Yuede Ji

Yuede Ji is an Assistant Professor from the Department of Computer Science and Engineering at the University of North Texas. Yuede Ji’s research interest is at the intersection of Cybersecurity, High-Performance Computing, Graph Learning, and Graph Analytics. He aims to build efficient systems to protect the emerging data and computing devices from various layers, such as vulnerability detection, malware detection, and network threat detection. Furthermore, he aims to build high-performance computing systems with emerging hardware (e.g., GPU) to improve the scalability of cybersecurity techniques. His research works have frequently appeared at prestigious cybersecurity and high-performance computing conferences, including USENIX Security, SC, HPDC, RAID, and AsiaCCS. His research has won the best paper award at NPC 2014.

Yuede Ji received the Ph.D. degree from the the George Washington University in 2021 (advisor Prof. H. Howie Huang). He received M.S. and B.E. from Jilin University in 2015 and 2012, respectively. Yuede Ji’s curriculum vitae can be found here.

Openings

I am looking for multiple self-motivated Ph.D. students (fully funded) and several interns or visitors (could be remote) to work with me on various cool projects in cybersecurity, high-performance computing, and graph analytics. If you are interested, please drop me an email with your CV.

News

[June 2021] : Our paper on analyzing exposed Android Init routines, DEFInit, is accepted to USENIX Security’21.

[Mar. 2021] : Our paper on identifying the compilation provenance of binary code, Vestige, is accepted to ACNS’21.

[Feb. 2021] : Our paper on detecting unknown advanced persistent threat (APT) is accepted to Computer Networks.

[Oct. 2020] : Our paper on binary code vulnerability analysis with graph neural network, BugGraph, is accepted to AsiaCCS’21.

[Oct. 2020] : Our paper on large scale small graphs computation with GPUs, SwarmGraph, is accepted to HPCC’20.

[June 2020] : Start working as a senior intern at Kryptowire (a company that specializes in Mobile Security and Code Analysis) mentored by Dr. Mohamed Elsabagh.

[May 2020] : Our paper on lateral movement detection with unsupervised graph learning is accepted to RAID’20.

[Mar. 2020] : Our paper on graph connectivity computation framework, Aquila, is accepted to HPDC’20.

[June 2018] : Our paper on strongly connected component detection, iSpan, is accepted to SC’18.