Projects are the key driver in DIREC. In total, we have initiated more than 40 projects. All projects are collaboration projects between several partners.
Bridge projects are multidisciplinary research and innovation projects directed by DIREC researchers in collaboration with companies, the public sector and GTS institutes with the aim of increasing the companies’ digitalisation and innovation capacity.
Spin-qubit quantum-dot arrays are one of the most promising candidates for universal quantum computing. However, with the size of the arrays, a bottleneck has emerged: Tuning the many control parameters of an array by hand is time-consuming and very expensive. The nascent spin-qubit industry needs a platform of algorithms that can be fine-tuned to specific sensing hardware, and which allows cold-start tuning of a device.
There is constant interest for Internet Voting by election commissions around the world. At the same time, there is a need for online voting in blockchain governance. However, building an internet voting system is not easy: The design of new cryptographic protocols is error-prone, and public trust in the elected body is easily threatened. This project aims to improve the security and quality of the internet voting system and influence regulation on minimum quality requirements for blockchains.
Together with industrial partners, this project will investigate production scenarios where a machine can be operated by untrained personnel by using low-code development for adaptive and re-configurable robot programming of logistic tasks.
Greenland’s election law was changed in 2020, which now permits the use of Internet Voting. Together with the authorities in Greenland, this project will investigate the effects of program verification on public trust in election technologies. The project aims to contribute to making internet elections more credible, which can strengthen developing and post-conflict democracies around the world.
Based on observations of the Earth, a range of Danish public organizations build and maintain important data foundations that are used for decision-making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark. This project aims to support the digital acceleration of the green transition by strengthening the data foundation for environmental data.
Remote and hybrid work will certainly be part of most work practices, but what should these future work practices look like? Should we merely attempt to fix what we already have or can we be bolder and speculate a different kind of workplace future? Together with companies, this project seeks a vision of the future that integrates hybrid work experiences with grace and decency.
This project aims to identify safety and security requirements for IoT systems and develop algorithms for quantitative risk assessment and decision-making. The aim is furthermore to create tools for designing and certifying IoT security training programs that will enable Danish companies to obtain security certification for their IoT devices, thus giving them a lead in a market that is likely to demand such certification in the near future.
AI currently relies on large data centers and centralized systems, necessitating data movement to algorithms. To address this limitation, AI is evolving towards a decentralized network of devices, bringing algorithms directly to the data. This shift, enabled by algorithmic agility and autonomous data discovery, will reduce the need for high-bandwidth connectivity and enhance data security and privacy, facilitating real-time edge learning.
Today, robots and drones take on an increasingly broad set of tasks. However, such robots are limited in their capacity to cooperate with one another and with humans. This project aims to address multi-robot collaboration and design and evaluate technological solutions that enable users to engage and control autonomous multi-robot systems.
Together with clinicians, this project aims to develop explanatory AI that can help medical staff make qualified decisions by taking the role as a mentor who provides feedback and advice for the clinicians. It is important that the explainable AI provides good explanations that are easy to understand and utilize during the medical staff’s workflow.
Together with industry, the project aims to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.
Together with industry, this project aims to develop methods and tools that enable the industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.
The amount of mobility-related data has increased massively which enables an increasingly wide range of analyses. When combined with digital representations of road networks and building interiors, this data holds the potential for enabling a more fine-grained understanding of mobility and for enabling more efficient, predictable, and environmentally friendly mobility.
Today, manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table.
Together with industrial partners, this project aims to develop and validate a method of automated imaging-based solutions that can replace subjective manual inspection and improve performance, robustness and consistency of the inspection.
Downtime of equipment is costly and a source of safety, security and legal issues. Today, organisations adopt a conservative schedule of preventive maintenance independent of the condition of equipment. This results in unnecessary service costs and occasional interruptions of production due to unexpected failures.
The rapidly growing application of machine learning techniques in cyber-physical systems leads to better solutions and products in terms of adaptability, performance, efficiency, functionality and usability. However, cyber-physical systems are often safety critical, e.g., self-driving cars or medical devices, and the need for verification against potentially fatal accidents is of key importance.
Strategic, multidisciplinary research projects managed by DIREC researchers that aim to deliver value for both the scientific world and society. The SciTech projects aim to expand the Danish universities’ research and education capacity.
Our focus is on improving optimization algorithms in online decision-making. Using techniques from online algorithms for solving optimization problems, we can provide worst-case guarantees, but normal (averagecase) behavior may not be satisfactory. Using techniques from machine learning, we can often provide good behavior in practice, but guarantees are lacking, and in particular missing for situations not captured by the training data. We aim at combining the best features from these two areas.
The search for nearest neighbors is essential but often inefficient in applications like clustering and classification, especially with high-dimensional big data. Traditional methods become impractical due to the curse of dimensionality, making approximate nearest neighbor (ANN) search methods a faster alternative despite their inexact results. ANN methods significantly enhance processing speed, impacting algorithmic decision-making processes by introducing trade-offs in accuracy, bias, and trustworthiness, which must be carefully considered for different use cases.
