Åbo Akademi Datavetenskap - Computer Science

Our two-year IT master programme is designed to provide you with an understanding of computation, communication and information processing in the context of our fast-changing society, and to enable you to be successful in your life-long career. It will provide you with the necessary knowledge and skills to join the IT-industry as well as data-driven industries, and to participate in demanding industrial and research projects. It also provides the required background to apply to a doctoral study programme.
Students can specialise in one of the three tracks offered by the programme:

• Computer Science
• Embedded Systems
• Software Engineering

The description for our Computer Science track follows, for more information on degree structure and admission process please visit http://www.abo.fi/fakultet/en/master_it

Computer Science Track

The Computer Science track paves the way for our students towards building a career in three hot and on-demand fields: data science, bioinformatics, critical systems. Our program offers an ideal combination of foundational and practical studies, and offers desirable options for a wide spectrum of students from those who seek a future in the industry to those who wish to build up a career in academia. We prepare our students for the life as professionals through an enjoyable, well-thought and challenging journey.

The first year of studies in computer science is focused on the foundational aspects of the program. Our students go through a number of core computer science courses and choose a specialization from three offered thematic modules. They also experience a lab internship in the department to get a hands-on experience with the department’s research, as well as with its culture. The second year of studies offers the practical aspect of the program with a focus on a large team project and on the Master thesis project.

Computer Science thematic modules:

Bioinformatics

Bioinformatics is one of the most attractive multidisciplinary areas for researchers which facilitates exploring biology inspired problems with different computational methods. Bioinformatics is used in a wide range of areas ranging from personalized medicine to forensic analysis to gene therapy and evolutionary studies. Computational biomodelling and simulation, that we focus on in this program, can provide valuable insight when the prospect of experimentation is limited due to the safety and feasibility issues. The application of this kind of approaches includes, but is not limited to, the evaluation of normal and diseased conditions, individualized drug design, discovery of the underlying functionalities of biological systems, and prediction of the medical procedures’ outcomes. The goal of this module is to provide the students with the knowledge of current tools and methods related to the nature inspired computing and modeling techniques. They will acquire the foundations to interpret complex biological data through dynamic models and simulations, and to perform analysis on them using well-known efficient bioinformatics software.

Computational Data Analytics
In the age of data explosion, the face of research done in scientific fields is being changed dramatically. Nowadays, scientists are dealing with massive amount of unstructured data which contains unprecedented amount of information which in turn can facilitate breakthrough data-driven discoveries and predictions. These skills can be employed in a wide range of fields including digital advertisements, recommender systems, image recognition, business intelligence, etc. That is why the demand for the “data scientists” with the ability to read through this entangled web of knowledge is on the rise. With this rising demand throughout the world, the number of data scientists is still very scarce. This module is designed to address the ever growing need for the skilled data scientists. At the end of this module the students will be able to build computational models to interpret and analyze given data sets ranging from medium sized datasets to the large ones. They will be able to effectively process the data and to use sophisticated software developed for handling big data.

Distributed and critical systems
This module is aimed to give the students tools for developing distributed and critical systems. Distributed Systems that need to be safe and reliable exist everywhere today, for instance, in form of control systems, networked sensor systems and embedded systems. Examples of these kind of systems are systems for self-driving cars, e-health, smart houses, as well as control systems for drones. The goal of this module is that the students will learn about the basics of these distributed systems and how to make them reliable. The students will also be provided methods and tools for developing such complex systems to function as required and in a safe way. Since many of these distributed systems deal with highly sensitive data, means for dealing with security issues are also provided.

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The Computational Biomodeling Laboratory (Combio) at our department received the Best Paper Award at the 14th International Conference on Computational Methods in Systems Biology (CMSB) for the article

Eugen Czeizler, Cristian Gratie, Wu Kai Chiu, Krishna Kanhaiya and Ion Petre - Target Controllability of Linear Networks

CMSB is the premiere conference on the computational methodological part of the systems biology research community.

The award comes with a great NVIDIA Tesla K40 GPU card.

It is the second time in a few years when the Combio team receives the best paper award at CMSB!