Princeton University

Samsung Research America (SRA) has joined Princeton University‘s new NextG Corporate Affiliates program, reports Telecoms News. The program brings together industry and academia for the sake of research in 6G wireless networks and related technologies.

“The best academic research to help realise the promise of future wireless networks is informed by close interactions between faculty and industry technology leaders. Our NextG program aims to foster those deep collaborations around wireless technology, as well as policy, that are required to drive meaningful innovation and global leadership in an era of rapid change”, said Andrea Goldsmith, Dean of Princeton’s School of Engineering and Applied Science.

The NextG initiative was launched last year at Princeton’s School of Engineering and Applied Science and aims to increasingly advance innovation in networking, sensing and other key areas that will be the foundation of next-generation data infrastructure.

“We encourage Princeton’s researchers to continue innovating and develop potentially disruptive 6G technologies,” said Dr Charlie Zhang, SVP at SRA.

With SRA chairing key 6G standardization groups, the company is now in a position to quickly translate Princeton’s insights into broader ecosystem developments.

With the new partnership, Samsung aims to take wireless networks into a new era in which 6G promises significantly higher speeds, lower latency and expanded opportunities for new mobile applications.

Read more:
1. GitHub Opens New Group for AI-Focused Open Source Projects
2. Voltron Data Releases Ibis 8.0, Developers Will Write Code Only Once
3. CEO’s Amazon Prime: “I Have no Plans to Adjust the Prime Team”

Top Ways to Save Your Career

Expand your horizons
In a rapidly changing industry like technology, adaptability is key to ensuring your relevance. Continuously upgrade your knowledge and skills and keep up with the latest trends, technologies and certifications in your field. By investing in training, you are not only increasing your competency, but also positioning yourself as a valuable asset to your organization. Expand your knowledge and explore adjacent fields in your industry. This will make you more flexible and allow you to navigate and explore new opportunities when needed.

Build a strong professional network
As you know, building and maintaining professional relationships is critical to your long-term career success. Attend relevant events and conferences where you can network with professionals, industry experts and why not potential mentors. These networking opportunities provide valuable connections and insights. Don’t underestimate the role of LinkedIn, which allows you to connect with colleagues, join industry-specific groups and participate in discussions. Active participation in online communities can expand your network and open up new opportunities.

Be confident in your abilities and skills
Demonstrating your value to the organisation is important to secure your position. Align your work with the company’s goals and communicate your progress regularly. Emphasize your accomplishments and the positive impact they have on the organization’s success. Invest in improving your soft skills, such as effective communication, teamwork and leadership abilities. These skills are highly valued in any organization and can set you apart from your colleagues.

API is a key feature of future networks to be able to offer users new real-time services that consume high bandwidth and require low latency. Cloud service providers and aggregators will play a major role.

“In the coming years there will be an explosion of new services that will impact our networks. To provide the best experience to our customers and offer them innovative services, the best connectivity is not enough; it is necessary to face this challenge with the involvement of the entire technological ecosystem. Our agreement with Microsoft is essential in this objective, which is why we are expanding our collaboration framework”.

said Mark Evans, Telefónica’s Chief Strategy & Development Officer.

Telefónica Kernel allows the creation of digital capabilities from complex telecommunications systems to develop new digital solutions that place the customer at the center, giving them full control over their data and guaranteeing privacy during their experience on all channels and devices.

Together with the company’s new generation networks, Telefónica Kernel forms the basis for advancing in the programmable networks of the future through Open Gateway and for creating new value-added services.

During the presentation with Microsoft at the Telefónica stand at MWC, Chema Alonso, Telefónica’s Chief Digital Officer, showed the first use cases that the company has developed internally and in collaboration with other companies. These are use cases based on an initial family of Telco APIs already available to promote new experiences in areas with great potential such as connectivity optimization, communications, security in financial services, as well as privacy and security. in general.

Telefónica has also agreed to incorporate the GitHub Enterprise platform, including GitHub Copilot, into its internal development process. GitHub will offer Telefónica the latest version of its services as soon as they are commercially available, and will provide the necessary services to align its technology stacks with Telefónica’s business objectives.

.NET division of software engineers at Microsoft outlined what they called “the most interesting and impactful changes in the networking field.” The updates reflect Microsoft’s belief, expressed last year by Richard Lander, project manager for the .NET team, that networks are of great importance to modern applications.

“There are also a lot of new developments and diversity of requirements across networking protocols and systems. The networking team focuses on delivering high-performance APIs, primarily for HTTP”,

Richard Lander, Program Manager on the .NET Core team.

HttpHeaders are more thread-safe in .NET 7. With .NET 6, it was easier for users to inadvertently access the headers at the same time, which could lead to the header values being duplicated, malformed or worse.

Header behavior in .NET is more intuitive, with the HttpHeaders matching thread-safety guarantees and concurrently supporting multiple reads – as long as they aren’t modified.

In addition, Microsoft has done more to support Quick UDP Internet Connections (QUIC), a transport layer tool that uses UDP as the underlying protocol and requires the use of TLS 1.3. QUIC.

