Mark Anderson

Particle Astrophysics Researcher | Engineering Physics Graduate


Queen's University

Doctor of Philosophy (Ph.D.)

Particle Astrophysics

  • Working on the SNO+ neutrino physics experiment
  • Focusing on the applications of machine learning to the large-scale, high dimensional data from the detector
  • Recipient of numerous academic and research based awards
Sep 2017 - Present

Queen's University

Bachelor of Applied Science (B.A.Sc.)

Engineering Physics: Electrical Engineering Option

  • Dean's scholar designation achieved every academic term; graduated with first class honours
  • Completed fourth year thesis on the use of machine learning to model robot arm movements
  • Completed relevant courses in topics such as:
    • Machine learning
    • Probability and random processes
    • Signal processing
    • Bioinformatic analytics
  • Recipient of numerous awards for academic excellence, outstanding achievement, and research potential
Sep 2013 - Jun 2017

Relevant Experience

Particle Astrophysics Researcher

Queen's University & SNOLAB | Kingston, ON & Sudbury, ON

Worked with the NEWS-G collaboration in the ongoing search for dark matter.

  • Helped implement and maintain a repository of software tools for the collaboration on GitHub
  • Developed software in C++ and Python to:
    • Process detector data
    • Facilitate both low-level and high-level data analysis
    • Assist researchers in easily accessing and visualizing detector data
  • Performed large-scale data analysis using C++/ROOT and Jupyter Notebooks
May 2016 - Aug 2016

Particle Astrophysics Researcher

Queen's University & SNOLAB | Kingston, ON & Sudbury, ON

Worked on the DEAP-3600 dark matter detection experiment.

  • Assisted with various aspects of the DEAP-3600 dark matter detection experiment
  • Helped design and construct a radon assay system at the SNOLAB underground laboratory
  • Conducted large-scale data analysis using C++/ROOT
May 2015 - Aug 2015

Other Experience

Teaching Assistant

Queen's University | Kingston, ON

Working as a teaching assistant for various first and second year undergraduate courses.

  • Assist first and second year students in physics experimentation (APSC 100 and ENPH 253)
  • Help students with data analysis in MATLAB and Python
  • Answer questions regarding experimental procedures, technical report writing, and code
Jan 2018 - Present

First Year Engineering Tutor

Queen's University | Kingston, ON

Worked as a Douglas tutor for the Faculty of Engineering and Applied Science.

  • Held regular general help sessions for up to fifty first year engineering students
  • Provided academic assistance for subjects including mathematics, physics, and programming
  • Explained challenging technical concepts in clear and concise terms
  • Presented explanations to students with different backgrounds and knowledge
Sep 2014 - Mar 2017


Machine Learning | Deep Learning | Neural Networks
Particle Astrophysics | Neutrino Physics

I am currently a doctoral student at Queen's University in the field of experimental particle astrophysics, which involves the observation and study of naturally produced particles (typically originating from astronomical phenomena). Here at Queen's, I work on the SNO+ experiment -- a versatile liquid scintillator detector located 2km underground at SNOLAB. As the successor to the SNO experiment, SNO+ will continue to allow us to study tiny particles called neutrinos. A better understanding of neutrinos can help answer long-standing open questions in physics, which will in turn help us to better understand the Universe.

As a graduate student, I focus on improving the analysis of SNO+ data with modern software tools, including machine learning. The neutrino interactions and nuclear decays we aim to observe are rare, but there are an abundance of other processes which tend to mask the processes of interest. A tremendous effort is made to reduce these backgrounds. Having the detector located underground provides rock shielding from cosmic rays. As well, the detector is constructed using extremely clean materials with very low levels of radioactivity. Despite this, background processes still dominate and thus lead to a large set of data that is increasing rapidly. I am exploring interesting techniques to handle such a large amount of data. Some interesting projects I am working on include background reduction, rare event classification, and event reconstruction.


Relevant Skills

Programming: Python, C/C++, MATLAB

Data Analytics Libraries/Tools: Pandas, NumPy, Matplotlib, Seaborn, SciPy, IPython/Jupyter Notebooks, ROOT

Machine Learning Libraries/Tools: TensorFlow (both pure and with Keras), Scikit-learn, ROOT TMVA

Markup: LATEX, HTML, CSS, Markdown

Other: Unix Shell, Git


In addition to academics and research, I thoroughly enjoy being outdoors. Born and raised in North Vancouver, I had easy access to both the ocean and the beautiful North Shore mountains. This allowed me to try many different hobbies and activities growing up, many of which I still enjoy to this day. In the summers, I like to hike, kayak, sail, and golf. In the winters, I enjoy showshoeing and skiing. I am also an avid reader and enjoy a variety of fiction and non-fiction.

Mt. Seymour, N. Vancouver, BC Mt. Seymour, N. Vancouver, BC
Sea to Sky Gondola, Squamish, BC

Contact Information

Feel free to contact me if you have any questions!

 Kingston, ON, Canada

 N. Vancouver, BC, Canada