SICSA Phd Conference 2022

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Last week I presented my poster at SICSA PhD Conference 2022 held at Glasgow Caledonian University. I was able to meet other PhDs at different stages of their projects, which was the perfect opportunity to reflect on my work and explore others’ doctoral journeys. Have fun!

The SICSA Conference

Every year the Scottish Informatics and Computer Science Alliance (SICSA) organises a general PhD conference open to Scottish universities. Students are able to display their work during the poster session or even take part in a dissertation competition. The organizers didn’t come up short on making the event more engaging and interesting by arranging insightful guest lectures. This year organizers added a Reverse Viva session where participants could ask questions about the PhD thesis to its authors. Additionally in this 2-day event, delegates were able to attend various workshops on Intellectual property; Getting the job you want after finishing your PhD; Post pandemic computing science education; Bricolage for blended spaces and Early career research.

Poster Session

It was my first attempt at a scientific poster. Seeing the massive amount of examples that almost always look the same, I came up with the new layout. Unfortunately, the layout seemed to be the sole strength of the poster since I was unable to win the competition lol. However, I managed to draw the attention of other PhD researchers, which gave me an opportunity to discuss my research and answer some questions which was a fun experience. In the past, I saw little to no purpose in events like that, however, this year I tried to get rid of any prejudices and try my best. It was not always smooth, some people genuinely liked the project and were keen on engaging in a conversation, whereas for some it wasn’t that interesting (I don’t blame you!).

My Poster (better when zoomed in!)

The poster session allowed me to meet some new interesting PhDs and explore some of their research as well. I learned about what my fellow peers work on now, which gives me a better idea of potential collaborators for future projects. One thing I found odd was the way the posters were judged. The session was announced to all participants after one of the guest lectures, which many students planning on checking out the posters for the first time. Together with the judges moving around and trying to assess each of the posters, this just resulted in a slight mess. Probably it would be beneficial in the future for the judges to remain anonymous, it would put less pressure on the presenters and also lead up to more natural discussions.

Guest Speakers

Although there were many keynote speakers, my favourite sessions were delivered by Kyle White and Stewart Whiting, who talked about their journeys in launching a start-up during a PhD. It was very inspirational to see how many things can be achieved in such a long time. I was amazed to see how with persistence and motivation you can achieve huge success (and be able to finish PhD, ha!). Their stories gave me a fresh perspective on my future plans and also some ideas, but too soon to share anything yet.

Workshop session: Bricolage for blended spaces

To be entirely frank, the only reason I attended this workshop was that I had no idea what bricolage was. Luckily my amazing colleagues from Edinburgh Napier University, Emilia Sobolewska and Callum Egan were happy to explain the concept. To keep it brief, bricolage is the methodological approach that involves applying multiple different methodologies for the same problem and testing them all at the same time. Together with the concept of blended spaces, where a physical environment and a virtual environment are deliberately integrated, these two make quite a strong case for project design process. If you are interested more in how bricolage can be used in research, I recommend Emilia’s piece on Tailoring methodological bricolage to investigate non-discretionary use of digital technology.

General thoughts

The SICSA PhD conference this year was great! It was good to see all of us PhDs together, discussing our research, sharing stories with each other and having fun doing so! The venue was great and the fact that such a big event remains free to attend says a lot about SICSA. I believe that initiatives like this benefit the research community and ensure that more people will choose to pursue their own research in the future. Looking forward to the next year’s edition and can’t wait to discuss my research progress with some of the people I met this year!

 

Edinburgh – Summer Institute in Computational Social Science (SICSS)

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I recently participated in my first summer school as a PhD student, held at Edinburgh University. It was an amazing experience and I would like to share some of it with you, enjoy!

What is SICSS?

Summer Institute in Computational Social Science (SICSS) is an initiative that started in 2017 but since then it grew to more locations and it is now running across nearly all continues (not in Antarctica yet!).  The main goal of the institutes is to provide a bridge between social and data science. This will allow the researchers to work with large amounts of digital data. Together with the information delivered by advanced researchers, the institutes create an excellent learning environment.

