Projects
PHD POSITION IN DATA ANALYTICS AND STATISTICAL MODELING KU Leuven
In many research fields data dimension reduction techniques are widely used. Fields such as chemometrics, signal processing, and video compression, try to deal with these issues with tools that transform high-dimensional data to lower dimensions where the meaningful properties of the data are retained. Principal Component Analysis (PCA) is a widely used tool for dimension reduction. However, it is known that PCA is not robust against ...
Data Analytics and Statistical Modelling KU Leuven
The project – in cooperation with the Joint Research Centre (JRC) of the European Commission – aims to develop and apply robust statistical and machine learning techniques for outlier/anomaly detection. Various approaches will be considered, such as distance/density or tree-based approaches, (generalised) Benford’s law, robust regression techniques, time series analysis and graphical models. Such methods will be applied to high-dimensional ...
Academic advising analytics dashboards: Towards artificial intelligence for student success KU Leuven
Academic advising is a growing professional practice with a developing scholarly field within Higher Education. Academic advising refers to “the intentional interactions between representatives from institutions of higher education (advisors) and students (advisees), and the ways by which advisors provide guidance and support for students on issues relating to the student's personal growth, academic studies, career, and ...
Understanding High-Dimensional Time-Series: Topological and Visual Analytics for Characterizing Sleep Apnea. Hasselt University
Tensor decompositions for multi-modal Big Data analytics KU Leuven
Tensor decompositions that generalize low-rank matrix decompositions to multi-modal data are gaining importance in several fields, such as machine learning, neuroscience, scientific computing, and signal processing. I propose a 4-step framework for Multimodal Exploratory Analysis of Data: first, the large-scale data is compressed; second, a suitable generalized additive tensor decomposition is constructed from the compressed tensor via ...
Theoretical and Practical Aspects of Advanced Risk Analytics in a Dynamic Insurance and Finance Environment. KU Leuven
Predictive Modelling and Big Data Analytics for Risk Management KU Leuven
We will be working on developing new analytical tools as well as models for accurate risk monitoring and subsequently for prediction. Grounded on the advances in data science (big data, alternative data) and financial technology, the target applications of the research project will cover industry-wide practices pertaining to the credit risk, systemic (cyber) risk, and global market risk that financial institutions are heavily exposed, ...
Optimization and analytics for stochastic and robust project scheduling KU Leuven
Project scheduling and other large-scale planning problems often involve high degrees of uncertainty. Important attributes of individual activities such as processing times, resource requirements and costs can often only be estimated stochastically and their actual values at the time of execution may deviate significantly from these estimates. The aim of this research project is to advance the methodology for anticipating and counteracting ...
Developing tailored geospatial analytics techniques for real estate KU Leuven
This project will investigate the development of new geospatial analytics techniques for several business applications, mostly focused towards real estate. Geospatial data has attracted interest for decades, however, more and more real big data sets are becoming available, partly driven by massive publicly available data. Classical statistical techniques often suffer from computational tractability in these environments. Recent advances in ...