Weiyue(Larry) Li

Graduate Student at Harvard University

  • Student Resercher, Intel, Jan 2023 - Present

    • Affiliated with Intel & HDSI DCA Research Project Collaboration. Details could not be exposed at the moment.
    • Member of the Web-Based Computing/UX Data Study Project Team, operating under the guidance of Professor Rayan Saab and Professor Jingbo Shang.

  • Data Scientist Intern, Qualcomm Institute, Aug 2022 - Present

    • Project 1:
      • Collaborate with physicians to collect patient data and relevant information on COVID-19 from two internal databases.
      • Conduct data cleaning, aggregation, and validation to ensure data accuracy and reliability.
      • Utilize AWS to build state-of-the-art machine learning pipelines for data analysis and statistical modeling.
      • Develop an interactive dashboard that presents statistically significant findings, enabling physicians to make data-driven decisions.
    • Project 2:
      • Topic and data are both sensitive. This block of text will be replaced once the project has ended.

  • Data Science Research Assistant, Institute for Neural Computation, Aug 2022 - Present

    • Conduct research on Adolescent Brain Cognitive Development (ABCD) using structural magnetic resonance imaging (sMRI), working alongside Neuroscience and Psychology professionals under the supervision of Ph.D. Candidate Margot Wagner.
    • Collect and organize data from 351 external databases by querying and web scraping to create data lookup tables, contributing to the development of a comprehensive understanding of the field.
    • Develop deep learning pipelines (SynthSeg, 3D-CNN, etc) for analyzing images and detecting depression, including conducting literature reviews, discussing model selection, and building the pipelines, contributing to ongoing research on the relationship between brain development and cognitive function in adolescents.

  • Undergraduate Teaching Assistant, US San Diego, Jan 2021 - Present

    • Teach 10 different courses across 3 departments, covering topics such as data science fundamentals, machine learning, graph theory, programming, calculus, and econometrics.
    • Lead discussion sessions for 1300+ students and hold 350+ office hours to help students better understand the material.
    • Develop interactive programming assignments, quizzes, exams, and a new course website, while building a Docker- based Gradescope Autograder for Python, Java, and Stata scripts, and unsupervised machine learning models to detect academic integrity violations on remote exams.
    • Initiate new course policies to improve the course experience and encourage better learning outcomes.