Broadly, my research interest spans three domains of computer science and engineering:

Software engineering

The software has become omnipresent, permeating all aspects of our lives and influencing both routine and critical tasks. Whether it’s tracking steps or assisting in complex medical procedures, software plays a vital role. Given our heavy reliance on software, the importance of efficient software engineering (SE) cannot be overstated. SE focuses on delivering techniques and methodologies that enable the development, evolution, and maintenance of software in a superior manner. It seeks to ensure the safety, robustness, and transparency of the software that people rely on so heavily. As an SE researcher, I primarily study the modularity and security aspects of the software. Modularity enables flexible, maintainable software development through reusable components, while security focuses on protecting against vulnerabilities and threats.

AI engineering

AI Engineering is an emerging area at the intersection of machine learning (ML) and software engineering, treating ML models as a distinct yet integral form of software. Unlike traditional software, these models are profoundly data-centric and exhibit less determinism, demanding a nuanced understanding from researchers. My research in AI Engineering specifically centers on investigating the adaptability, modularity, and reusability of deep learning (DL), a class of machine learning algorithms that has gained significant attention in recent times.

Deep learning

Deep Learning, with its exceptional capability to learn from large datasets, can truly have a transformative effect on people’s lives. I find myself deeply fascinated by the recent strides made in this area. As a Deep Learning enthusiast, I am interested in providing a machine-learning perspective on the software engineering challenges inherent in deep learning.