Hollan Haule

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Hollan Haule

Position

Enrichment Student

Cohort year

2023

Partner Institution

University of Edinburgh
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Research areas

Data structures Probabilistic programming Estimation theory Time series Probability Non-parametric & semi-parametric methods Information theory (Statistical methods & theory) High dimensional inference Causality Uncertainty quantification Stochastic optimisation Neural & evolutionary computing Artificial intelligence Neural networks Nonlinear dynamics Pattern formation Dynamical systems & differential equations Reinforcement learning Unsupervised learning Supervised learning Semi-supervised learning Pattern recognition Deep learning Applications (Machine learning)

Bio

Hollan Haule is a PhD student at The University of Edinburgh, where he is co-supervised by Dr. Javier Escudero Rodriguez, Dr. Chen Qin, and Dr. Milly Lo. He holds a BSc in Computer Engineering and Information Technology from the University of Dar es Salaam, Tanzania, and an MSc degree in Artificial Intelligence from the University of Edinburgh, UK. His research interests focus on the analysis of biomedical time-series data using machine learning techniques.

Research interests

Hollan's research aims to advance healthcare delivery in the ICU by utilizing machine learning tools to aid medical researchers in analyzing routinely collected physiological data at the bedside. In the first year of his PhD, he focused on investigating and developing unsupervised machine learning techniques for detecting noise in the data. This work directly addresses the laborious and expensive manual data cleaning process traditionally performed by experienced researchers before the data can be utilized for medical research purposes.

Looking ahead, Hollan plans to leverage his time at the Turing Institute to further develop and test interpretable machine learning models. These models aim to utilize the collected data to forecast life-threatening episodes, providing timely alerts to medical professionals for prompt intervention. By using his expertise in machine learning, Hollan seeks to contribute to improving patient outcomes and enhancing the efficiency of critical care in the ICU.