Chenyu Zhou

(+86)175-6915-8031

chenyuzhou1[AT]link.cuhk.edu.cn

Shenzhen, China

Hi , This is Chenyu (周宸宇 in Chinese)!

I am an M.Phil. student at The Chinese University of Hong Kong, Shenzhen, majoring in Computer and Information Engineering. I am working as a research assistant supervised by Prof. Wei Cai in the Human-Crypto Society Laboratory. I am working in the lab on exciting research related to decentralized finance(DeFi) and Web3. Prior to this, I completed my undergraduate studies at China University of Petroleum, Beijing, where I double-majored in Management Information Systems and Computer Science, earning dual degrees in Management and Engineering.

I am deeply passionate about the intersection of technology and finance, and firmly believe that technology-driven finance will revolutionize the financial industry. Specifically, my research interests center on the application of blockchain, AI, and machine learning in finance.

🚩 Recent Speical Focus:

Large-Scale Graph Neural Network Learning and Applications
Reinforcement Learning and LLM-driven Forecasting for Financial Markets
Security and Performance Analysis of DeFi / Blockchain Layer 2

I possess strong programming skills and take pleasure in coding. I am a gold medalist in the Asian regional contest of the International Collegiate Programming Contest(ACM-ICPC), the world’s most prestigious collegiate programming contest. I am proficient in Python and C/C++ and have gained experience working with R, Matlab, Java, Golang, and JavaScript.

I have a strong passion for quantitative investment. I have nearly two years of full-time or internship work experience before pursuing my graduate degree. My previous employers, which include Ubiquant Investment(九坤投资), Harvest Fund(嘉实基金), and JZL Capital(君理资本), have all acknowledged my exceptional abilities in this field.

I am a Web3er, and I believe that decentralized autonomous organizations (DAOs) built on Web3 have immense potential to enhance productivity. As a result, I am actively engaged with the SeeDAO I&R guild. I have great confidence in SeeDAO’s growth and am excited to witness its progress firsthand.

I am a technical partner of DIGICARBON(中财数碳), and my main responsibility is to develop machine learning algorithms and technical frameworks. In fact, I am also interested in topics related to technology-enabled carbon neutrality. I think this topic is the best representation of “Tech For Good”.

I was obsessed with business competitions for a long time. I led our team to the 7th place in China in the Global Management Challenge in 2021.

Services

Reviewer:
- IEEE Transactions on Computational Social Systems (TCSS)
- ACM The International Conference on the Foundations of Digital Games 2023 (FDG 2023)
- 1st Workshop on Fintech and Decentralized Finance (FiDeFix 2023)

news

Jan 23, 2024	Our paper ARTEMIS: Detecting Airdrop Hunters in NFT Markets with a Graph Learning System has been accepted by The 2024 ACM Web Conference (WWW 2024) !
Aug 8, 2023	I’ve joined the Tencent WeChat team as a research intern, focusing on large-scale graph neural network learning tasks!
Jul 9, 2023	Our paper Harnessing Web3 on Carbon Offset Market for Sustainability: Framework and A Case Study has been accepted by IEEE Wireless Communications !
May 15, 2023	I’ve joined Meituan as a deep learning research intern!
Jan 14, 2023	We proudly earned the gold medal 🥇 at the ACM-ICPC Asia Regional Contest, representing CUHK-SZ! Congratulations to me and my teammates, Hanfei and Zirong!

selected publications

2024

ARTEMIS: Detecting Airdrop Hunters in NFT Markets with a Graph Learning System

Chenyu Zhou, Hongzhou Chen, Hao Wu, Junyu Zhang, and Wei Cai

2024

Abs

As Web3 projects leverage airdrops to incentivize participation, airdrop hunters tactically amass wallet addresses to capitalize on token giveaways. This poses challenges to the decentralization goal. Current detection approaches tailored for cryptocurrencies overlook non-fungible tokens (NFTs) nuances. We introduce ARTEMIS, an optimized graph neural network system for identifying airdrop hunters in NFT transactions. ARTEMIS captures NFT airdrop hunters through: (1) a multimodal module extracting visual and textual insights from NFT metadata using Transformer models; (2) a tailored node aggregation function chaining NFT transaction sequences, retaining behavioral insights; (3) engineered features based on market manipulation theories detecting anomalous trading. Evaluated on decentralized exchange Blur’s data, ARTEMIS significantly outperforms baselines in pinpointing hunters. This pioneering computational solution for an emergent Web3 phenomenon has broad applicability for blockchain anomaly detection. The data and code for the paper are accessible at the following link: doi.org/10.5281/zenodo.10676801.

2023

Harnessing Web3 on Carbon Offset Market for Sustainability: Framework and A Case Study

Chenyu Zhou, Hongzhou Chen, Shiman Wang, Xinyao Sun, Abdulmotaleb El Saddik, and Wei Cai

IEEE Wireless Communications, 2023

Abs arXiv

Blockchain, pivotal in shaping the metaverse and Web3, often draws criticism for high energy consumption and carbon emission. The rise of sustainability-focused blockchains, especially when intersecting with innovative wireless technologies, revises this predicament. To understand blockchain’s role in sustainability, we propose a three-layers structure encapsulating four green utilities: Recording and Tracking, Wide Verification, Value Trading, and Concept Disseminating. Nori, a decentralized voluntary carbon offset project, serves as our case, illuminating these utilities. Our research unveils unique insights into the on-chain carbon market participants, affect factors of the market, value propositions of NFT-based carbon credits, and the role of social media to spread the concept of carbon offset. We argue that blockchain’s contribution to sustainability is significant, with carbon offsetting potentially evolving as a new standard within the blockchain sector.