About me

I am an Assistant Professor of Computer Science at the School of Computing and Information Systems at Singapore Management University. Previously, I obtained my PhD in 2023 at the Institute of Science and Technology Austria (ISTA), where I was fortunate to be supervised by Krishnendu Chatterjee and Petr Novotný. For my PhD work, I received the Outstanding PhD Thesis and the Outstanding Scientific Achievement awards at ISTA. Before that, I obtained bachelor’s and master’s degrees in mathematics at the University of Cambridge.

You can reach me at dzikelic@smu.edu.sg.

I have multiple openings for motivated PhD students, interns, and visiting PhD/master/undergrad students, to work with me on topics listed below, or more broadly on topics related to formal methods, trustworthy AI or program verification. See this page for more details.

Research Interests

My research is concerned with designing theories and automated methods for ensuring that software and AI systems are correct, safe, and trustworthy. To achieve this, I study Formal Methods and Programming Languages as well as Trustworthy AI and Safe Autonomy. My research is both foundational and application driven. On the foundational side, the long-term goal of my work is to contribute to laying theoretical and algorithmic foundations of automated formal verification and synthesis methods for probabilistic models and programs. Probabilistic models and programs provide a canonical framework for modelling and implementing computing systems that exhibit uncertain behaviour, with a broad range of applications such as randomised algorithms, communication protocols and networks, security and privacy protocols, control and autonomous systems, and AI. The central application domains that I focus on are trustworthy AI and safe autonomy, where my goal is to develop certified learning and formal verification methods for designing trustworthy AI and safe autonomous systems. My current research interests with some selected prior work include:

  1. Formal Methods for Probabilistic Models and Programs
  2. Trustworthy AI and Safe Autonomy
  3. Broader perspective and other applications of probabilistic system verification:

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