前言：本文为本人在准备夏令营和预推免期间准备的常见英文面试问答，也参考了很多学长学姐的经验，并且基于本人的经历进行修改，做成了模板的形式，希望能够给与学弟学妹或多或少的帮助。

老师好，我是xxx，是来自xxx大学xxx专业的一名学生。我的专业排名为第xxx名，四六级成绩较好，分别为xxx和xxx分。

在本科期间我有过xxx方面的科研经历，具体参与的科研为一个关于xxx的研究。在科研过程中，我负责的是xxx和xxx…x个部分的工作。前期我使用了xxx，测试xxx，以验证xxx。在论文撰写的后期，我负责实现xxx，计算xxx等指标。

另外也有过一个工程项目的经历，项目的具体背景是xxx。项目的核心是xxx，这个项目我主要负责的是研究xxx，给出xxx。

在竞赛方面，我主要参加的竞赛有xxx, xxx, xxx……，主要负责了xxx，队伍也斩获了xxx、xxx的成绩。

以上就是我在科研、竞赛、项目方面的主要经历，另外还有一些关于xxx, xxx……的原创性工作，如果老师感兴趣，我愿意后续介绍。

I am xxx, a student from xxx majoring in xxx. I rank x in my major. And the scores of my CET-4 and CET-6 are xxx and xxx respectively.

During my undergraduate study, I had scientific research experience in xxx, specifically participating in a research on xxx problem. I was responsible for xxx in the early stage and xxx in the late stage.

In addition, I also have an engineering project experience, the specific background of the project is that xxx. The core of the project is to xxx. In this project, I was mainly responsible for xxx and xxx.

In terms of competition, I mainly took part in xxx, xxx…… and was responsible for the xxx. Our team also won the xxx, xxx and xxx.

The above are my main experiences in scientific research, competitions and projects. In addition, I have also done some original work related to xxx and xxx. If you are interested, I would like to introduce them to you.

我的设计是一种 xxx 结合xxx 的方法，我们将xxx问题建模成一个xxx问题，问题的目标是

这部分还是要基于自己的项目写，基本介绍可以按照：

按照这个模式每点一句就行

MergeSort is a typical application of Divide and Conquer. It is an effective algorithm with a nlgn time complexity and it is stable but not in place. Merge sort is a good choice for external sort. The basic step of the algorithm is to merge the ordered subsequences into a complete ordered sequence.If two ordered subsequences are merged into one, it is called two-way merge.

Quicksort is an in-place sorting algorithm. it is still a commonly used algorithm for sorting. Quicksort is a divide-and-conquer algorithm. It works by selecting a ‘pivot’ element from the array and partitioning the other elements into two sub-arrays, according to whether they are less than or greater than the pivot. For this reason, it is sometimes called partition-exchange sort.[4] The sub-arrays are then sorted recursively. This can be done in-place, requiring small additional amounts of memory to perform the sorting.

Heap sort requires the use of heap data structures, which can be implemented using relationships between the indexes of an array. For example: if the father node’s index is i, then the child node’s is 2i or 2i + 1.

The heapsort algorithm can be divided into two parts.

In the first step, a heap is built out of the data. The heap is often placed in an array with the layout of a complete binary tree. The complete binary tree maps the binary tree structure into the array indices; each array index represents a node; the index of the node’s parent, left child branch, or right child branch are simple expressions. For a zero-based array, the root node is stored at index 0; if i is the index of the current node, then

In the second step, a sorted array is created by repeatedly removing the largest element from the heap (the root of the heap), and inserting it into the array. The heap is updated after each removal to maintain the heap property. Once all objects have been removed from the heap, the result is a sorted array.

Heapsort can be performed in place. The array can be split into two parts, the sorted array and the heap. The storage of heaps as arrays is diagrammed here. The heap’s invariant is preserved after each extraction, so the only cost is that of extraction.

Counting sort is not comparison based sort. Its time complexity is O(N) in the best, worst, and average cases. The input to counting sort is usually integers that have duplications in a small range. The algorithm needs to take advantage of extra space, that is, the algorithm is not in place. But the ordering is stable.

Radix sort is not comparison based sort, it is stable. The time complexity is order n. Radix sort is a sort by digit. The sorting of each digit can be implemented using counting sorting. Specific implementation process: all the values to be compared are unified into the same length of digits, and the number with shorter digits is filled with zero. Then, start at the lowest digit and sort it one by one. So when you go from the lowest sort to the highest sort, it becomes an ordered sequence.

Bucket sort is not a comparison based sort. The time complexity is O(n). The input to the algorithm is usually a decimal that satisfies a uniform distribution. The algorithm first puts the data to be sorted into buckets according to certain mapping relationships. Since data is uniformly distributed and there is less data in each bucket, insert sort can be performed in each bucket. After sorting the data in each bucket, merge the data in all buckets to obtain the final result.

