programming

Engagements

Parallel computing for interactions between fluids and flexible structures with application to suspended longline aquaculture farms
University of Maine, Augusta

The structural dynamics of the aquaculture farms in unsteady flow are essential to assess the performance and resilience of aquaculture farms in environmental change. Moreover, the feedback of the aquaculture farms to the flow is significant for the environment, ecology, and coastal management, such as hydrodynamics impacts, habitat resilience, nutrient transportation, wave attenuation, coastal erosion control, etc. The computational fluid dynamics (CFD) method is used to analyze the interaction between aquaculture farms and the flow. The longline aquaculture farms such as kelp farms and mussel farms are consisting of multiple flexible structures such as mussel droppers and kelp blades. Considering hundreds or thousands of large deformed structures in the fluid-structure interaction (FSI) computing is time-consuming. Therefore, computer science research and parallel computing implementation are essential to make progress on this project. The computer science aspects we initially envision are converting the FSI code to c++ from MATLAB, as well as parallelizing the code. If you have any ideas beyond that, we would love to hear them.

Understanding Covid-19 Pandemic through Social Media Discussion
Bryant University

Dr. Li has been collecting covid-19 tweets since March 2020 and currently has about 1.2 billion tweets. She is still collecting the tweets and expects to have more in the future. This project focuses on the understanding of the impact of covid-19 pandemic through social media discussion on Twitter. The following topics will be explored: 1). What are the top topics discussed regarding covid-19? How has the discussion of the topics changed over time? 2). What is sentiment/emotion of the topic by time, location, and gender? and 3). How to identify misinformation/fake news about covid-19.

The student will work on this project from start to finish using various data analytic methodology including data exploration, topic modelling, natural language processing and machine learning.

Model Mie scattering and light propagation through a high scattering medium using Monte Carlo simulation
Southern Connecticut State University

In this project, we will first use numerical approaches to model light scattering off single particles using Monte Carlo simulation. We will obtain results that follow Rayleigh scattering and Mie scattering. The program will then be extended to simulate light propagation in a highly scattering turbid medium like biological tissue which consists of various arrangements of particles and bulk geometry and calculate the light distribution in the medium and on the boundary. The program will eventually be used for imaging tumors in biological tissue, which will be achieved through an inverse problem.

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