We advance SE with insight-driven design to build software that redefines what’s possible.
WSELab pioneers advancements in classical and quantum software engineering by integrating cutting-edge techniques—including AI, large language models (LLMs), and evolutionary computing—across every stage of software development.
Integrating uncertainty modeling, quantification and mitigation across the software lifecycle—modeling, testing, and evolution—to enable resilient software systems that thrive in dynamic environments.
Developing principles, methodologies, and tools to design, implement, test, and optimize software for quantum and hybrid quantum-classical systems.
Utilizing precise digital models to enhance efficiency, consistency, and automation in software system development.
Harnessing advanced AI techniques to streamline testing processes, ensuring robust quality and adaptive performance across diverse software landscapes.
Applying both classical and quantum optimization techniques to enhance software performance, efficiency, and decision-making in complex engineering problems.
Explore our key research areas where we're making significant contributions.
Transforming models—spanning prior knowledge systems, machine learning frameworks, and more—into dynamic, context-aware collaborators.
Learn MoreDeveloping a series of novel methodologies for understanding, specifying, modeling, evolving and testing uncertainties of Cyber-Physical Systems (CPSs).
Meet the researchers and students who make our work possible.
Full Professor
Builts years of experience in SE research, tackling complex challenges and developing practical solutions.
Associate Professor
Specializes in software testing for complex software/system with a focus on AI-based techniques.
