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.
Benefit and Bias of Approximate Nearest Neighbor Search for Machine Learning and Data Mining
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.
DeCoRe: Tools and Methods for the Design and Coordination of Reactive Hybrid Systems
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.
Methodologies for scheduling and routing droplets in digital microfluidic biochips
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.
Automated Verification of Sensitivity Properties for Probabilistic Programs
Sensitivity measures how much program outputs vary when changing inputs. We propose exploring novel methodologies for specifying and verifying sensitivity properties of probabilistic programs.
Understanding Biases and Diversity of Big Data used for Mobility Analysis
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.
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 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.
Hardware/software Trade-off for the Reduction of Energy Consumption
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.
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.
Mobility Analytics using Sparse Mobility Data and Open Spatial Data
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.