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Michael C

- Research Program Mentor

PhD candidate at University of California San Diego (UCSD)

Expertise

genetics, cellular/molecular biology, computer science, neuroscience

Bio

I am a PhD Candidate in the Bioinformatics and Systems Biology Graduate Program at UC San Diego and a National Science Foundation Graduate Research Fellow. I am passionate about understanding human disease, and strongly believe that biomedical research with DNA sequencing and related technologies can enable this. Collaboration and interdisciplinary research are central to my approach, and I am dedicated to use reproducible, rigorous, and data-driven methods in these settings to make scientific discoveries. Outside the lab, I enjoy exploring San Diego County through hiking, swimming, and playing tennis, embracing the area’s natural beauty with an active lifestyle. I find joy in cooking and experimenting with new recipes, often drawing inspiration from various cuisines around the world, especially India and Italy. These activities provide a balance to my academic pursuits and keep me energized and creative.

Project ideas

Project ideas are meant to help inspire student thinking about their own project. Students are in the driver seat of their research and are free to use any or none of the ideas shared by their mentors.

Diagnosing Breast Cancer Subtypes Using Cancer Genome Sequencing Data

I specialize in disease biology, genomics, bioinformatics, and data analysis. I can help students explore topics such as cancer genomics, bioinformatics techniques, and the application of machine learning in healthcare. Knowledge and Skills to be Learned • Basics of cancer biology, particularly breast cancer subtypes • Principles of genome sequencing and data interpretation • Bioinformatics tools and software (e.g., R, Python) • Machine learning algorithms for DNA variant calling • Data visualization and scientific reporting Students will: 1. Learn about Breast Cancer Subtypes: Gain foundational knowledge about the biology of cancer and breast cancer subtypes (e.g., HER2-positive, triple-negative, hormone receptor-positive). 2. Explore Genome Sequencing: Understand the basics of genome sequencing technologies and the types of data generated. 3. Data Acquisition: Obtain publicly available cancer genome sequencing datasets from resources such as The Cancer Genome Atlas (TCGA). 4. Data Processing: Use bioinformatics tools to preprocess and clean the raw sequencing data. 5. Variant Calling: Apply machine learning algorithms to classify breast cancer subtypes based on the genetic data. 6. Visualization and Reporting: Create visualizations to represent findings and compile the results into a scientific research paper. Potential Student Outcomes • Scientific Research Paper: A detailed report outlining the methodology, analysis, and findings of the project. • Data Visualizations: Graphs and charts that illustrate key aspects of the data and the results of the machine learning models. • Presentation: A PowerPoint or poster presentation summarizing the project for academic or science fair settings. • Code Repository: A well-documented codebase for data processing and analysis, potentially shared on platforms like GitHub for community use. This project will provide students with hands-on experience in cancer genomics research, enhance their computational skills, and give them a taste of real-world applications of bioinformatics and machine learning in healthcare.

Coding skills

Python, R

Teaching experience

I have tutored undergraduate students in undergraduate genetics and biology courses. I have taught short bioinformatics courses to lab technicians and graduate students. I have mentored undergraduate students in the research lab setting.

Credentials

Work experience

Broad Institute of MIT and Harvard (2016 - 2020)
Research Associate
Dana Farber Cancer Institute (2014 - 2016)
Undergraduate Researcher

Education

Trinity College (CT)
BS Bachelor of Science (2016)
Cellular and Molecular Biology
University of California San Diego (UCSD)
PhD Doctor of Philosophy candidate
Bioinformatics and Systems Biology

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