SciTech- og Explore-projekter

Strategiske, tværgående forskningsprojekter, ledet af DIREC-forskere – og ofte i samarbejde med eksterne samarbejdspartnere – har til formål at levere værdi for både den videnskabelige verden og samfundet.   

Formålet med SciTech-projekterne er at opbygge forsknings- og uddannelseskapaciteten på universiteterne. Explore-projekterne er små agile forskningsprojekter, som har til formål hurtigt at screene nye ideer.

SciTech-projekter

SciTech project

Online Algorithms with Predictions

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.

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SciTech project

Benefit and Bias of Approximate Nearest Neighbor Search for Machine Learning and Data Mining

The search for nearest neighbors is an emerging and increasingly vital component in data analysis tasks, for example using vector embedding databases. Typically, the search is the bottleneck in terms of efficiency. Approximate nearest neighbor (ANN) search methods are often employed to speed up the application. However, different methods for ANN search come with different biases that can be positive or negative for the downstream application. In this project, the bias of different ANN methods and its impact on different applications will be studied.

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SciTech project

Privacy and Machine Learning

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. A lot of data are private and stored locally for good reasons, but combining the information in a global machine learning system could lead to services that benefit all.

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SciTech project

Machine Learning Algorithms Generalisation

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.

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Explore-projekter

Explore project

Cyber-Physical Systems with Humans in the Loop

Constructing cyber-physical systems with humans in the loop is important in many application areas to enable a close co-operation between humans and machines. However, there are also many challenges to overcome when constructing such systems with current software technologies and human-centered design approaches.

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Explore project

Re-Use of Robotic-data in Production through search, simulation and learning

In contrast to other fields in AI, the potential of exploiting large data collections is not realized in robotics yet. We aim to analyze the underlying scientific and technical challenges as well as associated legal and privacy issues by means of three half days meetings of university partners and companies, one public workshop, and the preparation of four deliverables.

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Explore project

Algorithms education via animation videos

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.

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Explore project

Ergonomic & Practical Effect Systems

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 that 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.

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Explore project

Verifiable and Robust AI

The challenge to the research community is how to extend existing verification technologies to cope with software systems comprising AI components. This is an unchartered territory and one of the most pressing research challenges in AI. The industrial importance of this topic is closely related to the question of liability in case of malfunctioning products. Over a 4-month period the explore project will provide a state-of-the-art survey and identify research directions to be followed.

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Explore project

Explainable AI

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.

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Bridge-projekter

Ud over SciTech- og Explore-projekterne støtter DIREC også Bridge-projekter, som er tværgående forsknings- og innovationsprojekter ledet af DIREC-forskere i samarbejde med virksomheder, det offentlige og GTS-institutter med det formål at øge kapaciteten inden for digitalisering og innovation i virksomhederne.

Udforsk vores strategiske, tværgående Bridge-projekter.