The Väisälä Prize is worth €15 000 and is awarded annually to young, already distinguished scientists in the field of mathematics and natural sciences. The two prizes were presented at an event hosted by the Finnish Academy of Science and Letters on Monday, 11 December 2023.
Professor Anu Kankainen’s field of research, nuclear astrophysics, aims to examine the origin of elements in the universe. In her research, Kankainen has focused especially on the precision mass measurements of radioactive isotopes. Through these masses, we can gain information on nuclear binding energies and how much energy is released in nuclear reactions in stars, which in turn affects the probability of nuclear reactions.
Kankainen defended her doctoral thesis in 2006 in the Department of Physics at the University of Jyväskylä and then worked as a postdoctoral researcher at the Academy of Finland and an Academy Research Fellow at the University of Jyväskylä. From 2013 to 2014, Kankainen worked as a researcher at the University of Edinburgh in Scotland.
“Following a series of twists and turns, I ended up studying physics at the University of Jyväskylä and during the course of my studies, I got more and more excited about research,” says Kankainen. “Towards the end of my studies, I completed a course in nuclear physics, which put me on the path to becoming a nuclear physicist: the course was extremely inspiring and, among other things, led me to become a summer trainee at the Accelerator Laboratory of the University of Jyväskylä. Soon after this, I applied for doctoral studies.”
In 2017, Kankainen received the esteemed and generous ERC Consolidator Grant from the European Research Council for her research project MAIDEN (Masses, Isomers and Decay Studies for Elemental Nucleosynthesis). She was appointed Associate Professor in 2019 and Professor in 2022.
Kankainen is one of the most distinguished young experts in her field of research and has an extensive international cooperation network in Europe and the United States. She has published almost 200 research articles, including several publications in leading journals in her field. In addition, she holds several positions of trust related to research.
“Research in nuclear astrophysics is very international by nature. We conduct measurements in large research groups, for one to two weeks at a time,” says Kankainen. “The best part about my job are the measurements and what we may discover through them, and working together with all kinds of people.”
Experimental research led by Kankainen has been conducted mainly at the Accelerator Laboratory of the University of Jyväskylä, but also in other leading research centres in the field, such as CERN, GSI-FAIR and NSCL Michigan State University. This research has produced extremely precise mass and binding energy data for more than one hundred rare radioisotopes and several metastable nuclear states.
“It always feels great to discover something that nobody else has discovered before. There is currently a lot going on internationally in nuclear astrophysics research. For example, a new accelerator laboratory has just been opened in the United States, which can produce extremely neutron-rich nuclei and study their properties. My personal dreams have to do with how much we can discover in the years to come.”
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Professor Lauri Oksanen’s research deals with inverse problems. Oksanen’s research is applied, for example, when there is a need to know what is inside an object in medical or soil imaging. Oksanen studies the mathematical methods needed to produce such images.
Oksanen earned his Master of Science degree in 2008 and his PhD in 2012. Following this doctoral thesis, he went on to work briefly as a post doc researcher at the University of Washington and then in 2013 as a lecturer at the University College London where he was appointed permanent Professor of Mathematics in 2020. In 2021, it was time to move to Finland to become Professor of Applied Mathematics at the University of Helsinki.
Oksanen became interested in mathematics at a young age for several reasons. “Ever since comprehensive school, I’ve had excellent mathematics teachers. I’ve definitely also had some natural inclination towards the field, in addition to a group of friends who are interested in mathematics. When I was younger, I enjoyed computer programming as a hobby, which is a lot like mathematics: both require logical thinking and the ability to express yourself in formal languages and enjoy this type of expression,” says Oksanen.
Oksanen’s research is exceptionally broad in scope and he is one of the world’s leading researchers in numerical methods for inverse problems. He has studied numerical methods for partial differential equations, especially element methods, and his studies on discretization have attracted wide attention. Oksanen has also achieved interesting results with medical imaging methods combining different physical models, for example in photoacoustic imaging.
“My working days are quite varied. When I’m working remotely, I do a lot of reading and thinking, whereas when I’m at the university, I meet with my research group and have lots of conversations with people, which stimulates my thinking on remote working days. Both sides are important.”
Oksanen’s work has gained remarkable visibility around the world, and he has been invited to speak at a number of international conferences. He has also received major funding for his research, including the ERC Consolidator Grant from the European Research Council for the years 2023–2028. This funding is aimed at distinguished researchers for consolidating their research group and establishing an impactful career in Europe. Oksanen also serves as the principal investigator at the Centre of Excellence of Inverse Modelling and Imaging of the Academy of Finland (2023–2025).
As for the future of mathematics in Finland, Oksanen feels optimistic: “We in Finland have extremely good, top-notch research groups and can conduct research in mathematics at the highest international level. Looking at the field more broadly, I wish that education in mathematics would lean slightly more towards computer science. The processing of large data sets and machine learning models, which are the hot topics today, sit largely in the intersection between mathematics, computer science and statistics.”
Oksanen’s dreams for the future have to do with solving certain mathematical problems, but the researcher does not want to give away too much: “Probably every mathematician has a favourite problem that they dream of solving.”