“Another interesting difference from well-known transport protocols such as TCP and UDP is that it has built-in transport layer stream multiplexing. This allows for multiple concurrent, independent data streams that do not affect each other”,

Microsoft engineers write.

Microsoft brought QUIC into a .NET 5 library for internal use for its implementation of HTTP/3. With .NET 7, Microsoft is making the System.Net.Quic library public and exposing its APIs. The new API can work with such platforms as Linux, macOS, and iOS as well as Windows.

.NET 7 allows IT administrators to see more details about WebSocket protocol response calls. In addition, information about HTTP response headers and status code can contain important information when a handshake success or error occurs, such as details about the handover situation or the tokens associated with the session.

What does the creator of this idea think?

Pablo Rodriguez, who is the federal heritage minister, says the bill will “increase fairness” in the digital news market by creating a framework and contracting process through which companies like Google and Meta, which own social networking sites Facebook and Instagram, can pay media outlets.

Pablo Rodriguez stands firmly behind the belief that he is making these reforms for the sake of protecting the future of a free and independent press. He argues that it is about ensuring that Canadians have access to fact-based information and protecting the strength of their democracy.

But what does the bill actually include? 

The bill provides for the creation of a system, overseen by the Canadian Radio-television and Telecommunications Commission, which would have the power to impose administrative monetary penalties on companies that do not comply with its provisions.

Is there a solution? 

Companies can exempt themselves from the contracting process set out in the proposed legislation, known as Bill C-18, provided they have agreements with media outlets that meet certain criteria.

The VMware University Research Fund has committed to awarding a U.S. $1.5 million grant to attract top researchers and scientists to the faculty, in support of the 10-year plan signed by ETH Zurich, EPFL, and the Bulgarian government. This founding grant aims to establish and develop the INSAIT faculty, as well as attract Ph.D. & MSc students for research in in diverse areas of computer science and systems engineering, including AI, security, storage and networking.

The strategic partnership aims to attract high calibre talent to the world-class faculty and train the next generation of technology researchers and engineers

Diana Stefanova, VMware VP of Regional Site Strategy says:

“For VMware, this is an investment in new technologies research, consistent with our innovation strategy. It is also a unique opportunity to increase the pool of key talent in Central and Eastern Europe.”

In addition to the financial support, VMware’s more than 1800 engineers in Bulgaria are uniquely positioned to help support the institute with hands-on experience in cloud, networking, computing, and security. The collaboration of renowned scientists with PhD and Masters students, supported by big tech capabilities, has the potential to lead to groundbreaking findings in critical areas, such as private trustworthy AI, programmable networks and the data economy.

Raghu Raghuram, CEO, VMware, commented:

“There is an intrinsic link between innovation and talent. As an organization that has a culture of innovation, we have seen first-hand how important it is to invest and nurture great minds and ideas. This relationship with INSAIT is another example of our global commitment to developing tech talent. It is especially relevant to VMware in Bulgaria given our world-class R&D operations there, which we are keen to keep well supplied with the best minds in technology.”

The focus of the relationship is to invest in developing world-class talent. Establishing unique technological facilities helps contribute to new collaborative research, for the benefit of the entire technology ecosystem.

Prof. Martin Vechev, Chair of INSAIT Supervisory Board said:

“As a global technological leader VMware understands the importance of investing in talent and promoting inclusive economic growth and recovery. Attracting and nurturing the top minds in turn helps with the democratization of science and solving some of the hardest challenges.”

For more than 15 years the VMware Academic Program has been supporting the advancement of academic research and teaching in universities and institutions in the United States and around the world. This extension of close collaboration with institutes such as INSAIT aims to increase the diversity of systems research people, ideas, and advances across the globe.

In this latest endeavour, VMware and INSAIT will continue to drive the program’s goals of attracting high-quality and diverse talent and creating state of the art educational programs. In this way, providing attractive career perspectives and globally competitive salaries in a flourishing industry encourages top young talent and improves local economic and social opportunities.

BumbleBee can also automatically generate boilerplate user-space code for developing eBPF tools and brings a Docker-like experience to packaging an eBPF program. This allows it to plug into other OCI-image workflows for publishing and distribution.Idit Levine, founder and CEO, Solo.io. Said:

“At Solo.io, we see eBPF as a critical enabling technology that will improve application networking. We’ve been working during the last year to leverage eBPF technology with Gloo Mesh, our Istio-based service mesh offering for the enterprise.”

eBPF comes from extended Berkeley Packet Filter and is a kernel technology that can run sandboxed programs in an operating system kernel, providing a safe way to enhance the kernel with observability, networking, and security technologies.

BumbleBee also includes a CLI (command line interface) and provides tooling to automate away the rest of the experience.

In a post on Facebook, Mark Zuckerberg stated:

“Meta has developed what we believe is the world’s fastest AI supercomputer. We’re calling it RSC for AI Research SuperCluster.”

However, companies such as Microsoft and Nvidia have already announced their AI supercomputers.

