Data science
Introduction
In a world where we are more and more connected, data is everywhere. You can extract knowledge and provide solutions benefiting society in a large variety of domains, including health, engineering, safety and security, business, and science. Methodologies for Data Engineering and Machine Learning are subjects of research and are constantly required to be improved to face new challenges brought by the increase of data complexity and new application scenarios. Most all off these fall under the broad umbrella of Artificial Intelligence (AI).
You can design or extend methods to mine, analyze, optimize, classify, or regress from data to solve a fundamental or applied research question of your choice. This method may follow a machine learning paradigm (e.g. supervised, unsupervised or reinforcement learning) or can belong to a related field, e.g. evolutionary computing. Problems in natural language processing, social networks, biometrics and computer vision, eHealth and medical related applications can be addressed. Concepts such as fairness, data quality, trust, and safety may also be considered.
Suggested Topics
Here is a list of current research topics, for inspiration:
Data engineering
Autonomous and robust extraction of information from the web and natural language text
Data integration and data cleaning
Online search engines and recommender algorithms
Process mining
Social media, social networks, and network science
Machine learning
Bayesian networks and probabilistic graphical models
Deep learning
Explainable AI
Evolutionary algorithms
Generative models
One-shot, transfer and multi-task learning
Reinforcement learning and multi-agent systems
Scalable neural networks
Biometrics and computer vision
Detection of manipulation in video and audio
Efficient searching of surveillance video
Finger vein, forensic face and biometric recognition
Mobile authentication
Systems fingerprinting for recognition and profiling
Further reading
A list of example project descriptions for this track: https://www.utwente.nl/en/eemcs/dmb/assignments/open/bachelor/
Information
For specific information on the content of this track, you may contact the track chairs: Decebal Mocanu and Nicola Strisciuglio.