about-me-example.Rmd

---
title: "About Me"
author: "Eric C. Anderson"
output:
  html_document:
    toc: yes
  word_document:
    toc: yes
  pdf_document:
    toc: yes
bibliography: references.bib
---

# Who I am and where I came from

I grew up in a small town in Southern California.  When I was 11 years old,
I spent five weeks living with a family friend who ran the hyperbaric chamber
adjacent to the University of Southern California Marine Lab on Catalina Island.
Days spent snorkeling the kelp beds, keeping intertidal organisms in an aquarium
I was allowed to fill, and watching researchers tag blue sharks convinced me that
I wanted to be a marine biologist.  

In high school, however, I got into rock climbing and became a little math/physics nerd.
I went to [Stanford University](https://www.stanford.edu/) planning to major in
Mathematics, but got super intimidated by all the smart, wonky people in my first
honors math class.  I took a year-long detour to
[Prescott College](https://www.prescott.edu/), where I had my first class in evolution
and wondered why the hell my high-school biology class was not taught in the context
of evolution.

I ended up studying human biology at Stanford, and then went to
[University of Washington](https://www.washington.edu/) for graduate work. I started
in Fisheries, but then got involved in genetics research that needed some
new statistical developments.  Thus, my math nerdiness was resurrected and
I did my PhD in Quantitative Ecology and Resource Management, focusing on
the use of Monte Carlo methods for inference from population genetic models.

After a two-year postdoc at Berkeley, I started working for the National Marine
Fisheries Service in Santa Cruz, CA.  I actually did become a marine biologist,
sort of! I still work for NMFS, but a year and a half
ago I moved to Fort Collins with Kristen, and have an affiliate position
in FWCB.

When I am not working I love getting out and being active. My top four things
to do are:

1. Snorkeling in rivers and creeks, backpacking, and hiking (all with my family),
1. Playing hockey (I learned to skate after moving here.  What a blast!),
1. Biking,
1. Playing with our pair of awesome, springy cats.  

Here is a picture of me with daughter Zoe, looking for aquatic invertebrates
above the CSU Mountain Campus.

```{r me_pic, echo=FALSE, out.width="500px"}
knitr::include_graphics("images/eric.jpg", auto_pdf = TRUE)
```




# Research Interests

I'm interested in all manner of statistical inference from genetic data. Lately I have
been working on the genetic basis of run timing in Chinook salmon and other salmonids.


## Influential papers

When I was a graduate student, I heard Peter Green speak about
his work on reversible jump MCMC for the analysis of finite mixture
models.  One of the problems I was working with at the time was
estimating proportions of salmon from different rivers that were being
caught in the ocean---the mixed stock fishery problem.  I spent a lot
of time working through @richardson1997bayesian and learned a lot about
MCMC and RJMCMC in the process.

Later on, much of my work on Bayesian inference of pedigrees from genetic data
[@anderson2016bayesian] builds upon the idea of factor graphs described by
@kschischang2001factor.

## The mathematics behind my research

I have worked a lot with the coalescent process, so let's put
down the expected time during which there are $k$ extant lineages
in a population of size $N$.
$$
\mathbb{E}T_k = \frac{4N}{k(k-1)}.
$$

And, while we are at, let's throw down a description of one of the 
update steps in the sum-product algorithm for acyclic factor graphs:
$$
\mu_{f_j\longrightarrow v_i}(x_i) =  
\sum_{x_{C\backslash i} \in \mathcal{X}_{C\backslash i}}
h_j(x_{C\backslash i}, x_i) \prod_{k\in C\backslash i} \mu_{v_k\longrightarrow f_j}(x_k).
$$

## My computing experience

I started programming in BASIC on our old Apple IIe in 1983.  In high school
I implemented a basic program to plot some fractal images.  After that, I didn't really
do any programming until grad school when I took a course in C.
Here is some C code that I wrote:
```c
	if(RU!=NULL) {
		RepUnitZSum = (int *)calloc(RU->NumRepUnits,sizeof(int));
		RepUnitPis = DvalVector(0,RU->NumRepUnits, 0.0, 1.0, .01);
		RepUnitPofZs = (dval ***)calloc(N,sizeof(dval **));
		for(i=0;i<N;i++) {
			RepUnitPofZs[i] = DvalVector(0,RU->NumRepUnits-1,  0.0,1.0,   -1);  /* no histograms for these */
		}
		if(BO->PiTraceInterval>0) {
			repPi_tracef = OpenTraceFile("rep_unit_pi_trace.txt", "trace file of reporting unit Pi values from gsi_sim.", Baselines, BO, RU, BO->PiTraceInterval);
		}
		if(BO->ZSumTraceInterval>0) {
			repZSumtracef = OpenTraceFile("rep_unit_zsum_trace.txt", "trace file of reporting unit ZSum values from gsi_sim.", Baselines, BO, RU, BO->ZSumTraceInterval);
		}
	}
```
Wow! That is pretty ugly.  

When I was a postdoc, John Novembre and the other members of Monty Slatkin's lab at
Berkeley got me hooked on using the Unix shell, programming in bash, and
writing short scripts in awk and sed. Here is a little awk script that takes the
output of SGE's `qacct` command and makes a nice, tidy table of it
```sh
#! /usr/bin/awk -f

# an awk script. 
# it expects the output of qacct like this:
# qacct -o eriq -b 09271925  -j ml

# make it executable and run it like this:
# qacct -o eriq -b 09271925  -j ml | tidy-qacct


# if you pass it a job number that was not one of your jobs it 
# just skips the error message that comes up.  

# note that the output of qacct is space delimited


/^==========/ {++n; next}  # increment run counter, then skip these lines
/^error:/ {next}  # skip it if you told it to get a wrong job number

# now, every data line it gets things.  It compiles the header 
# all the way through, in case some reports have more columns...
NF > 1 {
  tag = $1;
  if(!(tag in header_vals)) {
    header[++h] = tag;
    header_vals[tag]++;
  }
  $1 = "";  # remove the tag from the full line of stuff
  values[n, tag] = $0;  # assign the values to the tag
  
}

# at the end of it all, we print the header and then all the values:
END {
  # print the header
  printf("%s", header[1]);
  for(i=2;i<=h;i++) 
    printf("\t%s", header[i]);
  printf("\n");
  
  # cycle over individuals and fields and print em
  for(j=1;j<=n;j++) {
    printf("%s", values[j,header[1]]);
    for(i=2;i<=h;i++) 
      printf("\t%s", values[j,header[i]]);
    printf("\n");
  }
}
```

I used to rather dislike the R programming language and felt it was 
dreadfully slow.  It has gotten a lot better in the last two decades. 
The introduction of Hadley Wickham's tidy data analysis framework has
really improved things.

## What I hope to get out of this class

I hope that I will:

* Help students understand enough about Unix and programming to lessen the pain of learning to do bioinformatics.
* Be able to advance students' own research.
* Impart to the students an appreciation of the importance of making research reproducible.

# Evaluating some R code

I'm going to just simulate one million beta random variables from a $\mathrm{Beta}(2,5)$ distribution
and plot a histogram of it.
```{r, message=FALSE}
library(tidyverse)

beta_rvs <- tibble(
  x = rbeta(1e06, shape1 = 2, shape2 = 5)
)

ggplot(beta_rvs, aes(x = x)) +
  geom_histogram(binwidth = 0.01)
```


# Citations