I am currently a PhD candidate in the Department of Computer Science at Purdue University, where I am fortunate to work alongside Professor Christina Garman as part of the Boilermakers' Applied Research in Cryptography (BARC). Prior to joining Purdue, I contributed as a software and application developer at Pervasive Technology Institute and IU School of Optometry. I completed my bachelor and master degrees at Indiana University Bloomington.
My research interests lie at the intersection of cryptography, zero knowledge proof, protocol security evaluation, and software security. Specifically, I am intrigued by the potential of zk-SNARKs to enhance privacy and efficiency in various applications. I aim to bridge the gap between theoretical cryptography and practical security solutions, addressing critical challenges in the rapidly evolving digital landscape.
Feel free to contact with me and you can see my CV for more information.
Research Areas: Applied Cryptography, Software Security, and Privacy
Thesis title: Segmentation of Retinal Optic from a New Approach Hough Transform
at Purdue University
at York University
at Indiana University Bloomington
at Indiana University Bloomington
NDSS Workshop on AI System with Confidential Computing Assistant
International Journal of Applied Cryptography
IEEE/ACM Transactions on Computational Biology and Bioinformatics
ICLR Workshop on Backdoor Attacks and Defenses in Machine Learning
IEEE International Conference on Medical Artificial Intelligence
Financial Cryptography and Data Security
Yongming Fan, Yuquan Xu, and Christina Garman, “Snarkprobe: An automated security analysis framework for zksnark implementations,” in International Conference on Applied Cryptography and Network Security, Springer, 2024.
Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, and Xiaozhong Liu, “Semi-supervised semantic-topological iteration network for financial risk detection via news label diffusion,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
Zhixin Li, Rui Zhu, Zihao Wang, Jiale Li, Kaiyuan Liu, Yue Qin, Yongming Fan, Mingyu Gu, Zhihui Lu, Jie Wu, Hongfeng Chai, Xiaofeng Wang, and Haixu Tang, “Fairfix: Enhancing fairness of pre-trained deep neural networks with scarce data resources,” in 10th IEEE International Conference on Intelligent Data and Security (IDS), IEEE, 2024, (Best Student Paper Award).
Yongming Fan, “Segmentation of retinal optic from a new approach hough transform,” M.S. thesis, Indiana University Bloomington, May 2020.
Assisted instructor to teach graduate level data science and system security class including lecture and lab sections and coordinated other instructors and TAs to analyze student's learning status and draft the weekly plan for the course.
Developed and enhanced ophthalmic instruments software programs in MATLAB, including contrast sensitivity perimetry, retinal image segmentation, and realistic neural processing simulations to provide accurate and reliable ophthalmic diagnostics for patients.
Designed and developed multiple online course systems for both front-end and back-end development and created application programming interfaces (APIs) to ensure compliance with federal and state student record privacy regulations and facilitate seamless content integration for future system upgrades.
Provided registration related services including instruction and problem resolution for registration and schedule adjustment and aided new freshman and transfer students to to understand and learn the University’s academic system during the New Student Orientation.
Cooperated instructors to lecture the Introduction to Programming I and II courses and Tools for Computing for fundamental programming constructs, including loops, arrays, class, and file system in Python, Java, and Arduino.
Last Update: January 5, 2024 Admin