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.
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.
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
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