Institute of Computing Technology Supervisor: Researcher Shihong Xia Major: Computer Science and Technology
计算技术研究所 导师：夏时洪研究员 专业：计算机科学与技术
GPA 3.7/4.0 Weighted Average Score 87.87/100 Number of Courses Taken 86(including exemptions) 百分制加权平均分 87.87/100 修读课程数 86(含免修)
GPA: 3.7/4.0 Weighted Average Score: 88.45/100 Number of Courses Taken: 79(including exemptions) Lowest Score: 75/100 Number of Courses Scoring Less Than 80: 5 (President: Prof. Hua Huang, National high-level leading talents) (Tutors of Freshmen: Prof. Wenyi Zeng)
Core Courses: Pattern Recognition, Introduction to Computer Systems, Computer Vision, Natural Language Processing, Brain like and Cognitive Computing, Data Structure and Algorithm, Mathematical Basis of AI, Software Engineering, Digital logic and CPU, Modern Signal Processing
Professional Courses: Data Mining, ML Tools and Platforms, Database System, Linux OS, Discrete Mathematics, Cybersecurity, Game Theory, Large Models Based on MindSpore, Education Digitization
Basic Courses: Calculus, Linear algebra and its application, Probability theory and stochastic process, Interactive Python Programming, C and C++ programming, Basic Physics, Experiments of Basic Physics, Intro to AI
General Courses(Not all): Art of Algorithm and ACM Contest, Self-management in Mental Health, Intro to Philosophy, Emerging Biotechnology, Medical Basics, Swimming(Elementary Course), Prevention of Legal Risk, Art and Aesthetics, International Conflict and Crisis Management, Science Fiction Film Appreciation
GPA 3.7/4.0 百分制加权平均分 88.45/100 修读课程数 79(含免修) 最低分 75/100 80分以下课程数 5 (院长：黄华教授，国家高层次领军人才) (新生导师：曾文艺教授)
核心课程：模式识别、计算机系统导论、计算机视觉、自然语言处理、类脑与认知计算、数据结构与算法、人工智能数学基础、软件工程、数字逻辑与CPU基础、现代信号处理基础
专业课程：数据挖掘、机器学习工具与平台、数据库系统原理、Linux操作系统、离散数学、信息安全基础与实践、博弈论、基于昇思的大模型、教育数字化
基础课程：微积分、线性代数及其应用、概率论与随机过程、交互式Python编程入门、C与C++程序设计、基础物理、基础物理实验、人工智能导论
通识课程(不全)：算法艺术与ACM竞赛、心理健康自我管理、哲学入门、新兴生物技术、走近医学、游泳(初级)、法律风险防范、艺术与审美、国际冲突与危机管理、科幻电影欣赏
GPA 3.4/4.0 Weighted Average Score: 83.92/100 Number of Courses Taken: 7 Lowest Score: 74/100 Number of Courses Scoring Less Than 80: 2
Courses: Mathematical Statistics, Statistical Computation, Introduction to Statistics A, Microeconomics, Probability Theory, Applied Time Series Analysis, Nonparametric Statistics
GPA 3.4/4.0 百分制加权平均分 83.92/100 修读课程数 7 最低分 74/100 80分以下课程数 2
课程：统计学导论A、微观经济学原理、概率论、数理统计、统计计算、应用时间序列分析、非参数统计
| Academic Credits 学分 (1 credit 学分=16 class hours 学时) |
Total 总计 | Major 主修 (including exemptions 含免修) |
Minor 辅修 |
| 2021 Winter 冬季 | 22.25 | 22.25 | 0 |
| 2022 Summer 夏季 | 59.5 | 59.5 | 0 |
| 2022 Winter 冬季 | 89.75 | 89.75 | 0 |
| 2023 Summer 夏季 | 130 | 123 | 7 |
| 2023 Winter 冬季 | 161.25 | 143.25 | 18 |
| 2024 Summer 夏季 | 188.5 | 164.5 | 24 |
| 2024 Winter 冬季 | 188.75 | 164.75 | 24 |
| 2025 Summer 夏季 | 194 | 170 | 24 |
| Graduation Requirements 毕业要求 | 169 | 145 | 24 |
Science Experimental Class 理科实验班
