Miles Wang Henderson
About Miles Wang Henderson
Miles Wang Henderson is a Senior Associate Machine Learning Engineer with a strong background in computational biology and bioinformatics. He specializes in applying machine learning techniques to drug discovery, with a focus on sequential decision making and generative modeling.
Current Role at Terray Therapeutics
Miles Wang Henderson serves as a Senior Associate Machine Learning Engineer at Terray Therapeutics. He has been in this role since 2024, contributing to the company's efforts in leveraging machine learning for drug discovery. His work involves applying advanced machine learning techniques to improve the efficiency and effectiveness of drug development processes.
Previous Experience at ETH Zürich
Miles worked at ETH Zürich in various capacities. He served as a Research Assistant for five months in 2023, where he engaged in research activities. Additionally, he was a Teaching Assistant for four months in 2023, supporting students in their learning. He also completed his Master Thesis at ETH Zürich in 2023, focusing on computational biology and bioinformatics.
Experience in Biotech and Pharmaceuticals
Miles has significant experience in the biotech and pharmaceutical sectors. He worked as a Junior Software Engineer at AliveX Biotech for four months in 2021, followed by a role as a Research Intern at Roche in Basel, Switzerland, for five months from 2022 to 2023. These positions provided him with practical insights into software development and research in the life sciences.
Educational Background in Computational Biology
Miles holds a Bachelor of Science with Honours from the University of Amsterdam, which he completed from 2018 to 2021. He furthered his education at ETH Zürich, earning a Master of Science in Computational Biology and Bioinformatics from 2021 to 2023. His academic background supports his specialization in machine learning applications within drug discovery.
Specialization in Machine Learning for Drug Discovery
Miles specializes in the application of machine learning techniques in drug discovery. His focus areas include sequential decision making and generative modeling, which are critical for developing innovative solutions in the pharmaceutical industry. His strong foundation in computational biology and bioinformatics enhances his capability in this field.