I'm Marlene Lin, a graduate student pursuing a master’s degree in Health Data Science at UCSF. With a strong background in computational biology and statistics, I'm passionate about using data-driven insights to make a positive impact in healthcare.
I am actively seeking internship opportunities for the summer and part-time positions beginning in the fall.
About me
I have had extensive experience analyzing data from a variety of fields and therefore confident in my ability to adapt and thrive in diverse analytical environments. When working as an operation intern for Kaisa Group, I collected and evaluated complex population data and health statistics to complete a key study of medical resource distributions for the telemedicine department’s direct-to-patient service deployment. As a student researcher at UCLA and UCSF, I have motivated projects that derived diagnostic insights from imaging and EHR data with rigorous statistical analysis and implementation of machine learning/deep learning models and honed my expertise in R, Python, and MATLAB.
In addition to my technical proficiency, I further built my skills in problem-solving and collaboration while working in these cross-functional teams and as a teaching assistant for the library. I strive to contribute to an effective working environment through constant communications with members and the development and documentation of pipelines and tools that optimize the workflow. I believe that my proactive and detail-oriented approach to work would ensure my contribution to your team.
Education
University of California, San Francisco (2023 - 2025)
Master of Science in Health Data Science
Relevant coursework:
- Programming for Health Data Science in R
- Biostatistical Methods for Health Research
- Machine Learning in R for Biomedical Research
- Responsible Conduct of Research Training
University of California, Los Angeles (2019 - 2023)
Bachelor of Science in Computational and System Biology
Concentration: Biological Data Sciences
Minor: Statistics
Thesis: Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging. Metabolites. 2023; 13(7):835. https://doi.org/10.3390/metabo13070835
Relevant Coursework:
- Biotechnology and Society
- Digital Image Processing
- System and Signal
- Python with Applications
- Machine Learning
- Linear Models
- Mathematical Statistics
- Data Analysis & Regressions
- Statistical Programming in R
- Design and Analysis of Experiments
Work
Library Teaching Assistant
(October 2023 - Current)
- Co-teach and assist with R and Python programming workshops on topics including data visualization, statistical analysis, and machine learning.
- Offer 1:1 programming and data analysis help to UCSF community members during weekly data science help desk.
- Assist with other projects including updating online course materials, creating subject guides and help articles, and presenting library resources to UCSF members.
Student Researcher
UCLA Magnetic Resonance Research Labs, Los Angeles, CA
(March 2022 - July 2023)
- Developed MATLAB applications for in-depth processing and annotations of multidimensional Magnetic Resonance Spectra data.
- Tuned parameters and assessed the performance of various MR spectroscopy reconstruction methods, reducing MR spectroscopy scan time by at least two-fold.
- Characterized brain MR Spectroscopy data of obstructive sleep apnea and pediatric AIDS patients.
- Construct ensemble learning models to classify breast tumors based on 5D MR Spectroscopy quantitation, findings published in Metabolites.
Learning Assistant
UCLA Undergraduate Learning Assistant Program, Los Angeles, CA
(Sep 2022 - March 2023)
- Encouraged growth-oriented mindsets among students and created an equitable STEM learning environment by fostering student collaboration as a peer educator.
- Effectively explained course concepts and expanded peers’ understanding of the relevant materials in discussions, course workshops, and review sessions.
- Formulated ways to improve course structure and helped maintain clarity within the course by consistently communicating with instructors in weekly meetings.
Telemedicine Operation Analyst Intern
Kaisa Health Group, Shenzhen, China
(June 2021 - Sep 2021)
- Directed detailed regional analysis of the healthcare sector in major cities of China to provide insights on the department’s Direct-to-Patient service deployment.
- Collected and analyzed data on medical resources distribution, international and domestic health product sales, and health expenditures of different communities.
- Conducted on-site investigations of various pharmacy chains and delivered relevant reports to support the department’s business acquisition plan.
Projects
Research
Alzheimer’s Disease Neuropathological Subtype Identification
On-going with Rabinovici Lab, UCSF Memory and Aging Center
Identify Alzheimer’s Disease subtypes based on the topographic distribution of tau by applying robust data-driven clustering methods on baseline tau-PET of sporadic early-onset patients from the Longitudinal Early-Onset Alzheimer’s Disease Study
Prevalence of Diabetes Screening, Pre-diabetes Testing, and Nutrition Counseling
On-going with Dr. Fukuoka & Dr. Jiang, UCSF Dept. of Epidemiology and Biostatistics
Characterize the prevalence of encountered for diabetes screening, testing for pre-diabetes, and nutrition counseling among different demographic groups using electronic health record data queried from the UCSF Info Commons
Breast Lesion Classification with DWI and 5D MRSI data
June 2023 with Thomas Lab, UCLA Magnetic and Resonance Research Labs
Ensemble Learning Breast Lesion Classification using Magnetic Resonance Spectroscopic Imaging and Diffusion-weighted Imaging
MATLAB Magnetic Resonance Spectroscopy Processing Application
Bone Age Assessment with Added Features
Dec 2021
Re-implemented a deep-learning model for bone age regression with an attention-guided localization network and label distribution learning-based regression network. Incorporated ethnicity information from the Digital Hand Atlas data to improve the accuracy of bone age prediction.
Literature Review: Application of Gene Drive
Data Analysis
Python Blogposts
Pseudo-alignment Implementation
NIH Funding Dashboard
Kirby Message Bank
Cast-based Show Recommendations
Basic Classification 1: Cat v.s. Dog pics
Basic Classification 2: False v.s. Real news
Basic Classification 3: Penguin Classification
Dec 2022
Penguin Classification using multiple sklearn models and visualization using Seaborn.