Bayesian Active Learning for EMI Near-Field Emission Characterization of High-Speed Electronics

Bayesian Active Learning for Engineering Design and Optimization under Uncertainty

Bayesian Active Learning for Modeling and Optimization of Microwave Devices

Bayesian Optimization for Engineering Design and Process Optimization

Data-Efficient Machine Learning for Making Interpretable Decisions

Data-Efficient Physics-Informed Machine Learning

Doctoral project Anthony Andrea Faustine

Doctoral project Arun Kaintura

Doctoral project Bernard Meyer

Doctoral project Dirk Deschrijver

Doctoral project Domenico Spina

Doctoral project Hua Wei

Doctoral project Ivo Couckuyt

Doctoral project Joachim van der Herten

Doctoral project Joeri Ruyssinck

Doctoral project Karel Crombecq

Doctoral project Krishnan Chemmangat Manakkal Cheriya

Doctoral project Kyle Powers Foss

Doctoral project Leen De Baets

Doctoral project Nicolas Knudde

Doctoral project Prashant Singh

Doctoral project Rémi Delanghe

Doctoral project Roberto Medico

Doctoral project Sean Stijven

Doctoral project Selvakumar Ulaganathan

Doctoral project Sofie Van Gassen

Doctoral project Tom Van Steenkiste

Machine Learning and Design Techniques for Tolerant Photonic Circuits

Machine Learning for the Design of High-Frequency Systems

Machine Learning with Radar Sensors for Smart Healthcare

Adaptive Neural Networks for Robotic Sensing

Bayesian Preferential Many-Objective Optimization with Imperfect Knowledge

Data-efficient and explainable engineering design

Data-efficient generative models for innovative engineering design

Deep Learning on Time Series Data for Health Care

Doctoral project Aren Maes

Doctoral project Elizabeth Rita Samuel

Doctoral project Evy Troubleyn

Doctoral project Luca De Camillis

Doctoral project Simon De Ridder

Doctoral project Yufei Xing

Explainable Machine Learning Models for Time Series Data

Machine Learning for Accurate and Efficient Uncertainty Quantification in Radiological Waste Characterization

Machine Learning for Microstructure Analysis of Steel

Monitoring of Photovoltaic Systems and Forecasting of Power System Imbalance Using Neural Networks

Physics-Informed Machine Learning Based Identification and Reinforcement Learning Based Control of Flexibility in Smart Grid Applications

Predictive Models of Time Series Data in Health Care

Quantifying Uncertainty and Improving Reliability of Time-Series Based Deep Learning Models

Time-Series-Based Forecasting of Biomanufacturing Processes

Accurate parameterized macromodels for efficient design

Advanced 3D MOR-subgridding

Advancing Engineering Design and Optimization through Data-efficient Machine Learning

Development of machine learning techniques for flow cytometry data

Efficient ensemble systems for classifying large-scale data

Fast and Reliable Assessment of Electromagnetic Field Exposure using Sequential Sampling, Surrogate Modeling and Optimization

Generative design of linear passive electronic systems

High bitrate channel modeling and transceiver optimization for advanced chip-to-chip interconnects and VDSL applications

High-dimensional and stochastic macromodeling of parameterized high-speed systems

Incorporating error control in sparse modelling

Learning complex patterns with Gaussian processes

Machine Learning and Radar Sensors for Monitoring Patients and Elderly People in Healthcare

Machine learning for meta-modelling in distributed computing environments

Near-field measurement techniques for the efficient solving EMC emission Problem

Parameterized model order reduction of delayed and distrubuted systems (D-PMOR)

Parametrized Model Order Reduction (PMOR): Sparse Data and Sparse Models

Robuste techniques for multivariate surrogate modeling

Versatile surrogate-based optimization of medium-scale and large-scale problems.

Wideband parameterized behavioral models for passive and active electronic components,