Category: Careers

Designing for autistic people

by Daniel Gill and Paul Curzon, Queen Mary University of London

What should you be thinking about when designing for a specific group with specific needs, such as autistic people? Queen Mary students were set this task and on the whole did well. The lessons though are useful when designing any technology, whether apps or gadgets.

A futuristic but complicated interface
A futuristic but complicated interface with lots of features: feature bloat?
Image by Tung Lam from Pixabay

The Interactive Systems Design module at QMUL includes a term-long realistic team interaction design project with the teaching team acting as clients. The topic changes each year but is always open-ended and aimed at helping some specific group of people. The idea is to give experience designing for a clear user group not just for anyone. A key requirement is always that the design, above all, must be very easy to use, without help. It should be intuitively obvious how to use it. At the end of the module, each team pitches their design in a short presentation as well as a client report.

This year the aim was to create something to support autistic people. What their design does, and how, was left to the teams to decide from their early research and prototyping. They had to identify a need themselves. As a consequence, the teams came up with a wide range of applications and tools to support autistic people in very different ways.

How do you come up with an idea for a design? It should be based on research. The teams had to follow a specific (if simplified) process. The first step was to find out as much as they could about the user group and other stakeholders being designed for: here autistic people and, if appropriate, their carers. The key thing is to identify their unmet goals and needs. There are lots of ways to do this: from book research (charities, for example, often provide good background information) and informally talking to people from the stakeholder group, to more rigorous methods of formal interviews, focus groups and even ethnography (where you embed yourself in a community).

Many of the QMUL teams came up with designs that clearly supported autistic people, but some projects were only quite loosely linked with autism. While the needs of autistic people were considered in the concept and design, they did not fully focus on supporting autistic people. More feedback directly from autistic people, both at the start and throughout the process, would have likely made the applications much more suitable. (That of course is quite hard in this kind of student role-playing scenario, though some groups were able to do so.) That though is key idea the module is aiming to teach – how important it is to involve users and their concerns closely throughout the design process, both in coming up with designs and evaluating them. Old fashioned waterfall models from software engineering, where designs are only tested with users at the end, are just not good enough.

From the research, the teams were then required to create design personas. These are detailed, realistic but fictional people with names, families, and lives. The more realistic the character the better (computer scientists need to be good at fiction too!) Personas are intended to represent the people being designed for in a concrete and tangible way throughout the design process. They help to ensure the designers do design for real people not some abstract tangible person that shape shifts to the needs of their ideas. Doing the latter can lead to concepts being pushed forward just because the designer is excited by their ideas rather than because they are actually useful. Throughout the design the team refer back to them – does this idea work for Mo and the things he is trying to do? 

An important part of good persona design lies around stereotypes. The QMUL groups avoided stereotypes of autistic people. One group went further, though: they included the positive traits that their autistic persona had, not just negative ones. They didn’t see their users in a simplistic way. Thinking about positive attributes is really, really important if designing for neurodivergent people, but also for those with physical disabilities too, to help make them a realistic person. That group’s persona was therefore outstanding. Alan Cooper, who came up with the idea of design personas, argued that stereotypes (such as a nurse persona being female) were good in that they could give people a quick and solid idea of the person. However, this is a very debatable view. It seems to go against the whole idea of personas. Most likely you miss the richness of real people and end up designing for a fictional person that doesn’t represent that group of people at all. The aim of personas is to help the designers see the world from the perspective of their users, so here of autistic people. A stereotype can only diminish that.

Multicolour jigsaw ribbon
Image by Oberholster Venita from Pixabay

Another core lesson of the module is the importance of avoiding feature bloat. Lots of software and gadgets are far harder to use than need be because they are packed with features: features that are hardly ever, possibly never, used. What could have been simple to use apps, focusing on some key tasks, instead are turned into ‘do everything’ apps. A really good video call app instead becomes a file store, a messaging place, chat rooms, a phone booth, a calendar, a movie player, and more. Suddenly it’s much harder to make video calls. Because there are so many features and so many modes all needing their own controls the important things the design was supposed to help you do become hard to do (think of a TV remote control – the more features the more buttons until important ones are lost). That undermines the aim that good design should make key tasks intuitively easy. The difficulty when designing such systems is balancing the desire to put as many helpful features as possible into a single application, and the complexity that this adds. That can be bad for neurotypical people, who may find it hard to use. For neurodivergent people it can be much worse – they can find themselves overwhelmed. When presented with such a system, if they can use it at all, they might have to develop their own strategies to overcome the information overload caused. For example, they might need to learn the interface bit-by-bit. For something being designed specifically for neurodiverse people, that should never happen. Some of the applications of the QMUL teams were too complicated like this. This seems to be one of the hardest things for designers to learn, as adding ideas, adding features seems to be a good thing, it is certainly vitally important not to make this mistake if designing for autistic people. 

Perhaps one of the most important points that arose from the designs was that many of the applications presented were designed to help autistic people change to fit into the world. While this would certainly be beneficial, it is important to realise that such systems are only necessary because the world is generally not welcoming for autistic people. It is much better if technology is designed to change the world instead. 

