CV

Contact Information

• Current Position: Postdoctoral Fellow
• Host Institution: imec - IDLab - Ghent University
• Email: lorin.werthenbrabants@ugent.be

---

Education

• PhD in Computer Science
  Ghent University (2020–2024)
  Dissertation: “Quantifying Uncertainty and Improving Reliability of Time-Series Based Deep Learning Models”
  Promotors: Prof. Tom Dhaene, Prof. Dirk Deschrijver

• MSc in Computer Science
  Ghent University (2018)
  Thesis: Focused on machine learning methods for time-series data.

---

Research Interests

• Trustworthy Machine Learning (TML)
• Uncertainty Quantification in Deep Learning
• Event-Based Time Series Analysis
• Multimodal Data Integration
• Self-Supervised Learning for Healthcare Applications

---

Professional Experience

• Postdoctoral Researcher
  IDLab, imec, Ghent University (2023–Present)
  Research focuses on event-based deep learning techniques for healthcare diagnostics and energy-efficient machine learning.

• PhD Student
  IDLab, imec, Ghent University (2019–2023)
  Title of dissertation: “Quantifying Uncertainty and Improving Reliability of Time-Series Based Deep Learning Models”.

• Machine Learning Engineer
  Robovision (2018–2019)
  Developed machine learning pipelines for computer vision applications, transitioning to academia for deeper exploration of research questions.

---

Key Publications

1. Werthen-Brabants, L., et al. (2024). “Deep Learning-Based Event Counting for Apnea-Hypopnea Index Estimation using Recursive Spiking Neural Networks.” IEEE Transactions on Biomedical Engineering.
   Developed novel RSN-Count method for event-based models in sleep apnea.

2. Werthen-Brabants, L., et al. (2022). “Split BiRNN for real-time activity recognition using radar and deep learning.” Scientific Reports.
   Proposed a split computation method for radar-based activity recognition.

3. Werthen-Brabants, L., et al. (2022). “Uncertainty quantification for appliance recognition in non-intrusive load monitoring using Bayesian deep learning.” Energy and Buildings.
   Applied Bayesian deep learning to quantify uncertainty in load monitoring.

4. Bhavanasi, G., Werthen-Brabants, L., et al. (2022). “Patient activity recognition using radar sensors and machine learning.” Neural Computing and Applications.
   Guided research on radar-based patient activity monitoring.

5. Castillo-Escario, Y., Werthen-Brabants, L., et al. (2022). “Convolutional neural networks for Apnea detection from smartphone audio signals: effect of window size.” IEEE EMBC Conference.
   Guided model development for sleep apnea detection using smartphone audio.

---

Awards and Recognitions

• Selected as a PhD representative for the Flanders AI Research Program (2023).
• Keynote speaker at imec’s “Save Data” event on Trustworthy AI in Healthcare (2024).

---

Leadership and Teaching

• Teaching Assistant: Courses on Computer Science, Machine Learning, and Logic at Ghent University (2019–Present).
• Supervised multiple Master’s theses, with students publishing conference papers under guidance.

---

Mobility and Research Stays

• Visiting Researcher:
  University of Sydney, Australia (2022)
  Facilitated collaboration and guided local researchers in time series modeling.

---

Skills

Technical Skills

• Programming Languages: Python, C, C++, MATLAB
• Deep Learning Frameworks: TensorFlow, PyTorch
• Data Science:
  • Time Series Analysis
  • Uncertainty Quantification
  • Statistical Modeling
  • Data Visualization

Languages

• English (Professional)
• Dutch (Native)
• French (Intermediate)

---

Outreach and Science Communication

• Featured in public AI discussions, including television appearances and online dissemination (e.g., Karrewiet 2019, VRT NWS Laat 2024).
• Regular reviewer for leading journals including IEEE and Nature.

---

References

• Prof. Dirk Deschrijver
  IDLab, imec, Ghent University
• Additional references available upon request.