Dynamic programming is an extension of the divide-and-conquer idea applied to optimization problems. There are four steps in Dynamic programming：

Dijkstra algorithm is suitable for finding single source shortest paths in non-negative weight graphs. The algorithm uses the idea of dynamic programming, traversing to the nearest node from the source point every time, and updating other unvisited nodes until the expansion to the end point. The algorithm can use priority queue optimization every time to find the nearest node. The time complexity of the algorithm is O(VE), where V is the number of nodes and E is the number of edges in the graph.

Bellman-ford algorithm is suitable for finding single source shortest paths in graphs with negative weights. The algorithm uses all edges for n rounds of relaxation. Time complexity is O(VE). Where V is the number of nodes and E is the number of edges.

Huffman coding is a specific type of prefix code commonly used for lossless data compression. A Huffman tree is constructed with the weight of the frequency of characters, and then the characters are encoded by the Huffman tree, which is called Huffman coding.

Specifically, the character to be encoded is taken as the leaf node, and the frequency of the character in the file is taken as the weight of the leaf node. In a bottom-up manner, the required tree is constructed by performing n-1 times of “merge” operation, that is, the Huffman tree. The core idea of this tree is to make the leaf with large weight closest to the root

Freudian algorithm is an algorithm that uses the idea of dynamic programming to calculate multi-source shortest paths in any weight graph, including negative weight graph or non-negative weight graph. The time complexity of the algorithm is O(N ^3). Use knots for relaxation. After modification, the algorithm can be used to operate transitive closure.

Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node and explores as far as possible along each branch before backtracking. Extra memory, usually a stack, is needed to keep track of the nodes discovered so far along a specified branch which helps in backtracking of the graph.

Dreadth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. Breadth-first search (BFS) is an algorithm for searching a tree data structure for a node that satisfies a given property. It starts at the tree root and explores all nodes at the present depth prior to moving on to the nodes at the next depth level. Extra memory, usually a queue, is needed to keep track of the child nodes that were encountered but not yet explored.

My hometown is xxx county, a small town located in xxx, famous for its beautiful scenery. My hometown not only has xxx, xxx and other famous attractions, but also has xxx, xxx and other famous cuisine. All in all, xxx is a remarkable place to travel and live in.

My school is xxxx, also known as xxx, which is located in xxxx. xxxx（学校名） has a strong atmosphere of science and technology, possessing many specialties with national characteristics, the most famous is some of the xxxx direction of the major. I majored in xxx.

During my undergraduate study, I took compulsory curriculum like computer communication and network, computer composition principle, operating system, database system and so on. I also participated in the debate team and other clubs, which enriched my extracurricular activities.

My favorite book is the Romance of The Three Kingdoms, which is set in China during the later Han Dynasty. I appreciate the firm friendship between the LiuGuanZhang, and even more admire zhuge Liang’s strategizing.

Reading can broaden my horizon, and cultivate my mind. （说原因必备）

My advantages are that I can learn quickly, I am focused and self-disciplined, which can be seen from my transcript. I also have a relatively complete scientific research experience, enabling me to master some scientific research skills in advance. My weakness may be that I can be a little stubborn.

My favorite course is the computer composition principles. Because this course provide me with a chance to understand the computer from scratch in an all-round way, so that I can really have a systematic understanding of my major after entering the university.

根据自己的城市介绍吧~

First of all, I think xxx University is a top academic university in domestic even international. There is no doubt that a higher and better platform can contribute to my personal development. In addition, I like the direction of network and system very much. I think it is very desirable to do my favorite direction in a first-class university.

In my opinion, the biggest problem encountered in scientific research is how to optimize and solve the problem when my model has a defect in a certain aspect or fails to reach the expected goal. Specifically reflected in my project is that when I was doing xxx problems, the xxxx （出现的问题）, and I hope to xxx（希望解决到什么程度）. Finally, I solved the problem by xxxx（用什么方法解决的）.

I want to engage in the research of distributed machine learning as a graduate student.

First of all, I think AI is a very exciting field.

The current development trend of AI is that the ability of machine learning models are becoming more and more powerful, but at the same time, AI is increasingly facing bottlenecks in systems——topics such as the design of AI accelerators, large-scale machine learning, whcih are outside the focus of traditional machine learning. Therefore, I think Machine Learning System as a cross research direction is very promising and important for the development of AI and systems.

Personally, I have been interested in AI and systems since I was an undergraduate. Recently, when I really got in touch with MLSys, I felt very interested in this field and I was very motivated to learn. and I also hope to make meaningful and valuable work to promote the progress of the field during this period.

First of all, I like computer, and I am motivated to learn and do research, hoping to do meaningful and valueable work in this area.

In addition, I have received a lot of help and advice from teachers during my undergraduate period. I hope I can continue to work in the academic community in the future and give feedback to this help.

In combination with these two points, I think the choice of studying for a doctorate and doing research is very consistent with my life orientation.

In lower grade, I think lay a good professional foundation is important. I plan to learn excellent courses in domestic and abroad, not just limited to the class.

The second grade I can focus on secientific rearch, read paper as more as possible, and do some valuable and meaningful work to promote the prograss in my area.

I mainly took part in xxx competition, and was responsible for the xxxx. Our team also won the xxx and xxx. （然后万能的broaden horizon……）