Professional Activities

Talks

  • Guest Speaker, Data4All, University of Chicago (November 23, 2024)
  • Invited Talk, “Recent Advances in the Analysis of Partial Differential Equations”, SIAM Great Lakes Meeting, Michigan State University (October 14, 2023)
  • Poster Presentation, Association for Women in Mathematics, Joint Math Meetings (Online) (January 8, 2021)

Mentorship


Leadership and Service

  • President, SIAM Student Chapter, OSU (Aug 2022 – Apr 2023)
  • Vice President, AWM Chapter, OSU (Aug 2021 – Apr 2023)
  • Vice President, Math Graduate Student Association, OSU (Aug 2020 – May 2022)
  • Founder & Organizer, Student Analysis Seminar, OSU (Jan 2020 – May 2022)
  • Graduate Student Representative, Diversity Committee, OSU Math Department (Aug 2020 – May 2021)
  • Outreach Coordinator, SIAM Chapter, IISc (Aug 2017 – May 2018)

Selected Projects

  • Mathematical Problems in Industry (MPI 2025):
    Collaborated with Kwaai, an open-source AI lab, to study privacy-preserving query methods for vector databases in Personal AI. Explored encryption techniques like dimensional scrambling, noise injection, ElGamal, and CKKS, and developed and evaluated new homomorphic encryption algorithms.

  • Mathematical Problems in Industry (MPI 2024):
    Analyzed Vironix Health’s de-identified datasets on disease progression during remote patient monitoring to identify positive health outcomes and predict adverse episodes, patient compliance, and engagement.

  • Erdős Institute Data Science Boot Camp (Fall 2023):
    Built predictive models to forecast S&P 500 index behavior and compared results across stock indices.

  • Wolfram|Alpha (Summer 2023):
    Contributed to a project extracting and validating mathematical assertions from scientific papers, using LaTeX parsing, pattern matching, and regular expressions.

  • Mathematical Problems in Industry (MPI 2022):
    Collaborated with Vironix Health to conducted exploratory data analysis of hospital admission data to identify key features and symptoms predictive of heart failure severity.

  • Graduate Student Math Modeling Camp (GSMMC 2022):
    Analyzed geospatial travel data to balance transparency, privacy, and utility using statistical and randomization methods.

  • Erdős Institute Data Science Boot Camp (Summer 2021):
    Used predictive modeling (KNN, Decision Trees, SVMs) to identify success factors in clinical trials for cancer interventions.