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Front cover of CS4FN issue 29 - Diversity in Computing

EPSRC supports this blog through research grant EP/W033615/1,

Neurodiversity and what it takes to be a good programmer

by Paul Curzon, Queen Mary University of London

A multicoloured jigsaw pattern ribbon
Image by Oberholster Venita from Pixabay

People often suggest neurodiverse people make good computer scientists. For example, one of the most famous autistic people, Temple Grandin, an academic at Colorado State University and animal welfre expert, has suggested programming is one of the jobs autistic people are potentially naturally good at (along with other computer science linked jobs) and that “Half the people in Silicon Valley probably have autism.”.. So what makes a good computer scientist? And why might people suggest neurodiverse people are good at it?

What makes a good programmer? Is it knowledge, skills or is it the type of person you are? It is actually all three though it’s important to realise that all three can be improved. No one is born a computer scientist. You may not have the knowledge, the skills or be the right kind of person now, but you can improve them all.

To be a good programmer, you need to develop specialist knowledge such as knowing what the available language constructs are, knowing what those constructs do, knowing when to use them over others, and so on. You also need to develop particular technical skills like an ability to decompose problems into sub-problems, to formulate solutions in a particular restricted notation (the programming language), to generalise solutions, and so on. However, you also need to become the right kind of person to do well as a programmer. 

Thinking about what kind of person makes a good programmer, to help my students work on them and so become better programers, I made a list of the attributes I associate with good student programmers. My list includes: attention to detail, an ability to think clearly and logically, being creative, having good spatial visualising skills, being a hard worker, being resilient to things going wrong so determined, being organised, being able to meet deadlines, enjoying problem solving, being good at pattern matching, thinking analytically and being open to learning new and different ways of doing things.

More recently, when taking part in a workshop about neurodiversity I was struck by a similar list we were given. Part of the idea behind ‘neurodiversity’ is that everyone is different and everyone has strengths and weaknesses. If you think of ‘disability’ you tend to think of apparent weaknesses. Those ‘weaknesses’ are often there because the world we have created has turned them into weaknesses. For example, being in a wheelchair makes it hard to travel because we have built a world full of steps, kerbs and cobbles, doors that are hard to manipulate, high counters and so on. If we were to remove all those obstacles, a wheelchair would not have to reduce your ability to get around. Thinking about neurodiversity, the suggestion is to think about the strengths that come with it too, not just the difficulties you might encounter because of the way we’ve made the world.

The list of strengths of neurodiverse people given at the workshop were: attention to detail, focussed interest, problem-solving, creative, visualising, pattern recognition. Looking further you find both those positives reinforced and new positives. For example, one support website gives the positives of being an autistic person as: attention to detail, deep focus, observation skills, ability to absorb and retain facts, visual skills, expertise, a methodological approach, taking novel approaches, creativity, tenacity and resilience, accepting of difference and integrity. Thinking logically is also often picked out as a trait that neurodiverse people are often good at. The similarity of these lists to my list of what kind of person my students should aim to turn themselves into is very clear. Autistic people can start with a very solid basis to build on. If my list is right, then their personal positives may help neurodiverse people to quickly become good programmers.

Here are a few of those positives others have picked out that neurodiverse people may have and how they relate to programming:

Attention to detail: This is important in programming because a program is all about detail, both in the syntax (not missing brackets or semicolons) but more importantly not missing situations that could occur so cases the program must cover. A program must deal with every possibility that might arise, not just some. The way it deals with them also matters in the detail. Poor programs might just announce a mistake was made and shut down. A good program will explain the mistake and give the user a way to correct it for example. Detail like that matters. Attention to detail is also important in debugging as bugs are just details gone wrong. 

Resilience and determination: Programming is like being on an emotional roller coaster. Getting a program right is full of highs and lows. You think it is working and then the last test you run shows a deep flaw. Back to the drawing board. As a novice learning it is even worse. The learning curve is steep as programming is a complex skill. That means there are lots of lows and seemingly insurmountable highs. At the start it can seem impossible to get a program to even compile never mind run. You have to keep going. You have to be determined. You have to be resilient to take all the knocks.

Focussed interest. Writing a program takes time and you have to focus. Stop and come back later and it will be so much harder to continue and to avoid making mistakes. Decomposition is a way to break the overall task into smaller subtasks, so methods to code, and that helps, once you have the skill. However, even then being able to maintain your focus to finish each method, so each subtask, makes the programming job much easier.

Pattern recognition: Human expertise in anything ultimately comes down to pattern matching new situations against old. It is the way our brains work. Expert chess players pattern match situations to tell them what to do, and so do firefighters in a burning building. So do expert programmers. Initially programming is about learning the meaning of programming constructs and how to use them, problem solving every step of the way. That is why the learning curve is so steep. As you gain experience though it becomes more about pattern matching: realising what a particular program needs at this point and how it is similar to something you have seen before then matching it to one of many standard template solutions. Then you just whip out the template and adapt it to fit. Spot something is essentially a search task and you whip out a search algorithm to solve it. Need to process a 2 dimensional array – you just need the rectangular for loop solution. Once you can do that kind of pattern matching, programming becomes much, much simpler.

Creativity and doing things in novel ways: Writing a program is an act of creation, so just like arts and crafts involves creativity. Just writing a program is one kind of creativity, coming up with an idea for a program or spotting a need no one else has noticed so you can write a program that fills that need requires great creativity of a slightly different kind. So does coming up with a novel solution once you have a novel problem. Developing new algorithms is all about thinking up a novel way of solving a problem and that of course takes creativity. Designing interfaces that are aesthetically pleasing but make a task easier to do takes creativity. If you can think about a problem in a different way to everyone else, then likely you will come up with different solutions no one else thought of.

