People

Duality Lab is a software-engineering research group at Purdue University. Our work is carried out by graduate and undergraduate researchers working across the lab’s research programs.

Mentorship is a central part of the lab. Graduate researchers develop independent research programs, while undergraduate researchers participate through sustained research teams, senior design, independent study, SURF, REU, and related programs.

Faculty

James C. Davis, PhD

Assistant Professor, Elmore Family School of Electrical and Computer Engineering, Purdue University

Graduate researchers

Dharun Anandayuvaraj

Ph.D. candidate

Learning engineering knowledge from software failures

Purvish Jajal

Ph.D. candidate · co-advised with Y.H. Lu

Efficient neural network design

Nicholas J. Eliopoulos

Ph.D. candidate · co-advised with Y.H. Lu

Machine learning systems and efficient inference

Kelechi Gabriel Kalu

Ph.D. candidate

Software supply chains, signing, and trustworthy reuse

Daniel Lugo

Ph.D. student

U.S. Space Force

Government software acquisition in the GenAI era

Andrew Rozema

Ph.D. student

Phishing measurements and tooling

Berk Çakar

Ph.D. student

Software engineering for domain-specific constructs (regexes, PTMs)

Huiyun Peng

Ph.D. student

Agentic software engineering

Ricardo Andrés Calvo Méndez

Ph.D. student

Reliability and security of embedded software

Undergraduate research

Undergraduates are a substantial part of Duality Lab. Students participate through Vertically Integrated Projects, senior design, independent research, SURF, NSF REU, and related programs.

Since 2020, 175+ undergraduate researchers have worked with the group. At least 27 have become authors on peer-reviewed papers, posters, and other scholarly outputs, alongside 12 senior-design projects supervised through these research activities.

Learn more about undergraduate research and mentoring →

Current VIP team

Software Engineering with Pre-Trained Models
Students study the reuse, integration, reliability, and security of pre-trained models.

Alumni