Experiences

I received a BEng in Computer Science and Technology in 2024 from GDUT (Guangdong University of Technology, Guangzhou) where I experienced a two-year research internship in the DMIR lab (Data Mining and Information Retrieval) that focus on the interface between Causal Inference and Artificial Intelligence.

Reminder: For I would only document things as to my career path unfolded step by step, my experiences directly pertain to my work; I have been always gratitude to these nice people along my career trajectory. As for emotional thoughts towards this world, I keep jotting them down on my blogs as sources of motivation.

June. 2024 - Present

Preparation for 2025-Fall Postgraduate Application

Completing my undergrad research work, I’ve been preparing for the TOEFL and GRE tests, while also investigating graduate programs in relation to computer science; Since I hadn’t decided to study abroad until the final undergrad year, I’ve experienced twists and turns through my application process.

Meanwhile, since nearly all my undergraduate studies majoring in computer science consist in the research of Causal Discovery, in November I was recommended to write a research report regarding my past completed work in relevant areas of causality to better assist my postgraduate application.

Finally, to make the most of this time, I may plan to write down reflections on my blog after this period of busy. In fact, I recognize that there would still be many moments in life, where we need to learn a skill, take an “exam”, re-examine past work, and make a decision under time constraints and information gap.

Sep. 2023 - Apr. 2024

Personal Work about Popularization of Causal Science

Diexue Free Workplace

I spent additional half of a year in writing a “special paper”, where I try to interpret popular notions in causation from where the causal diagram sits. In light of the paper, I also created video series (see representative episode) to help intuitively illustrate causation, with familiar (but simplified) context such as climate change and COVID-19.

This paper serves as a farewell to my two-year undergrad research experience on “causal discovery”.

The photo serves as a memento of the moment when I was trying to understand “causal indirected effect (CID)” — a typical notion in the end of The Book of WHY. I couldn’t grasp it quite well the first time I read the book, but now have the opportunity to review it based on my own experience in causal science.

Sep. 2021 - Sep. 2023

Research Internship in Non-Linear Causal Inference | Guangzhou, China

Data Mining and Information Retrieval Lab (Advisors: Wei Chen，Ruichu Cai)

Starting in sophomore, I joined to Prof. Ruichu Cai’s lab as part of National Key R&D Programs in Causal Inference and Decision Theories. Curious about how to teach AI in brain science spectrum to manoeuvre causation entailed by generic fMRI data, we ended up developing a “hybrid-based” causality algorithms.

Applying the algorithm over fMRI data, our findings highlight that generic cause-and-effect among different brain regions (showcased by blood-oxygen activity measurements) can be computationally inferred, drawing on the assumption of “structural causal asymmetry” tied to the specific math form of brain functions.

Tuning in to generic causal discovery (ie. involving unknown confoundings), we continue our study under the context of time — does a cause always occurs ahead of an effect? Our investigation led to a research review reporting the techniques by which modern approaches handle cause-and-effect over time dimension.

A picture of me and Prof. Ruichu Cai (right). The background is the 727-room (at top right, an unforgettable number for me), where I first joined to a group-meeting as a way of joining to the lab.