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Python Developer

Location:
Boston, MA
Posted:
October 03, 2020

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Resume:

Mengqi Chen

adglz6@r.postjobfree.com *** Princeton St., East Boston, MA

617-***-****

Work Experiences

Bioinformatician Intern

Massachusetts General Hospital June 2019 – exp October 2020

• De novo transcriptome assembly with both Nanopore sequencing data and Illumina sequencing data, achieving the most complete transcriptome reconstruction of Vargula hilgendorfii.

• Processing Bulk/Single-cell RNA-seq from different Platform from raw data to differentially expression analysis

• Develop a novel Graph-based clustering algorithm to remove redundancy, which has far better unsupervised clustering than CDHIT-EST and Trinity-Pseudogene.

• Pipeline automation: write meta-scripts that can generate and automate other scripts, which make evaluation of clustering more convenience and robust.

Research Associate

Zhejiang University December 2017 - June 2018

• Develop a web tool to predict transcription factor binding site based on JASPAR database.

• Automated web scraping and text processing script in python, achieving web information retrieval every month.

• Optimize algorithm in R script with pre-computing and k-mer tricks that accelerate scripts 100 times Education

Northeastern University, Boston, MA May 2020

Master of Science in Bioinformatics GPA: 4/4

Courses: Biostatistics in R; Bioinformatics computational method; Advanced genomics; Introductory to machine learning Skills and Techniques

Programming environment: R-project, Python, Perl, MySQL, Scala, Linux commands Tools: samtools; bedtools; GATK; STAR; IGV; Apache Spark; Docker; Airflow; Django Python Packages: numpy, pandas, tensorflow, scikit-learn R Packages: tidyverse, fgsea, Deseq2, Seurat

Visualization and presentation packages:

Web development: HTML, CSS, SASS, d3.js

Python: matplotlib, plotly, dash

R: ggplot2, shiny

Experiences of dealing with different sequencing platforms: Illumina rna-seq, scRNA-seq from 10X and inDrop, Long read sequencing from Nanopore Sequencing and PacBio Personal Projects

•Develop a web application to plot interactive visualization with AWS EC2 and S3

•Somatic Variants Calling on a chordoma dataset via GATK, collecting all SNPs in VCF format.

•Build regression models to predict Soccer Players’ values, following CRISP-DM Principle

•Develop an SVM model to predict cancer classification based on diagnostic features with 96% accuracy.

•Classify normal, SARS and COVID19 chest X-ray images with Convolutional Neural Network (CNN) with 95% accuracy. Link

LinkedIn: https://www.linkedin.com/in/mengqi-chen

GitHub repo (contain above Personal Projects): https://github.com/chenpoi/CodeExample Professional Certification

Ultimate AWS Certified Developer Associate 2020

Interactive Python Dashboards with Plotly and Dash Machine Learning and AI: Support Vector Machines in Python Build Data Visualizations with D3.js & Firebase



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