Heng Le Chen
About Heng Le Chen
Heng Le Chen is a Bioinformatics Intern at Yale University, where he has enhanced data processing capabilities and contributed to genomic research. He holds a Bachelor's degree in Mathematical, Computational, and Statistical Sciences from Yale-NUS College and has previous experience as a Machine Learning Intern at Yale University School of Medicine and as an Emergency Medical Technician in the Singapore Armed Forces.
Work Experience at Yale University
Heng Le Chen has held multiple positions at Yale University. He worked as a Machine Learning Intern at the Yale University School of Medicine for three months in 2023. Currently, he is serving as a Bioinformatics Intern at Yale University, a role he has been in since 2023. In this position, he has enhanced data processing capabilities by implementing optimized algorithms, which improved efficiency in analyzing large-scale genomic datasets.
Education and Expertise
Heng Le Chen studied at Yale-NUS College, where he pursued a Bachelor's degree in Mathematical, Computational, and Statistical Sciences from 2021 to 2025. Prior to this, he attended Anglo-Chinese School (Independent) from 2016 to 2018 and Hong Kong International School from 2006 to 2011. His academic background provides a strong foundation in computational techniques and data analysis, which he applies in his current bioinformatics role.
Previous Experience in Healthcare
Before his internships at Yale University, Heng Le Chen worked as an Emergency Medical Technician with the Singapore Armed Forces (SAF) from 2019 to 2020. This role involved providing medical assistance and emergency care, contributing to his skills in high-pressure environments. Additionally, he served as a Student Researcher at Yale-NUS College from 2022 to 2023, where he focused on defining the roles of rare and structural variants in gene regulation using the EN-TEx personal epigenome resource.
Research Contributions
During his internship at Yale University School of Medicine, Heng Le Chen contributed to the adaptation of ChIP-Seq datasets for deep learning models. This work aimed to predict the regulatory effects of structural variants, showcasing his ability to integrate computational methods with biological research. His experience in enhancing data processing capabilities through optimized algorithms further demonstrates his expertise in bioinformatics.