Pitching competition on research topics

Vote for the best pitch on each topic!

Data intensive engineering is a one-year research-based project course in which we are having very talented international students. The course is organized within the framework of our Erasmus Mundus Joint Master’s Degree Programme on the Engineering of Data Intensive Software Systems (EDISS). 

The objectives of this course are to give the students a real-life experience through a one-year hands-on project-based course. The course project is defined together either with academic and/or industrial partners. The tasks of the projects are carefully defined in small blocks conforming to each theoretical objective of our four courses on Data Science, Artificial Intelligence, Machine Learning and Embedded AI. These blocks cumulatively will form a well-designed real-life case study, from setting up the project to the theoretical research, and data processing to modelling and deployment.

This year, for the first time, the team of students are going to compete to win their favourite 1-year project topic through a pitching competition.  The students have gone through intensive pitching training and now they share the result of their 2-week hard effort as a 3-min pitching video with the Åbo Akademi students and staff for public voting.

Each topic is won by the team with highest pitching ranking on that topic. 

Voting may take only a few minutes of your time but your vote has an imperative impact on matching topic and teams.

All pitching videos will be available from 10am on Monday 26.09.2022. All videos are available from the topic sections bellow or from the following YouTube playlist.

Voting starts on 26.09.2022 at 12:00 and it ends on 29.09.2022 at 8:00. Voting is only open to students and staff members of Åbo Akademi University.

Topic: Critical Systems - Developing novel adaptive Artificial Intelligence (AI) based software testing approaches.

Competing pitches:

Vote for the best pitch on topic 2 here!

Topic: Medical AI - Developing Artificial Intelligence (AI) based solutions to identify Parkinson's disease signatures.

Competing pitches:

  • Team 3: Debayan Bhattacharya, Juan Carlos Pichardo, Sofiia Charnota. Link to the video
  • Team 4: Alex Montoya Franco, Faridun Mamadbekov, Ikram Ul Haq, Mudita Shakya. Link to the video
  • Team 6: Aarohi Garg, Nicolas Restrepo Torres, Sherkhan Azimov. Link to the video

Vote for the best pitch on topic 3 here!

Topic: Critical Systems - Developing Machine Learning (ML) models to predict testing effort in software project management.

Competing pitches:

Vote for the best pitch on topic 4 here!

Topic: Critical Systems - Developing a Machine Learning (ML) based framework to find suitable system configurations in critical adaptive distributed systems.

Competing pitches:

Vote for the best pitch on topic 5 here!

Topic: Digital Attacks - Developing an Artificial Intelligence (AI) based adversarial attack framework to test convolutional neural network.

Competing pitches:

Vote for the best pitch on topic 6 here!

Topic: Smart Banking - Developing Machine Learning (ML) models to automatically recognise and exploit document images.

Competing pitches:

  • Team 1: Aicha Moussaid, Ricardo Chavez Tapia, Saad Waseem, Zahid Hasan. Link to the video
  • Team 3: Debayan Bhattacharya, Juan Carlos Pichardo, Sofiia Charnota. Link to the video
  • Team 4: Alex Montoya Franco, Faridun Mamadbekov, Ikram Ul Haq, Mudita Shakya. Link to the video

Vote for the best pitch on topic 7 here!