The Format

Participants

During this 2-week summer school, a group of 24 people were selected. We all came from different backgrounds and the only common thing was our interest in computational social science (CSS), although, most of us were PhDs.

Schedule

During the first week, we attempted a series of lectures/workshops on various data analysis methods including:

  • Reproducible workflows, data carpentry
  • APIs, Web scraping, digital research methods
  • Networks and network analysis
  • Social networks and simulations
  • Computational text analysis and natural language processing
  • Machine learning and prediction

Because lectures were delivered by experienced academics, we were able to discuss the application of such techniques in their research. This offered a chance to understand the given concept beyond its theoretical description. Furthermore, by utilizing them on the actual data, we could explore the limitations and strengths of learned methods.

Guest Speakers

Each day of learning was preceded by a guest lecture, from speakers who work or are associated related to the CSS field. I particularly enjoyed the talk delivered by Anita Gohdes on her research paper entitled: Distract and divert: How world leaders use social media during contentious politics. In her study, she explored the digital communication strategies of world leaders, using their social media interactions. It was interesting to see the differences in practices among various political ideologies.

Another great talk was delivered by Benjamin Bach who talked about the various visualisation techniques and tools available for the researchers. We learned how to appropriately choose the visualisation technique so that it would achieve the desired outcome. Benjamin is responsible for running a VisHub, where he works toward more understandable visualisation solutions for all.

Group Work

The second week consisted of group work on any topic related to CSS. Our group decided to explore the position of US Twitter users on gun control from an analysis of tweet text and mentions and retweets network. During this 4-day project, we were able to use some of the methods learned during week 1 but also improve our team-working skills. The time constraints prevented us from performing a detailed investigation into the topic. However, we managed to gather a collection of 1000 tweets related to gun control and based on our annotations (pro-/against-gun control) we performed our analysis. With some of the insightful findings regarding the retweet networks (very clustered) and promising results from trained classifiers (f1 score of 80% in the best case), I believe we utilized the given time well.

Furthermore, this task showed me that working in a group of talented and motivated researchers is not an easy task. Don’t get me wrong, I worked on a number of group projects before but never at the doctoral level. The experience was both amazing and challenging at the same time, we spend some time working out the dynamics of our group, however, in the end, we were able to fulfil most of our initial goals and were happy with how things went. Special thanks to my amazing group members: Alisha Kelkar, Bruno Schmidt-Feuerheerd, François t’Serstevens, Alessio Scopelliti and awesome teaching assistant Aybuke Atalay for guiding us in the right direction. 

Social Aspect

Apart from the high standard of teaching provided by the SICSS, another great thing about this summer school was the social aspect of it. The organizers did an amazing job at not only planning the summer school but also at ensuring that we would connect as a group by organizing social events. While during the day everyone stayed focused on the learning part after the classes ended we were able to get to know each other a little bit more, which made the whole experience more fulfilling. Here I would like to thank Christopher Barrie, for organizing the whole event and being a great teacher!

SICSS- Edinburgh 2022 cohort in their natural habitat

To Sum up

The SICSS- Edinburgh 2022 exceeded my expectations. Beginning with the fact that it it fully funded by external sponsors, this summer school offers a great opportunity for researchers from all different backgrounds to learn about how digital data can be effectively used in various domains. It was the best experience, as a PhD student, so far and it allowed me to develop as a researcher and also grow as a person. Through the mix of classes and various situations, I have gained a fresh perspective on my own research and was able to improve my teamwork abilities, which I believe are crucial in both life and work. SISCA runs every year in various locations, I strongly recommend to anyone to apply and see for themselves how awesome it is! However, make sure you do that early in the year as the institutes are very popular.

Navigating Wellbeing & Reimagining Resistance During the PhD (SGSSS Symposium)

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Recently I participated in the SGSSS student-led symposium on Navigating Wellbeing & Reimagining Resistance During the PhD. The event was held at the University of Strathclyde in Glasgow and gathered over 30 doctoral students associated with the Scottish Graduate School of Social Science. As one of the participants, I would like to talk about my experience and share with you a couple of things I learned.

