Mike Lynch: sequencing success

Mike Lynch was one of Britain’s most successful entrepreneurs. An electrical engineer, he built his businesses around machine learning long before it was a buzz phrase. He also drew heavily on a branch of maths called Bayesian statistics which is concerned with understanding how likely, even apparently unlikely, things are to actually happen. This was so central to his success that he named his super yacht, Bayesian, after it. Tragically, he died on the yacht, when Bayesian sank in a freak, extremely unlikely, accident. The gods of the sea are cruel.

Synthesisers

A keyboard synthesiser
Image by Julius H. from Pixabay

Mike started his path to becoming an entrepreneur at school. He was interested in music, and especially the then new but increasingly exciting, digital synthesisers that were being used by pop bands, and were in the middle of revolutionising music. He couldn’t afford one of his own, though, as they cost thousands. He was sure he could design and build one to sell more cheaply. So he set about doing it.

He continued working on his synthesiser project as a hobby at Cambridge University, where he originally studied science, but changed to his by-then passion of electrical engineering. A risk of visiting his room was that you might painfully step on a resistor or capacitor, as they got everywhere. That was not surprising giving his living room was also his workshop. By this point he was also working more specifically on the idea of setting up a company to sell his synthesiser designs. He eventually got his first break in the business world when chatting to someone in a pub who was in the music industry. They were inspired enough to give him the few thousand pounds he needed to finance his first startup company, Lynett Systems.

By now he was doing a PhD in electrical engineering, funded by EPSRC, and went on to become a research fellow building both his research and innovation skills. His focus was on signal processing which was a natural research area given his work on synthesisers. They are essentially just computers that generate sounds. They create digital signals representing sounds and allow you to manipulate them to create new sounds. It is all just signal processing where the signals ultimately represent music.

A curving roof made of triangles of glass.
Image by Kang-Rui LENG from Pixabay

However, Mike’s research and ideas were more general than just being applicable to audio. Ultimately, Mike moved away from music, and focussed on using his signal processing skills, and ideas around pattern matching to process images. Images are signals too (resulting from light rather than sound). Making a machine understand what is actually in a picture (really just lots of patches of coloured light) is a signal processing problem. To work out what an image shows, you need to turn those coloured blobs into lines, then into shapes, then into objects that you can identify. Our brains do this seamlessly so it seems easy to us, but actually it is a very hard problem, one that evolution has just found good solutions to. This is what happens whether the image is that captured by the camera of a robot “eye” trying to understand the world or a machine trying to work out what a medical scan shows. 

This is where the need for maths comes in to work out probabilities, how likely different things are. Part of the task of recognising lines, shapes and objects is working out how likely one possibility is over another. How likely is it that that band of light is a line, how likely is it that that line is part of this shape rather than that, and so on. Bayesian statistics gives a way to compute probabilities based on the information you already know (or suspect). When the likelihood of events is seen through this lens, things that seem highly unlikely, can turn out to be highly probably (or vice versa), so it can give much more accurate predictions than traditional statistics. Mike’s PhD used this way of calculating probabilities even though some statisticians disdained it. Because of that it was shunned by some in the machine learning community too, but Mike embraced it and made it central to all his work, which gave his programs an edge.

While Lynett Systems didn’t itself make him a billionaire, the experience from setting up that first company became a launch pad for other innovations based on similar technology and ideas. It gave him the initial experience and skills, but also meant he had started to build the networks with potential investors. He did what great entrepreneurs do and didn’t rest on his laurels with just one idea and one company, but started to work on new ideas, and new companies arising from his PhD research.

Fingerprints

Fingerprint being scanned
Image by alhilgo from Pixabay

He realised one important market for image pattern recognition, that was ripe for dominating, was fingerprint recognition. He therefore set about writing software that could match fingerprints far faster and more accurately than anyone else. His new company, Cambridge Neurodynamics, filled a gap, with his software being used by Police Forces nationwide. That then led to other spin-offs using similar technology

He was turning the computational thinking skills of abstraction and generalisation into a way to make money. By creating core general technology that solved the very general problems of signal processing and pattern matching, he could then relatively easily adapt and reuse it to apply to apparently different novel problems, and so markets, with one product leading to the next. By applying his image recognition solution to characters, for example, he created software (and a new company) that searched documents based on character recognition. That led on to a company searching databases, and finally to the company that made him famous, Autonomy.

