The Knowledge Media Institute (KMi) Annual Summer Scholarship for people from a Black, Asian and Minority Ethnic Background.
KMi is a Computer Science Research & Development Lab. We are a diverse, multi-national bunch who are passionate about what we do. We treat everyone as a valued team member, be they professors, researchers, post-grad students or other non-academic staff. We believe in research that has an impact in the real world with real users.
Among the visitors who come to KMi each summer, there has been an under-representation of visitors from a Black, Asian and Minority Ethnic Background. We aim to ensure consistent representation through this annual scholarship and create greater awareness of computer science research in not-yet-reached communities.
What does a Scholarship from KMi offer?
Taking part in a project will enhance your employability, develop computer science skills and/or provide a social benefit.
KMi invites applications from UK Black, Asian and Minority Ethnic applicants aged 16 to 20. You will carry out a short computer science project in KMi, lasting up to 6 weeks during June-August. The project may be conducted online, at the Open University campus or through a combination of both. While we appreciate applicants with existing computing skills, not all of our scholarship projects will require prior knowledge of programming. We value and promote theoretical diversity in computing.
What does KMi Scholarship funding mean?
The award will be based on an assessment of the project proposals by a selection panel.
An award winner will receive non-repayable funding grant of up to £4,000, which can be used for project costs or other living expenses. A payment schedule will be agreed which is appropriate to the needs of the awardee and their project.
An award winner will carry out their project under the mentorship of KMi Academic Researchers and/or PhD Students. The project findings will be shared with KMi, for example, by depositing a report in the OU's Open Research Online repository (ORO) or depositing data or code in the Open Research Data Online repository (ORDO).
Funds are limited, and no guarantee of an award can be made.
How to apply?
Before completing an application, make sure you've read the terms and conditions and the application form. Then read the topics below and decide which interest you. Next, Email or Call Miss Ortenz Rose at email@example.com / Phone +44 (0)1908 654774. She will take your details and put you in touch with one of the named supervisors for your topic of interest, who will give more information and support with developing your project proposal and application.
Benchmarking environmental sensors
Subject Area: Human-Computer Interaction for Climate
Supervisory Team: Dr. Lara Piccolo
Pitch: The United Nation's Sustainable Development Goals address the global challenges we face, including climate change, environmental degradation, access to clean energy, etc. In this scholarship project, we want to experiment with some low-cost and DIY technology with sensors that could help people and local communities to understand their own context related to some SDGs, for example, related to air and water pollution.
You will look at existing solutions to collect environmental data using these sensors and will try to set up yourself a couple of them, reporting your difficulties and the solutions you found, and how the data you managed to collect helps you to understand the environment.
As an output of your scholarship, you will co-author a paper with the supervisor and the material you generated will be available online as a video or blog.
Identifying bias in mainstream news sources
Pitch: The negative consequences deriving from a distorted media landscape have been apparent for many years, for instance in the context of the UK's Brexit Referendum or the 2016 US Election. Hence, news monitoring has become extremely important for tackling issues of social and political polarisation, media diversity and concentration of power in the media industries. This monitoring is, however, massively resource demanding and therefore new computational techniques are needed to facilitate this monitoring task. In particular, we need systems able first to effectively identify all news relevant to a topic, e.g., the 2016 US Election, and then also able to facilitate the analysis of the various viewpoints expressed by different media sources on the topic in question.
The summer project will focus on using both novel technologies for news analytics, as well as standard tools for searching news content, to perform analysis of media content that examine topic and viewpoint coverage, focusing on issues and events drawn from a preliminary list that includes immigration, climate, the COVID-19 pandemic, the Black Lives Matter movement, Brexit, the storming of the US Capitol and the kneeling protests of American football players in the US. In particular, we are interested in carrying out analyses of viewpoints in the news on the basis of vocabulary, sentiment and the types of arguments that are put forward. The final project output will be a study providing insights on the degree of fairness and balance shown by the main UK news sources with respect to key current issues. The project will also produce a dataset that will be published on GitHub and will also result in a scientific publication co-authored by the student.