Problem solving and analytical minds: Programming is problem solving on steroids. Being able to think analytically is an important part of problem solving and is especially powerful if combined with creativity (see above). You need to be able to analyse a problem, come up with creative solutions and be able to analyse what is the best way of solving it from those creative solutions. Being analytical helps with solid testing too.

Visual thinking: research suggests those with good visual, spatial thinking skills make good programmers. The reasons are not clear, but good programs are all about clear structure, so it may be that the ability to easily see the structure of programs and manipulate them in your head is part of it. That is part of the idea of block-based programming languages like Scratch and why they are used as a way into programming for young children. The structure of the program is made visual. Some paradigms of programming are also naturally more visual. In particular object-oriented programming sees programs as objects that send messages to each other and that is something that can naturally be visualised. As programs become bigger that ability to still visualise the separate parts and how they work as a whole  is a big advantage.

A methodological approach: Novice programmers just tinker and hack programs together. Expert programmers design them. Many people never seem to get beyond the hacking stage, struggling with the idea of following a method to design first, yet it is vital if you are to engineer serious programs that work. That doesn’t mean that programming is just following methods, tinkering can be part of the problem solving and coming up with creative ideas, but it should be used within rigorous methodology not instead of it. More time is also spent by good programming teams testing programs as writing them, and that takes rigorous methods to do well too. Software engineering is all about following rigorous methods precisely because it is the only way to develop real programs that work and are fit for purpose. Vast amounts of software written is never used because it is useless. Rigorous methods give a way to avoid many of the problems.

Logical thinking: Being able to think clearly and logically is core to programming. It combines some of the things above like attention to detail, and thinking clearly and methodologically. Writing programs is essentially the practice of applied logic as logic underpins the semantics (ie meaning) of programming languages and so programs. You have to think logically when writing programs, when testing them and when debugging them. Computers are machines built from logic and you need to think in part like a computer to get it to do what you want. A key part of good programming is doing code walkthroughs – as a team stepping through what a program does line by line. That requires clear logical thinking to think in teh steps the computer performs.

I could go on, but will leave it there. The positives that neurodiverse people might have are very strongly positives for becoming a good programmer. That is why some of the best students I’ve had the privilege to teach have been neurodiverse students.

Different people, neurodiverse or otherwise, will start with different positives, different weaknesses. People start in different places for different reasons, some ahead, some behind. I liked doing puzzles as a child, so spent my childhood devouring logic and algorithmic puzzles. That meant when I first tried to learn to program, I found it fairly easy. I had built important skills and knowledge and had become a good logical thinker and problem solver just for fun. I learnt to program for fun. That meant it was if I’d started way beyond the starting line in the race to become a programmer. Many neurodiverse people do the same, if for different reasons.

Other skills I’ve needed as a computer scientist I have had to work hard on and develop strategies to overcome. I am a shy introvert. However, I need to both network and give presentations as a computer scientist (and ultimately now I give weekly lectures to hundreds at a time as an academic computer scientist). For that I had to practice, learn theory about good presentation and perhaps most importantly, given how paralysing my shyness was, devise a strategy to overcome that natural weakness. I did find a strategy – I developed an act. I have a fake extrovert persona that I act out. I act being that other person in these situations where I can’t otherwise cope, so it is not me you see giving presentations and lectures but my fake persona. Weaknesses can be overcome, even if they mean you start far behind the starting line. Of course, some weaknesses and ways we’ve built the world mean we may not be able to overcome the problems, and not everyone wants to be a computer scientist anyway so has the desire to. What matters is finding the future that matches your positives and interests, and where you can overcome the weaknesses where you set your mind to it.

Programming is not about born talent though (nothing is). We all have strengths and weaknesses and we can all become better by practicing and finding strategies that work for us, building upon our strengths and working on our weaknesses, especially when we have the help of a great teacher (or have help to change the way the world works so the weaknesses vanish).

My list above gives some of the key personal characteristics you need to work on improving (however good at them you are or are not right now) If you do want to be a good programmer. Anyone can become better at programming than they are if they have that desire. What matters is that you want to learn, are willing to put the practice in, can develop strategies to overcome your initial weaknesses, and you don’t give up. Neurodiverse people often have a head start on those personal attributes for becoming good at new things too.

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Front cover of CS4FN issue 29 - Diversity in Computing

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This blog is funded by EPSRC on research agreement EP/W033615/1.

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Designing robots that care

Happy robot mobile image by Alexandra_Koch from Pixabay

by Nicola Plant, Queen Mary University of London

Think of the perfect robot companion. A robot you can hang out with, chat to and who understands how you feel. Robots can already understand some of what we say and talk back. They can even respond to the emotions we express in the tone of our voice. But, what about body language? We also show how we feel by the way we stand, we describe things with our hands and we communicate with the expressions on our faces. Could a robot use body language to show that it understands how we feel? Could a robot show empathy?

If a robot companion did show this kind of empathetic body language we would likely feel that it understood us, and shared our feelings and experiences. For robots to be able to behave like this though, we first need to understand more about how humans use movement to show empathy with one another.

Think about how you react when a friend talks about their headache. You wouldn’t stay perfectly still. But what would you do? We’ve used motion capture to track people’s movements as they talk to each other. Motion capture is the technology used in films to make computer-animated creatures like Gollum in Lord of the Rings, or the Apes in the Planet of the Apes. Lots of cameras are used together to create a very precise computer model of the movements being recorded. Using motion capture, we’ve been able to see what people actually do when chatting about their experiences.