Fetch

A puppy fetching a stick
Image from Pixabay

One of his great loves was his dog, Toby, a friendly enthusiastic beast. Mike’s take on the idea of a search engine was fronted by Toby – in an early version, with his sights set on the nascent search engine market, his search engine user interface involved a lovable, cartoon dog who enthusiastically fetched the information you needed. However, in business finding your market and getting the right business model is everything. Rather than competing with the big US search engine companies that were emerging, he switched to focussing on in-house business applications. He realised businesses were becoming overwhelmed with the amount of information they held on their servers, whether in documents or emails, phone calls or videos. Filing cabinets were becoming history and being replaced by an anarchic mess of files holding different media, individually organised, if at all, and containing “unstructured data”. This kind of data contrasts with the then dominant idea that important data should be organised and stored in a database to make processing it easier. Mike realised that there was lots of data held by companies that mattered to them, but that just was not structured like that and never would be. There was a niche market there to provide a novel solution to a newly emerging business problem. Focussing on that, his search company, Autonomy, took off, gaining corporate giants as clients including the BBC. As a hands-on CEO, with both the technical skills to write the code himself and the business skills to turn it into products businesses needed, he ensured the company quickly grew. It was ultimately sold for $11 billion. (The sale led to an accusation of fraud in hte US, but, innocent, he was acquitted of all the charges).

Investing

From firsthand experience he knew that to turn an idea into reality you needed angel investors: people willing to take a chance on your ideas. With the money he made, he therefore started investing himself, pouring the money he was making from his companies into other people’s ideas. To be a successful investor you need to invest in companies likely to succeed while avoiding ones that will fail. This is also about understanding the likelihood of different things,  obviously something he was good at. When he ultimately sold Autonomy, he used the money to create his own investment company, Invoke Capital. Through it he invested in a variety of tech startups across a wide range of areas, from cyber security, crime and law applications to medical and biomedical technologies, using his own technical skills and deep scientific knowledge to help make the right decisions. As a result, he contributed to the thriving Silicon Fen community of UK startup entrepreneurs, who were and continue to do exciting things in and around Cambridge, turning research and innovation into successful, innovative companies. He did this not only through his own ideas but by supporting the ideas of others.

Man on rock staring at the sun between 2 parallel worlds
Image by Patricio González from Pixabay

Mike was successful because he combined business skills with a wide variety of technical skills including maths, electronic engineering and computer science, even bioengineering. He didn’t use his success to just build up a fortune but reinvested it in new ideas, new companies and new people. He has left a wonderful legacy as a result, all the more so if others follow his lead and invest their success in the success of others too.

In memory of a friend

Paul Curzon, Queen Mary University of London

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Front cover of CS4FN issue 29 - Diversity in Computing

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Superhero Syllogisms

Kapow
Image by Andrew Martin from Pixabay

Superheroes don’t just have physical powers. Often they come out on top because of their mental abilities. Sherlock is a good example, catching villains through logical thinking. Anyone can get better at thinking! Just practice.

It is important for everyone to be able to think clearly. It is especially true for programmers, detectives and lawyers as well as superheroes. You need to be able to work things out from the facts you know. The Ancient Greeks were very good at logic. They invented the idea of a ‘syllogism’. These are common patterns that combine facts where you figure out a conclusion only using the facts.

For example, if we know facts 1 and 2 below (where you can swap in anything for X, Y and Z) then we can create a new fact as shown.

FACT1 ALL X Y FACT 2 Z IS A X NEW FACT Z Y
Image by Paul Curzon

So let’s replace X with the word superheroes, Y with fight crime and Z with my favourite superhero, Ghost Girl. If we put them in to the picture above we get the new picture:

FACT 1 ALL super heroes fight crime FACT 2 Ghost girl is a super hero NEW FACT Ghost Girl fights crime
Image by Paul Curzon

In this case we can deduce the new fact that Ghost Girl fights crime. Notice how you use the plurals in Fact 1 and singular words in the other facts to make the English work.

