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Contact Information
Contact Information
Em@il: mark AT shma DOT dev
About Me
About Me
I hold a Ph.D. in Computer Science with a specialization in Blockchain, Cryptography, and Data Analysis. My passion lies at the intersection of technology and innovation, driving advancements in Web3, cryptographic solutions, and fintech. I am actively seeking opportunities where I can leverage my expertise to contribute to groundbreaking projects in these areas. Let's connect and explore how we can build the future together.
If you're looking for a dedicated professional to push the boundaries of innovation, let's connect. Feel free to reach out via email for potential collaborations.
Professional Experience
Professional Experience
Projects
Projects
Anti-Cryptocurrency Laundering 2021 - 2024
Built a data analysis platform using ClickHouse and Neo4j to support SQL and graph algorithms over multi-terabyte of data.
Developed a graph technology-based detection system to identify incoming funds and trace the tainted funds to their origin, helping prevent cryptocurrency laundering.
Conducted extensive research to understand the latest trends and challenges in the field of cryptocurrency laundering, which informed the development of our detection system.
Fraud Buster 2020-2021 paper, video
Capitalized on the threshold cryptosystem and proxy re-encryption methodologies to architect a confidential, blockchain-based routing system for decentralized delivery networks, enhancing both security and efficiency.
Demonstrated the system using Mininet to simulate a virtual Software Defined Network (SDN), effectively highlighting its capability in addressing telecom fraud while ensuring route confidentiality.
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain 2020-2021 paper
Exploited secure multiparty computation to accurately compute model weights in a federated learning environment, enabling transparent evaluation of participant contributions while protecting privacy during model-building.
Benchmarking blockchain-based gene-drug interaction data sharing methods 2019-2020 paper, code, web
Earned 1st Place at the 2019 iDASH Privacy & Security Workshop for developing the most efficient Solidity smart contract in terms of data storage and query performance.
Conducted pioneering research on blockchain strategies applied to distributed methods for sharing gene-drug interaction records.
Blockchain-based access audit 2019-2020 paper, code
3rd Place, iDASH PRIVACY & SECURITY WORKSHOP 2018 - secure genome analysis competition, Blockchain Track
A secure and transparent access control audit module plays an essential role in ensuring accountability in a cross-site genomic data sharing system. A centralized access log audit system is vulnerable to the single point of attack and also lacks transparency since the log could be tampered with by a malicious system administrator or internal adversaries. We designed a blockchain-based log system that can provide a lightweight and widely compatible module for existing blockchain platforms.
Publications
Publications
Shuaicheng Ma, Yang Cao, Li Xiong, "Transparent Contribution Evaluation for Secure Federated Learning on Blockchain", IEEE International Conference on Data Engineering Workshops (ICDEW), 2021.
Shuaicheng Ma, Tamraparni Dasu, Yaron Kanza, Divesh Srivastava, Li Xiong, "Fraud Buster: Tracking IRSF Using Blockchain While Protecting Business Confidentiality", Conference on Innovative Data Systems Research (CIDR), 2021.
Tsung-Ting Kuo*, Tyler Bath*, Shuaicheng Ma*, Nicholas Pattengale*, Meng Yang*, Yang Cao, Corey M. Hudson, Jihoon Kim, Feng Mu, Guangming Pan, Kai Post, Shuang Yang, Ye Yin, Li Xiong, and Lucila Ohno-Machado, "Benchmarking Blockchain-Based Gene-Drug Interaction Data Sharing Methods: A Case Study from the iDASH 2019 Secure Genome Analysis Competition Blockchain Track". International Journal of Medical Informatics (IJMI), 2021.
Shuaicheng Ma, Yang Cao, Li Xiong, "Efficient logging and querying for Blockchain-based cross-site genomic dataset access audit", BMC Genomics, 2019.
Jinfei Liu, Juncheng Yang, Li Xiong, Jian Pei, Jun Luo, Yuzhang Guo, Shuaicheng Ma, Chenglin Fan. "Skyline Diagram: Efficient Space Partitioning for Skyline Queries", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
*co-first authors
Awards
Awards
1st Place, iDASH PRIVACY & SECURITY WORKSHOP 2019 - secure genome analysis competition, Blockchain Track
3rd Place, iDASH PRIVACY & SECURITY WORKSHOP 2018 - secure genome analysis competition, Blockchain Track
Teaching Assistant Experience
Teaching Assistant Experience
2021 Spring, Introduction to Computer Science ||
2020 Fall, Systems Programming
2019 Fall, Introduction to Computer Science I
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