第二学位申请材料

申请者需上传报名材料如下：

1.《哈尔滨工业大学攻读第二学士学位申请表》一份，（需加盖档案所在单位或所在学校公章），申请表须在网上报名系统填写完成后打印。√

2.原本科专业或者报考专业相关领域的副教授及以上职称（或相当专业技术职称）专家的推荐信两封（模板见附件1）。√

3.考生本人“现实表现情况表”（模板见附件2，须加盖考生档案所在单位学工部门公章）。√

4.本科阶段的课程学习成绩单（须授课单位教务处或档案保存单位盖章），外语等级证书或成绩单。√

5.可以体现考生本人综合素质、水平和能力的德育获奖证书、创新创业项目、学科竞赛奖励、文章专利等，以及其他方面的相关证明材料。

以上材料由申请者添加目录，并按顺序合成为一个PDF版本的文件（以申请者报名号作为文件名）提交到招生报名系统中，无需邮寄任何纸质材料，未按规定提交的材料将不予受理。

申请者必须确保填写的信息和提交的材料真实、准确，填写虚假信息或提供虚假材料的申请者一经发现将随时被取消考核和录取资格，且今后不再被允许申请我校各类考试入学。

个人陈述

Motivation Letter Proposed Programme: MSc in Artificial Intelligence Applicant: Chenxi Zhang  My fascination with Artificial Intelligence ignited when I witnessed its transformative potential through hands-on experimentation. Motivated by the practical capabilities of large language models (LLMs), I engineered a Python-based middleware integrating an LLM API with QQ—China’s prominent social platform. This system now actively serves diverse user queries daily, vividly demonstrating AI’s power to democratize information access. Such tangible impact solidified my resolve to master the foundational principles driving these innovations and contribute meaningfully to their evolution.  During my Bachelor’s in Computer Science and Technology at Harbin Institute of Technology, Shenzhen (HITSZ), I cultivated both theoretical rigor and practical ingenuity. Core courses like Artificial Intelligence and Information Retrieval not only stimulated intellectual curiosity but also yielded outstanding academic results, reflecting my proficiency in computational theory. Beyond the classroom, I thrived in competitive environments: receiving departmental recognition as a freshman and securing third prize in HITSZ’s 2025 CTF challenge. A defining moment occurred during a CTF competition focused on adversarial attacks using Projected Gradient Descent (PGD). Initially unfamiliar with the domain, I immersed myself in self-directed learning—methodically dissecting the problem until I successfully crafted an attack solution. This experience crystallized my passion for navigating complex technical challenges through persistent innovation.  My dedication to AI research culminated in my Bachelor’s thesis, inspired by observing Meta’s Llama-3 generate coherent financial risk assessments from fragmented news data. Recognizing a critical industry gap—traditional models’ inability to process unstructured signals versus large models’ excessive resource demands—I designed a real-time financial risk warning system. The solution featured a novel dual-channel architecture: a structured data stream incorporating a dynamically recalibrated Altman Z-Score that achieved 89.4% accuracy on HKEX datasets, paired with an unstructured NLP pipeline using a Llama-3 semantic analyzer enhanced by keyword-attention gating. This innovation boosted sentiment analysis F1-scores to 93.7%, marking a 65.6% improvement over baselines. To operationalize the system, I built an interactive Vue.js/Flask dashboard with WebSocket streaming, enabling real-time visualization of risk trajectories for entities like Tencent and Baidu within five seconds of data capture.  My career vision is to evolve from an AI Engineer/Researcher into a Technical Lead architecting responsible AI products. The MSc in Artificial Intelligence at CentraleSupélec is uniquely positioned to equip me with the advanced theoretical depth and specialized skills necessary for this trajectory. The program’s Foundations of Deep Learning course is absolutely critical, providing the bedrock understanding of neural architectures essential for my work in CV and FL. Furthermore, courses such as Foundations of Optimization and Stochastic Optimization are vital for mastering the algorithms underpinning efficient model training and resource-aware deployment. Big Data for AI will be indispensable for handling the scale of data inherent in modern AI systems, while AI and Ethics aligns perfectly with my long-term vision of architecting responsible and trustworthy AI products. This comprehensive curriculum, blending core theory with essential practical considerations like ethics and large-scale data management, directly addresses the multifaceted knowledge required to excel in my target roles within leading technology firms.   CentraleSupélec’s esteemed reputation for excellence in engineering and its strong focus on cutting-edge AI research provide the ideal environment for my postgraduate studies. The program’s emphasis on both foundational rigor and applied innovation resonates deeply with my academic background, research experience, and career ambitions. I am eager to contribute my dedication, proven problem-solving abilities, and passion for leveraging AI for tangible benefit to your vibrant academic community. I am confident that this MSc program is the pivotal next step in my journey to becoming a leader in the AI field.