It turns out that we share our understanding of things like a headache by performing it together. We share the actions of the headache as if we have it ourselves. If I hit my head, wince and say ‘ouch’, you might wince and say ‘ouch’ too – you give a multimodal performance, with actions and words, to show me you understand how I feel.

So should we just program robots to copy us? It isn’t as simple as that. We don’t copy exactly. A perfect copy wouldn’t show understanding of how we feel. A robot doing that would seem like a parrot, repeating things without any understanding. For the robot to show that it understands how you feel it must perform a headache like it owns it – as though it were really theirs! That means behaving in a similar way to you; but adapted to the unique type of headache it has.

Designing the way robots should behave in social situations isn’t easy. If we work out exactly how humans interact with each other to share their experiences though, we can use that understanding to program robot companions. Then one day your robot friend will be able to hang out with you, chat and show they understand how you feel. Just like a real friend.

multimodal = two or more different ways of doing something. With communication that might be spoken words, facial expressions and hand gestures.

Related Magazine …

See also (previous post and related career options)

AMPER: AI helping future you remember past you
Click to read about the AMPER project

We have recently written about the AMPER project which uses a tablet-based AI tool / robot to support people with dementia and their carers. It prompts the person to discuss events from their younger life and adapts to their needs. We also linked this with information about the types of careers people working in this area might do – the examples given were for a project based in the Netherlands called ‘Dramaturgy for Devices’ – using lessons learned from the study of theatre and theatrical performances in designing social robots so that their behaviour feels more natural and friendly to the humans who’ll be using them.

Job, Netherlands (Utrecht): PhD in Performing Arts and Robotics, clos 21 Jan 2024
Click to see one of the four jobs in this area with another three linked from it

See our collection of posts about Career paths in Computing.

Career paths in Computing

EPSRC supports this blog through research grant EP/W033615/1.

AMPER: AI helping future you remember past you

by Jo Brodie, Queen Mary University of London

Old photos image by Karolina Grabowska from Pixabay

Have you ever heard a grown up say “I’d completely forgotten about that!” and then share a story from some long-forgotten memory? While most of us can remember all sorts of things from our own life history it sometimes takes a particular cue for us to suddenly recall something that we’d not thought about for years or even decades. 

As we go through life we add more and more memories to our own personal library, but those memories aren’t neatly organised like books on a shelf. For example, can you remember what you were doing on Thursday 20th September 2018 (or can you think of a way that would help you find out)? You’re more likely to be able to remember what you were doing on the last Tuesday in December 2018 (but only because it was Christmas Day!). You might not spontaneously recall a particular toy from your childhood but if someone were to put it in your hands the memories about how you played with it might come flooding back.

Accessing old memories

In Alzheimer’s Disease (a type of dementia) people find it harder to form new memories or retain more recent information which can make daily life difficult and bewildering and they may lose their self-confidence. Their older memories, the ones that were made when they were younger, are often less affected however. The memories are still there but might need drawing out with a prompt, to help bring them to the surface.

old newspaper
Perhaps a newspaper advert will jog your memory in years to come… Image by G.C. from Pixabay

An EPSRC-funded project at Heriot-Watt University in Scotland is developing a tablet-based ‘story facilitator’ agent (a software program designed to adapt its response to human interaction) which contains artificial intelligence to help people with Alzheimer’s disease and their carers. The device, called ‘AMPER’*, could improve wellbeing and a sense of self in people with dementia by helping them to uncover their ‘autobiographical memories’, about their own life and experiences – and also help their carers remember them ‘before the disease’.

Our ‘reminiscence bump’

We form some of our most important memories between our teenage years and early adulthood – we start to develop our own interests in music and the subjects that we like studying, we might experience first loves, perhaps going to university, starting a career and maybe a family. We also all live through a particular period of time where we’re each experiencing the same world events as others of the same age, and those experiences are fitted into our ‘memory banks’ too. If someone was born in the 1950s then their ‘reminiscence bump’ will be events from the 1970s and 1980s – those memories are usually more available and therefore people affected by Alzheimer’s disease would be able to access them until more advanced stages of the disease process. Big important things that, when we’re older, we’ll remember more easily if prompted.

In years to come you might remember fun nights out with friends.
Image by ericbarns from Pixabay

Talking and reminiscing about past life events can help people with dementia by reinforcing their self-identity, and increasing their ability to communicate – at a time when they might otherwise feel rather lost and distressed. 

“AMPER will explore the potential for AI to help access an individual’s personal memories residing in the still viable regions of the brain by creating natural, relatable stories. These will be tailored to their unique life experiences, age, social context and changing needs to encourage reminiscing.”

Dr Mei Yii Lim, who came up with the idea for AMPER(3).

Saving your preferences

AMPER comes pre-loaded with publicly available information (such as photographs, news clippings or videos) about world events that would be familiar to an older person. It’s also given information about the person’s likes and interests. It offers examples of these as suggested discussion prompts and the person with Alzheimer’s disease can decide with their carer what they might want to explore and talk about. Here comes the clever bit – AMPER also contains an AI feature that lets it adapt to the person with dementia. If the person selects certain things to talk about instead of others then in future the AI can suggest more things that are related to their preferences over less preferred things. Each choice the person with dementia makes now reinforces what the AI will show them in future. That might include preferences for watching a video or looking at photos over reading something, and the AI can adjust to shorter attention spans if necessary. 