Puzzles

Can you solve these Superhero Syllogism puzzles? Work out which conclusion is the one that follows from the given facts. Use our coloured template above to help.

Superhero syllogism puzzle 1

FACT 1: ALL superheroes do good.
FACT 2: The Invisible Woman is a superhero.

Which statement below (a, b, c or d) can we say from these facts alone? Don’t use anything extra, just use fact 1 and fact 2. (ANSWERS at the bottom of the page).

a) The Invisible Woman has superpowers.
b) The Invisible Woman does good.
c) The Invisible Man does good.
d) The Invisible Woman does not do good

Superhero syllogism puzzle 2

FACT 1: ALL superheroes sometimes accidentally do harm.
FACT 2: Jamila is a superhero.

What can we say from these facts alone?

a) Jamila sometimes accidentally does harm.
b) Jamila is not a superhero
c) Those with superpowers only do good.
d) Jamie is a superhero

Superhero syllogism puzzle 3

FACT 1: ALL supervillains laugh in an evil way.
FACT 2: The Spider is a supervillain.

What can we say from these facts alone?

a) The Spider sometimes accidentally does harm.
b) The Spider does not laugh in an evil way.
c) Supervillains are evil.
d) The Spider laughs in an evil way.

As long as the facts are true the conclusion follows, though if the facts are not true then nothing is really known.

Superhero syllogism puzzle 4

The following logic is good but something has gone wrong because the conclusion is not true. The superhero called the Angel does not actually have any superpowers! The Angel just wears a flying suit! Can you work out what has gone wrong with our logic?

1. ALL superheroes have superpowers.
2. The Angel is a superhero.

Therefore we can conclude from these facts alone that

3.The Angel has superpowers.

Answers are at the bottom of the page.

Fun to do

Take the pattern of the above syllogisms and invent your own. Just substitute your own words, but keep the pattern.See how silly the “facts” you can deduce are.

– Paul Curzon, Queen Mary University of London, first appeared in A BIT of CS4FN 2

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Answers

Superhero syllogism puzzle 1

Answer: b.
FACT 1: ALL superheroes … do good.
FACT 2: The Invisible Woman is a superhero
Therefore we can conclude from these facts alone that
NEW FACT: The Invisible Woman … does good.

Superhero syllogism puzzle 2

Answer: a.
FACT 1: ALL superheroes … sometimes accidentally do harm.
FACT 2: Jamila is a superhero
Therefore we can conclude from these facts alone that
NEW FACT: Jamila … sometimes accidentally does harm.

Superhero syllogism puzzle 3

Answer: d.
FACT 1: ALL supervillains … laugh in an evil way.
FACT 2: The Spider is a supervillain.
Therefore we can conclude from these facts alone that
NEW FACT: The Spider … laughs in an evil way.

Superhero syllogism puzzle 4

Something has gone wrong. We are told  that The Angel has no superpowers. They just wear a special flying suit. The new fact is therefore not true. This means that one of the original ‘facts’ was not actually true. If we start from things that are not true then the things we deduce will not be true either! In this case

EITHER: 

Some superheroes do NOT have superpowers

OR: 

The Angel is NOT a superhero.

Eating at Quonk: a tough puzzle?

cafe empty chairs
Image from pixabay

A group of friends: 2 women (Alice and Babs) and 2 men (Zach and Yabu) like to go out on dates to cool restaurants in pairs. There are four combinations they date in (Alice-Zach, Alice-Yabu, Babs-Zach and Babs-Yabu).

The favourite restaurant of one of the men and one of the women is a place called Quonk. However if those two eat together they always try new restaurants as do the other pair if together. Therefore when exactly one and only one of the particular man and woman in question is on a date they eat at Quonk.

When Alice goes out with Zach they go to Quonk.

Which, if any, other pair eat at Quonk?

