Jeanna Matthews

Jeanna Matthews is a Full Professor of Computer Science at Clarkson University and an affiliate at Data and Society. She holds a PhD from UC Berkeley (1999) and teaches courses ranging from operating systems to cybersecurity. Her research focuses on algorithmic transparency, AI ethics, and societal impacts of automated systems. Matthews is a prominent ACM leader, serving on multiple committees including the Technology Policy Subcommittee on AI Accountability. She has pioneered work on forensic software analysis in criminal justice systems and delivered DEF CON presentations on virtualization security and adversarial testing. Awards include ACM Distinguished Speaker and Fulbright Specialist roles. Her work emphasizes open-source tools and critical thinking in education, extending to global service learning programs in the Dominican Republic and Brazil.

Education: PhD in Computer Science (UC Berkeley, 1999), B.S. in Math/Computer Science (Ohio State, 1994), B.A. in Spanish (SUNY Potsdam, 2016).

Research Interests: Cybersecurity vulnerabilities, algorithmic accountability frameworks, automated decision systems in justice contexts, and ethical AI design. Recent projects include investigating bias in DNA forensic software through a Brown Institute grant and analyzing political polarization on social platforms.

Awards & Recognition:

• ACM Distinguished Speaker (2018-present)
• Fulbright Specialist (2018-present)
• 2018-2019 Brown Institute Magic Grant
• ACM SIGOPS Chair (2011-2015)
• ACM Special Interest Group Governing Board Chair (2016-2018)

Teaching & Outreach: Designed courses integrating open-source tools and critical inquiry, including abroad programs in Mexico, Brazil, and the Dominican Republic. Advocates for lifelong learning strategies and questioning underlying assumptions in computing systems.

Key Projects:

• Forensic Software Accountability: Examining discrepancies in DNA analysis tools
• Algorithmic Transparency: Frameworks for auditing automated systems
• Cybersecurity Education: Adversarial testing methodologies for justice software