Automated single-step retrosynthetic reaction prediction with energy-weighted molecular graphs
October 2019 – December 2019
RetroCHEM is a command-line tool for graph-based, single-step retrosynthetic reaction prediction. It offers a variety of options, including SMILES tutorials, SMILES parsing, conversion to MDL MolFile and graph formats, and predicting decomposition reactions. This project reaches high accuracy segmenting small molecules and could be used as a framework for de novo drug design.
In silico discovery and classification of competing endogenous RNA (ceRNA) molecules for multi-omic network diffusion and novel microRNA-sequestering drug design
September 2018 – April 2019
CeRNetwork is an independent research project using graph theory and large genomic datasets in order to identify previously overlooked competing endogenous RNA (ceRNA) drug targets that are highly involved in chronic disease. Thorough statistical analysis suggest that these molecular protagonists may be more effective in creating personalized medicine than microRNA therapeutics.
AWARDS
2019 Intel International Science and Engineering Fair (ISEF): 4th in Category of Computational Biology and Bioinformatics
A mechanism for rapid identification of aquatic microorganisms via magneto-fluorescent nanoparticles (MFNP)
December 2018 – June 2019
What's in Your Water is a biotechnology project exploring ways to prevent the spread of waterborne illness. It combines novel, inexpensive, magneto-fluorescent nanoparticles (MFNPs) and bacterial antibodies to visibly identify the presence of and aggregate harmful microorganisms in solution.
AWARDS
2019 Virginia Technology-Student Association: 1st Place Overall in the Category of Biotechnology Design
Modeling teenage risk for developing substance use addictions with network topology
March 2019
This project used mathematical modeling to represent the growth and harms of substance use over time. Using statistical data, a stochastic network model was created and weighted by socioeconomic influences to accurately predict the likelihood of various teenagers to use substances based on their environments.
AWARDS
2019 MathWorks Math Modeling (M3) Challenge Honorable Mention (top 4% of submitted papers)
Spontaneous selective carbon dioxide adsorption using cyclodextrin metal-organic framework (CD-MOF) conjugated with alkali ions
November 2017 – May 2018
This project engineered cyclodextrin metal-organic frameworks (CD-MOF) to spontaneously and selectively adsorb carbon dioxide from the atmosphere. It proposed a new and inexpensive method for synthesizing the crystals based on vapor diffusion techniques. Optimization of coordination with alkali ions led to the most ideal CD-MOF for the task.
AWARDS
2018 Virginia Governor's Award in Science and Technology
2018 Intel International Science and Engineering Fair (ISEF) Finalist