“Reminiscence therapy is a way of coordinated storytelling with people who have dementia, in which you exercise their early memories which tend to be retained much longer than more recent ones, and produce an interesting interactive experience for them, often using supporting materials — so you might use photographs for instance”

Prof Ruth Aylett, the AMPER project’s lead at Heriot-Watt University(4).

When we look at a photograph, for example, the memories it brings up haven’t been organised neatly in our brain like a database. Our memories form connections with all our other memories, more like the branches of a tree. We might remember the people that we’re with in the photo, then remember other fun events we had with them, perhaps places that we visited and the sights and smells we experienced there. AMPER’s AI can mimic the way our memories branch and show new information prompts based on the person’s previous interactions.

​​Although AMPER can help someone with dementia rediscover themselves and their memories it can also help carers in care homes (who didn’t know them when they were younger) learn more about the person they’re caring for.

*AMPER stands for ‘Agent-based Memory Prosthesis to Encourage Reminiscing’.

Suggested classroom activities – find some prompts!

  • What’s the first big news story you and your class remember hearing about? Do you think you will remember that in 60 years’ time?
  • What sort of information about world or local events might you gather to help prompt the memories for someone born in 1942, 1959, 1973 or 1997? (Remember that their reminiscence bump will peak in the 15 to 30 years after they were born – some of them may still be in the process of making their memories the first time!).

See also

If you live near Blackheath in South East London why not visit the Age Exchange and reminiscence centre which is an arts charity providing creative group activities for those living with dementia and their carers. It has a very nice cafe.

Related careers

The AMPER project is interdisciplinary, mixing robots and technology with psychology, healthcare and medical regulation.

We have information about four similar-ish job roles on our TechDevJobs blog that might be of interest. This was a group of job adverts for roles in the Netherlands related to the ‘Dramaturgy^ for Devices’ project. This is a project linking technology with the performing arts to adapt robots’ behaviour and improve their social interaction and communication skills.

Below is a list of four job adverts (which have now closed!) which include information about the job description, the types of people that the employers were looking for and the way in which they wanted them to apply. You can find our full list of jobs that involve computer science directly or indirectly here.

^Dramaturgy refers to the study of the theatre, plays and other artistic performances.

Dramaturgy for Devices – job descriptions

More on …

1. Agent-based Memory Prosthesis to Encourage Reminiscing (AMPER) Gateway to Research
2. The Digital Human: Reminiscence (13 November 2023) BBC Sounds – a radio programme that talks about the AMPER Project.
3. Storytelling AI set to improve wellbeing of people with dementia (14 March 2022) Heriot-Watt University news
4. AMPER project to improve life for people with dementia (14 January 2022) The Engineer

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This blog is funded by EPSRC on research agreement EP/W033615/1.

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Collaborative community coding & curating

Equality, diversity and inclusion in the R Project

Concentric Rings of rainbow people as triangles holding hands
Image by Gordon Johnson from Pixabay

You might not think of a programming language like Python or Scratch as being an ‘ecosystem’ but each language has its own community of people who create and improve its code (compilers, library code,…), flush out the bugs, introduce new features, document any changes and write the ‘how to’ guides for new users. 

R is one such programming language. It’s named after its two co-inventors (Ross Ihaka and Robert Gentleman) and is used by around two million people around the world. People working in all sorts of jobs and industries (for example finance, academic research, government, data journalists) use R to analyse their data. The software has useful tools to help people see patterns in their data and to make sense of that information. 

It’s also open source which means that anyone can use it and help to improve it, a bit like Wikipedia where anyone can edit an article or write a new one. That’s generally a good thing because it means everyone can contribute but it can also bring problems. Imagine writing an essay about an event at your school and sharing it with your class. Then imagine your classmates adding paragraphs of their own about the event, or even about different events. Your essay could soon become rather messy and you’d need to re-order things, take bits out and make sure people hadn’t repeated something that someone had already said (but in a slightly different way). 

When changes are made to software people also want to keep a note not just of the ‘words’ added (the code) but also to make a note of who added what and when. Keeping good records, also known as documentation, helps keep things tidy and gives the community confidence that the software is being properly looked after.

Code and documentation can easily become a bit chaotic when created by different people in the community so there needs to be a core group of people keeping things in order. Fortunately there is – the ‘R Core Team’, but these days its membership doesn’t really reflect the community of R users around the world. R was first used in universities, particularly by more privileged statistics professors from European countries and North America (the Global North), and so R’s development tended to be more in line with their academic interests. R needs input and ideas from a more diverse group of active developers and decision-makers, in academia and beyond to ensure that the voices of minoritised groups are included. Also the voices of younger people, particularly as many of the current core group are approaching retirement age.

Dr Heather Turner from the University of Warwick is helping to increase the diversity of those who develop and maintain the R programming language and she’s been given funding by the EPSRC* to work on this. Her project is a nice example of someone who is bringing together two different areas in her work. She is mixing software development (tech skills) with community management (people skills) to support a range of colleagues who use R and might want to contribute to developing it in future, but perhaps don’t feel confident to do so yet. 

Development can involve things like fixing bugs, helping to improve the behaviour or efficiency of programs or translating error messages that currently appear on-screen in the English language into different languages. Heather and her colleagues are working with the R community to create a more welcoming environment for ‘newbies’ that encourages participation, particularly from people who are in the community but who are not currently represented or under-represented by the core group and she’s working collaboratively with other community organisations such as R-Ladies, LatinR and RainbowR. Another task she’s involved in is producing an easier-to-follow ‘How to develop R’ guide.

