Related research

The Algorithms group designs and evaluates algorithms to solve problems in complex systems where decentralisation, uncertainty, conflicting interests, and time constraints are major issues. Think of real-time balancing of energy supply and demand in communities of producers and consumers, or of coordinating schedules for service providers at airports in order to ensure that planes are cleaned at the right time and provided with fuel and food services. To design such algorithmic solutions we build upon fascinating fundamental scientific findings in computer science.

In our group you learn how to design and use advanced algorithms using methods from planning and scheduling, algorithmic game theory, and sequential decision making under uncertainty. Also you will learn to implement these algorithms efficiently and to use the right methods to evaluate their performance.

Example Projects
Smart grids, transportation systems, surveillance or maintenance. Quite a number of these projects involve industrial partners such as Alliander, NedTrain and Thales.

More information about the research projects of The Algorithms Group.

The section Cyber Security focuses on technical aspects of cybersecurity.

Example projects
The three main areas of research are "computing with encrypted data", "data analytics and machine learning for cyber security" and "information theoretic security & privacy".

During their MSc-thesis research, students can contribute to running projects in the mentioned research areas . In addition, students can work on projects proposed by companies and other organisations including Fox-IT, Deloitte, IBM, ENCS, TNO, or may also come up with a project based on their own ideas.

In order to do a Msc-thesis in the Cyber Security group, students are expected to have a relevant cyber security knowledge background: they should select the core and elective courses from the Cyber Security specialisation programme.

More information about the research projects of the Cyber Security Group.

The Computer Graphics and Visualization Group develops new algorithms to generate, represent, interpret, display and interact with data. Especially today, with the explosion of data complexity, algorithms in these domains are particularly important.

Visualization is concerned with the analysis and display of large and complex data sets, such as multi-field data and an extensive variety of imaging types. Feature extraction, and data-mining techniques are combined with visualization methods to develop adequate visual analysis tools. Hereby, insights are gained by extracting important information and supporting interactive data exploration, which is beneficial for many purposes, for example, decision-making or education. We propose novel illustration solutions, improved 3D interaction tools, efficient algorithms for large scale data visualization, and investigate the perceptual impact of the illustrations.

Example projects
One of our focus areas is the medical domain, where we develop solutions for the exploration of brain connectivity, blood flow, surgery planning and population imaging studies. Other areas also benefit from our results, such as navigation, as well as environmental and climate data analysis.

Large-data generation, interaction, and visualisation are all essential topics and will be even more important in the future

Our research is of high relevance to scientific, medical, entertainment, and engineering applications. Hereby, a large variety of industrial domains benefit from our results.

More information about the research projects of the Computer Graphics and Visualization Group.

The Embedded Software group has the ambition to improve the software development and maintenance process for embedded systems, ranging from simple, 8-bit microcontrollers in consumer products to complex, networked controllers in industrial applications.

Example projects
Most of the research activities are focused on wireless systems ranging from developing software for individual mobile phone users, via leveraging crowd sourcing, to deploying large-scale networks of tiny sensor/actuator nodes collaborating in an ad-hoc fashion.

Prospective master students are expected to have a hands-on mentality as the Embedded Software  Group employs an experimental style in which (software) systems are not only designed, but also implemented, evaluated and improved. As such experience with low-level programming in C is considered a big plus, but not necessary as the Embedded Software Group also tackles fundamental issues at an abstract level. Master students can do their thesis work both within the group (preferred) usually collaborating with a PhD candidate on one of the research projects, or at a company like Philips, NXP, TNO active in the embedded systems market.

More information about the research projects of the Embedded Software Group.

“Interactive intelligence” is the intelligence that underlies and emerges by the repeated interaction of systems that cooperate to achieve a joint goal. It specifically refers to the multi-faceted higher-level intelligence that human or artificial actors need to interact purposefully, including abilities to predict the behaviour of others and anticipate on that, and the ability to support team members towards the achievement of a common goal while respecting the interests of each individual actor.

In the Data Science and Technology track the Interactive Intelligence Group develops methods and techniques for engaging humans in fluent and robust data collection and information exchange, for analysing incoming data and interpreting the social situation, data transformation techniques to present information to different types of users in appropriate ways.

Example projects
SHINE: developing data science techniques for crowdsensing, Negotiation to create automated negotiation strategies and negotiation support systems, COMMIT to create socially adaptive apps for sharing location data to support families and elderly, TRADR to create team decision support for human-robot teams that explore a  disaster area and build and maintain a shared awareness of this area, and PAL to develop a Personal Assistant for a healthy Lifestyle (i.e., a physical and virtual robot) of children with diabetes, harmonized to the social support of family and caregivers.