  • Alice and Yabu eat at Quonk
  • Babs and Zach eat at Quonk
  • Babs and Yabu eat at Quonk
  • None of the other pairs eat at Quonk

Find the answer here.

Paul Curzon Queen Mary University of London, from the archive

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From a handful of sand to a fistful of dollars

Where computer chips come from


Sandy beach image by Peggychoucair from Pixabay

Sitting at the heart of your computer, mobile phone, smart TV (or even smart toaster) is the microprocessor that makes it all work. These electronic ‘chips’ have millions of tiny electronic circuits on them allowing the calculations needed to make your gizmos work. But it may be surprising to learn that these silicon chips, now a billion pound industry worldwide are in fact mostly made of the same stuff that you find on beaches, namely sand.

A transistor is just like a garden hose with your foot on it

Sand is mostly made of silicon dioxide, and silicon, the second most abundant substance in the earth’s crust, has useful chemical properties as well as being very cheap. You can easily ‘add’ other chemicals to silicon and change its electrical properties, and it’s by using these different forms of silicon that you can make mini switches, or transistors, in silicon chips.

House Hose

A transistor on a chip can be thought of like a garden hose, water flows from the tap (the source) through the hose and out onto the garden (the drain), but if you were to stand on the hose with your foot and block the water flow the watering would stop. An electronic transistor on a chip in its most basic form works like this, but electrical charge rather than water runs through the transistor (in fact the two parts of a transistor are actually called the source and drain). The ‘gate’ plays the part of your foot; this is the third part of the transistor. Applying a voltage to the gate is like putting your foot on and off the hose, it controls whether charge flows through the transistor.

Lots of letter T’s

A billion pound industry made of sand

If you look at a transistor on a chip it looks like a tiny letter T, the top crossbar on the T is the source/drain part (hose) and the upright part of the T is the gate (the foot part). Using these devices you can start to build up logic functions. For example, if you connect the source and drain of two transistors together one after another it can work out the logic AND function. How? Well think of this as a long hose with you and a friend’s foot available. If you stand on the hose no water will flow. If your friend stands on the hose no water will flow. If you both stand on the hose defiantly no water will flow. It is only when you don’t stand on the hose AND your friend also doesn’t stand on the hose that the water flows. So you’ve build a simple logical function.

Printing chips

From such simple logic functions you can build very complex computers, if you have enough of them, and that’s again where silicon comes in. You can ‘draw’ with silicon down to very small sizes. In fact a silicon chip is printed with many different layers. For example, one layer has the patterns for all the sources and drains, the next layer chemically printed on top are the gates, the next the metallic connections between the transistors and so on. These chips take millions of pounds to design and test, but once the patterns are correct it’s easy to stamp out millions of chips. It’s just a big chemical printing press. It’s the fact that you can produce silicon chips efficiently and cheaply with more and more transistors on them each year that drives the technology leaps we see today.

Beautiful silicon

Finally you might wonder how the chip companies protect their chip designs? They in fact protect them by registering the design of the masks they use in the layer printing process. Design registration is normally used to protect works of artistic merit, like company logos. Whether chip masks are quite as artistic doesn’t seem to matter. What does matter is that the chemical printing of silicon and lots of computer scientists have made all today’s computer technology possible. Now there is a beautiful thought to ponder when next on the beach.

– Paul Curzon, Queen Mary University of London

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  • Computational Lithography promo [EXTERNAL]
    • You probably won’t be surprised to learn that computer science can now also help improve the creation of computer chips. Computational lithography (literally ‘stone writing’) improves the resolution needed to etch the design of these tiny components onto the wafer thin silicon, using ultraviolet light (photoglithography = ‘stone writing with light’). Here’s a promotional video from ASML about computational lithography.

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To be (CEO) or not to be (CEO)

Just because you start a start-up doesn’t mean you have to be the boss (the CEO) running the company… Hamit Soyel didn’t and his research-based company, DragonFlyAI is flourishing.

