Holly Cui
About Holly Cui
Holly Cui is a Data Science Intern at Hiya Inc. and a Student Research Affiliate at Duke AI Health, with a background in statistics and computer science.
Current Positions
Holly Cui currently holds several positions in the fields of data science and research. She is a Data Science Intern at Hiya Inc., a Student Research Affiliate at Duke AI Health, and a Research Assistant at both the University of Pennsylvania and Duke University. These roles involve extensive engagement in data analysis, machine learning, and probabilistic modeling.
Past Work Experience
Holly Cui has amassed various experiences throughout her career. She worked as a Reviewer & Research Assistant at The ZeD Lab at the University of Chicago, a Student Brand Ambassador at ByteDance, a Data Analyst in the Department of Linguistics at UC Santa Barbara, and a Machine Learning Researcher at Tsinghua University. These roles have enabled her to gain valuable insights and skills across multiple research and industry projects.
Education and Degrees
Holly Cui has pursued multiple degrees in the realms of statistics and computer science. She earned a Bachelor of Science (BS) in Statistics & Computer Science from Duke University from 2021 to 2023. Prior to this, she achieved a Bachelor of Science (BS) in Statistics and Data Science from UC Santa Barbara from 2019 to 2021. These academic credentials underline her strong foundation in data science.
Research and Publications
Holly Cui has a distinguished research background, demonstrated by her publication in IEEE, which focused on probabilistic modeling and machine learning. She participated in the Health Data Science Summer 2022 Program, concentrating on machine learning applications in healthcare like lab test harmonization. Additionally, she was the sole undergraduate selected among 11 participants in a competitive research program under Dr. Ricardo Henao and Ms. Shelley Rusincovitch.
Interdisciplinary Research Interests
Holly Cui is deeply interested in applying her interdisciplinary research experiences to real-world industry applications. She has engaged in advanced machine learning research projects in health and medicine, working closely with clinicians and professors. Her focus lies in optimizing data models and leveraging her research knowledge for practical solutions in the industry.