Congratulations on your PhD! What was your background when you started your PhD?
"I have a bachelor's degree in computer science from Free University of Tbilisi in Georgia (where I'm from) and two years in a master's program in Mathematics at Ilia State University in Georgia with one Erasmus year in Uppsala."
What is it like to be a PhD student at IDA?
"My time as a PhD was quite pleasant overall. Despite having no prior research experience, close support from my supervisors helped me get up to speed quite quickly, and I have been enjoying research ever since. I was part of Wallenberg AI autonomous systems and software program (WASP) graduate school, which made the experience even more interesting with various courses around Sweden, summer and winter schools and international trips."
You have just finished your PhD. What do you want to do next?
"I am continuing to work on research directions that emerged in the later years of my PhD, now with the help of an International postdoc grant from the Swedish Research Council. It allows me to travel for two years outside Sweden, and my plan is to visit two universities in the UK: Oxford University and Royal Holloway University of London."
A summary of the thesis by George Osipov
Many real-world tasks can be phrased in terms of assigning values to variables subject to a set of constraints. This makes the Constraint Satisfaction Problem (CSP) a versatile modelling tool both in practice and in theory of computing. In applications, it is often impossible to satisfy all constraints, but there exist assignments that satisfy almost all constraints.
In his thesis, George Osipov has studied the complexity of solving almost satisfiable CSPs. Classical complexity theory strongly suggests that finding optimal assignments to CSPs is computationally intractable. However, by adopting the lens of parameterized complexity, many problems become tractable by leveraging the special structure of the instances. The thesis is at the intersection of theoretical computer science and AI and is not guided by an immediate application. One possible application is efficient data processing and automatic reasoning, which can be crucial for drawing insights from the drastically increasing amounts of data. An attractive feature of parameterized algorithms in this regard is that they scale favourablyto larger inputs.
To the thesis:
