School of Electronic Engineering and Computer Science

Dr Arkaitz Zubiaga

Arkaitz

Lecturer

Email: a.zubiaga@qmul.ac.uk
Room Number: People's Palace, PP5.01

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.

Data Analytics (Work based)

This module focuses on the range of approaches, methodologies, techniques and tools for data analysis, and the use of data analysis findings to inform decision-making in an industrial / business context. It is a work-based module only available to students on relevant degree apprenticeship programmes.

Research

Research Interests:

My research revolves around Human Factors in Natural Language Processing, interdisciplinary research bridging NLP and Social Computing. 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.