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School of Electronic Engineering and Computer Science
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Dr Arkaitz Zubiaga

Arkaitz

Senior Lecturer and Deputy Director of Graduate Studies

Email: a.zubiaga@qmul.ac.uk
Room Number: People's Palace, PP5.01
Website: http://www.zubiaga.org

Teaching

Applied Statistics ()

The module introduces core statistical concepts for practical data analysis. It will provide students with the skills to model data sources, analyze their statistical properties, visualize them in different ways and fit the samples to a known probabilistic model.

Big Data Processing ()

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:

My research revolves around Social Data Science, interdisciplinary research bridging NLP and Computational Social Science. I'm particularly interested in linking online data with events in the real world, among others for tackling problematic issues on the Web and social media that can have a damaging effect on individuals or society at large, such as hate speech, misinformation, inequality and other forms of online harm.
Check my website (http://www.zubiaga.org/) and the Social Data Science lab (https://sds.eecs.qmul.ac.uk/) for more info.
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