What was the Event About?

For many of us, it is hard to find the right work-life balance. While keeping mentally and physically well is important for everybody, the doctoral study requires (usually 3 to 4 years long) commitment. Therefore, a suitable working environment is essential. By ensuring our wellbeing, we can utilize available resources (such as time) to their fullest, thus allowing us to excel at our research and other academic or professional commitments. The event was organised by SGSSS student reps, including a colleague from my research institute, Katherine Stephen.

How the Event Looked Like?

Before going to the event I was not sure what to expect. I thought it will look similar to a conference or a research group meeting, with representatives and speakers giving talks about how to improve your wellbeing during a PhD. While partly, it was like that 😀, the whole event was very informal, which gave us (students) a chance to meet each other and discuss relevant matters in a casual atmosphere. Everyone quickly started talking to each other and then we all proceeded to the main room which was followed by a couple of networking exercises.

Living Libraries Sessions

Once everybody got to know each other roughly, the Living Libraries session began. The session consisted of 7 different tables and for each one, a single book was assigned. The book was a person facilitating the conversations and serving as an expert on a topic. Some of the themes included: First Generation Academic; Doing a PhD with a Disability or Managing Your Mental Helth During a PhD. It is worth mentioning that mentioned books, were other PhD students, hence in our conversations we could all relate to each other. I was able to explore other students’ experiences and their journeys, which opened my eyes to various challenges and opportunities available to us.

Other Sessions

Following these sessions, we had a great talk with Dr Jo Ferrie on Why are PhDs Traumatic? and Dr Maddie Breeze on Imposter Syndrome in academia. However,  I found the Reframing Failures Panel held by Dr Ashley Rogers, Dr Mhairi Mackenzie and Dr Colin Atkinson the most interesting. The speakers presented their journeys as the PhD researchers and shared useful tips on how to manage failures. The conversations were more insightful as each of the presenters had gone through different failures and found unique techniques or strategies in dealing with failures useful. Additionally, the session was followed by the Q&A which allowed us to hear their perspectives on our struggles.

What was the Best About the Event?

I personally enjoyed the earlier mentioned informal setting created by the organizers. That way we could talk about the problems we face and things we are not happy about in a safe environment. Moreover, the fact that we were able to actively discuss with each other, created a sense of unity and from my perspective facilitated more honest conversations.

Apart from that I really enjoyed the Reimagining the Future of Higher Education held by Jessica Cleary, which allowed us to reflect on the current state of HE concerning our PhD as well as academia in general in a funny yet constructive way.

Wellbeing & Resistance: Top Takeaways

For the person who never heard about the Imposter Syndrome, learning about it and realising its relation to academia was of huge help. Although, personally I never felt like an imposter, exploring the topic allowed me to reflect on some of the situations from my past and understand them better. In short, all of the symposium’s sessions were useful, while I could not relate to all of the topics raised, each one of them broaden my perspective, thus allowing me to further develop as both a PhD researcher and a person. I believe that simply listening to other people’s stories and having a chance to discuss them, gives you the opportunity to re-think some of the past situations and ultimately allow you to grow.

My Approach to Wellbeing

For me, wellbeing is having a sense of structure while remaining free. Having a proper diet, nurturing my hobbies, allocating time for work and relaxing and, most importantly, being open to new experiences and opportunities. By putting yourself through new situations you provide yourself with a fresh perspective, which might not be useful at first but it is sure to be helpful at some point in the future. It is perfectly fine and normal to feel uncomfortable when doing something new, however, getting comfortable with the uncomfortable is what ultimately will allow you to become a better researcher and a person, at least that’s what I think 😉.


I am looking forward to some of the future SGSSS sessions and hope that the pleasant and welcoming environment from this session would accompany future meetings. It was great to meet so many new people who are also pursuing PhD research and listen to experienced individuals on their approach to wellbeing at work.

 

Scottish AI Summit 2022

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Last week I participated in the first-ever Scottish AI Summit in Edinborough at the Dynamic Earth venue. It was my first experience with the conference and wanted to share my thoughts on the topics discussed as well as the event itself.