There are also people who work in universities but who aren’t academics (they don’t teach or do research but do other important jobs that help keep things running well) and some of them use R too and can contribute to its development. However their contributions have been less likely to get the proper recognition or career rewards compared with those made by academics, which is a little unfair. That’s largely because of the way the academic system is set up. 

Generally it’s academics who apply for funding to do new research, they do the research and then publish papers in academic journals on the research that they’ve done and these publications are evidence of their work. But the important work that supporting staff do in maintaining the software isn’t classified as new research so doesn’t generally make it into the journals, so their contribution can get left out. They also don’t necessarily get the same career support or mentoring for their development work. This can make people feel a bit sidelined or discouraged. 

To try and fix this and to make things fairer the Society of Research Software Engineering was created to champion a new type of job in computing – the Research Software Engineer (RSE). These are people whose job is to develop and maintain (engineer) the software that is used by academic researchers (sometimes in R, sometimes in other languages). The society wants to raise awareness of the role and to build a community around it. You can find out what’s needed to become an RSE below. 

Heather is in a great position to help here too, as she has a foot in each camp – she’s both an Academic and a Research Software Engineer. She’s helping to establish RSEs as an important role in universities while also expanding the diversity of people involved in developing R further, for its long-term sustainability.

Further reading

Related careers

QMUL

Below is an example of a Research Software Engineer role which was advertised at QMUL in April 2024 – you can read the original advert and see a copy of the job description / person specification information which is archived at the “Jobs in Computer Science” website. This advert was looking for an RSE to support a research project “at the intersection of Natural Language Processing (NLP) and multi-modal Machine Learning, with applications in mental health.”

Closed, QMUL, Research Software Engineer (0.5FTE), ~£63.6k pro rata, closed April 2024

QMUL also has a team of Research Software Engineers and you can read about what they’re working on and their career here (there are also RSEs attached to different projects across the university, as above).

Archived job adverts from elsewhere

Below are some examples of RSE jobs (these particular vacancies have now closed but you can read about what they were looking for and see if that sort of thing might interest you in the future). The links will take you to a page with the original job advert + any Job Description (JD – what the person would actually be doing) and might also include a Person Specification (PS – the type of person they’re looking for in terms of skills, qualifications and experience) – collectively these are often known as ‘job packs’.

Note that these documents are written for quite a technical audience – the people who’d apply for the jobs will have studied computer science for many years and will be familiar with how computing skills can be applied to different subjects.

1. The Science and Technology Facilities Council (STFC) wanted four Research Software Engineers (who’d be working either in Warrington or Oxford) on a chemistry-related project (‘computational chemistry’ – “a branch of chemistry that uses computer simulation to assist in solving chemical problems”) 

2. The University of Cambridge was looking for a Research Software Engineer to work in the area of climate science – “Computational modelling is at the core of climate science, where complex models of earth systems are a routine part of the scientific process, but this comes with challenges…”

3. University College London (UCL) wanted a Research Software Engineer to work in the area of neuroscience (studying how the brain works, in this case by analysing the data from scientists using advanced microscopy).

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This blog is funded by EPSRC on research agreement EP/W033615/1.

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Exploring mazes, inventing algorithms (part I) 

by Paul Curzon, Queen Mary University of London

A maze with mouse searching for cheese.
Image by CS4FN

Computer science research in part involves inventing new algorithms or improving new ones. But what does that mean. Let’s explore some mazes to explore algorithms.

What does computer science research involve? It is very varied: from interviewing people to find out what the real problems that need solving in their lives or jobs are; to running experiments to find out what works and what doesn’t; to writing programs to solve problems.

Improving algorithms

A core part of much research is coming up with new and better algorithms that solve particular problems. The kind of algorithm could be anything from a new more secure cryptographic protocol, or a better way to rank the results of a search engine, to a new more effective machine learning algorithm that is less likely to make things up, or perhaps can better explain how it came to its conclusions.

What does it mean to come up with a better algorithm though? Once a problem is solved, isn’t it solved? Let’s explore a simple problem to see. Let’s explore mazes. Solve the simple maze puzzle above before you go on. Find a route that gets the mouse to the cheese.

Wandering around mazes, finding algorithms

If you’ve ever been in a hedge maze in the garden of some stately home, or a corn field maze, the chances are you just dived in and wandered rather aimlessly. Perhaps you tried to remember which way you went at each junction, to avoid going down the same dead-ends more than once. How about solving the paper version of a maze puzzle like the one above? Now perhaps you looked ahead to spot dead-ends to avoid tracing wrong paths with your pencil.

Probably what you are doing is at least a little random. You could, in theory at least, end up going back over the same paths, never taking the right one and and never getting to the middle. Could we come up with an algorithm that guarantees to solve mazes? To be an algorithm it would need to guarantee you ended up finding a path to the centre of the maze if you followed the steps of the algorithm precisely. It should also work for any maze, or at least all mazes of a particular kind. Ideally, the algorithm gives you a path that can then be followed by anyone without them having to run the algorithm themselves. They can just follow the path generated by the algorithm for that maze.

