I am available to supervise MSc and FYP projects in the following areas.
Visualising Neural Networks
Using Information Visualisation to help developers to understand the operation of Neural Networks by showing them which neurons and pathways fire for different types of data. This technique should allow developers to ‘debug’ networks, by showing how the network structure can be adapted, or training data can be supplemented, to resolve classification errors. The project will use deeplearning4J and some standard benchmark data-sets as a case-study.
Visualising the Blockchain
Using Information Visualisation in Blockchain interfaces to help with users’ understanding and improve trust in the protocol for different application areas.
Mobile Information Visualization
We have developed guidelines and demo applications for visualizing different types of data on mobile devices such as tablets and smartphones . A project in this area might involve testing one of the visualizations with a new type of data or using the guidelines to develop a new visualization.
Mobile app development
We are working with a Suzhou company to develop an app to scan biological chips monitoring antibiotic pollution. The app needs to take a mobile phone image of the chip and create a regular normalised (flattened) image for processing on a central on-line database. Results will be visualised and presented on a map or another type of visual representation. A project can consider the scanning and normalisation of the image and/or the visualisation of the data.
Smartwatch Information Visualization
A project in this area would involve using graphics to better display basic data on an android smartwatch. This would allow us to investigate the development of graphical displays for very small screens and develop guidelines for this sort of development. Examples of applications to develop might be;
- A smartwatch visualization display for running (showing pace, distance covered etc)
- A smartwatch visualization of fitness data
- A visualization of the time (showing daylight hours, weather etc)
We have developed a fish-eye map for android and tested this map using a location based game . Projects building on this might involve;
- Developing a tourist or shopping app using the map
- Implementing the map on an android smartwatch
- Using the fish-eye projection for a course map on a running watch
We have developed a method for visualizing security protocols. A project building on this work might develop an interactive application based on our method.
We have developed an edge bundling method for reducing clutter in visualizations of graph data based on work at Stanford university . A project in this area might involve making our algorithm more efficient or testing it with different types of data such as flight path data or pedestrian movement data.
We have developed an algorithm that traces the edges of simple 2D images and converts them into 3D data for 3D printing. We would like develop an application that allows people to draw the images on a tablet which automatically generates the 3D images and sends them to a 3D printer. It’s hoped that this application can help teach children about 3D printing.
Location based games
We have developed a simple location based game where players can use a mobile android smartphone to collect coins or other objects located around the university campus. Any project in this area would involve modifying the code to investigate some different aspect of location based gaming such as;
- Competitive gaming
- Collaborative Gaming
- Social aspects of gaming
Library book search app
To create an android app that allows users to search the book collection in the library. We have already developed a basic app . This uses the principles of Human-computer Information Retrieval  to cluster books together according to co-borrowing statistics. We would like to finish the app off and do a full user evaluation.
- Paul Craig. 2015. Interactive animated mobile information visualisation. In SIGGRAPH Asia 2015 Mobile Graphics and Interactive Applications (SA ’15). ACM, New York, NY, USA, Article 24, 6 pages. DOI: https://doi.org/10.1145/2818427.2818458
- Paul Craig, Huayue Chen, and Fidaly Houssen. 2016. In ETIS Suzhou. A Task Based Evaluation of Fisheye Maps for Mobile Navigation
- Selassie, David, Brandon Heller, and Jeffrey Heer. “Divided edge bundling for directional network data.” IEEE Transactions on Visualization and Computer Graphics 17.12 (2011): 2354-2363.
- Paul Craig and Sebastian Tsikata, 2016, ETIS Suzhou, Diversity Based Human-Computer Information Retrieval for University Library Collections
- Marchionini, G. (2006). Toward Human-Computer Information Retrieval Bulletin, in June/July 2006 Bulletin of the American Society for Information Science