Gang Wang
Gang Wang
Assistant Professor
(217) 244-1008
4316 Siebel Center for Comp Sci

For More Information


Gang Wang received his Ph.D. from UC Santa Barbara in 2016 (under the direction of Ben Y. Zhao and Heather Zheng), and a B.E. from Tsinghua University in 2010. After working as an Assistant Professor at Virginia Tech (2016 - 2019), he joined the University of Illinois at Urbana-Champaign in 2019. He is now an Assistant Professor in the Department of Computer Science, and also has affiliate faculty appointments in the Department of Electrical and Computer Engineering (ECE) and the Informatics Program of School at the University of Illinois. He is a recipient of the NSF CAREER Award (2018), Amazon Research Award (2021), Google Faculty Research Award (2017), and Best Paper Awards from IMWUT 2019, ACM CCS 2018, and SIGMETRICS 2013. His projects have been covered by media outlets such as MIT Technology Review, The New York Times, Boston Globe, CNN, and ACM TechNews.

Gang Wang's research interests are Security and Privacy, Internet Measurement, and Data Mining. His work takes a data-driven approach to addressing emerging security threats in massive communication systems (social media, email services), crowdsourcing systems, mobile applications, and enterprise networks. His major contribution is a series of measurement methodologies that have revealed previously overlooked security threats, including crowdturfing activities in online social networks, security certification failures in the payment card industry, email spoofing vulnerabilities in major email providers, and the deep link usage in mobile app ecosystems. Another key contribution is his work on applying machine learning in security applications (e.g., bot detection, malware classification) to handle adversarial behaviors and concept drift.

Gang Wang's current focus is on developing robust data-driven methods (e.g., machine learning models, graph models) to enable accurate, scalable, and user-friendly security applications for malware analysis and online abuse mitigation. The key challenge Wang is tackling is to effectively engineer human-machine collaboration pipelines to improve the efficacy and efficiency of both parties against adaptive attackers in the respective fields. Wang is currently collaborating with industry partners and researchers from related fields such as HCI and AI to address these problems.

Academic Positions

  • Affiliate Faculty, Informatics Programs, School of Information Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, Sep. 2021 - Present
  • Affiliate Faculty, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, Nov. 2020 - Present
  • Assistant Professor, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Aug. 2019 - Present

Selected Articles in Journals

Articles in Conference Proceedings


  • ACM CCS Best Reviewer Award, ACM CCS (2022)
  • Amazon Research Award (2021)
  • Teacher Ranked as Excellent/Outstanding by their Students, UIUC (2021)
  • CSAW '20 Applied Research Competition Finalist (2020)
  • Teacher Ranked as Excellent/Outstanding by their Students, UIUC (2020)
  • Teacher Ranked as Excellent/Outstanding by their Students, UIUC (2019)
  • IMWUT Distinguished Paper Award (2019)
  • Outstanding New Assistant Professor Award at Virginia Tech (2019)
  • ACM CCS Outstanding Paper Award (2018)
  • NSF CAREER Award (2018)
  • Google Faculty Research Award (2017)
  • Outstanding Dissertation Award, UC Santa Barbara (2016)
  • Graduate Division Dissertation Fellowship, UC Santa Barbara (2015)
  • SIGMETRICS Best Practical Paper Award (2013)

Recent Courses Taught

  • CS 463 (ECE 424) - Computer Security II
  • CS 562 - Mach Lrng for Sys, Netwk & Sec
  • CS 598 GW - ML for Sys, Netwrks & Security