Conference Agenda

The three main topics of the summit were Trustworthiness, Ethics and Inclusivity in AI. These were a base for the discussion for eight distinct panels and 3 workshops. As it wasn’t possible to attend all of them, I had to choose which one will be the most relevant for my PhD and interests. While I could spend hours discussing all of them, I will share with you the ones that were the most insightful from my perspective.

Panel 5: Why is Explainable AI still a challenge?

Explainability in AI is becoming a very hot topic in this domain right now. Increasing access to the software and easily accessible programming packages, allowed researchers from non-programing backgrounds to employ and test machine learning models on their ideas. Furthermore, the rise of artificial neural networks has led to sophisticated methods being widely available. While they often provide superior results to traditional methods, they are associated with explainability issues, resulting from their complexity. This phenomenon is known as the ‘Black Box” model, which results often elude human interpretation. Rather than trying to explain the models, researchers focus on interpreting the results by isolating the important features. However, this approach still does not provide sufficient information on why and how the model makes the final decision, merely gives the idea of what is being considered.

Panel Discussions

Panellists consisted of AI experts from both academia and industry. While in some cases, the lack of explainability might not be an issue, for example, the Netflix recommendation algorithm, when it comes to the areas such as law enforcement or healthcare it poses a huge threat. Whether it’s academic researchers or companies, it is crucial to contextualize the AI system. Some applications might favour performance over explainability while some should not, according to one of the panellists, the importance of explainability is linked to human participation in the system. In other words, the system in which the final decisions are made by humans does not necessarily has to provide complete explainability, as human experts are only using it to aid their decisions. Another area in which explainability might not play an important role in, is the detection of rare, often catastrophic events, which otherwise wouldn’t be discovered soon enough (i.e., nuclear reactor faults).

My Views

While I can definitely agree with the latter case, in which AI is the only to only tool at the disposal of the human when it comes to lack of explainability in high-stake sectors such as law enforcement or healthcare, not being able to understand the results of the AI system, might lead to terrible consequences. For one, people might begin to rely on the system too much, and while it might provide satisfactory results at the beginning, not being able to understand its decision process, effectively does not improve the understanding of the specific disease or legal verdict. If sometime in the future the system would come out to be faulty, people would demand compensation, court judgements would have to be revoked additionally stigmatising the AI for years. I agree with panellists, that there is no “one fits all” when it comes to the approach to the explainability in AI. Each case is different, nevertheless keeping the human in the loop could limit the risks coming from lack of explainability.

Workshop 2: What does Responsible Innovation Mean to You?

The main reason I attended this workshop, was that I wanted to see how it differed from the panel discussions. To my surprise the conversations there were much more interactive, with participants having a chance to talk with each other and vote on the questions raised.

Discussion

Again people from the industry and academia were invited and in this case, I was able to explore approaches to responsibility in AI from different perspectives. It was interesting to see how aspects such as ethics, transparency, inclusion and accountability align across all of these different parties. Speakers talked about how the approach to responsibility in AI changed over time, with people now taking a more proactive approach.

My views

While I consider the aspect mentioned in the previous paragraph important to every AI system. I think that the speakers failed to discuss the issue of participation, which I believe is essential to the well functioning of the AI system. Without participation, there is no reason to work on all of the remaining as there is no one to benefit from it. While these concepts become highly interconnected later in the process, I think more has to be done to ensure people are not afraid of AI and are able to trust it, especially older generations.

General Remarks

I very much enjoyed the summit and it was amazing to meet important people from the industry and catch up on the latest developments in this area. It was also a great place to meet new people and establish new contacts that might be relevant to my research. I was able to learn about the Scottish companies working within the AI domain and the talks I participated in gave me some interesting ideas on how to proceed with my research.


All of the talks mentioned in this post along with the original recordings can be found on the summit’s website here. All you need to do is to log into their virtual platform and you will have access to all of the materials online for the next five months for free.

Rewire Annotation project

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For the past month, I was participating in Rewire’s abusive speech detection project in the role of entries annotator. Its been an amazing last couple of weeks in which I learned more about what such a project looks like from the inside and was able to meet very interesting individuals from the NLP field.