Hamit’s computer science research (with Peter McOwan) at Queen Mary concerns understanding human (and animal) vision systems. Building on the research of neuroscientists they created computational models of vision systems. These are just programs that work in the way we believe our brains process what we see. If our understanding is correct then the models should see as we see. For example, one aspect of this is how our attention is drawn to some things and not others. If the model is accurate, it should be able to predict things we will definitely notice, and predict things we probably won’t. It turned out their models were really good at this.

They realised that their models had applications in marketing and advertising (an advert that no one notices is a waste of money). They therefore created a startup company based on their research. Peter sadly died not long after the company was founded leaving Hamit to make it a success. He had a choice to make though. Often people who start a startup company set themselves up as the CEO: it is their company so they want control. To do this you need good business skills though and also to be willing to devote the time to make the business a success. You got to this point though because of your technical and creative skills,

When you start a company you want to make a difference, but to actually do that you need a strong team and that team doesn’t have to be “behind” you, they can be “with” you – after all the best teams are made up of specialists who work to their strengths as well as supporting and working well with each other. Perhaps your strengths lie elsewhere, rather than in running a business,

With support from Queen Mary Innovations who helped him set up DragonflyAI and have supported it through its early years, Hamit decided his strengths were in the creative and technical side of the business, so he became the Chief Scientist and Inventor rather than the CEO. That role was handed to an expert as were the other senior leadership roles such as Marketing and Sales, Operations and Customer Success. That meant Hamit could focus on what he did best in further developing the models, as well as in innovating new ideas. This approach also gives confidence to investors that the leadership team do know what they are doing and that if they like the ideas then the company will be a success.

As a result, Hamit’s business is now a big success having helped a whole series of global companies improve their marketing, including Mars and Coca-Cola. DragonflyAI also recently raised $6m in funding from investors to further develop the business.

As Hamit points out:

By delegating operations to a professional leadership team, you can concentrate on areas you truly enjoy that fuel your passion and creativity, ultimately enhancing your fulfilment and contribution to your company and driving collective success.”

To be the CEO or not be the CEO depends on your skills and ambition, but you must also think about what is best for the company, as Hamit has pointed out. It is important to realise though that you do not have to be the CEO just because you founded the company.

Paul Curzon, Queen Mary University of London,

based on an interview between Hamit Soyel and Queen Mary Innovations

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The Blue Planet?

A Blue planet
Image by spieriz from Pixabay (cropped)

How much should we change the world to make it easier for our machines to work?

Plant scientists have spotted a problem they can solve. Weeding robots are finding it difficult to weed. It is a hard problem for them. All those weeds look just like the real crop which they aren’t supposed to destroy. So the robots are pulling up the wrong things. What is a robot to do? Should we make it easy for them?

Plant Scientists have seen a need for their technology which is looking for solutions any where it can. Robots are good at distinguishing colour. That is easy. So why not just genetically modify weeds to be blue. This is possible as there are already lots of genes causing blueness in plants (think blueberries). Problem solved. The robots then won’t get it wrong again and the crops are safe.

What could possibly go wrong? Well, to work the genes will need to be spread wildly and perhaps they could escape and get into our crops or other plants that are just there to be plants, or just plants in the food chain, We could end up with a blue planet a bit like the red one the martians brought int he War of the Worlds. Alternatively, evolution might step up and continually produce mutant weeds that subverted that gene, given that gene killed them. Perhaps all the problems can guarantee to be avoided, though the wise person does not bet against natural selection finding a way round problems presented to it in the long term.

Isn’t it time we learnt our lesson and stopped changing the planet to make our machines lives easier? Of course we have been doing that for a long time – think of all the roads scarring the countryside so cars work or rails so trains work. Perhaps we should think more about the needs of the planet as well as of people, rather than the needs of our machines when innovating, especially when undoubtedly eventually (if we don’t destroy ourselves first) we will have machines clever enough to work it out.

There are always lots of ways of solving problems and it is important to think about the planet now not just our machines. Perhaps robots should just not weed until they can do it without us having to change the problem (and the planet) for them so they can!

