Extracting meaning from data

Doctor

Extracting meaning from data is the central business of the information era. ML Market is a European consortium of leading researchers that span a range of areas in information processing, data analysis, statistics and machine learning.

ML Market groups are formed from world-leading research groups within the Pascal European network that also actively engage to find business solutions to challenging real-world problems. ML Market exists to promote the academic and industrial expertise of its researchers and provides a platform to engage and broker industrial contacts.

Case Studies

What are you looking at?

You’re waiting at the station for your train and you glance at the electronic poster next to you. It notices that you’re looking at it, and from your gaze it works out what you would most like to see. It’s as though it’s reading your mind – but really it’s reading your eyes.

The Desktop Doctor

DoctorHundreds of years of medical experience. An infinite patience and the ability to take every symptom into account. Precise and logical, up-to-date, and never short on ideas. All just casually sitting on your doctor’s desk. It may not have much of a bedside manner, but then its job is not to meet patients.

Bonaparte Disaster Victim Identification System

Society is increasingly aware of the possibility of a mass disaster. Recent examples are the WTC attacks, the tsunamis, and various airplane crashes. In such an event, the recovery and identification of the remains of the victims is of great importance, both for humanitarian as well as legal reasons. Disaster victim identification (DVI), i.e. the identification of victims of a mass disaster, is greatly facilitated by the advent of modern DNA technology. In forensic laboratories, DNA profiles can be recorded from small samples of body remains which may otherwise be unidentifiable.

Topics

Machine Vision

Machine vision is a branch of engineering that uses computer vision in the context of manufacturing. While the scope of Machine Vision is broad and a comprehensive definition is difficult to distil, a "generally accepted definition of machine vision is '... the analysis of images to extract data for controlling a process or activity.'" Put another way, Machine Vision processes are targeted at "recognizing the actual objects in an image and assigning properties to those objects--understanding what they mean." The commercial applications of Machine Vision include tracking of people in crowds, numberplate recognition, reconstructing the 3D geometry of an environment.

Multimodal Interaction and Adaptive Learning

Traditional Pattern Recognition (PR) and Machine Learning (ML) have generally focused on full automation; that is, in developing technologies ultimately aiming at fully replacing human beings in tasks that require complex perceptive and/or cognitive skills. However, full automation often proves elusive or unnatural in many applications where technology is expected to assist rather than replace the human agents. This asks for a paradigm shift which should place PR/ML within the framework of human interaction.

Multimodal Interaction and Adaptive Learning deals with the fundamental work needed to address the research challenges and opportunities entailed by this paradigm shift. These include: interaction analysis and modelling, multimodal processing and fusion, interactive performance estimation and measurement, and several emerging forms of machine learning that look especially promising in the interactive framework (online, adaptive, active, semi-supervised, limited feedback, reinforcement, etc.).

Time Series Analysis

Timeseries appear in a variety of disciples, from finance to physics, computer science to biology. The origins of the subject and diverse applications in the engineering and physics literature at times obscure the commonalities in the underlying models and techniques. Modern timeseries applications include financial timeseries prediction, video-tracking, music analysis, control theory and genetic sequence analysis.