Wall-following

In fact lots of maze algorithms have been invented. Perhaps the one most people have heard of, if they know of any maze algorithm, is called Wall-following. It is very simple to do, You just pick a wall at the entrance either to the left or right and then follow it, If in a garden maze, keep your hand on the hedge as you walk round. If doing a paper puzzle, draw the path sticking to the chosen wall. Try it on the following simple maze.

A simple maze with mouse and cheese.
Image by CS4FN

Simply connected

The wall-following algorithm will guarantee to get you to the centre of the maze, and back out again too, but only as long as the maze is what is called simply connected. That just means the maze is constructed from a single hedge (or one unbroken drawn line) not a series of unconnected hedges. If you look at both examples above you will see I created them by just drawing a single wiggly line.

If a maze is simply connected then it cannot have looping paths, so no going round in circles for ever. It will also only have one entrance/exit. That shows the first aspect of inventing algorithms that is important. They often only work for some situations, not all. You must be sure you know what situations they do and don’t work.

Often the earliest algorithms invented to solve a problem are like wall-following: they only work for simple situations. Other people then come along and find new algorithms that can cover more problems (here more mazes). Can you tweak the wall-following maze algorithm to work even if there are multiple exits from the maze, for example? As it stands our algorithm could just take you from the entrance straight out of another exit without exploring much of the maze at all! See the end for one simple way to tweak the algorithm. What if there are paths that take you round in circles? Can you come up with an algorithm to deal with that?

Some times the improvements invented just involve tweaking an existing algorithm as with dealing with multiple exits in a maze. Some times a whole new algorithm is needed.

Faster, higher, stronger?

Even for a simple constrained version of the problem, like simply connected mazes, people can invent better algorithms. What does better mean for a maze? Well one way you might have a better algorithm is if it is faster in coming up with a solution. Another is that the solution it comes up with is faster. For a maze that means a shorter (ideally the shortest) path to the centre. Wall following may get you in to the centre (and out again) but you probably will have discovered a very long path that takes you in and out of lots of dead-ends needlessly. You do find a path to the centre, but it may be a very long path. Can you come up with an algorithm that finds shorter paths?

We will explore an algorithm that does next.

More to come…

Some solutions

The result of wall following on our simple maze

A route for the mouse to follow that takes it to the cheese.
Image by CS4FN

One way to deal with multiple exits

To deal with a maze that has multiple exits, so multiple breaks in the outer wall, tweak the wall-following algorithm as follows. First mark the exit you use to enter the maze, so you know when you return to it. If you come to any other exit then pretend there is a gate there and keep following the wall as though it were unbroken and there were no exit.

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This blog is funded by EPSRC on research agreement EP/W033615/1.

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Black in Data

Lightbulb in a black circle surrounded by circles of colour representing data

Image based on combining bid data and lightbulb images by Gerd Altmann from Pixabay

Careers do not have to be decided on from day one. You can end up in a good place in a roundabout way. That is what happened to Sadiqah Musa, and now she is helping make the paths easier for others to follow.

Sadiqah went to university at QMUL expecting to become an environmental scientist. Her first job was as a geophysicist analysing seismic data. It was a job she thought she loved and would do forever. Unfortunately, she wasn’t happy, not least about the lack of job security. It was all about data though which was a part she did still enjoy, and the computer science job of Data Analyst was now a sought-after role. She retrained and started on a whole new exciting career. She currently works at the Guardian Newspapers where she met Devina Nembhard … who was the first Black woman she had ever worked with throughout her career.

Together they decided that was just wrong, but also set out to change it. They created “Black in Data” to support people of colour in the industry, mentoring them, training them in the computer science skills they might be short of: like programming and databases; helping them thrive. More than that they also confront industry to try and take down the barriers that block diversity in the first place.

Paul Curzon, Queen Mary University of London

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What the real Pros say

by Paul Curzon, Queen Mary University of London

Originally Published in the CS4FN “Women are Here” special

Rebecca Stewart CC BY 2.0 Thomas Bonte
“Rebecca Stewart” by Thomas Bonte, CC BY 2.0 via Flikr (cropped)

Some (female) computer scientists and electronic engineers were asked what they most liked about their job and the subject. Each quote is given with the job they had at the time of the quote. many have moved on or upwards since.

Here is what the real Pros think …

“Building software that protects billions of people from online abuse … I find it tremendously rewarding…Every code change I make is a puzzle: exciting to solve and exhilarating to crack; I love doing this all day, every day.”

Despoina Magka, Software engineer, Facebook

“Taking on new challenges and overcoming my limitations with every program I write, every bug I fix, and every application I create. It has and continues to inspire me to grow, both professionally and personally.”

Kavin Narasimhan, Researcher, University of Surrey

“Because computer science skills are useful in nearly every part of our lives, I get to work with biologists, mathematicians, artists, designers, educators and lately a whole colony of naked mole-rats! I love the diversity.”

Julie Freeman, artist and PhD student, QMUL

“The flexibility of working from any place at any time. It offers many opportunities to collaborate with, and learn from, brilliant people from all over the world.”

Greta Yorsh, Lecturer QMUL, former software engineer, ARM.

“Possibilities! When you try to do something that seems crazy or impossible and it works, it opens up new possibilities… I enjoy being surrounded by creative people.”

Justyna Petke, Researcher, UCL

“That we get to study the deep characteristics of the human mind and yet we are so close to advances in technology and get to use them in our research.” – Mehrnoosh Sadrzadeh, Senior Lecturer, QMUL

“I get the opportunity to understand what both business people and technologists are thinking about, their ideas and their priorities and I have the opportunity to bring these ideas to fruition. I feel very special being able to do this! I also like that it is a creative subject – elegant coding can be so beautiful!”

