Inka Lehtimäki awarded best Master’s thesis in engineering and architecture

Aalto University graduate Inka Lehtimäki has been awarded the best Master's thesis award by tech trade unions TEK and TFiF.
Aalto University's master's degree student Inka Lehtimäki.
Lehtimäki completed her thesis in the Master's Programme in Life Science Technologies at Aalto's Department of Computer Science. She majored in complex systems and graduated in 2021. Image: Jari Härkönen

Lehtimäki developed a novel method to differentiate epileptic and functional seizures with machine learning from video data. She worked with Tampere-based epilepsy monitoring company Neuro Event Labs and supported the company's product development in epilepsy monitoring.

‘I am inspired by the practical approach of coding to analytical reasoning,’ said Lehtimäki. ‘The best thing is when I can apply my own knowledge in a field that interests me and at the same time help others.’

The 5,000-euro prize was awarded by the Finnish tech trade union, TEK and its Swedish-speaking counterpart TFiF. The annual awards celebrate the best Doctoral and Master's theses in the Finnish engineering and technology scene.

‘The awarded thesis is a novel contribution at the intersection of computation and epileptic seizures,’ says Juhani Nokela, director of public affairs at TEK. ‘It has been completed independently in a company with added value for the company’s product development. It reads well and is sufficiently understandable to readers outside the field.’

After graduating in 2021 from Aalto, Lehtimäki has continued to work with machine learning.

‘I’m a technology consultant at Accenture and I help our healthcare clients with machine learning projects. My studies at Aalto, and the skill I learned when working on my Master's thesis, have supported my career tremendously.’

Lehtimäki completed her Bachelor’s degree in chemical and bio engineering. She switched to the Master's Programme in Life Science Technologies to pursue her interest in the healthcare sector and to learn more about machine learning.

‘Students should pursue their interests, wherever they may lead. The possibilities will open up. You don’t have to know everything – the key thing is to be willing to learn. My friends and I had some pretty late nights with machine learning courses in the beginning, but hard work paid off in the end.'

Link to the Master's thesis: Automatic Video-based Classification of Convulsive Epileptic and Functional Seizures, supervisor Riku Linna and advisor Andrew Knight

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