Sitemap
Welcome to my personal page. Here you'll find everything you need to know about my scientific career path and digital portfolio. It might seem confusing at first since I'm both a laboratory scientist and a programmer
Main menu
Let me tell you where to go if you're looking for something specific. At the top of each page is a main menu that will direct you to this homepage, the portfolio/showcase, my blog 'Not Very Humerus', my short resume, and a page of links.
If you're looking for information about my current open source projects, I'd take a look at the portfolio page, where I go into more detail about my interest in alignment-free methods, sequencing formats, and code profiling.
But on this page you'll learn more about me, and I'll tell you what you'll find next.
About me
If you're in a hurry, I'd suggest you look at my current projects, skillsets, and education below. If you can take 5 minutes, there's a more detailed about section below that, at the bottom of this page.
Social Media
At the top and bottom of every page are links to my social media accounts. Please consider reaching out to me there and I'd be happy to talk about most anything!
Projects
I am currently working on tuning and benchmarking an algorithm for processing k-mer spectra under GPLv3+. The project is detailed further under my Portfolio Showcase. The project features theoretical investigations, code profiling, report generation, and Github project management.
- CLI Performance Tuning
- K-mer Database (.kdb)
- Performance Benchmarking (Rmd)
- Kmer.js
- NGS Complexity Index
- Customized Arch Linux OS
Work History
In the past, I worked for Terry Papoutsakis, a metabolic engineer of the bacterium C. acetobutylicum and the biopharmaceutical company Bristol-Myers Squibb in two departments: Computational Genomics and Computer Assisted Drug Design. If you'd like to see more about my professional life, please check out the associated information in my resume, my LinkedIn, or contact me on social media for my long-form curriculum vitae.
Skills
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THEORETICAL
- Biochemistry
- Physiology
- Genetics/Genomics
- Algorithms
- Biostatistics
- Machine Learning
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LABORATORY
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Organic, Phyical, Analytical Chemistry:
- GC-MS
- HPLC
- ITC
-
Microbiology:
- Fed-batch fermentation
- Plate/colony culture
- Flux-balance analysis
-
Immunoscience:
- Fluorescence-Activated Cell Sorting (FACS)
- Immunofluorescence / Immunohistochemistry
- Western blot
-
Molecular Biology:
- Northern blot
- q-RT-PCR
- Next Generation Sequencing
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Organic, Phyical, Analytical Chemistry:
-
CODING
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Database:
- MySQL/PostgreSQL/Oracle
- MongoDB/DynamoDB
- Sqlalchemy/Rails/Express ORMs
-
Statistics / Visualization:
- R + KnitR + Shiny
- Data-Driven Documents (D3.js)
-
Web Development:
- HTML + CSS + JS
- REST-APIs
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UNIX Shell:
- Bash on RHEL/Debian/Arch Linux/OSX
-
Scripting / Full-stack:
- Bash
- Python / Flask
- Ruby / Rails
- Perl
- NodeJS / Express
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Functional Programming:
- Scheme
- Haskell
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Database:
Education
- B.S. Biochemistry
- M.S. Bioinformatics
About Me.
Bioinformatician who enjoys studying microbiological genomics and algorithms. Not currently enrolled in a program but still studying the disciplines nonetheless. I look for disciplines that are synergistic with biological fundamentals (chemistry, compuster science, and mathematics) to balance potential applied science areas with my quantitative reasoning.
Philosophically, I admire the spirit of the Gnu Public License, Creative Commons, Arduino, and open-science communities. Some day, I'd love to work on some blog posts about citizen science with Arduino instruments.
A challenge in biological sciences is system complexity and the number of system components available to study. For this reason, there has been an increase in the number of multiplexed measurement technologies, as well as an increase in the cost of instrumentation. Multiplexed assay formats like the microarray and Illumina sequencing provide a broad, survey arm to detect changes worth investigating at the classical level. However, determining the sensitivity of such measurements or confronting the quantitative aspects of biology remains a challenge that is often addressed by software and statistical thresholding.
Quantitative fundamentals are often pushed aside in exchange for a broad applied science survey in many undergraduate biology programs. For this reason, I elected to focus on classical biochemistry book work, applied molecular fundamentals in a cancer laboratory, and a masters degree in computer science with a focus on sequencing technology and multiplexed gene expression.
If there was one thing I'd fix from my masters degree, it would be that I didn't build the library from scratch with cheap ligases and random hexamers. I elected instead to use a Illumina TruSeq sequencing kit, which produced an RNA library with good fragment length and excellent fastq quality scores. However, these kits should be used as a high-caliber basis to judge the quality of alternative library preparation protocols utilizing cheaper reagents and custom barcoding strategies. In short: I wish I had used a more DIY approach.
Open source fanboy, gamer, guitarist.
Feel free to contact me with questions, requests, or feedback