Paul Curzon, Queen Mary University of London

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Mixing Research with Entrepreneurship: Find a need and solve it

A mixing desk
Image by Ida from Pixabay

Becoming a successful entrepreneur often starts with seeing a need: a problem someone has that needs to be fixed. For David Ronan, the need was for anyone to mix and master music but the problem was that of how hard it is to do this. Now his company RoEx is fixing that problem by combining signal processing ans artificial intelligence tools applied to music. It is based on his research originally as a PhD student

Musicians want to make music, though by “make music” they likely mean playing or composing music. The task of fiddling with buttons, sliders and dials on a mixing desk to balance the different tracks of music may not be a musician’s idea of what making music is really about, even though it is “making music” to a sound engineer or producer. However, mixing is now an important part of the modern process of creating professional standard music.

This is in part a result of the multitrack record revolution of the 1960s. Multitrack involves recording different parts of the music as different tracks, then combining them later, adding effects, combining them some more … George Martin with the Beatles pioneered its use for mainstream pop music in the 1960s and the Beach Boys created their unique “Pet Sounds” through this kind of multitrack recording too. Now, it is totally standard. Originally, though, recording music involved running a recording machine while a band, orchestra and/or singers did their thing together. If it wasn’t good enough they would do it all again from the beginning (and again, and again…). This is similar to the way that actors will act the same scene over and over dozens of times until the director is happy. Once happy with the take (or recording) that was basically it and they moved on to the next song to record.

With the advent of multitracking, each musician could instead play or sing their part on their own. They didn’t have to record at the same time or even be in the same place as the separate parts could be mixed together into a single whole later. Then it became the job of engineers and the producer to put it all together into a single whole. Part of this is to adjust the levels of each track so they are balanced. You want to hear the vocals, for example, and not have them drowned out by the drums. At this point the engineer can also fix mistakes, cutting in a rerecording of one small part to replace something that wasn’t played quite right. Different special effects can also be applied to different tracks (playing one track at a different speed or even backwards, with reverb or auto-tuned, for example). You can also take one singer and allow them to sing with multiple versions of themselves so that they are their own backing group, and are singing layered harmonies with themselves. One person can even play all the separate instruments as, for example, Prince often did on his recordings. The engineers and producer also put it all together and create the final sound, making the final master recording. Some musicians, like Madonna, Ariana Grande and Taylor Swift do take part in the production and engineering parts of making their records or even take over completely, so they have total control of their sound. It takes experience though and why shouldn’t everyone have that amount of creative control?

Doing all the mixing, correction and overdubbing can be laborious and takes a lot of skill, though. It can be very creative in itself too, which is why producers are often as famous as the artists they produce (think Quincy Jones or  Nile Rogers, for example). However, not everyone wanting to make their own music is interested in spending their time doing laborious mixing, but if you don’t yet have the skill yourself and cant afford to pay a producer what do you do? 

That was the need that David spotted. He wanted to do for music what instagram filters did for images, and make it easy for anyone to make and publish their own professional standard music. Based in part on his PhD research he developed tools that could do the mixing, leaving a musician to focus on experimenting with the sound itself.

David had spent several years leading the research team of an earlier startup he helped found called AI Music. It worked on adaptive music: music that changes based on what is happening around it, whether in the world or in a video game being played. It was later bought by Apple. This was the highlight of his career to that point and it helped cement his desire to continue to be an innovator and entrepreneur. 

With the help of Queen Mary, where he did his PhD, he therefore decided to set up his new company RoEx. It provides an AI driven mixing and mastering service. You choose basic mixing options as well as have the ability to experiment with different results, so still have creative control. However, you no longer need expensive equipment, nor need to build the skills to use it. The process becomes far faster too. Mixing your music becomes much more about experimenting with the sound: the machine having taken over the laborious parts, working out the optimum way to mix different tracks and produce a professional quality master recording at the end.

David  didn’t just see a need and have an idea of how to solve it, he turned it into something that people want to use by not only developing the technology, but also making sure he really understood the need. He worked with musicians and producers through a long research and development process to ensure his product really works for any musician.

