Liam
Steadman

My research focuses around spatio-temporal data such as vehicle count, air temperature and rainfall. I'm interested in how we can take vast quantities of data and make them small enough to process quickly on a laptop. By using these techniques, we can reduce a dataset's footprint by 95% while incurring minimal error. This includes:

• Reducing Spatio-Temporal Data
  Data generated by sensors in the environment, such as traffic count data, is growing in volume at increasing rates. Yet processing this data quickly for analysis is becoming impractical using traditional techniques. My work investigates how we can reduce a dataset's volume using partitioning techniques and summaritive models without losing meaningful information.
• Linking Spatio-Temporal Data
  Often, datasets become more meaningful when they are analysed in the context of each other. However, linked datasets can be even larger than the sum of the original datasets. My work therefore investigates methods for linking and reducing datasets simultaneously to enable learning from the linked data.

My research is performed in partnership with TRL, using datasets collected from the UK's road network and weather stations.

I'm a creative and people-oriented Computer Scientist with experience in research, programming and application development. I have experience delivering research-led software in industry and creating applications used by thousands of users in multiple industries.

Data Science and Reduction

• Experience in designing, implementing and evaluating algorithms for data reduction.
• Working knowledge in data science, particularly working with real world spatial and spatio-temporal data.

Programming Languages

• Highly proficient in Python, including data science and machine learning libraries, and frameworks for processing large data.
• Working knowledge of Java, Swift and C++. Highly proficient in PHP (including Laravel) and HTML with experience in Javascript and UI design.

Experimental Analysis & Software Development

• Experience building large experiments in software to test hypotheses and analyse results. Includes working with clusters (inc. Slurm) and disitributed computing for large test deployments.
• Experience delivering research-led software as Python packages for users in industry.
• Experience working with common programming paradigms and data processes (inc. object oriented, distributed systems, online processing).
• Highly proficient in Linux/Unix-based systems (Linux, macOS, iOS), Git, agile methodologies (Scrum, test-driven, continuous deployment), database systems (SQL and NoSQL), and mobile app development (iOS and iPadOS). Experience working in HPC systems.

Teaching, Presentations and Writing

• Member of several programming committees, as well as experience presenting at several conferences and workshops, both academic and industrial. Experience working and presenting within outreach programmes.
• Experience in creating seminar series' for teaching (below). Experience delivering seminars and training to large groups in both higher education and industry.
• Proficient in academic writing and summarising technical documents for management.

Managing Teams

• Experience leading multiple teams both within academia and outside.
• Managed a team of 36 for TEDxWarwick, Europe's largest student-led TEDx conference. Delivered multiple conferences with large budgets.

Throughout my PhD I've been fortunate to teach in the following modules:

• Artificial Intelligence (with Prof. Nathan Griffiths)
• Operating Systems and Networks (with Dr. Adam Chester)
• Mobile Robotics (with Dr. Hongkai Wen and Dr. Claire Rocks)
• Project Management (with Dr. Sarah Kalvala)