推荐信

To Whom it May Concern,  It is with genuine pleasure that I provide this letter of recommendation for Mr. Zhang Chenxi, an exceptional student who completed my Artificial Intelligence and Machine Learning course at Harbin Institute of Technology, Shenzhen. I am confident that his academic diligence and analytical skills will make him a valuable asset to your esteemed program.  I have known Chenxi since September 2023, when he enrolled in my core course on foundational and advanced AI methodologies. The curriculum covered key topics including neural network architectures, supervised/unsupervised learning paradigms, reinforcement learning frameworks, natural language processing techniques, and ethical implications of AI deployment. Throughout the semester, Chenxi consistently demonstrated a remarkable capacity for absorbing complex concepts. His assignments—ranging from implementing convolutional neural networks (CNNs) for image classification to optimizing Q-learning algorithms—were executed with precision and submitted with thorough documentation. Notably, he achieved outstanding results in all assessments, with a final grade placing him within the top 10% of his cohort.  Beyond technical proficiency, Chenxi exhibited a proactive approach to mastering practical tools. He independently honed his skills in TensorFlow and PyTorch, developing fluid competency in model construction, hyperparameter tuning, and performance evaluation. His coursework submissions frequently exceeded expectations; for instance, his final project on sentiment analysis leveraged BERT embeddings with customized attention mechanisms, yielding accuracy rates surpassing baseline requirements by 12%. This ability to independently explore extensions of course material underscored his intellectual curiosity and commitment to excellence.  What distinguishes Chenxi is his systematic problem-solving methodology. When confronted with challenges—such as debugging gradient vanishing issues in recurrent networks—he methodically dissected the problem, consulted academic literature, and iteratively refined his solutions. His analytical rigor, combined with quiet perseverance, enabled him to produce consistently reliable results without external prompting.  I firmly believe Chenxi possesses the technical acumen, self-discipline, and intellectual maturity to thrive in advanced studies. His aptitude for rapidly assimilating new frameworks and deriving actionable insights from theoretical foundations aligns seamlessly with research-intensive programs. I endorse his application without reservation and welcome any further inquiries regarding his qualifications.  Sincerely, [Your Full Name] Professor of Artificial Intelligence Harbin Institute of Technology, Shenzhen Email: [Your Institutional Email] Tel: [Your Phone Number] Address: [Your Department Address]

推荐信

To Whom it May Concern,  It is with sincere enthusiasm that I recommend Mr.Zhang Chenxi for your consideration. Mr. Zhang was an outstanding student in my Data Structures and Algorithms course at Harbin Institute of Technology, Shenzhen during the first semester of first year. Based on his exceptional grasp of core concepts and formidable programming abilities, I am confident he will excel in your program and become a significant asset.  Throughout the course, which covered fundamental and advanced data structures (including arrays, linked lists, stacks, queues, trees, heaps, hash tables, and graphs) and their associated algorithms (searching, sorting, traversal, shortest path, minimum spanning trees), Mr. Zhang demonstrated a profound understanding of the material. His assignments were not merely completed but executed with remarkable precision and efficiency. He consistently implemented complex data structures like balanced trees and graph representations (adjacency list/matrix) flawlessly in programming languages such as C and Python. His solutions were characterized by clean, well-documented code and insightful algorithmic choices, consistently placing him within the top tier of the class.  Mr. Zhang’s programming prowess was particularly impressive. He possesses a rare combination of deep theoretical understanding and the practical skill to translate that knowledge into robust, efficient, and correct code. For instance, his implementation of Dijkstra’s algorithm for shortest path or his AVL tree insertion/deletion routines with full rebalancing showcased not only technical accuracy but also a keen awareness of time and space complexity trade-offs. He not only met all requirements but delivered a solution that was both algorithmically sophisticated and meticulously engineered, demonstrating his ability to apply data structures to solve non-trivial problems effectively.  Beyond technical skill, Mr. Zhang exhibited a systematic and analytical approach to problem-solving. When tackling challenging problems, such as optimizing a complex sorting routine or debugging intricate pointer manipulation in linked structures, he approached them methodically. He would decompose the problem, implement carefully, and rigorously test his code. This diligence resulted in consistently reliable and high-performance submissions. His quiet determination and ability to work independently towards optimal solutions were hallmarks of his performance.  In summary, Mr. Zhang Chenxi possesses an exceptional command of data structures and algorithms, coupled with truly strong programming capabilities. His analytical rigor, commitment to producing high-quality work, and ability to independently solve complex problems make him exceptionally well-prepared for advanced studies and research in computer science. I recommend him for your program without any hesitation and am certain he will achieve significant success. Please feel free to contact me if you require further information.  Sincerely,  [Your Full Name] [Your Title, e.g., Associate Professor of Computer Science] [Your University Name] Email: [Your Institutional Email] Tel: [Your Phone Number] (Optional) Address: [Your Department Address] (Optional)