Dr Olga Tveretina

Dr Olga Tveretina completed her PhD in Computer Science at Eindhoven University of Technology in The Netherlands, and she earned her degree in Applied Mathematics with distinction from University of Tartu in Estonia.

During her PhD, she focused on decision procedures for decidable subsets of First-Order Logic such as Equality Logic with Uninterpreted Functions that plays a significant role in various applications:

• Formal verification: an important role in formal verification processes, particularly in ensuring the equivalence and correctness of both software and hardware systems.
• Constraint solving: these solvers are key in various optimisation and problem-solving applications where certain functions are not fully defined.
• Satisfiability modulo theories solvers: extend traditional SAT solvers by incorporating various theories.
• Automated theorem proving: supports the development of automated theorem provers, which are vital for reasoning about abstract concepts.

Following her PhD, Dr Tveretina conducted postdoctoral research at the following istitutions: University College Cork, Radboud University Nijmegen and Karlsruhe Institute of Technology.

She is a member of Association for the Advancement of Artificial Intelligence (AAAI) and London Mathematical Society (LMS).

She also has industry experience, including work as a Software Engineer in financial sector, contributions to a rule-based AI system for urban water supply management, applied AI classification in a medical context, and the practical implementation of her PhD research in a verification tool.

Dr Olga Tveretina's research interests lie within the fields of AI and Theoretical Computer Sience, with an emphasis on analysis of dynamical systems, constraint solving, logical reasoning, and computational optimisation. Her current research includes the following areas:

• Analysis of hybrid dynamical systems: a focus on the decidability and complexity analysis of the reachability and mortality problems in low-dimensional hybrid systems. Hybrid systems, characterised by their combination of continuous and discrete dynamics, are essential for modeling a wide array of real-world systems such as automated control systems, robotics, and biological systems.
• Automatic differentiation: efficient computing derivatives of functions specified by computer programs which plays an important role in optimisation, sensitivity analysis, and training of machine or deep learning models.
• Propositional proof systems: comparative analysis of various standard propositional proof systems, contributing to a deeper understanding of their relative efficiency and complexity. Propositional proof systems are used in computer algorithms that automatically prove or disprove logical statements. This has applications in AI, software verification, and formal methods.
  

In addition to her academic research, Dr Olga Tveretina has industry experience as a Software Engineer at the Ministry of Finance of Estonia. She has also worked on rule-based AI systems supporting urban water supply management, and applied AI classification in a medical context. Her PhD research on OBDD-based reasoning for equality logic with uninterpreted functions has been implemented in practice as part of a verification tool used by the Software Quality Lab at Eindhoven University of Technology.

Dr Olga Tveretina's teaching philosophy emphasises fostering a deep comprehension of the subject matter while also inspiring students to develop a passion for continuous learning. She is dedicated to helping her students grasp the fundamental concepts that form the foundations of Computer Science, while also encouraging them to explore and engage with the material beyond the classroom. The subjects she has taught include:

• Models and methods in computing: theoretical foundations in mathematical concepts that are crucial for understanding computational models and methods, essential for formal reasoning and algorithm development.
• Algorithms and data structures: analysis of algorithms, complexity, and problem-solving strategies.
• Programming: fundamentals of programming using languages like Python and C/C++, with the emphasis on the importance of writing efficient, readable, and maintainable code, preparing students for real-world software development.
• Mathematics for robotics and AI: mathematical groundwork for students interested in robotics and AI. 
  
• AI principles: foundational concepts and methodologies in AI, preparing students for advanced studies and research in the field.
• Advanced AI: recent developments in AI with an aim to prepare students for both academic research and industry roles in AI, ensuring they are equipped with the skills to thrive in a rapidly evolving field.