Lokasi ngalangkungan proxy:   [ UP ]  
[Ngawartoskeun bug]   [Panyetelan cookie]                
Skip to content
View cmk323's full-sized avatar

Block or report cmk323

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
cmk323/README.md

Hi, I'm Mae.

lnp2

I am an mRNA and lipid nanoparticle (LNP) process development scientist with 3+ years of industry experience in mRNA-LNP formulation, mixing optimization, enzymatic reactions, preparative chromatography, and membrane filtration sciences with an emphasis on automation, process robustness, and scale feasibility. I have extensive experience in tangential flow filtration (TFF) for nucleic acid drug products, including development of automated small-scale screening systems and GMP-representative scale-down models using flat sheet cassettes, hollow fiber filter modules, and microfluidic membrane chips. I am highly skilled in Design of Experiments (DoE) methodology using JMP to optimize mixing systems across varied geometries, including turbulent T-mixers, impingement jet mixers, and stirred-tank bioreactors. With an educational background in biochemistry and computer science, I am highly passionate about the intersection between experimentation and computation. I am fluent in Python and SQL and have diverse hands-on experience developing machine learning models, data analysis pipelines, and visualization tools to aid bioprocess development. I have been recognized for leading technical TFF training initiatives, partnering cross-functionally to advance innovative nanoparticle characterization strategies, and for my work accelerating data-driven process development cycles using Benchling.

Pinned Loading

  1. computational-drug-discovery-project computational-drug-discovery-project Public

    The goal of this project is to generate a linear regression model that accepts ChEMBL inhibitor data for a target of interest as input and produces inhibitor bioactivity predictions with respect to…

    Jupyter Notebook 6 2