There is an unmet need for decentralised privacy-preserving machine learning. Cloud computing has great potential, however, there is a lack of trust in the service providers and there is a risk of data breaches. This project aims to develop AI methods and tools that enable secure and privacy-preserving use of sensitive data for machine learning. The goal is to address the lack of trust in cloud service providers and the risk of data breaches, while still enabling the use of analytical tools.
AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data.
Strategic, multidisciplinary research projects managed by DIREC researchers that aim to deliever value for both the scientific world and society. The Explore projects are small agile research projects to screen new ideas.
Constructing cyber-physical systems with humans in the loop enables new applications like bio-computing, active learning systems, and intelligent medical systems. These applications allow humans and machines to collaborate on real-world tasks, integrating aspects of both Cyber-Physical Systems (CPS) and Socio-Technical Systems (STS). They feature close cooperation between software technologies, focusing on situational awareness, safety, privacy, usability, and easy error handling.
A robot database with information on previous robot solutions can save manufacturing companies time and money and allow for smaller-scale companies to automate their production as well. This is the conclusion of the ReRoPro project. Although it sounds simple, there are several challenges involved with creating a robot database. With input from industry and international experts, the researchers have now gained a much better understanding of the challenges.
A recurring problem of digitalised industries is to design and coordinate hybrid systems that include IoT (Internet of Things), edge, and cloud solutions. Currently adopted methods and tools are not effective to this end, because they rely too much on informal specifications that are manually written and interpreted by humans.
Several highly popular YouTube channels for mathematics and other scientific content (e.g., 3blue1brown, Numberphile, Veritasium) with millions of views indicate that learners may respond very positively to professionally produced educational videos. This project aims at creating and evaluating an initial library of such videos to supplement teaching in algorithms.
This project aims to combine secure multiparty computation and blockchain techniques, to enable efficient privacy-preserving computation with accountability, allowing computation on private data while maintaining an audit trail for third-party verification. The project can potentially help fight discrimination, catch unethical and fraudulent behavior, and generate positive publicity for honest participation.
As cyber-physical systems (CPSs) are becoming ever more ubiquitous, many of them are considered safetycritical. We want to help CPS manufacturers and regulators with establishing high levels of trust in automatically synthesized control software for safety-critical CPSs.
The overall purpose of this project is to define, investigate, and provide preliminary methodologies for scheduling and routing microliter-sized liquid droplets on a planar surface in the context of digital microfluidics.
Sensitivity measures how much program outputs vary when changing inputs. We propose exploring novel methodologies for specifying and verifying sensitivity properties of probabilistic programs.
This project will quantify the biases and uncertainties associated with human mobility data collected through digital means, such a smartphone GPS traces, cell phone data, and social media data.
Effect systems are currently a hot research subject in type theory. Yet many effect systems, whilst powerful, are very complicated to use, particularly by programmers who are not experts at type theory. Effect systems with inference can provide useful guarantees to programming languages while being simple enough to be used in practice by everyday programmers.
Computing devices consume a considerable amount of energy. Implementing algorithms in hardware using field-programmable gate arrays (FPGAs) can be more energy efficient than executing them in software in a processor. This project explores classic sorting and path-finding algorithms and compare their energy efficiency and performance when implemented in hardware.
Artificial Intelligence brings the promise of technological means to solve problems that previously were assumed to require human intelligence, and ultimately provide human-centered solutions that are both more effective and of higher quality in a synergy between the human and the AI system than solutions that are provided by humans or by an AI system alone.
The Educational projects aim to supplement and create synergy with the existing education programs at all Danish universities, promote gender diversity, and strengthen the cooperation between researchers, students, and the business community to ensure the best possible exploitation of the capacity.
Denmark needs more IT specialists. But how do we get more young people to study computer science and become IT specialist? This project, consisting of two subprojects, focuses on initiatives that can improve both recruitment and retention of a larger but also more diverse group of young people e.g., female students and students without prior programming experience.
To teach many recent topics within digital technology at scale requires proper software infrastructures to support the teaching for lab exercises and projects. Some of these topics are data-driven systems, AI and cloud computing. Commercial providers are offering cloud computing and AI resources, however, in many situations these are ill fit for teaching activities as they are complex for early learners, are problematic due to GDPR, make teaching material obsolete by rapidly changing their UIs and when scaled add a significant cost.
Teaching quality and student feedback is negatively impacted by lack of teachers and many students. There is a need to consider how learning technologies can help improve teaching quality and student feedback both in physical and digital learning environments.
The mix of students in digital technology is low in diversity (e.g. female students). This is a problem on a societal level which also impacts the study environment.
Entrepreneurship projects and events aim to strengthen science-based digital startups and equipt reesarchers to create digital spinouts.
We are actively in dialog with all our partners about creating new projects that will fullfill the goal of DIREC. If you have an idea for a project, please contact us to hear about potential collaboration.
