hunt for the international award. During the Mathematical Contest in Modeling, based on the layout of checkout counters in supermarkets, I devised a honeycomb-shape design of toll plaza, which merges the traffic flow in two steps and can reduce the pressure at joining points. Next day, starting from scratch, I self-learned the queuing theory and proved that our new design was more economical and robust than the traditional one. Although our preparation was somewhat inadequate, those two points secured us from collapsing. Months later, as one of the 13 teams among more than 8,000 worldwide, we won the top prize, Outstanding Award.

Research is what I'm doing when I don't know what I'm doing.

In 2014, Rodriguez's DPC algorithm brought clustering algorithms into the Cartesian coordinate system; in early this year, my SNN-DPC algorithm brought neighborhoods into his two-dimensional world. Though it sounds exaggerated, from conception to implementation and then to the final manuscript, I undertook all the work of this research project. I added the neighborhood information to the properties determining centers so that cross-winding datasets could be well handled. Also, I developed a two-step distribution strategy which resolved the vicious domino effect and further improved the robustness. Referring to Rodriguez's code, I composed my implementation and published to GitHub. After two months' intensive works, the manuscript was finally submitted to Information Science, an SCI journal with impact factor 4.832, and was accepted in March this year.

We keep moving forward, opening new doors, and doing new things, because we're curious and curiosity keeps leading us down new paths.

This July, I went to the National University of Singapore for the summer workshop. Together with other Chinese students from colleges of 211 and/or 985 program, we spent three weeks full of challenges. For the first week, we spent two days on each of the three classes, with only one rest day between each switch. For the rest two weeks, endless assignments and research came to us one after another. Though hard, it brought me the precious sleepless experience learning abroad. But when I knew I got an A+, I felt all my efforts finally paid off.

The measure of intelligence is the ability to change.

In-depth research is always necessary for the creation of cutting-edge techniques. While the master's degree is a process of learning existing knowledge, Ph.D. is more likely to explore the unknown space and convert ideas into realities. My goal belongs to the latter category, that's part of the reasons I apply for the Ph.D. program.

In the area of multimedia, captioning is a challenging problem remains to be resolved. Though there are already many research works, the end-user product is still at the preliminary stage, especially for video processing. In the Ph.D. period, first I'll familiarize with the necessary knowledge on CV and NLP. As for the deep learning frames, I've already learned some like TensorFlow, PyTorch, and Keras and had used them to implement practical neural networks. Next, through cutting-edge research publications, locating existing problems and potential fixes, followed with tentative experiments. In this stage, apart from public datasets, other sources are also necessary. I'll seek chances to participate in joint research programs held by large companies to acquire this part of data; at the same time, it'll give me the initial experience working in an industrial environment.

The trend of multimedia will not last forever, and no one can predict when it will start to decline. If there is no much fluctuation in the next few years, the natural choice would be becoming a researcher in the company, where both computing power and data are adequate. Also, there would be a whole team working in the same field, rendering quick iteration from idea to product. On the other side, if the job market shows a gloomy trend, research institutions would be a better choice. The research pressure here is far less than a commercial environment, and I can fully immerse myself into long-term research.

My undergraduate school was not an obstacle in my academic journey, but a touchstone highlighted my ability and potential. Also, I'll be thankful for all my friends, lecturers, and my supervisor, without whose help, I would never have the chance to realize my academic aspiration.