Kyung Ha

I was a postdoctoral researcher at Purdue University, where I implemented and analyzed vertex-based plant cell models using Matlab. I have a Ph.D. in Mathematics from UCLA, where I modeled and published various bioengineering and microfluidic problems. My core competencies include mathematical programming, Machine Learning, and Optimization.

In my recent work experience, I have contributed to several interdisciplinary projects that involve applying mathematical techniques to real-world data. For example, I clustered COVID-related papers using language processing and matrix factorization. I am motivated by the challenge and potential of using mathematics to solve real-world problems and advance scientific knowledge.

I can bring diverse perspectives and experiences to the team, as I have collaborated with experts from different fields and countries.

RESEARCH POSTDOC Aug 2022 - Apr 2023Purdue UniversityWest Lafayette, IN

• Devised a code that from a flat photo of the cylindrical xylem distinguishes the spirals from the rings, and quantifies the number, width, and curvature of each pattern. Used image processing to clean the noisy photos and detect patterns.
• Developed a code that models fern growth. The gametophyte of a fern forms a notch that protects the sex cells of the organism.

  Reproduced the notch formation by dividing the cells into two groups and controlling the growth and division rate. Applied state-of-the-art optimization techniques, and utilized supercomputer clusters. 

• Invented a model of capsules that capture single cells, which will at least half the current DNA sequencing time. To work properly, these capsules need to capture a uniform collection of bubbles.

  Proved that the uniform distribution is the minimizer for capsules with energy functions containing two inflection points. Calculated two capsule designs that satisfy this criteria. Created an interaction model to calculate the convergence time to achieve the uniform distribution.

• Analyzed particle movement in microsized spiral ducts, which is used in cancer research.

  Due to inertial migration, particles behave differently depending on three parameters. Constructed a simplified model that combines the three into a single parameter. Applied bifurcation analysis to show the particle behavior agrees with the results of the full model.

• Clustered 25k+ COVID-related papers, using language processing and matrix factorization techniques.

  Vectorized papers with TD-IDF, clustered them according to their topics and extracted the keywords of each cluster. Clusters were further divided according to their sub-topics and verified by comparing topic similarities between different clusters. 

UNIVERSITY OF CALIFORNIA, LOS ANGELES Aug 2016 - Jun 2022Ph.D. in Mathematics (GPA: 3.85)Los Angeles, CA

• Studied Applied Mathematics at the second-best applied mathematics department in the world.
• Published 7 papers in NeurIPS workshop, Physics Review E, SIAM Dynamical Systems, Journal of Engineering Mathematics, etc.