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#+TITLE: Resume
- #+AUTHOR: Don Armstrong
+ #+AUTHOR: Don L. Armstrong
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- \href{mailto:\myemail}{\myemail} \hfill
- +1~714-813-8531 \\
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- \href{https://github.com/dondelelcaro}{https://github.com/dondelelcaro}
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+ # #+LATEX_CLASS_OPTIONS: [10pt,breaklinks]
+ * Experience
+ ** Research Scientist at UIUC \hfill 2015--2017
+ + Epigenetic modifications associated with PTSD, the genomic basis of
+ the development of parturition in mammals, and detecting adverse
+ pregnancy outcomes using urinary exosomes.
+ ** Postdoctoral Researcher at USC \hfill 2013--2015
+ + Identifying genes and causal alleles associated with Systemic Lupus
+ Erythematosus using genome-wide association, next-generation
+ sequencing, computational and biochemical approaches.
+ ** Postdoctoral Researcher at UCR \hfill 2010--2012
+ + Identifying genes which are associated with Systemic Lupus
+ Erythematosus using prior information and targeted trio-based
+ studies.
+ ** Debian Developer \hfill 2004--Present
+ + \emph{Debian Project}, Developer; Technical Committee Member
+ (2010--2016), Technical Committee Chair (2015--2016).
* Education
- ** UC Riverside
- + *PhD* in Cell, Molecular and Developmental Biology
- + *BS* in Biology
+ ** Doctor of Philosophy (PhD) \hfill UC Riverside
+ + Cell, Molecular and Developmental Biology.
+ ** Batchelor of Science (BS) in Biology \hfill UC Riverside
+
* Skills
-** Genomics and Epigenomics
-+ *NGS* and array-based Genomics and Epigenomics of complex human
- diseases using *RNA-seq*, targeted DNA sequencing, *RRBS*, Illumina
- bead arrays, and Affymetrix microarrays from sample collection to
- publication.
-+ Reproducible, scalable bioinformatics analysis using make,
- *nextflow*, and *cwl* based workflows on cloud- and cluster-based
- systems on terabyte-scale datasets
-+ Alignment, annotation, and variant calling using existing and custom
- software, including *GATK*, *bwa*, *STAR*, and *kallisto*.
-+ Correcting for and experimental design to overcome multiple
- testing, confounders, and batch effects using Bayesian and
- frequentist methods approaches
-# + Using evolutionary genomics to identify causal human variants
-** Statistics
-+ Statistical modeling (regression, inference, prediction, and
- learning in very large (> 1TB) datasets)
-+ Addressing confounders and batch effects
-# + Reproducible research
+** Data Science
++ Reproducible, scalable analyses using *R*, *perl*, and python with
+ workflows on cloud- and cluster-based systems on terabyte-scale
+ datasets
++ Experimental design and correction to overcome multiple testing,
+ confounders, and batch effects using Bayesian and frequentist
+ methods
++ Design, development, and deployment of algorithms and data-driven
+ products, including APIs, reports, and interactive web applications
++ Statistical modeling (regression, inference, prediction/forecasting,
+ time series, and machine learning in very large (> 1TB) datasets)
++ Data mining, cleaning, processing and quality assurance of data
+ sources and products using tidydata formalisms
++ Visualization using *R*, ggplot, Shiny, and custom written routines.
+** Software Development
++ Languages: perl, R, C, C++, python, groovy, sh, make
++ Collaborative Development: git, travis, continuous integration,
+ automated testing
++ Web, Mobile: Shiny, jQuery, JavaScript
++ Databases: Postgresql (PL/SQL), SQLite, Mysql, NoSQL
++ Office Software: Gnumeric, Libreoffice, \LaTeX, Word, Excel,
+ Powerpoint
** Big Data
+ Parallel and Cloud Computing (slurm, torque, AWS, OpenStack, Azure)
+ Inter-process communication: MPI, OpenMP