SHINE - Sensing Heterogeneous Information Network Environment. The starting point of the SHINE research project is simple: better data leads to better understanding leads to better decisions. This holds for both individual citizens and government authorities. For this reason, SHINE develops data science techniques to collect, process, and visualise environmental data in urban environments. Heavy rainfall is a good example of this. We collect data from physical sensors, e.g., weather stations, as well as through crowdsensing: citizens collecting and sharing weather data through social media or mobile apps.

More information about the research projects of the Interactive Intelligence Group.

The Multimedia Computing Group develops algorithms for enriching, accessing, and searching large quantities of data. Such algorithms lie at the core of tomorrows’ search engines and large-scale recommender systems.

Example projects
The group sets its focus on developing systems that are oriented to the needs of users, and that solve the challenges faced by large-scale online content and service providers. Multimedia data analytics also has applications in the full range of fields that benefit from data science, including health, telecom, and geosciences. The group has a track record of developing technologies that make possible optimized interaction with large collections of multimedia data (e.g., images, video, and music) in real-world contexts (e.g., within social networks). Our work requires a combination of mathematical models, machine learning techniques, and practical skills in algorithm development and evaluation.

The members of the MMC group share expertise in multimedia information retrieval, recommender systems, multimedia signal processing, social network analysis, human computation (crowdsourcing) and quality of experience. Collaborations include joint work with researchers from Yahoo Labs, Telefónica Research, Microsoft Research and Google.

More information about the research projects of the Multimedia Computing Group.

The Network Architectures and Services Group

More information about the research projects of the Network Architectures and Services Group.

Today, parallel and distributed systems are everywhere: the Internet, online social networks, online games, wireless systems consisting of cooperating hand-held computers, clouds for compute-intensive applications, there is no end to the examples.

Example projects
The Parallel and Distributed Systems group performs research on the modeling, the design, the implementation, and the analysis of parallel and distributed systems. Most of our research is experimental: we try to build prototypes of systems, preferably used in the real world, to demonstrate the quality of the proposed solutions, and we try to measure, benchmark, and model real-world distributed systems.

The main areas covered by the Parallel and Distributed Systems Group are cluster, datacenter, and cloud computing, P2P systems, distributed privacy-enhancing technologies, online social networks, and massively multiplayer online games.

More information about the research projects of the Parallel and Distributed Systems Group.

Many societal, industrial and scientific problems can be solved by a computer through learning from examples, i.e. through pattern recognition. We aspire to obtain a full conceptual understanding of learning methodologies and to exploit them in analysing complex data. Our methodological and conceptual developments are predominantly inspired by problems encountered in computer vision and bioinformatics, as these are two prime yet distinct examples of application areas yielding such complex data.

Example projects
Our pattern recognition research aims to understand the intricate interplay between representation, generalisation and evaluation, the core elements of a pattern recognition system. To come to advances in insight, we focus both on a deeper understanding of existing methodologies, such as combining classifiers, deep learning, and on developing new strategies, such as similarity-based learning, semi-supervised learning, and multiple instance learning.

In our computer vision research we are particularly interested in approaches that learn from big data and that model spatiotemporal relations. We use our approaches to solve key computer vision problems such as object detection, object recognition, and object tracking. We apply our approaches in a variety of domains, including social psychology, consumer science, technical art history, and health.

With our bioinformatics research we try to unravel the enormous amounts of data that are currently being generated in the life sciences field, such as next-generation sequencing data. We design novel algorithms that infer and exploit simple models of complex interactions, by coupling biological insights and available prior knowledge to high-throughput measurements.

More information about the research projects of the Pattern Recognition and Bioinformatics Group.

More information about the research projects of the Programming Language group

More information about the research projects of the Software Engineering group

The Web Information Systems Group (WIS) concentrates in its research on engineering and science of the Web. The research specifically considers the role of Web data in the engineering of Web-based information systems. The group's research is aimed at improving the understanding of people's actions, motivations, and behaviours on the Web, and subsequently leveraging that knowledge to build Web applications that are semantic, personalised and adaptable.

Example topics
This includes topics in harvesting, integrating, transforming, analysing, and retrieving Web data, with focus on the special properties of Web data. A large portion of Web data is human-made, e.g. in social networks or Twitter streams, and this brings scientific challenges in how to effectively attribute meaning to Web data. The size of the Web brings challenges in how to efficiently store, index, and analyse data at Web scale.

The Web Information Systems Group’s researchers and students strive to advance the state-of-the-art in relevant areas like user modelling, Web science, information retrieval, Web engineering, Web data management, and crowdsourcing.

Therefore, WIS offers a well-structured thesis trajectory and aims for students to submit their research work to a conference or journal. Students who would like to pursue an industrial placement for their thesis will benefit from WIS strong links with various Dutch and international companies; about half of WIS Master students choose an industrial placement.

More information about the research projects of the Web Information Systems Group.