Jill Hamilton, Vice President, Morgan Stanley

“You never know what research area the solution to your problem will come from, so every conversation is valuable.”

Vanessa Pope, PhD student, QMUL

“I get to ask questions about people, and set about answering them in an empirical way. computer science can lead you in a variety of unexpected directions”

Shauna Concannon, Researcher, QMUL

“It is fascinating to be able to provide simpler solutions to challenging requirements faced by the business.”

Emanuela Lins, Vice President, Morgan Stanley

“I think the best thing is how you can apply it to so many different topics. If you are interested in biology, music, literature, sport or just about anything else you can think of, then there’s a problem that you can tackle using computer science or electronic engineering…I like writing code, but I enjoy making things even more.”

Becky Stewart, Lecturer, QMUL

“… you get to be both a thinker and a creator. You get to think logically and mathematically, be creative in the way you write and design systems and you can be artistic in the way you display things to users. …you’re always learning something new.”

Yasaman Sepanj, Associate, Morgan Stanley

“Creating the initial ideas, forming the game, making the story… Being part of the creative process and having a hands on approach“,

Nana Louise Nielsen, Senior Game Designer, Sumo Digital

“Working with customers to solve their problems. The best feeling in the world is when you leave … knowing you’ve just made a huge difference.“

Hannah Parker, IT Consultant, IBM

“It changes so often… I am not always sure what the day will be like“

Madleina Scheidegger, Software Engineer, Google.

“I enjoy being able to work from home“

Megan Beynon, Software Engineer, IBM

“I love to see our plans come together with another service going live and the first positive user feedback coming in“

Kerstin Kleese van Dam, Head of Data Management, CCLRC

“…a good experienced team around me focused on delivering results”

Anita King, Senior Project Manager, Metropolitan Police Service

“I get to work with literally every single department in the organisation.”

Jemima Rellie, Head of Digital Programme, Tate

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“The thundering engines vibrate throughout your body”

The space shuttle lifting off
A space shuttle launch.
Image by WikiImages from Pixabay

Computer scientist Jason Cordes tells us what it was like to work for NASA on the International Space Station during the time of Space Shuttle launches. (From the archive)

Working for a space agency is brilliant. When I was younger, I often looked up at the stars and wondered what was out there. I visited Johnson Space Center in Houston, Texas and told myself that I wanted to work there someday. After completing my college degree in computer science, I had the great fortune to be asked to work at NASA’s Johnson Space Center as well as Kennedy Space Center.

Johnson Space Center is the home of the Mission Control Center (MCC). This is where NASA engineers direct in-orbit flights and track the position of the International Space Station (ISS) and the Space Shuttle when it is in orbit. Kennedy Space Center, situated at Cape Canaveral, Florida, is where the Space Shuttle and most other space-bound vehicles are launched. Once they achieve orbit, control is handed over to Johnson Space Center in Houston, which is why when you hear astronauts calling Earth, they talk to “Houston”.

Space City

Houston is a very busy city and you get that feeling when you are at Johnson. There are people everywhere and the Space Center looks like a small city unto itself. While I was there I worked on the computer control system for the International Space Station. The part I worked on was a series of laptop-based displays designed to give astronauts on the station a real-time view of the state of everything, from oxygen levels to the location of the robotic arm.

The interesting thing about developing this type of software is realising that the program is basically sending and receiving telemetry (essentially a long list of numbers) to the hardware, where the hardware is the space station itself. Once you think of it like that, the sheer simplicity of what is being done is really surprising. I certainly expected something more complex. All of the telemetry comes in over a wire and the software has to keep track of what telemetry belongs to what component since different components all broadcast over the same wire. Essentially the program routes the data based on what component it comes from and acts as an interpreter that takes the numbers that the space station is feeding and converting them into a graphical format that the astronauts can understand. The coolest part of working in Houston was interacting with astronauts and getting their feedback on how the software should work. It’s like working with celebrities.

Wild times

While at Kennedy Space Center, I was tasked with working on the Shuttle Launch Control System for the next generation of shuttles. The software is very similar to that used to control the ISS. The thing I remember most about working there was the environment.

Kennedy Space Center is about as opposite as you can get from the big city feeling at Johnson. It’s situated on what is essentially swampland on the eastern coast of Florida. The main gates to Johnson are right on major streets within Houston, but at Kennedy the gate is on a major highway, and even then, travel to the actual buildings of the Space Center is a leisurely 30 minute drive through orange groves and trees as well as bypassing causeways and creeks. Along the way you might spot an eagle’s nest in one of the trees, or a manatee poking its head from the waters. Kennedy is in the middle of a wildlife preserve with alligators, manatees, raccoons and every other kind of critter you can imagine. In fact, I was prevented from going home one evening by a gator that decided to warm itself up by my car.

The coolest thing about working at NASA, and specifically Kennedy Space Center, was being able to watch shuttle launches from less than 10 miles away. It’s an incredible experience. The thundering engines vibrate throughout your body like being next to the speakers at an entirely too loud rock concert. Night launches were the most amazing, with the fire from the engines lighting up the sky. It is very amazing to watch this machine and realise that you are the one who wrote the computer program that set it in motion. I’ve worked in a few development firms, but few of them gave me as much emotion when I saw it in action as this did.

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