About Rewire

Rewire is a start-up company established by researchers from Alan turning Institute, Bertie Vidgen and Paul Röttger who were both involved in similar projects before deciding to start their own company. Rewire is delivering socially responsible AI solutions for online safety. Their proprietary algorithm is trained for various problems ranging from hate speech to sexism detection or in this case, abuse identification.

Why Have I participated?

I learned about this project through one of my colleagues at the Social Informatics Research Group and since it was related to my current research, I decided to apply. I wanted to learn more about what such a project looks like from the inside, explore the team’s working dynamics and what could be expected from the participants. On top of that, I was hoping to meet people from the AI field so that I could incorporate some of their expertise into my own work.

How did it go?

Throughout the whole duration of the project, I enjoyed the working environment created by the company. From the beginning, we were given a clear picture of what the whole project will look like and what is expected from us.  I was surprised by how much our input was considered in the entire annotation process. We were given a chance to discuss harder cases and could always count on help from the expert annotators, providing explanations on top of the answers. That way the entire process run smoothly and deadlines could be easily met. I have to say that the Rewire team was really flexible when it came to the working conditions which made working for them an amazing experience. Moreover, we were also given a chance to discuss the results and any doubts during weekly meetings which gave the team the feeling of being involved in the process, thus resulting in greater productivity (in my opinion).

During the project, I learned how severe the issue with abuse on the internet really is. After the first batch of entries, I immediately understood the importance of such work. The issue is very severe and users are getting more sophisticated in creating their abusive entries in a way that the traditional safety systems won’t work. Working towards something with a positive impact on the agenda gave me the motivation to excel at the job.

What Have I learned?

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First of all, I explored how such a project progresses. If I were to use a similar method in the future for my research or even my PhD I now know where to start, what tools can I use for the communication and what is the remuneration for such task. The knowledge I gained will definitely help me set up a similar task in the future and made me more confident about working in a large team environment.
As for my second objective, I was able to establish contact with the company’s CTO Paul Röttger, who was keen enough to organise a meeting to discuss the algorithms used by a company as well as share some insights regarding the project itself as well as his personal experience in the field. Hopefully, I would be able to discuss my work with him and will be given an opportunity to hear feedback from the experienced AI expert. I want my research to be most impactful for the LM experts, policymakers and most importantly job seekers. To do that I need to ensure that all of the inefficiencies are addressed accordingly and that the methods I use will lead to the utmost results. Having a chance to speed up with someone with this experience in such early stages of research is extraordinary and likely to improve the entire process.

Thank you Rewire for such a great experience and looking forward to your future projects!

 

How to Read Machine Learning Papers

The Importance of Literature Review

The literature review is an essential part of every research. Identifying the potential areas of development, limitations in the current research and most importantly, gaining the essential topic knowledge, are the key reasons for the high importance of literature review. While many papers are easily absorbable even by people from outside the area of expertise, others with their domain-specific jargon can prove difficult to digest, even by the people from within the field.

In my case, it is usually the ML papers that I have a hard time understanding. With their high association to the mathematic theory and complex explanation of the models applied, they usually require multiple reads to fully comprehend. Based on the information from other blogs and websites I managed to develop a system that facilitates my ML paper’s reading and understanding process.

Why ML Papers Can Be Hard to Understand?

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The main reason behind the “challenging” nature of many ML papers is possibly their interdisciplinary nature. To deliver an effective ML model it is necessary to consider statistics, mathematics, programming and in some cases economics and finance theory. Given their complex nature, it is normal for them to be difficult to understand. Therefore, the main recommendation for reading ML papers is:

Do not get frustrated if you can’t grasp all of the concepts from the first read

That is the most important part of reading every paper, however, in my opinion, crucial when it comes to understanding ML papers and using my guide.

Secondly:

Take Regular breaks at least 5 minutes for 1h of work

It is crucial to give your brain rest,  especially when working on a computer and looking at the screen all the time. Make yourself a coffee, meditate, sit down, everything that would make you stand up from your chair and change the environment (even for a while).

