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. Methods and 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. Almost all of these falls under the broad umbrella of Artificial Intelligence (AI).
Within this track, 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.
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 (NLP)
Data integration and data cleaning
Online search engines and recommender algorithms
Process mining
Prognostics and health management
Social media, social networks, and network science
Machine learning
Bayesian networks and probabilistic graphical models
Explainable AI
Evolutionary algorithms
Continual learning
One-shot, transfer and multi-task learning
Reinforcement learning and multi-agent systems
Model robustness
Generative models
Deep learning
Scalable artificial neural networks
eHealth, clinical medicine and machine learning
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
Efficient implementation on resource limited devices
A list of example project descriptions for this track: https://www.utwente.nl/en/eemcs/dmb/assignments/open/bachelor/
For specific information on the content of this track, you may contact the track chairs: Nacir Bouali and Estefania Talavera.