Doctoral track takes Bachelor’s students directly into PhD studies

12.12.2016

The Doctoral track is available in three majors: Machine Learning and Data Mining, Bioinformatics and Complex Systems.

Detection of dense communities in social networks and attributes that describe the communities.png

Detection of dense communities in social networks and attributes that describe the communities.

Aalto University wants to offer the most promising Bachelor’s students the opportunity to move directly onto PhD studies. The chosen students must be accepted onto a Master's programme based on the normal criteria and must indicate in their motivation letter their interest in PhD studies. The best applicants are then interviewed before selection. The doctoral track will be launched for the 2017–2018 academic year in three majors: Machine Learning and Data Mining, Bioinformatics and Complex Systems. The total length of studies for those on the track will be around five years and all teaching will be provided in English.

‘There is demand for top-level experts especially in the area of machine learning, an area in which Aalto University has already for a long time been an attractive choice. Doctoral studies in a good research group provide just the kinds of skills needed for independently solving challenging problems. This has been noticed both by universities and recently also by interesting new technology companies. With a good PhD thesis, a very large number of career opportunities opens up in both universities and companies,’ explains Samuel Kaski, who is responsible for the Machine Learning and Data Mining major.

There is demand for top-level experts especially in the area of machine learning, an area in which Aalto University has already for a long time been an attractive choice.

‘Demand for bioinformatics experts is also very high world-wide. New measurement technologies generate increasingly large biological data sets, and talented bioinformatics and machine learning experts are needed to develop novel computational methods to analyze massive amounts of various genomic, -omics and health data," says Harri Lähdesmäki, the responsible Professor for the Bioinformatics major.

Involved in the doctoral track are also many other professors who represent the forefront of research in their field. For example, Professor Juho Rousu is studying the identification of metabolites, an area that relates to both personalised health care and machine learning. Professor Aki Vehtari has received an award as co-author of the leading textbook on Bayesian Data Analysis. Professor Jari Saramäki, on the other hand, is one of the pioneers of network theory and well known especially for research on social networks.

‘Doctoral track students are included in research groups right from the start of their studies. They receive top-level individual supervision right from a supervising professor.

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Detection of multivariate associations between 3D drug descriptors and biological response profiles
of cancer cells.

In addition, the best students are granted a scholarship to cover their tuition fees,’ explains Rousu, who is responsible for the LifeTech Master's Programme.

In machine learning, solutions should both be rigorous and work in practice, and we proudly foster this combination at Aalto University. PhD graduates in Bioinformatics may, for example, end up as experts in biology research groups, where they are expected to master the field sufficiently to be able to answer any questions about their field.

After graduation

The doctoral track and Master’s programmes at Aalto University for Machine Learning and Data Mining, Bioinformatics and Complex Systems are attractive to many employers, and and also an entrepreneur career is increasingly popular. CybelAngel CEO Stevan Keraydy, the winner of the Slush start-up event’s pitching competition, graduated with a major in Machine Learning and Data Mining and wrote his Master’s thesis on noise robust speech recognition.

‘I took the first steps towards my doctoral research already during my master’s studies by studying machine learning and, in particular, deep neural networks. I am very satisfied that the transition from master’s to PhD studies in machine learning and data mining has now received official attention,’ emphasises Kyunghuyn Cho. After his doctoral studies, he worked for two years as a researcher at the University of Montreal and is now Assistant Professor at New York University.

The international and friendly environment and the lack of bureaucracy are clear advantages in Finland and the Aalto University.

‘Aalto University Postgraduate studies come out very well in comparisons with other universities. I chose Aalto University because of Samuel Kaski and statistical machine learning, and I was very satisfied with the comprehensive coverage of the courses and the PhD study process. The international and friendly environment and the lack of bureaucracy are clear advantages in Finland and the Aalto University,’ says Melih Kandemir. He began his studies in Aalto in 2008 and is currently transferring from Heidelberg University to work as Assistant Professor at Özyeğin University in Turkey.

‘Aalto University is a very open environment, and this can be seen in the communication and the relationships between the research groups. Research into machine learning and data mining is very strong in Aalto and also very broad. At the moment, I’m missing the Aalto University environment,’ says  Hongyu Su. In 2012 he transferred from the University of Helsinki to Aalto University to work in Rousu’s research group and is currently working as Lead Data Scientist at Nordea.

‘I chose to study neural networks and machine learning because of academician Teuvo Kohonen. Aalto University offered an absolutely unique study option which included the opportunity to study also brain research. Unsupervised learning was the central perspective in the studies,’ explains Harri Valpola. He began his studies in Helsinki University of Technology in 1992 and is currently CEO of The Curious AI Company, which focuses on artificial intelligence.

For health and biology applications, Aalto University's machine learning and bioinformatics teaching and research are among the best in the world. For information technology, Aalto University is currently ranked at 60 in US News’s global rankings, up from its previous ranking of 83. In the field of machine learning, Aalto University is especially strong, with exceptional results in field-specific rankings. For example, Aalto was ranked among the top ten in the Microsoft Academic Search service.

The Helsinki region is also a globally significant hub of startup activity, which enables attractive internships during studies and opens up post-graduation career opportunities in the same companies.

The next application period for studies starting in 2017 will be held 15 December 2016 - 25 January 2017.

Machine Learning and Data Mining http://www.aalto.fi/en/studies/education/programme/machine_learning_and_data_mining/

Bioinformatics http://www.aalto.fi/en/studies/education/programme/bioinformatics/

Complex Systems http://www.aalto.fi/en/studies/education/programme/complex_systems/