In recent years artificial intelligence in general, and machine learning in particular, has made a real impact on a wide range of disparate fields such as self-driving cars, natural language understanding and medical diagnosis. At the University of Southampton we have several groups working on the cutting edge of the machine learning revolution. In this two part event we want to bring together researchers from a wide range of academic disciplines with our machine learners to build interdisciplinary understanding and collaborations.
In the morning session PhD students will present lectures that introduce machine learning in a non-technical way and that showcase a range of interdisciplinary work that our machine learning departments are doing today. There will also be a panel discussion with our presenters giving the audience a chance to ask questions. This morning session is open to students and staff with interest in using machine learning or finding out about the exciting work done in this university.
Only for PhD students (registration through Gradbook): In the afternoon session we invite PhD students from any academic discipline who have data that they are looking to extract information from. We will then have a smaller interactive session where our machine learning PhD students will be here to discuss problems you have and potential methods and techniques to solve them. We are interested in any PhD student from any department who has data-sets - the aim is to foster greater interdisciplinary research.
Detailed schedule (open to all PGRs, staff and students):
- 09:30: Coffee in Avenue Campus foyer
- 10:00: Introduction by Steve Squires and Ethan Harris
- 10:20: Topic/data specific talks
- 11:30: Panel
- 12:00: End of the morning session
Speakers:
- Jenny Walker - "Learning in the Deep: Applying Computer Vision and Machine Learning to Deep Sea Photography"
- Enrique Marquez - "Using Deep Learning for Speech Enhancement"
- Amy Parkes - "Predicting Shaft Powering of Liquid Natural Gas Carriers in Different Sea States with Operational Data"
- Iris Kramer - "Gaining Insights from Multiple Remote Sensor Images with Deep Learning to Detect Unknown Archaeological Sites"
- Greg Parkes - "Using Machine Learning in Medicine: The Transcriptome-Proteome Interface"
- Tristan Millington - "Correlation and Causation with Financial Networks