Assistant Professor of Computer Science
The recent computational advances in artificial intelligence (AI) and machine learning promise to enable many future technologies such as autonomous driving, smart home technologies, human-assisted robotics, and smart healthcare. Over the next decade, large amounts of data will be generated and stored as devices that control and sense the physical world are becoming more affordable, e.g., robots, smart watches. While the availability of data creates many exciting opportunities, it also gives rise to fundamental research challenges on the interface between control theory, machine learning and AI, and formal methods. Not only are new theoretical frameworks needed to address these new research challenges, but wide availability of these future systems and technologies will also require more efficient computational approaches that allow for near real-time learning and control. The key research challenges that I focus on include the development of new theoretical and computational frameworks for:
While there has been tremendeous success over the past years towards creating data-driven intelligent systems, there is still a lack of understanding of the complex interplay between the fields of control theory, machine learning and AI, and formal methods. My research helps to bridge these fields towards the design of scalable data-driven intelligent systems. Specifically, my personal research agenda revolves around safe and distributed AI-enabled autonomous systems with a focus on addressing the aforementioned key challenges. My contributions span the development of theoretical frameworks with stringent performance and safety guarantees to their scalable implementation by means of distributed computationally efficient algorithms.