John Robin Inston

Ph.D. Student in Statistics and Applied Probability

Thanks for visiting!

I am a 3rd year Ph.D. student in the Statistics and Applied Probability Department at the University of California Santa Barbara.

My broad research interests include quantitative finance, mean field games, control theory and environmental statistics. Currently I am investigating the implementation of Hidden Markov Models for precipitation modelling in California under the supervision of Professor Mike Ludkowski.

If you would like to reach out to discuss any of my written work or my research topics please send me a note using the contact page.

About me

I am passionate about learning and teaching topics in mathematics and statistics and I firmly believe in sharing and collaboration in academia. On my website I am committed to writing posts, sharing my project work and publishing all of the course material I produce for my teaching work.

Random walk simulation.

In addition to the posts on my website I will also be posting the same content to my profile on Medium, as well as (hopefully) publishing content on YouTube aiming to provide advice and helpful content for students in STEM.

Education Timeline

  • In 2013 I commenced my undergraduate study at Lancaster University studying Theoretical Physics.

  • Between 2014 and 2016 I changed course twice, first to Mechanical Engineering and then to Mathematics with Statistics.

  • I graduated in 2018 with a First Class (Honors) BSc in Mathematics with Statistics.

  • During the COVID19 pandemic in 2020 I returned to Lancaster to obtain my MSc in Quantitative Finance.

  • In 2022 I began working towards my doctorate in Statistics and Applied Probability at UC Santa Barbara in California.

flowchart TD
  A(<b>BSc Mathematics with Statistics</b> <br> Lancaster University <br> <i>2013-2018</i>) --> B(<b>MSc Quantitative Finance </b> <br> Lancaster University <br> <i>2020-2021</i>)
  B --> C(<b>Ph.D. Statistics and Applied Probability</b> <br> UC Santa Barbara <br> <i>2022 - Present</i>)

Areas

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