– Paul Curzon, Queen Mary University of London

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Avoiding loneliness with StudyBuddy

A girl in a corner of a red space head on knees
Lonely Image by Foundry Co from Pixabay

University has always been a place where you make great friends for life. Social media means everyone can easily make as many online friends as they like, and ever more students go to university, meaning more potential friends to make. So surely things now are better than ever. And yet many students suffer from loneliness while at university. We somehow seem to have ever greater disconnection the more connections we make. Klara Brodahl realised there was a novel need here that no one was addressing well and decided to try to solve it for the final year project of her computer science degree. Her solution was StudyBuddy and with the support of an angel investor she has now set up a startup company and is rolling it out for real.

A loneliness epidemic

In the digital age, university students face an unexpected challenge—loneliness. Although they’re more “connected” than ever through social media and virtual interactions, the quality of these connections is often shallow. A 2023 study, for example, found that 92% of students in the UK feel lonely at some point during their university life. This “loneliness epidemic” has profound effects, contributing to issues like anxiety, depression, and struggling with their programme.

During her own university years, Klara Brodahl  had experienced first hand the challenge of forming meaningful friends in an environment where everyone seemed socially engaged online but weren’t always connected in real life. She soon discovered that it wasn’t just her but a shared struggle by students across the country. Inspired by this, she set out to write a program that would fill the void in student’s lives and bridge the gap between studying and social life.

Combatting loneliness in the real world

She came up with StudyBuddy: a mobile app designed to combat student loneliness by supporting genuine, in-person connections between university students, not just virtual ones. Her aim was that it would help students meet, study, and connect in real time and in shared spaces. 

She realised that technology does have the potential to strengthen social bonds, but how it’s designed and used makes all the difference. The social neuroscientist John Cacioppo has pointed out that using social media primarily as a destination in its own right often leaves people feeling distant and dissatisfied. However, when technology is designed to serve as a bridge to offline human engagement, it can reduce loneliness and improve well-being. StudyBuddy embodies this approach by encouraging students to connect in person rather than trying to replace meeting face-to-face.

Study together in the real world

Part of making this work is in having reasons to meet for real. Klara realised that the need to study, and the fact that doing this in groups rather than alone can help everyone do better, could provide the excuse for this. StudyBuddy, therefore, integrates study goals with social interaction, allowing friendships to form around shared academic interests—an ideal icebreaker for those who feel nervous in traditional social settings.

The app uses location-based technology to connect students for co-study sessions, making in-person meetings easy and natural. Through a live map, students can see where others are checked in nearby at study spots like libraries, cafes, or student common areas. They can join existing study groups or start their own. The app uses university ID verification to help ensure connections are built on a trusted network.

From idea to startup company

Klara didn’t originally plan for StudyBuddy to become a real company. Like many graduates, she thought starting a business was something to perhaps try later, once she had some professional experience from a more ‘normal’ graduate job. However, when the graduate scheme she won a place on after graduating was unexpectedly delayed, she found herself with time on her hands. Rather than do nothing she decided to keep working on the app as a side project. It was at this point that StudyBuddy caught the attention of an angel investor, whose enthusiasm for the app gave Klara the confidence to keep going.

When her graduate scheme finally began, she was therefore already deeply invested in StudyBuddy. Trying to manage both roles, she quickly realised she preferred the challenge and creativity of her startup work over the graduate scheme. And when it became impossible to balance both, she took a leap of faith, quitting her graduate job to focus on StudyBuddy full-time—a decision that has since paid off. She gained early positive feedback, ran a pilot at Queen Mary University of London, and won early funding for investors willing to invest in what was essentially still an idea, rather than a product with a known market. As a result StudyBuddy has gradually turned into a useful mission-driven platform, providing students with a safe, real-world way to connect.

Making a difference

StudyBuddy has the potential to transform the university experience by reducing loneliness and fostering authentic, in-person friendships. By rethinking what engagement in the digital age means, the app also serves as a model for how technology can promote meaningful social interaction more generally. Klara has shown that with thoughtful design, technology can be a powerful tool for bridging digital and physical divides, creating a campus environment where students thrive both academically and socially. Her experience also shows how the secret to being a great entrepreneur is to be able to see a human need that no one else has seen or solved well. Then, if you can come up with a creative solution that really solves that need, your ideas can become reality and really make a difference to people’s lives.