Summary for Reading ML Papers

The guide you can see below is a summary of Andrew NG lecture on how to read the ML papers with the addition of my personal tips and other information I have found online on the topic (the rest of the sources are listed in the references section). Nevertheless creating a “manual” on how to read ML papers might prove useful to any of you, hence I decided to collect all information in one place in a form of a structured guide.  

First Pass- Title -> Abstract -> Graphs

Reading every ML paper should begin with identifying the Context of the research, which can be easily inferred from the paper’s title and abstract. Moreover, it is well documented that people can process images much faster than text and more importantly we are more likely to remember information stored within pictures. That said when it comes to ML papers, understanding the familiarizing with pictures can complement the information from the abstract.

Second Pass- Introduction and Conclusion (ONLY!)

The main idea behind skipping the rest of the text and focusing on the introduction and conclusion is that they are likely to contain all of the information on the author’s Motivation and Results. Understanding those two is essential to comprehend the tools and techniques used in an article. In my experience, they often made novel and complex concepts, easier to grasp providing the reader with a bigger picture regarding the whole idea of the paper. Although they usually contain less domain-specific jargon, they mention crucial to the topic concepts and terms. You can easily spot challenging expressions and learn about them before undertaking the whole document.

However, remember if you are new to a field, even Introduction and a Conclusion might require some more time to understand.

Third Pass- Read the Whole Article

Provided you have already gone through the first two steps, you should have sufficient knowledge to understand how authors have Implemented their methodology. Very often ML papers (the good ones should) will be accompanied by the mathematics equations, to explain the basis of the theory/model. In my experience, if you read the mathematics formula couple of times and you have a hard time understanding it, just skip it (for now). Do not get stuck and try to read them over and over again, in the hope of finally understanding the concept. By progressing with the text, you are more likely to understand the maths behind it, so keep reading and don’t get frustrated with complex mathematics concepts.

Fourth Pass- Read the Article Again 😉 

Are you still there? Good now go over the thing again! As harsh it might sound it is often necessary to read the article couple of times to fully understand it. However, bear in mind that if there are still areas that you have a hard time understanding, it is better to skip them. It is very likely for you not to understand all of the concepts mentioned in the article. As long as you feel comfortable with what authors did and how they did it, it should be enough to understand the article. It is all part of the learning process and we are unable to comprehend everything at one go. Try exploring other authors’ work or related research to learn about what concepts are crucial and which aren’t anymore.

What about the Code?

Some ML papers include the code which authors have used. In some cases, authors put their code on Github sometimes including thorough explanations of their programming choices. If you are interested in using the authors’ approach, you can download the code from the linked repository and try to run it yourself or recreate it using the methods you know.

Conclusion

Hoepfully now, reading ML articles will be less of a struggle and more of a joy to you. Remember that ML is a vastly developing discipline, hence it can initially be confusing and hard to understand. However, I hope that this guide will make the whole process more pleasant.


References:

Into the Metaverse- tomorrow’s future or just a gimmick?

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What is Metaverse?

As probably most of you already know, Facebook changed its name to Meta during the Fall of last year. Along with the new brand name, the company introduced a new service called Metaverse, which in the words of Philip Rosedale, the creator of the online game Second Life, is three-dimensional internet populated with people. Nevertheless, when it comes to Meta’s metaverse, the simple descriptions just won’t cut it anymore. The project evolved into an entirely new industry, which is now gaining more and more interest from the private and public sectors.

Why is it Special?

While it still remains a mystery for common people, entrepreneurs from all kinds of trades begin to look into ways how they can monetize from this new “thing”. Although digital real estate accompanied various metaverses for quite a while now, since Facebook decided to go all-in on the virtual world, digital estate prices saw an increase between 400% and 500%. A report by Grayscale estimated the potential value of the sector at $1 trillion dollars in the near future.

This valuation might seem crazy to you (it was to me when I found out), bear with me for a while.

