Sofie Van Hoecke

Associate professor

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Belgium

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Dr. Sofie Van Hoecke is an Associate Professor at UGent-imec in Belgium. Her research focuses on knowledge graphs, time-series data visualization, AI in industrial applications, and health informatics, as evidenced by her recent publications. She has contributed to advancements in context-aware recommender systems, anomaly detection in industrial environments, and methods for tracking health conditions using technology.

Sofie Van Hoecke

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Articles (20)

RR-GCN: Exploring Untrained Random Embeddings for Relational Graphs

Tracking Migraine Symptoms: A Longitudinal Comparison of Smartphone-Based Headache Diaries and Clinical Interviews

Analysis of free-living daytime movement in patients with migraine with access to acute treatment

VisCARS: Knowledge Graph-Based Context-Aware Recommender System for Time-Series Data Visualization and Monitoring Dashboards

Flame monitoring and anomaly detection in steel reheating furnaces based on thermal video using a hybrid AI computer vision system

Cross-Room CO2-Based Presence Detection for Occupancy Profiling

Feedback-Driven Pattern Matching in Time Series Data

A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests

Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts

Do not sleep on traditional machine learning

Benchmarking Whole Knowledge Graph Embedding Techniques

Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings

Parameter Efficient Neural Networks With Singular Value Decomposed Kernels

Leak localization in water distribution networks using GIS-Enhanced autoencoders

From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization

Variable Length Motif Discovery in Time Series Data

A semi-supervised approach with monotonic constraints for improved remaining useful life estimation

Hierarchical pattern matching for anomaly detection in time series

Physics-Based Neural Network Models for Prediction of Cam-Follower Dynamics Beyond Nominal Operations

Neural Network Augmented Physics Models for Systems With Partially Unknown Dynamics: Application to Slider–Crank Mechanism

Collaborators (7)

Nicolas Vandenbussche

Femke Ongenae

Steven Verstockt

Filip De Turck

Bruno Volckaert

Pieter Bonte

Agusmian Partogi Ompusunggu

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