From Mars to machine learning
As an undergraduate, current Aalto PhD student Tuomas Kynkäänniemi helped calibrate the sensors of a Mars rover for the Finnish Meteorological Institute (FMI). Now, his research is worlds away.
Left: Artist's impression of Perseverance’s landing, Credit: Nasa/JPL Right: GAN image samples Credit: Kynkäänniemi et al. (2019) Improved Precision and Recall Metric for Assessing Generative Models.
A project that Aalto University PhD Student Tuomas Kynkäänniemi was working on in the summer of 2017 has reached worldwide attention, as part of the Perseverance rover that just landed on Mars on Thursday night.
In 2016, as an undergraduate student at Aalto University studying engineering physics, Kynkäänniemi got a summer research job at the Finnish Meteorological Institute. He worked in what is now the Planetary Research and Space Technology Group at FMI. Kynkääniemi’s job was to help with the calibration of the temperature and pressure sensors that make up MEDA a Martian weather station that takes a wide range of atmospheric measurements that FMI helped develop.
“The sensors give an electrical signal, and my job was to take that electrical signal and turn it into a ‘real world’ unit, like degrees celsius”.
Kynkäänniemi’s work now is “on another planet” as he puts it, from his engineering work from his student internship days. He now works in the Computer Science research group of Jaakko Lehtinen, developing machine learning methods called generative models for generating highly realistic images.
“When I finished my job with FMI, the field of machine learning, and generative models, were very exciting and interesting to me. I got an internship at Nvidia Finland and started working in this area instead, so when it came to choosing what to focus on for my Master’s project, I swapped from engineering physics to computer science.”
Professor Lehtinen also worked at Nvidia as a principal research scientist, and Kynkäänniemi did his master’s project with him, developing a method for evaluating the performance of AI that generates images.
“A model that generates images needs to meet two criteria”, explains Kynkäänniemi, “they need to be realistic looking, but they also need to be highly variable. It’s no good if your model can only turn out one realistic looking image and they all look the same. It needs to make a wide range of realistic looking pictures.”
Kynkäänniemi's master's thesis was given the MSc Thesis Award of 2018-2019 by the Finnish Society for Computer Science, and was also published as a paper for the NeurIPS 2019 conference.
Kynkäänniemi describes his research path from undergraduate to now as a bit of a “random walk”, but he hopes it inspires other students and people thinking about studying technology at university.
“I hope my example shows that if you are open-minded and curious you can get the opportunity to work on lots of very different and fascinating projects”.
Read more
For more information about the FMI involvement with the Mars Perseverance lander, you can read about it on their website
You can read Kynkäänniemi’s master’s thesis here: https://aaltodoc.aalto.fi/handle/123456789/39830
Link to research article: https://proceedings.neurips.cc/paper/2019/file/0234c510bc6d908b28c70ff313743079-Paper.pdf