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Miss Joanne Guo

Joanne

Email: b.guo@qmul.ac.uk
Room Number: Engineering, Eng 104

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.

Computability, Complexity and Algorithms (Undergraduate)

A theoretical course, which concerned with the theoretical core of Computer Science. The course covers some of the most successful algorithms as well as some of the most central decision problems. A large part of the course will focus on the NP versus P problem as well as other famous unsolved problem in Computer Science. To understand this problem we consider the issue of how one programming problem can be disguised as another apparently very different problem. This idea is very important in designing algorithms and plays a crucial role in the theory of NP-completeness.

Digital Media and Social Networks (Postgraduate)

Content description: ------- How does the way we feel and express emotional behaviour affect our interaction with technology? What if we could use a ''head nod'' for ''liking'' things on Facebook? Can we create assistive technology to help people suffering from social disorders (e.g., autism)? Affective and Behavioural Computing is a multidisciplinary field of research and practice concerned with these questions, and understanding, recognizing and utilizing human emotions and communicative behaviour in the design of computational systems. ----- The following list aims to clarify the content and provides a representative list of topics: ¿ Overview: affective and behavioural computing; ¿ Theories in psychology, cognitive science and neuroscience: affect, emotion and social signal processing; ¿ Computational models; ¿ Emotion, affect and social signals in Human-Computer Interaction (HCI); ¿ Sensing: vision, audio, bio signals, text; data acquisition and annotation, databases and tools; ¿ Processing: extracting meaningful information and features; ¿ Recognition: applying machine learning techniques; ¿ Programming refresher: Hands-on lecture on programming for affective and behavioural computing using relevant libraries; ¿ Evaluation: automatic analysers, and emotionally and socially intelligent systems; ¿ Affect and social signal expression and generation (virtual characters, robots, etc.); ¿ Affect and social signals for Mobile HCI; ¿ Applications (entertainment technology/gaming/arts; clinical and biomedical studies, e.g., autism, depression, pain; etc.; implicit (multimedia) tagging; affective wearables); ¿ Ethical issues.

Digital Media and Social Networks (Undergraduate)

Introduction to Online Social Networks (OSN) Characteristics of OSNs Basic Graph Theory Small World Phenomenon Information propagation on OSNs Influence and Content Recommendation Sentiment Analysis in Social Media Privacy and ethics

Image Processing (Undergraduate/Postgraduate)

This course gives students an introduction to image processing. Areas covered include image representation, and image transforms, image enhancement using point and spatial operations, image filtering, image restoration, image compression and image segmentation.

Machine Learning for Visual Data Analysis (Postgraduate)

The module will cover the following topics: The Discrete Fourier Transform and the frequency content of images. The design and use of Gabor filters. Principal Component Analysis for denoising and compression. Unsupervised classification via feature space clustering. Texture segmentation with Gabor filters.

Research

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