School of Electronic Engineering and Computer Science

Dr Johan Pauwels

Johan

Postdoctoral Research Assistant

Email: j.pauwels@qmul.ac.uk
Room Number: Engineering, Eng 408

Teaching

Big Data Processing (Undergraduate)

Parallel computing, which implies the simultaneous execution of several processes for solving a single problem, is a mainstream subject with wide ranging implications for computer architecture, algorithms design and programming. The UK has been at the forefront of this technology through its involvement in the development of several innovative architectures. Queen Mary has been actively involved with Parallel Computing for more than a decade. In this module, you will be introduced to parallel computing and will gain first hand experience in relevant techniques. Laboratory work will be based on the MPI (Message Passing Interfaces) standard, running on a network of PCs in the teaching laboratory. The module should be of interest to Computer Scientists and those following joint programmes (eg CS/Maths, CS/Stats). It is also suitable for Chemistry and Engineering students and all those who are concerned with the application of high performance parallel computing for their particular field of study (eg Simulation of chemical Behaviour). The 12-week module involves two hours of timetabled lectures per week. Laboratory sessions are timetabled at two hours per week, normally spanning half the semester only. The module syllabus adopts a hands-on programming stance. In addition, it focuses on algorithms and architectures to familiarise you with messagepassing systems (MPI) as adopted by the industry.

Big Data Processing (Postgraduate)

The 12 week module involves 2 hours of timetabled lectures per week. Laboratory sessions are timetabled at 2 hours per week for 6 to 7 weeks only. The module syllabus adopts a hands-on programming stance. In addition it focuses on algorithms and architectures to familiarise students with message-passing systems ((MPI) as adopted by industry. Parallel computing, which implies the simultaneous execution of several processes for solving a single problem, is a mainstream subject with wide ranging implications for computer architecture, algorithms design and programming. The UK has been at the forefront of this technology through its involvement in the development of several innovative architectures.Queen Mary has been involved with Parallel Computing for more than a decade. In this module, students will be introduced to parallel computing and will gain firsthand experience in relevant techniques.

Research

Research Interests:

music information retrieval
artificial inteligence
digital signal processing
machine learning

I am an AI researcher specialising in automatic music description. My goal is to give machines the ability to recognise advanced musical concepts to the level of a trained musician. Examples of such concepts include musical chords, keys, instruments and structure. My research has applications both for musicians, who can benefit from new assistive tools for music production and learning, and the general public, who can enjoy a better navigation of the large music collections available on streaming platforms.

Publications