我是陳信睿。這個部落格主要用來記錄我的學習與成長歷程，內容多半是我在學校修課時學到的知識與心得，也包含一些自己整理的筆記。其中有些與數學奧林匹亞相關，也有部分是大學數學課程的筆記。
以下為我的簡歷，實驗室經歷的敘述更新至 2026 年 3 月。若希望快速瀏覽，也可以參考我的一頁式簡歷。
以下為我的學歷：
在臺大資工就讀期間，我接觸並認識了許多不同領域的知識，也因此對未來可能投入的方向有更廣的想像。大一剛入學時，我對數學特別有興趣，因此修了不少數學系的課程，也取得了不錯的成績。後來因為希望在資訊領域（尤其是系統相關方向）有更深入的能力，修課的重心逐漸轉向系統相關課程。同時，我對當前相當熱門的機器學習與深度學習領域也有一定程度的接觸與了解。
以下為我在奧林匹亞競賽中的主要成績：
剛進入高中時，我一直對數學競賽抱有興趣，但也是到了高中之後才真正知道「數學奧林匹亞」這個競賽體系。後來我參加了一些由前國手舉辦的營隊（IMOC），逐漸接觸並學習這個競賽中常見的數學知識與解題方法。
在高一（2020 年）時，我成功進入第一階段選訓營。不過在營隊中的各次考試表現都不太理想，最終也沒有進入第二階段選訓營。這段經驗讓我意識到自己還有許多需要加強的地方。
經過一年的練習與學習後，我對一些重要的觀念與解題技巧有了更好的掌握。隔年我順利進入第三階段選訓營（最後一階段），但由於在第二階段的整體表現並不突出，即使在第三階段的表現相對不錯，仍然沒有取得最終代表隊（IMO）的資格。同一年我在 APMO 獲得銅牌，對我來說也算是對那段時間努力的一種肯定。再隔一年因為需要準備學測，沒有再投入太多時間在競賽訓練上，但仍然獲得了 APMO 的榮譽獎。
在大學到研究所期間，我一共在三個不同的實驗室或研究環境中累積過一些研究經驗：
中研院暑期實習：指導老師為中研院研究員呂及人。在這段時間，我剛開始接觸深度學習，因此希望透過一個具體問題來深入了解相關技術。我選擇研究數學手寫辨識的問題，也是在這段時間第一次比較系統地學習深度學習如何應用在語言模型相關任務上，並體會到這類方法的能力。同時，我也在實習過程中進一步提升了閱讀程式碼以及閱讀研究論文的能力。
大學專題研究：無線網路及嵌入式系統實驗室。這個實驗室由臺大多位老師共同主持，而我當時是在施吉昇教授的團隊中。老師的研究方向主要與 vehicle-to-everything（V2X）communication 相關。我選擇加入這個實驗室，是因為希望對網路與系統相關技術有更深入的了解。雖然以專題生的身分能接觸到的研究內容相對有限，但在與學長姐的交流中仍然學到不少東西。例如，我曾經嘗試研究 Named Data Networking 的中介層軟體 Zenoh，並閱讀與修改部分程式碼，看看是否能對實驗室的研究有所幫助。
研究所實驗室：無線網路及嵌入式系統實驗室。在研究所階段，我選擇加入張原豪教授的實驗室。一方面是因為這個實驗室同樣屬於系統相關領域，另一方面老師對學生的指導也非常投入。實驗室目前主要的研究方向包括 NAND Flash 壽命議題以及 in-flash computing。目前我仍處於大量閱讀論文並尋找研究題目的階段，不過即使只經過了短短一段時間，我已經學到了不少新的知識與研究方法。
I am Hsin-Jui Chen. This blog is mainly used to record my learning journey and personal growth. Most of the content consists of notes and reflections from courses I have taken at university, along with some personal notes I have written. Some of these notes are related to mathematical olympiads, while others cover topics from undergraduate mathematics courses.
Below is a detailed overview of my CV, with laboratory experiences updated as of March 2026. For a quick overview, you may also refer to my one-page resume.
My educational background is as follows:
During my undergraduate studies at NTU CSIE, I had the opportunity to explore many different areas of computer science, which broadened my perspective on potential directions for future work. When I first entered university, I was particularly interested in mathematics, so I took many courses offered by the mathematics department and achieved strong academic results.
Later, I shifted my focus toward computer science, especially systems-related topics, because I wanted to build deeper expertise in that area. At the same time, I have also developed a reasonable understanding of modern machine learning and deep learning, which are currently very active research areas.
Some of my notable achievements in mathematical olympiads include:
When I first entered high school, I had a strong interest in mathematics competitions, but it was only then that I learned about the mathematical olympiad system. Later, I attended several training camps organized by former national team members (IMOC), where I began learning the mathematical topics and problem-solving techniques commonly used in olympiad competitions.
In my first year of high school (2020), I was admitted to the first stage of the national olympiad training program. However, my performance in the camp examinations was not very good, and I did not advance to the second stage. This experience made me realize that there was still a lot of room for improvement.
After a year of intensive practice and study, I developed a stronger grasp of several important concepts and techniques. The following year, I advanced to the third stage of the training program (the final stage). However, because my overall performance in the second stage was not strong enough, I was unable to secure a position on the final IMO national team, even though my performance in the third stage was relatively good.
In the same year, I received a bronze award at APMO, which I see as recognition of the progress I had made during that period. The following year, because I needed to prepare for the Taiwanese college entrance exam (GSAT), I spent less time on olympiad training and practicing. Nevertheless, I still received an APMO Honourable Mention.
From my undergraduate studies to graduate school, I have gained research experience in three different laboratories and research environments.
Summer Internship at Academia Sinica
I worked under the supervision of Research Fellow Chi-Jen Lu at Academia Sinica. During this internship, I had just begun learning about deep learning and wanted to explore the field through a concrete problem. I chose to study handwritten mathematical expression recognition (HMER), which was also my first opportunity to systematically learn how deep learning techniques can be applied to tasks related to language models. Through this experience, I also improved my ability to read source code and research papers.
Undergraduate Research Project – Wireless Networking and Embedded Systems Laboratory
This laboratory is jointly led by several professors at National Taiwan University, and I worked in the group of Professor Chi-Sheng Shih. One of the lab’s research mainly focuses on vehicle-to-everything (V2X) communication technologies. I joined the lab because I wanted to gain a deeper understanding of networking and systems-related technologies. Although the scope of research available to undergraduate project students was somewhat limited, I still learned a lot through interactions with senior students. For example, I explored Zenoh, a middleware for Named Data Networking, and attempted to read and modify parts of its source code to see whether it could contribute to the lab’s research.
Graduate Laboratory – Wireless Networking and Embedded Systems Laboratory
For my graduate studies, I joined the lab led by Professor Yuan-Hao Chang. One reason for this choice is that the lab focuses on systems research, which aligns with my interests. In addition, Professor Chang is highly dedicated to advising students. The lab’s current research directions include NAND Flash lifetime issues and in-flash computing. At the moment, I am still in the stage of extensively reading papers and exploring potential research topics. Nevertheless, even within a short period of time, I have already learned many new concepts and research methods.