For some virtual world is as important as the real one, with the Covid-19 facilitating the move into the online channels, setting up some offices in the virtual world seem like a natural next step. The largest investment bank JP, recently published a white paper, illustrating the hype versus the reality. The company is now looking into new opportunities and potential ways to develop the platform in terms of commercial perspectives. Disney is another firm, already taking steps in ensuring their future Metaverse presence will be profitable and successful. This entertainment giant recently appointed an executive to specifically lead the entertainment giant’s strategy for the metaverse. Even China started to highly invest in Metaverse’s constituents.

Nevertheless,  as with every novel technology, this concept is very risky. Moreover, being crypto-based it carries some degree of volatility from cryptocurrencies.

What’s the Future of Metaverse?

Well… If only I knew. Jokes aside, with the company whose values are: “Move fast”, “build awesome things” and “live in the future”, we can be sure that the road to success will be a bumpy one. However, seeing the initial interest and the relatively fast adoption on a commercial level, I believe there is a huge potential in this service, accompanied by countless paths of development. Estate market, advertising, banking, entertainment and who know what more will see its virtual equivalent.


For More Information on Metaverse visit this page.

Innovation Voucher Project

What is this about?

In December last year, I got involved in an innovation voucher project with a risk assessment company, Trubshaw Cumberlege. Their main area of operation is currently South Sudan and their evaluation processes are now expert-based.  With the future aim to expand the service into other regions, they were looking into ways how their process can be automated and more standardized. Together with a team of academics from Edinburgh Napier University, Dr Dimitra Gkatzia and Peter Cruickshank, we investigated their current approach, in an attempt to find possible development paths.

My Experience

It was my first project that was commissioned by a company and required cooperation with the project director (Sean Kelly), and the company’s tech team. From the beginning, I was quite anxious as this was completely new to me and wasn’t sure what to expect. However, with the project progressing I gained more confidence in my work and begin to understand the working dynamics of such project. It provided me with the necessary knowledge on how to function in a professional environment, the knowledge which I will definitely use in my future career. Overall, I would say that I got lucky with the coherent and communative project director, helpful teammates and my general fascination with the topic. While I understand that my experiences in the future might differ from this one, I feel more prepared for challenges ahead and more confident to learn from them.

What Have I learned?

The idea of the project was to analyse the text data collected from the news reports, then based on the company’s methodology try to extract the valuable information from raw text. The rough project plan can be seen below:

At this point, we exhausted all of the time attributed to this project and are currently working on a report summarizing all of our findings. As this was my first real-world task that required using NLP I learned a lot, especially in the area of language models, about which I will definitely make a post in the future :). Although the results at this point weren’t satisfactory enough for the model to be implemented into the company risk assessment processes straight away, they bring the firm one step closer to the fully working system in the future and provide a basis for further research into the topic.


Check out Trubshaw’s website for more information on the company. 

Baby Steps- How is going so far?

How did it all start?

After finishing my masters I was ready to start a job as a data analyst, didn’t put too much thought into this, just felt like a natural next step after 4 years of studying. It was during the 21′ summer when I received an email from my current project leader Dr David Brazier, talking about the opportunity for a PhD research in Enhancing the provision of Labour Market Intelligence through Machine Learning. It immediately caught my attention and I begin to think how, given my current skill set and knowledge, I would be of fit for this project. I have to say the more I looked into this, the more it became apparent that it is the right development path for me. I realized that doing my own research will not only be more fulfilling but also it will be much more in line with who I am as a person.

How is it going?

My first three months were a great experience, I have met a lot of new people, many of them talented academics and I could not have imagined a better PhD team. While there is still a long way ahead of me, I have a feeling that I made the right decision and feel good where I am right now in my life. I enjoy the amiable atmosphere created by the Centre for Social informatics, especially the degree to which researchers are sharing ideas, information on seminars and conferences, it feels almost like we are all working towards the common goal, which is great! Although it was three months, I already participated in multiple insightful seminars, guest lectures and workshops, and even took part in an innovation voucher (later on that in future posts) which wouldn’t be possible without the help and guidance from my PhD team. Overall it is a great building experience for me as a person and independent researcher and I am looking forward to what the future will bring.