Jakob Nordström obtained his MSc degree in Computer Science and Mathematics at Stockholm University in 2001, and his PhD degree in Computer Science at KTH Royal Institute of Technology in 2008. During 2008-2010 he was a postdoctoral researcher at the Massachusetts Institute of Technology (MIT), after which he returned to KTH in 2011, where he became an associate professor and received his Docent degree (habilitation) in 2015. In 2019 he moved to the University of Copenhagen, where he is now a full professor, and since 2020 he also has a part-time affiliation with Lund University.

In 2006 Jakob Nordström received the best student paper award at 38th ACM Symposium on Theory of Computing (STOC '06), and his PhD thesis received the Ackermann Award 2009 for "outstanding dissertations in Logic in Computer Science" from the European Association for Computer Science Logic. More recently, in 2022 he received a distinguished paper award at the 36th AAAI Conference on Artificial Intelligence (AAAI '22) and a best paper award at the 25th International Conference on Theory and Applications of Satisfiability Testing (SAT '22). During the years 2018-2023 Jakob Nordström was a member of the Young Academy of Sweden, where he served on the board 2020-2023. His current research is mainly funded by grants from the Independent Research Fund Denmark, the Swedish Research Council, and the Wallenberg AI, Autonomous Systems and Software Program (WASP).

In 1997-1998, Jakob Nordström served as a military interpreter at the Swedish Armed Forces Language Institute (Försvarets tolkskola), graduating as the best student of the 1998 class. In parallel with his studies and later his research, he worked for a number of years as a Russian interpreter, engaged among others for His Majesty the King of Sweden and the Swedish Prime Minister. He also has a Diploma in Choir Conducting with extended Music Theory from the Tallinn Music Upper Secondary School, Estonia. During the period 1994-1999, he was the artistic director of Collegium Vocale Stockholm, a vocal ensemble performing mainly Renaissance and Baroque music.

Computers are everywhere today—at work, in our cars, in our living rooms, and even in our pockets—and have changed the world beyond our wildest imagination. Yet these marvellous devices are, at the core, amazingly simple and stupid: all they can do is to mechanically shuffle around zeros and ones. What is the true potential of such automated computational devices? And what are the limits of what can be done by mindless calculations? Understanding this kind of questions is ultimately what my research is about.

Computational complexity theory gives these deep and fascinating philosophical questions a crisp mathematical meaning. A computational problem is any task that is in principle amenable to being solved by a computer—i.e., it can be solved by mechanical application of mathematical steps. By constructing general, abstract models of computers we can study how to design efficient methods, or algorithms, for solving different tasks, but also prove mathematical theorems showing that some computational problems just cannot be solved efficiently for inherent reasons.

I am particularly interested in understanding combinatorial optimization problems, which are of fundamental mathematical importance but also have wide-ranging applications in industry. My goal is, one the one hand, to prove formally that many such problems are beyond the reach of current algorithmic techniques, but also, on the other hand, to develop new algorithms that have the potential to go significantly beyond the current state of the art. Recently, I have also been doing research on how complexity theory can be harnessed to produce certificates that algorithms are actually computing correct results. It is an open secret in combinatorial optimization that even the best commercial tools sometimes produce wrong answers, but there has been no really principled way of addressing this problem. Our research has begun changing this, and we have already received several prestigious awards for our work.