menu

School of Electronic Engineering and Computer Science

People menu

Ms Katrin Hänsel Hänsel

Katrin Hänsel

Email: k.hansel@qmul.ac.uk
Room Number: Peter Landin, CS 413
Website: https://miezelkat.github.io

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.

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

Research

Return to top