Lyse Naomi Wamba
Leuven
Summary
Passionate about applied data-driven research, leveraging statistical and machine learning methods to enhance decision-making. Currently serving as a biostatistics research fellow at the EORTC, specializing in cancer clinical trial data analysis, trial design, and retrospective data analysis. Expertise in using retrospective data to gain insights into critical factors affecting patient safety, such as chemotherapy-induced hematologic toxicity, neurocognitive function, and quality of life.
Overview
7
7
years of professional experience
Work History
Biostatistics Research Fellow
European Organization for Research and Treatment of Cancer (EORTC)
11.2024 - Current
- Applying mixed-effects model, joint modelling and survival analysis to study prognostic markers in brain tumor patients.
- Drafting Statistical Analysis Plans (SAPs)
- Collaborating with oncologists to translate clinical questions into statistical modelling.
- Attending conferences on statistical modelling in oncology.
Ph.D. Researcher
KU Leuven
01.2020 - 10.2024
- Proposed a LoS prediction approach that employs domain adaptation and discriminative learning to leverage shared information across multiple hospital units.
- Applied and compared different sequence-to-sequence models for continuous hourly LoS predictions.
- Developed an interactive dashboard to view timely predictions of patients’ LoS and enable end-users of the prediction system to closely monitor patients’ recovery trajectories.
- Implemented a time-distributed algorithm to capture temporal dependencies in imaging and text data.
- Utilized cross-attention mechanisms to enhance LoS prediction by fusing different clinical data modalities in events of missing modalities.
- Prepared ethical documents for data acquisition requests and set up a local MongoDB instance to organize and access patient data from UZ Leuven in a structured format.
- Disseminated research work in the form of talks, posters, and webinars at different venues.
Teaching Assistant
KU Leuven
01.2020 - 10.2024
- In charge of tutorials sessions of Masters’ students for the course, system identification and modelling.
- Supervised several (10) masters’ theses.
Data Science Intern
PwC
02.2019 - 05.2019
- Constructed a user-interface tool (shiny dashboard) for the automatization of Martingale tests on ESG for various insurance companies.
- Applied machine learning algorithms to predict house prices for insurance valuation.
Data Science Intern
Complidata
07.2018 - 12.2018
- Tuned machine learning algorithms for suspicious activity detection.
Financial Engineering Intern
RiskConcile
09.2018 - 09.2018
- Developed a generic algorithm for pricing a diversified range of autocallables structured products handling early redemption, guaranteed or conditional coupon payments, with or without memory using both the Black Scholes and the Heston pricing models.
Education
Ph.D. - Electrical Engineering
KU Leuven
01.2024
M.Sc. - Statistics
KU Leuven
01.2019
M.Sc. - Applied Mathematics
University of Buea
01.2017
Skills
- Programming in Python, R, SAS, SQL
- Data Science & Modelling: Statistical analysis, survival analysis, machine learning, deep learning (PyTorch, TensorFlow), time series modelling, multimodal data modelling
- AI Techniques: Natural language processing, computer vision
- Data Infrastructure: High-performance computing (HPC), database management (MySQL, MongoDB, PostgreSQL)
- Project Management: Ethical document preparation, collaborative project setup
- Programming languages: Python, pytorch, Tensorflow, R, SAS
- Databases: SQL, PostgreSQL, Python
- Data visualization: Python, R, tableau, PowerBI, MS office
RESEARCH INTERESTS
My research interests lie in applied data science, with experience in healthcare data analysis, risk modelling, and financial engineering. I focus on using statistical modelling, machine learning, and deep learning to support data-driven decision-making. My expertise spans time series analysis, multimodal data modelling, survival analysis, natural language processing and computer vision. I have hands-on experience with data analysis, predictive modelling, dashboard visualization development, and high-performance computing applied to structured and unstructured data.
Publications
- L. N. W. Momo, V. Scheltjens, V. Verbeke, F. Rademakers, and B. De Moor, “Understanding deep learning models for length of stay prediction on critically ill patients through latent space visualization,” Computer Methods and Programs in Biomedicine, p. 108 832, 2025, ISSN: 0169-2607. DOI: https://doi.org/10.1016/j.cmpb.2025.108832.
- L. N. W. Momo, N. Moorosi, E. O. Noseise, F. Rademakers, and B. De Moor, “Length of stay prediction for hospital management using domain adaptation,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108 088, 2024.
- V. Scheltjens, L. N. Wamba Momo, V. Verbeke, and B. De Moor, “Target informed client recruitment for efficient federated learning in healthcare,” en, BMC Med. Inform. Decis. Mak., vol. 24, no. 1, p. 380, Dec. 2024.
- V. Scheltjens, L. N. W. Momo, V. Verbeke, and B. De Moor, “Client recruitment for federated learning in icu length of stay prediction,” in 2023 IEEE 19th International Conference on e-Science (e-Science), IEEE, 2023, pp. 1–9
LANGUAGE SKILLS
English: Native speaker
French: Native speaker
Spanish: Intermediate user (B1)
Awards
Travel grant: WiML travel grant to NeurIPS 2022 conference, October 2022
Research grant: Black in AI summer research grant, May 2021
Best modelling prize: KU Leuven Datathon
Best data visualization: Data For Good Challenge by Emergent Leuven
Research grant: AMSI-PHILLIP program for M.Sc. research
References
Bart, De Moor, Professor at KU Leuven, bart.demoor@kuleuven.be
Frank Rademakers, Emeritus Professor at UZ Leuven/KU Leuven, frank.rademakers@kuleuven.be
Elaine O. Nsoesie, Professor at Boston University, onelaine@bu.edu
Timeline
Biostatistics Research Fellow
European Organization for Research and Treatment of Cancer (EORTC)
11.2024 - Current
Ph.D. Researcher
KU Leuven
01.2020 - 10.2024
Teaching Assistant
KU Leuven
01.2020 - 10.2024
Data Science Intern
PwC
02.2019 - 05.2019
Financial Engineering Intern
RiskConcile
09.2018 - 09.2018
Data Science Intern
Complidata
07.2018 - 12.2018
M.Sc. - Statistics
KU Leuven
M.Sc. - Applied Mathematics
University of Buea
Ph.D. - Electrical Engineering
KU Leuven
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