Misogynoir: Intersectionality in Online Abuse
Pitch: Online abuse and hate speech is growing online. Finding new ways of studying this phenomenon, measuring it and reducing its presence or its influence is an important research field of computer science. However, current methods of studying and interpreting online abuse rarely consider intersectional categories like someone's gender identity or ethnicity, class or religion, and how social biases impact experiences of online abuse. One area, for example, is in online abuse and hate speech directed at Black women. Social scientists and activists have documented online abuse toward Black women as having both racist and sexist undertones that White women and Black men, for example, don't experience. How can we accurately detect this particular occurrence online, using computational techniques? In this summer project, you will be using a technique that is commonly used in computer science to detect online abuse and hate speech, Natural Language Processing (NLP). With NLP we can search for words, phrases and parts of speech, which we have categorised previously. Then, we can look for how often those categories appear in a given data set. This involves an interdisciplinary approach between social and computer scientists to achieve. You do not need to have any special skills to participate, but we do hope you'll have some interest in social justice topics, to ensure that this project will be meaningful for you. We will guide you in creating a dataset, annotating data, and interpreting patterns that emerge through NLP methods. Your final project will be published on GitHub under your name, and you will co-author a paper with your supervisors.
Modern Day Mind Control.. Can we use sensors to help learn to control the flow of blood to our brains?
Pitch: Hands up everyone that's stressed out! We are often told to use various techniques to reduce stress and increase concentration, but do they really work? How can we really tell? Well, it turns out that stress and poor health have huge impacts on our brain's blood flow activity. Using a Hegduino (an open source Hemoencephalography biofeedback device) to measure surface blood flow to your brain, you will build a small testing suite to measure the effectiveness of various small wellness interventions such as paced breathing, a small walk, listening to music, on blood brain flow, and by inference, their effectiveness as tools against stress.
You will design and run the relevant appropriate tests, and use numerical analysis tools to analyse your data. Your final project output will be a technical design of a test protocol and resulting annotated dataset, which you will publish to your own name on GitHub. You will also co-author an article with the project team.
Robot Assistants in the Wild
Pitch: Robots can help with many of our daily tasks, especially when it is impractical or unsafe for us to intervene. For example, they can operate under the extreme weather conditions imposed by space explorations, monitor hazardous manufacturing environments, or deliver essential services when social distance needs to be maintained. However, before we can safely delegate tasks to robots, we need to ensure that they can operate reliably in real-world scenarios. Succeeding in the real-world - or "in the wild" - is a challenge because it requires to orchestrate the various functionalities of the robot and the data coming through its sensors (such as cameras, depth sensors, laser rangefinders, and others). In this scholarship project, you will acquire practical expertise in using the Robot Operating System (ROS) through simulation and remote control. We will guide you in developing at least one of the functionalities that contribute to implementing a robot fit for operating in the wild. This final project will be published on GitHub under your name and showcased as a live demo.
- The call opens 15th March
- The deadline for getting in touch with an academic is midnight on 20th April
- The deadline for applications is midnight on 10th May 2021
- If you are successful, we'll send you an email and letter on or before 31st May.
- The project will start no earlier than 21st June and no later than 26h July and will run for 6 weeks.
The selection process:
The award will be made by a panel of OU staff, comprised of a Senior KMi academic, a People Services Representative and an Equality, Diversity & Inclusion Representative from the STEM Faculty.
To qualify you'll need to meet the following criteria:
- You identify as being from a black, Asian or minority ethnic background
- You are aged 16-20 on 31st May 2021.
- You can demonstrate an interest in technology
- You are ordinarily resident in the UK
- You submit a project proposal on one of the listed KMi topics that you have co-developed with a KMi academic or PhD Student
- You will have 2 referees (one or both from your educational institute, one can be a personal referee).