Summary
Overview
Work History
Education
Skills
Websites
RESEARCH INTERESTS
Publications
LANGUAGE SKILLS
Awards
References
Timeline
Generic

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
Lyse Naomi Wamba