Shanchieh Yang Headshot

Shanchieh Yang

Professor

Department of Computer Engineering
Kate Gleason College of Engineering
Director of Research, ESL Global Cybersecurity Institute
jay.yang@rit.edu
585-475-6434
Office Location
GLE-3435
Office Mailing Address
GLE-3475

Shanchieh Yang

Professor

Department of Computer Engineering
Kate Gleason College of Engineering
Director of Research, ESL Global Cybersecurity Institute

Education

BS, National Chiao-Tung University (Taiwan); MS, Ph.D., University of Texas at Austin

Bio

Dr. Shanchieh (Jay) Yang received his BS degree in Electronics Engineering from National Chaio-Tung University in Taiwan in 1995, and MS and Ph.D. degrees in Electrical and Computer Engineering from the University of Texas at Austin in 1998 and 2001, respectively. He is currently a Professor in Computer Engineering and the Director of Global Outreach for Global Cybersecurity Institute at Rochester Institute of Technology. His research focuses on advancing machine learning, modeling, and simulation for predictive cyber intelligence and anticipatory cyber defense. His research group has been supported by NSF, IARPA, DARPA, NSA, AFRL, ONR, and ARO. His earlier work introduced Variable Length Markov Models (F-VLMM), Virtual Terrain (VTAC), and Attack Social Graphs (ASG) for predictive cyber situation awareness (FuSIA, VTAC, & ViSAw). More recently, his team develops a holistic body of work that contains ASSERT to continuously learn and update emerging statistical attack models, CASCADES to simulate synthetic scenarios grounded with a theoretical understanding of adversary behaviors, and CAPTURE to forecast cyberattacks using unconventional signals in the public domain. He was a 2019 NSF Trusted CI Open Science Fellows and a 2020 NSF Trusted CI TTP Fellow. He received IEEE Region 1 Outstanding Teaching in an IEEE Area of Interest Award for outstanding leadership and contributions to cybersecurity and computer engineering in 2019. He received Norman A. Miles Award for Academic Excellence in Teaching in 2007, and was also a co-chair for IEEE Joint Communications and Aerospace Chapter in Rochester NY in 2005, when the chapter was recognized as an Outstanding Chapter of Region 1. As an innovative and collaborative leader in academia, he has also established several international partnership programs and collaborations with universities across Europe and Asia.

jay.yang@rit.edu
585-475-6434
Personal Links
Personal Website

Select Scholarship

  • A. Nadeem, S. Verwer, S. Moskal, and S. J. Yang, “Alert-driven Attack Graph Generation using S-PDFA,” in IEEE Transactions on Dependable and Secure Computing, 2021, doi: 10.1109/TDSC.2021.3117348.
  • S. Moskal and S. J. Yang, “Translating Intrusion Alerts to Cyberattack Stages using Pseudo-Active Transfer Learning,” in Proceedings of IEEE Conferences on Communications and Network Security, Oct 4-6, 2021.
  • C. Sweet, S. Moskal, and S. J. Yang, “On the Variety and Veracity of Cyber Intrusion Alerts Synthesized by Generative Adversarial Networks,” ACM Transactions on Management Information Systems, Special Issue on Analytics for Cybersecurity and Privacy, Vol.11, Issue 4, No. 22, December 2020, https://dl.acm.org/doi/abs/10.1145/3394503.
  • A. Okutan and S. J. Yang, “ASSERT: Attack Synthesis and Separation with Entropy Redistribution towards Predictive Cyber Defense”, Springer Journal on Cybersecurity, 2:15, May 2019.
  • S. Moskal, S. J. Yang, and M. Kuhl, “Cyber Threat Assessment via Attack Scenario Simulation using an Integrated Adversary and Network Modeling Approach,” Journal of Defense Modeling and Simulation, Vol. 15, No.1, pp.13-29, 2018.
  • S. J. Yang, H. Du, J. Holsopple, and M. Sudit, “Attack Projection for Predictive Cyber Situation Awareness,” book chapter in A. Kott, R. Erbacher, and C. Wang (Eds.), Cyber Defense and Situational Awareness, Springer, pp. 239-261, 2014.
  • S. J. Yang, A. Stotz, J. Holsopple, M. Sudit, and M. Kuhl, “High Level Information Fusion for Tracking and Projection of Multistage Cyber Attacks,” Elsevier International Journal on Information Fusion, Vol. 10, No. 1, pp.107-121, January 2009.
  • D. Fava, S. Byers, S. J. Yang, “Projecting Cyber Attacks through Variable Length Markov Models,” IEEE Transactions on Information Forensics and Security, Vol. 3, No. 3, pp.359-369, September 2008.

Currently Teaching

CMPE-110
Introduction to Computer Engineering
1 Credits
This course overviews the field of computer engineering, the computer engineering curriculum at RIT, and research and career opportunities. The topics covered include basic circuit analysis, number systems, digital logic, programming, robotics, laboratory equipment, teamwork, critical thinking, technical writing, modern and contemporary issues, ethics, diversity, and communication skills.
CMPE-570
Data and Communication Networks
3 Credits
This course gives an overview of the technologies, architectures, and protocols used to build various types of computer and communication networks. The course emphasizes various network design problems and solution approaches. Specific issues covered include framing and coding, error detection, multiple access control, addressing, routing, flow and congestion control, scheduling, and switching.
CMPE-670
Data and Communication Networks
3 Credits
This course gives an overview of the technologies, architectures, and protocols used to build various types of computer and communication networks. The course emphasizes various network design problems and solution approaches. Specific issues covered include framing and coding, error detection, multiple access control, addressing, routing, flow and congestion control, scheduling, and switching.
CMPE-788
Machine Learning for Cybersecurity Analytics
3 Credits
This course is a semester-long project-based course, where students learn to select and apply machine learning and data science (ML/DS) techniques to solve cybersecurity problems. Through learning-by-doing, students will discover cybersecurity challenges and how ML/DS can help overcome the challenges as well as the limitations of ML/DS. Students will explore and choose appropriate ML/DS approaches, design and conduct experiments with open-domain cybersecurity data, and deduce and present findings to practice analytical and critical thinking skills. The course will progress in tightly guided and coupled stages: data and feature analysis, literature review and problem discovery, ML technique exploration, experimental design, result interpretation and analysis, professional project dissemination, and constructive peer reviews.
CMPE-789
Special Topics
3 Credits
Graduate level topics and subject areas that are not among the courses typically offered are provided under the title of Special Topics. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor.
EGEN-232
Leadership/Ethics/Sustainability
1 Credits
The third course in a series of courses for engineering honors students focused on how innovative products are developed, designed and manufactured to effectively meet the expanding needs of a global economy. This course highlights key issues that decision-makers in industry need to understand as they shape their companies to be more competitive in a global context. A series of presentations by guest speakers address the topics of leadership, ethics, and sustainability.

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