Juan Zhai is an Assistant Professor in the Manning College of Information & Computer Sciences (CICS) at University of Massachusetts, Amherst (UMass). She co-directs the Laboratory for Advanced Software Engineering Research (LASER) lab. She is also a member of UMass NLP group. She works on trustworthy computing systems, with a focus on ensuring the correctness, reliability, and security of modern software and machine learning systems, including emerging agentic AI systems. Her research has been recognized with an NSF CAREER Award. Her work is supported by NSF, NEC Labs and Dolby.
I am always looking for students to work with. Drop me an email if you are interested in working with me!
Trustworthy AI for Software and ML Systems
I build trustworthy computing systems, with a focus on ensuring the correctness, safety, and reliability of modern machine learning and software systems, including emerging agentic AI systems.
My research draws on foundations from software engineering, security, and systems, and develops methods for testing, formal specification, and runtime assurance to make complex systems auditable and dependable in real-world environments. I am particularly interested in agentic AI, where autonomous systems execute multi-step workflows and interact with external tools, creating new challenges for verification, security, and control.
Formal Methods and Verification
A core focus is improving software correctness through formal specifications and verification-oriented techniques. I work on methods to derive precise behavioral specifications from natural language and code artifacts, enabling stronger testing, analysis, and reasoning about system behavior.
I am also interested in how formal reasoning can be integrated with AI-based development workflows so that intelligent code generation is grounded in explicit correctness constraints.
Reliability and Robustness of AI Systems
I develop practical techniques to test, diagnose, and improve the reliability of AI software stacks, including deep learning frameworks, model training pipelines, and deployment workflows. The emphasis is on identifying failures early and reducing costly downstream errors.
This includes work on robustness testing, automated debugging and repair, and system-level quality assurance for machine learning tools and models.
Responsible and Accountable AI Systems
Another key direction is improving fairness and accountability in AI systems. I investigate how bias emerges across the machine learning lifecycle and develop approaches to detect, explain, and mitigate those issues.
I also study software documentation and communication artifacts, such as comments and natural language specifications, to better align implementation behavior with intended requirements and strengthen trustworthy AI development workflows.
Current Students
PhD Students (Advisor)
Jinsong Mao: started in Fall 2022; co-advised with Dr. Shiqing Ma
Gehao Zhang: started in Fall 2024
Yi Su: Starting Sep. 2026
PhD Students (Committee Member)
Zhanna Kaufman: started in Fall 2021
Master's Students
Xiaofan Lu: started in Fall 2025
Chetan Kodand Reddy Madadi: started in Fall 2025
Alumni PhD students (committee member)
Juan Altmayer Pizzorno: received his PhD degree in 2026 at UMass Amherst
Alumni Undergraduate Students
Justin Chen: received his BS degree in May 2026 at UMass Amherst; enrolled as a Master's student at UMass Amherst
Sammie (Sheyan) Yu: received her BS degree in May 2026 at UMass Amherst; enrolled as a Master's student at UPenn
Join Our Research Group
For Prospective PhD Students
I am actively seeking motivated PhD students interested in Software Engineering, AI Safety, and Trustworthy AI research. Strong background in programming and interest in research is essential.
Application: Apply through the UMass CS PhD program and mention my name in your application.
For Undergraduate and Master's Students
I welcome motivated undergraduate and Master's students to join our research activities. Research experience provides valuable skills and insights for your academic and professional development.
Opportunities: Independent study courses, research assistant positions, and co-authorship on publications.
📧 Contact: Please email me with your CV, transcript, and research interests.
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- Fall 2026: CS621: Advanced Software Engineering: Analysis and Evaluation
- Fall 2025: CS520: Theory and Practice of Software Engineering
- Fall 2024: CS692P: Hot Topics in Software Engineering Research
- Fall 2024: CS520: Theory and Practice of Software Engineering
- Spring 2024: CS520: Theory and Practice of Software Engineering
- Spring 2023: CS431: Software Engineering
- Spring 2023: CS111: Introduction to Computer Science
- Fall 2022: CS112: Data Structure x2
- Fall 2022: CS111: Introduction to Computer Science
- Spring 2022: CS112: Data Structure x2
- Spring 2021: CS112: Data Structure x2
- Spring 2021: CS111: Introduction to Computer Science
- Fall 2020: CS112: Data Structure
- Fall 2020: CS111: Introduction to Computer Science
- Spring 2020: CS431: Software Engineering
- Spring 2020: CS112: Data Structure
- Spring 2020: CS111: Introduction to Computer Science
- Fall 2019: CS112: Data Structure
- Fall 2019: CS111: Introduction to Computer Science