CV.Rmd

---
name: Adam
surname: Kirosingh
position: "PhD Candidate"
address: "Stanford University, California"
#profilepic: "./img/me.jpg"
phone: +1 702 350 0660
email: "adamsk@stanford.edu"
github: akirosingh
linkedin: adamkirosingh
orcid: 0000-0003-0500-9269
date: "`r format(Sys.time(), '%B %Y')`"
headcolor: 414141
docname: CV
output: vitae::awesomecv
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      warning = FALSE,
                      message = FALSE)


pkgs <- c('vitae' ,'here' ,'tinytex' ,'tibble' ,'glue' ,'rmarkdown' ,'dplyr', 'readr' ,'yaml')
xfun::pkg_attach2(pkgs)



packages <- c('vitae'     # The CV's LaTeX Template
             ,'tibble'    # Data sctructure for tribbles
             ,'magrittr'  # The Pipe %>%
             ,'here'      # Relative Filepaths
             ,'readr'     # Data Loading
             ,'tidyverse' # Needed for bind_rows and whatever
             ,'scholar'   # Updating publications from google scholar
             ,'kableExtra'# Adding technicalskills table
            )
library(magrittr)
# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages])
}

# Packages loading
lapply(packages, library, character.only = TRUE) %>%
  invisible()

source(file = here("r","data.r"))

```




# Education

```{r EDUCATION}
structure(list(what = c("PhD Microbiology & Immunology", "BSc Molecular Microbiology & Immunology with Minors in Mathematics & Chemistry"), when = structure(c("2022", "2017"
), class = c("glue", "character")), with = c("Stanford University", 
"University of Nevada, Reno"), where = c("Stanford, CA", 
"Reno, NV"), why = list("Dissertation - Malaria susceptibility: genetic factors and immune adaptation during pregnancy", "Honors Thesis - In vivo distribution and clearance of purified capsular polysaccharide from Burkholderia pseudomallei in a Murine Model")), row.names = c(NA, 2L), groups = structure(list(
    what = c("BSc Molecular Microbiology & Immunology with Minors in Mathematics & Chemistry with Honors", 
    "High School Diploma", "PhD Microbiology & Immunology"), when = structure(c("2017", 
    "2013", "2022"), class = c("glue", "character"
    )), with = c("University of Nevada, Reno", 
    "Stanford University"), where = c("Reno, NV", "Stanford, CA"
    ), .rows = structure(list(2L, 1L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -2L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("vitae_detailed", 
"vitae_preserve", "grouped_df", "tbl_df", "tbl", "data.frame"
), preserve = c("what", "when", "with", "where", "why"), protect = TRUE)

```

# Research

```{r Research, results = "asis"}

research %>%
  detailed_entries(
    with = title,
    what = unit,
    why = detail,
    when = glue::glue("{startMonth} {startYear} - {endMonth} {endYear}",.na = ""),
    where = where, .protect = F
  )

```

# Teaching

```{r Teaching, results = "asis"}

teaching %>%
  detailed_entries(
    with = title,
    what = unit,
    why = detail,
    when = glue::glue("{startMonth} {startYear} - {endMonth} {endYear}",.na = ""),
    where = where, .protect = F
  )

```

# Leadership

```{r Leadership, results = "asis"}

leadership %>%
  detailed_entries(
    with = title,
    what = unit,
    why = detail,
    when = glue::glue("{startMonth} {startYear} - {endMonth} {endYear}",.na = ""),
    where = where, .protect = F
  )

```

# Relevant Courses

```{r}

UNR <- c("MATH 420: Mathmatical Modeling",
"MATH 461: Probability Theory",
"MATH 462: Introduction to Stochastic Processes",
"STAT 467: Statistical Theory")
Stanford <- c("BIOS 221: Modern Statistics for Modern Biology","CME 193: Introduction to Scientific Python")
 
classes <- tibble(title =c(rep("Stanford University",length(Stanford)),rep("University of Nevada, Reno", length(UNR))), detail = c(Stanford,UNR))

classes %>%
  detailed_entries(
    what = title,
    why = detail, .protect = F
  )
```


# Technical Skills 

<!-- \noindent\makebox[\linewidth]{\rule{\textwidth}{0.4pt}} -->

<!-- \par\noindent\nobreak\vspace{-30pt}\rule{\textwidth}{1pt} -->

```{r techtable, results='markup'}
tribble(
   ~`Coding Languages`, ~Software, ~`Markup Languages`,
  "R, Python, Mathematica, MATLAB",
  "GIT, SLURM, High-Performance Computing",
  "CSS, HTML, \\LaTeX, RMarkdown, Bootstrap"
) %>%
  kable(booktabs = T, align = 'c', escape = F, format = "latex") %>%
  kable_styling(full_width = F, font_size = 9,latex_options = "hold_position") %>% 
  row_spec(0, bold = T, color = "#414141") %>% 
  row_spec(1, bold = F, color = "#7f7f7f") %>% 
  column_spec(column = 1:3, width = '4.75cm')
```


<!-- \smallskip -->


# Honors

```{r HONORS}

honors %>%
  detailed_entries(
    with = area,
    what = accomplishment,
    why = detail,
    when = year,
    where = where
  )

```


# Publications

- **Kirosingh, A.S.**, De La Parte, L., Ty, M., Kakuru, A., Muhindo, M. K., Thulin, N., Kamya, M., Feeney, M., Dorsey, G., Wang, T.T., Jagannathan P., Cellular correlates for protection against malaria acquired across multiple pregnancies (manuscript in preparation)

- **Kirosingh, A.S.**, Gupta, A.S., Chevee, V., Davis, N., Cumnock, K., Lissner, M., Schneider, D.S. Malaria Susceptibility Loci Identified in the Diversity Outbred Mouse Population  (manuscript in preparation)

- Hurtado, P.J., **Kirosingh, A.S.**, 2019. Generalizations of the ‘Linear Chain Trick’: incorporating more flexible dwell time distributions into mean field ODE models. J. Math. Biol. 79, 1831–1883. https://doi.org/10.1007/s00285-019-01412-w

- Nualnoi, T., **Kirosingh, A.**, Pandit, S.G., Thorkildson, P., Brett, P.J., Burtnick, M.N., AuCoin, D.P., 2016. In vivo Distribution and Clearance of Purified Capsular Polysaccharide from Burkholderia pseudomallei in a Murine Model. PLOS Neglected Tropical Diseases 10, e0005217. https://doi.org/10.1371/journal.pntd.0005217

- Nualnoi, T., **Kirosingh, A.**, Basallo, K., Hau, D., Gates-Hollingsworth, M.A., Thorkildson, P., Crump, R.B., Reed, D.E., Pandit, S., AuCoin, D.P., 2018. Immunoglobulin G subclass switching impacts sensitivity of an immunoassay targeting Francisella tularensis lipopolysaccharide. PLOS ONE 13, e0195308. https://doi.org/10.1371/journal.pone.0195308

## Published Abstracts

- Hurtado, P., **Kirosingh, A.**, 2018. The Generalized Linear Chain Trick: A new tool to build ODE models with more flexible dwell-time distributions. Presented at the 2018 ESA Annual Meeting (August 5 -- 10), ESA.

- **Kirosingh, A.**, 2017. PROBABILITY DISTRIBUTIONS OF SYSTEM AVERAGE INTERRUPTION FREQUENCY INDEX. Presented at the 2017 AAAS Annual Meeting (February 16-20, 2017), AAAS.