Jonas Van Der Donckt

Orcid: 0000-0003-1440-7410

According to our database1, Jonas Van Der Donckt authored at least 15 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Addressing Data Quality Challenges in Observational Ambulatory Studies: Analysis, Methodologies and Practical Solutions for Wrist-worn Wearable Monitoring.
CoRR, 2024

2023
Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring.
Biomed. Signal Process. Control., March, 2023

tsdownsample: high-performance time series downsampling for scalable visualization.
CoRR, 2023

MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB.
CoRR, 2023

Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines.
CoRR, 2023

MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB.
Proceedings of the 2023 IEEE Visualization and Visual Analytics (VIS), 2023

2022
tsflex: Flexible time series processing & feature extraction.
SoftwareX, 2022

Assessing the added value of context during stress detection from wearable data.
BMC Medical Informatics Decis. Mak., 2022

mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients.
BMC Medical Informatics Decis. Mak., 2022

Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring.
CoRR, 2022

Plotly-Resampler: Effective Visual Analytics for Large Time Series.
CoRR, 2022

Towards Knowledge-Driven Symptom Monitoring & Trigger Detection of Primary Headache Disorders.
Proceedings of the Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25, 2022

Plotly-Resampler: Effective Visual Analytics for Large Time Series.
Proceedings of the 2022 IEEE Visualization and Visual Analytics (VIS), 2022

2021
MIRRA: A Modular and Cost-Effective Microclimate Monitoring System for Real-Time Remote Applications.
Sensors, 2021

2020
Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals.
Proceedings of the SEAMS '20: IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Seoul, Republic of Korea, 29 June, 2020


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