2021-College Entrance Examination Score 2021年高考成绩 634/750 Provincial Ranking 省排名 732/68294
Chinese 语文 126/150 Math 数学 141/150 English 英语 125/150 Science 理综 242/300
Science Island - Summer Camp 科学岛夏令营
Excellent Student 优秀营员
College of Engineering - Summer Camp 工学院夏令营
Completion 结业
School of Software Technology - Summer Camp 软件学院夏令营
Completion 结业
School of Intelligent Software and Engineering - Summer Camp 智能软件与工程学院夏令营
Excellent Student 优秀营员
School of Engineering - Summer Camp 工学院夏令营
Completion 结业
Oxford Prospects Programmes - 2023 Summer Tutorial Programme 牛津展望计划——2023年暑期导师制项目
Topic: Machine Learning Grade: A- 主题：机器学习 成绩：A-
This course is designed to give a broad overview of Artificial Intelligence(Al) and Machine Learning(ML). The course will introduce the core concepts and terminology used in these fields. We will explore the role of Al/ML in topics such as Computer Vision, Data Science, and Robotics. In addition to developing a broad understanding, the course also delves deeper into the powerful (and popular) technique of Neural Networks.
本课程旨在概括介绍人工智能（Al）和机器学习（ML）。我们将探索Al/ML在计算机视觉、数据科学和机器人学等领域中的作用，并介绍这些领域中使用的核心概念和术语。除了提升广泛的理解，本课程还深入研究了强大的（和流行的）神经网络技术。
Performance: Tailin's performance in the tutorials was very good. Tailin was prepared for the tutorial and was always attentive to the material being presented. Tailin's final presentation during the tutorial was well-presented. His presentation style was informative, distilling important points from the SLAM literature and bringing in open questions for discussion.
表现：Tailin(我)在教程中的表现非常好。Tailin已经为教程做好了准备，并一直关注所提供的材料。Tailin在教程中的最后一次演示非常精彩。他的演讲风格内容丰富，从SLAM文献中提炼出重要观点，并提出了开放式问题供讨论。
The Elite Class mainly conducts practical training in artificial intelligence and programming.
菁英班主要进行人工智能和程序设计的实训。
Excellent Student 优秀学员
The camp mainly taught the structure and evolution of the big model in natural language processing.
训练营主要讲授了自然语言处理领域大模型的结构和演变历程。
Second Prize 二等奖
Host: China Foreign Cultural Exchange Center of the Ministry of Education 主办：教育部中外人文交流中心
Courses: Challenge and Governance of Artificial Intelligence and Modern Technology 95.2, Research Methods and Data Processing Methods in report and paper writing 98.8, Emerging Biotechnology Development and Global Governance 100, The Belt and Road Initiative Based on Sustainable Development 100, International Conflict and Crisis Management 98, Music Therapy and Emotion Management 100, Iinnovation and Entrepreneurship in the Global Context 100
课程：人工智能与现代科技的挑战及治理 95.2，报告和论文撰写中常用的研究方法与数据处理 98.8，新兴生物技术发展与全球治理 100，国际经济视角分析“一带一路”与可持续发展 100，国际冲突与危机管理 98，音乐康复治疗与情绪管理 100，全球合作模式中的创新与创业 100
Artificial Intelligence Summer School 人工智能 暑期学校
Merit, Best Presenting Team 良好 最佳小组
Leadership and Innovation Program 领导力和创新项目
Global Business Plan Competition - Third Place 全球商业大赛 三等奖
Member 13th Group of 15th Issue 成员 第十五期第十三组
Excellent Student, Comprehensive Ranking 6/172 优秀学员 综合排名 6/172