Thomas Davidson

About Me

I am a third year PhD Student working with Jonathan Mace in the Cloud Software Systems Group at the Max Planck Institute for Software Systems in Saarbrücken, Germany. I currently work on developing visualisation techniques for distributed tracing, actively collaborating with Emily Wall from Emory University's CAV Lab. I completed my Undergraduate and Masters degree at Corpus Christi College, Cambridge and in my final year there I worked on developing novel visualisation techniques for English Literature.

Outside of my work I am an active runner, rugby and cricket player. I also entertain an unhealthy obsession with public transport metro systems.

Projects

My current research focusses on developing novel visualisation techniques in in the field of distributed tracing. More generally, my research interests lie in creating visualisations that can facilitate explorative analysis of datasets. I am also very interested in how users interact with visualisations and software and in the use of visualisation to explore user generated information.

MPI

Distributed Tracing Visualisation

I am currently carrying out a design study working with collaborators from industry to design improved visualisations for use in distributed tracing analytics. This work involves first understanding the user-facing aspects of distributed tracing, a field that has very little attention. We currently have a paper under submission outlining the findings of our qualitative interview study which characterises the domain and exposes existing problem areas as well as identifying several interesting applications and directions for future visualisation research in this area. This includes work focussing on interlinked visualisations, large data visualisation, and graph comparison along multiple dimensions. This work is currently under submission and is available on request.

The State of Visualisation in Systems Research

When assessing the existing state of visualisation in distributed tracing it became clear that little to no work has been done which focuses on the human aspect of distributed tracing. I undertook a survey study which examined the presentation of user facing aspects in the more general field of systems research focussing on human-in-the-loop tools. The findings from this work suggested that despite the implemented tools often being a fundamental contribution of the work, they were frequently under presented, designed, and explained.

TraVista

I helped to develop and design the visualisations used in a piece of work that investigated augmenting individual trace visualisations with aggregate data for performance debugging.

Science Gallery Youth Symposium

Digital Relationships

Digital Relationships is a data visualisation project that allows users to generate explorative and informative visualisations based on their own Facebook messaging data. The website lets you explore your messages over time and produce customisable infographics directly linked to your digital relationships with others. The work was selected and presented at the 2021 Science Gallery Youth Symposium.

Cambridge

An Exploratory Analysis Visualisation Tool for Classic English Literature

During my masters I worked on developing a tool which facilitated exploratory analysis of literature through visualisation and interaction techniques. The approaches I implemented built on previous approaches to abstract text visualisation and augmented them to enable users to gain insight into sentence structure, character and location references, as well as the age of language used throughout the text. I was supervised by Tim Regan from Microsoft Research and Alan Blackwell from the University of Cambridge.

Direct Solo Piano Sonography

As part of the L312 Digital Signal Processing with Computer Music course I implemented and presented my work on visualising piano performances in real time.

Using Machine Learning to Solve a Rubik's Cube

For my undergraduate dissertation I analysed the performance of a machine learning approach to solving a semi-scrambled rubik's cube when compared to human participants with varying levels of experience.