– Klara Brodahl, StudyBuddy and Paul Curzon, Queen Mary University of London

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Why is your Internet so slow?

Red and white lights of cars on a a motorway at night
Image from Pixabay

The Internet is now so much a part of life that, unless you are over 50, it’s hard to remember what the world was like without it. Sometimes we enjoy really fast Internet access, and yet at other times it’s frustratingly slow! So the question is why, and what does this have to do with posting a letter, or cars on a motorway? And how did electronic engineers turn the problem into a business opportunity?.

The communication technology that powers the Internet is built of electronics. The building blocks are called routers, and these convert the light-streams of information that pass down the fibre-optic cables into streams of electrons, so that electronics can be used to switch and re-route the information inside the routers.

Enormously high capacities are achievable, which is necessary because the performance of your Internet connection is really important, especially if you enjoy online gaming or do a lot of video streaming. Anyone who plays online games would be familiar with the problem: opponents apparently popping out of nowhere, or stuttery character movement.

So the question is – why is communicating over a modern network like the Internet so prone to odd lapses of performance when traditional land-line telephone services were (and still are) so reliable? The answer is that traditional telephone networks send data as a constant stream of information, while over the Internet, data is transmitted as “packets”. Each packet is a large group of data bits stuck inside a sort of package, with a header attached giving the address of where the data is going. This is why it is like posting a letter: a packet is like a parcel of data sent via an electronic “postal service”.

But this still doesn’t really answer the question of why Internet performance can be so prone to slow down, sometimes seeming almost to stop completely. To see this we can use another analogy: the flow of packet data is also like the flow of cars on a motorway. When there is no congestion the cars flow freely and all reach their destination with little delay, so that good, consistent performance is enjoyed by the car’s users. But when there is overload and there are too many cars for the road’s capacity, then congestion results. Cars keep slowing down then speeding up, and journey times become horribly delayed and unpredictable. This is like having too many packets for the capacity in the network: congestion builds up, and bad delays – poor performance – are the result.

Typically, Internet performance is assessed using broadband speed tests, where lots of test data is sent out and received by the computer being tested and the average speed of sending data and of receiving it is measured. Unfortunately, speed tests don’t help anyone – not even an expert – understand what people will experience when using real applications like an online game.

Electronic engineering researchers at Queen Mary, University of London have been studying these congestion effects in networks for a long time, mainly by using probability theory, which was originally developed in attempts to analyse games of chance and gambling. In the past ten years, they have been evaluating the impact of congestion on actual applications (like web browsing, gaming and Skype) and expressing this in terms of real human experience (rather than speed, or other technical metrics). This research has been so successful that one of the Professors at Queen Mary, Jonathan Pitts, co-founded a spinout company called Actual Experience Ltd so the research could make a real difference to industry and so ultimately to everyday users.

For businesses that rely heavily on IT, the human experience of corporate applications directly affects how efficiently staff can work. In the consumer Internet, human experience directly affects brand perception and customer loyalty. Actual Experience’s technology enables companies to manage their networks and servers from the perspective of human experience – it helps them fix the problems that their staff and customers notice, and invest their limited resources to get the greatest economic benefit.

So Internet gaming, posting letters, probability theory and cars stuck on motorways are all connected. But to make the connection you first need to study electronic engineering.

– Paul Curzon, Queen Mary University of London.

This article was originally published on the CS4FN website. It was also published in our 2023 Advent Calendar.

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Global Entrepreneurship Week

Global Entrepreneurship Week is in November each year and to celebrate we’ve put together a portal resource page on Tech Entrepreneurs to inspire you. It features, for example, Jacquie Lawson (who created a digital greetings card enterprise), Freddie Figgers (who runs the first Black-owned telecoms company in the US), Dragonfly AI (a computer vision company founded at QMUL) and Sophie Wilson (who designed the chip for the BBC Micro). We will keep adding more people and companies. There are also links to careers resources.

– Jo Brodie, Queen Mary University of London

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