Chenyu Zhou

Hi , This is Chenyu (周宸宇 in Chinese)!

I am currently a first-year PhD student in Institute of Intelligent Computing at Shanghai Jiao Tong University. I’m very proud to be advised by Prof. Yinyu Ye and Prof. Dongdong Ge. Prior to this, I obtained my bachelar’s degree from China University of Petroleum, Beijing and my master’s degree from The Chinese University of Hong Kong, Shenzhen, where I was advised by Prof. Wei Cai.

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 machine learning (large language models, deep learning and optimization) and blockchain (decentralized computing, smart contract and cryptocurrency) in finance.

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).

I like conducting my research in real industrial settings. I have served as an AI researcher at Cardinal Operations(杉数科技), Tencent(腾讯), and Meituan(美团).

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

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 Conference on Human Factors in Computing Systems 2024 (CHI 2024)
- ACM The International Conference on the Foundations of Digital Games 2023 (FDG 2023)
- 1st Workshop on Fintech and Decentralized Finance (FiDeFix 2023)
Teaching Assistant:
- MGTS3401: Data Structure, by Prof. Qi Deng at SJTU (2024 Autumn)

news

Sep 21, 2024	Our paper Decentralized Web3 Non-Fungible Token Community for Societal Prosperity? A Social Capital Perspectivey has been accepted by The 27th ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW 2024) !
Sep 18, 2024	I officially joined the Institute of Intelligent Computing at SJTU and became a PhD student!
Jul 19, 2024	I’ve joined Cardinal Operations as a large langeuage models research intern!
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 08, 2023	I’ve joined the Tencent WeChat team as a research intern, focusing on large-scale graph neural network learning tasks!

selected publications

CSCW 2024

Decentralized Web3 Non-Fungible Token Community for Societal Prosperity? A Social Capital Perspectivey

Hongzhou Chen, Chenyu Zhou, Abdulmotaleb El Saddik, and Wei Cai

The 27th ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW), 2024
WWW 2024

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

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

The ACM Web Conference 2024 (WWW) (Oral), 2024

Abs Code

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.
IEEE Wirel Commun

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.