Yi-Chun Lai

Yi-Chun Lai

Student at North Carolina State University

    Yi-Chun Lai is a M.S. Candidate in Analytics at the Institute for Advanced Analytics, North Carolina State University. Prior to joining the master's program, she earned her PhD in Environmental Engineering at NC State and gained one year of experience as a research scientist in a biomedical device company. Her doctoral research primarily focused on microalgal bioprocessing for biodiesel production and participated projects aimed at optimizing anaerobic digestion performance in wastewater treatment plants. With a decade of hands-on laboratory research experience, Yi-Chun developed a passion for data science. She is now eager to leverage her scientific background and analytical skills to bridge the gap between research and practical applications.

    All Sessions by Yi-Chun Lai

    Day 1 extra event 04/23/2024
    2:00 pm - 4:00 pm

    Women in Data Science Ignite: Sharing Insights and Networking Session

    This session is designed to empower and connect women working in the exciting field of data science. Whether you're a seasoned professional or just starting out, this is your chance to: Gain valuable insights from experienced data scientists on a variety of topics relevant to the field (consider mentioning specific examples here, e.g., career advice, overcoming challenges, best practices in a specific area of data science). Share your own experiences and perspectives in an open and supportive environment. Build connections with other women in data science, fostering collaboration and mentorship opportunities. This session is a great opportunity to learn, grow, and be inspired by the incredible women shaping the future of data science.

    Day 1 04/23/2024
    2:30 pm - 2:40 pm

    Utilizing XGBoost to Predict High-Performance Microbial Communities in Wastewater Treatment Plants

    For a full-scale treatment plant, it is hard to identify the optimal microbial community assembly (MCA) for treating wastewater. Therefore, leveraging the power of the XGBoost model can help unlock the process performance. We used real-world data from a wastewater treatment plant in North Carolina to evaluate MCA and its treatment quality. The results revealed the relationships between alpha diversity, beta diversity, process performance, and MCA. The model can be applied to regional North Carolina wastewater treatment plants to help inform and monitor the changes in MCA and identify the optimal MCA for the process.

    Open Data Science




    Open Data Science
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    Cambridge, MA 02142

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