In a statement which announced the news, the Meta engineers Kevin Lee and Shubho Sengupta said:

“RSC will help Meta’s AI researchers build new and better AI models that can learn from trillions of examples; work across hundreds of different languages; seamlessly analyze text, images, and video together; develop new augmented reality tools; and much more.”

They also hope that RSC will help them build entirely new AI systems that can, for example, power real-time voice translations to large groups of people, each speaking a different language, so they can seamlessly collaborate on a research project or play an AR game together.

The work on RCS began about a year and a half ago when Meta’s engineers designed the machine’s various systems such as cooling, power, networking, etc. The first half of it is already done and it is running and consists of 760 Nvidia GGX A100 systems containing 6,080 connected GPUs.

The work will be done by the end of this year. In a blog post, Meta’s engineers say that through 2022 they will be working on increasing the number of GPUs from 6,080 to 16,000 which is supposed to make the AI training performance better.

How and why did you decide to dive into the tech industry? What and/ or who has inspired you to do so?

It would be tough to point out one exact thing and say that’s exactly how it started. But if I had to do so, I would probably say it began by simple curiosity. And the way that usually goes is:

Hey, you know, what is this?

This is a computer.

Oh, how interesting!

And the… WOW, I can programme it to do what I want it to do and, and if it makes a mistake, it’s not because you know someone else’s fault, because I screwed something and that kind of combination of control and self-accountability but also the freedom. All those things that you can do with a computer can quickly get to be really addictive, so it’s really an addiction-type-of-thing. And yes, I guess that’s how it started.

Then I started doing this and it was all about getting this high and scratching this edge. It was more of a selfish endeavour in that sense when I really started loving what I do with technology but this was much later, I guess maybe five years ago. I was only doing it for myself but five years ago something happened. I ran across this interesting thing called Software-Defined Networking which was at the time, a really disruptive idea. Then I really understood that the power of these ideas can have to change everything, to challenge the status quo of the industry, I guess. And those pioneers at the time were really trying to do exactly that. Because networking is so very complicated and involves so many protocols. If you look at a diagram of protocols and want to print them out and put them on your wall, it will probably take you an entire wall. There are so many of them – hundreds of thousands and these guys were all about making things accessible for everyone else. Simplifying it so that anyone can get involved, innovate, contribute and attend. And I guess that’s when I really started gaining some perspective on things and I’ve been doing that ever since.

We’re not doing technology just for the sake of doing technology. Actually, I was kind of doing that before. Then I realised that technology is just a tool, something that everyone can use and needs to access.

What have you been working on for the past few years?

I think the last few years have been the most interesting years in my career, but then I could have said the same for every year, whenever you asked me that.

So before I get into details I think I need to come clean with something. In my bio and on my LinkedIn account I say that I work for SUSE. That is a lie. Let me explain. You know they say “Choose a job and do something that you love so you never have to work a day in your life.” And that’s the case with me. That’s how it feels working with an open-source company like SUSE. They say that open source is in our genes there and that’s true.

So, I started working maybe five, or more than five years ago as an OpenStack cloud software developer. OpenStack is a complex cloud platform software. It was also a disruptive technology cloud for a while, hence, I started to do that. Then, a few years after I made the transition to something else equally interesting to containerization, Kubernetes and containers. I was also a cloud-native application developer for a while.

But I guess the really interesting story of mine is when I started working in doing things with artificial intelligence and machine learning. In 2020, late 2020, I got involved with this amazing group of people, and we created this open-source project called FuseML. This is partially the reason why I gave this talk to the ОpenFest. And we recently launched it. We’re trying to improve things for everyone that wants to do something with machine learning.

You have a lot of experience in the tech world, starting from virtualization to SDN, to cloud computing, to containerization and cloud-native applications. Now you are exploring the AI/ML realm. Can you share what are the main challenges that stand in front of the AI/ML world?

Sure, so it depends on who you ask. For companies, I would say the challenge is to not really read the benefits of machine learning.

So far, only the big giants and the companies have been able to successfully do that and that’s why you hear all these interesting stories about artificial intelligence and machine learning coming from companies like Google, Netflix, Uber and so on. The real challenge here is to democratise that type of success and to give everyone a fair chance. I think that’s what these new disciplines of ML ops, machine learning operations are trying to do together with a set of best practices to help with that. So, it aims to help facilitate everyone’s access to building machine learning systems that everyone can put in production and get the revenue out of that.

For us, end-users, well, not for me because I’m a developer but I guess I’m also an end-user of machine learning… So, for us and users and maybe even the society at large the challenge is huge because it’s the world we need to understand. I don’t think there are a lot of people that really understand what machine learning is all about and what artificial intelligence really does. So I think that would be the next challenge for us, to inform ourselves and for those who know about it, to popularise it and democratise it in a way that makes it accessible to anyone. In that way, we’ll make it easy for everyone to understand what the implications of machine learning are and how we affect them personally because they will affect us as well.

And I think that that is one way to do it. So, again, I keep going with the democratisation of machine learning. I think that is one of the best ways to tackle this problem and this challenge. Also the popularisation. I hope there’ll come a time when we need to standardise what we’re doing with machine learning in the industry.

What specialized interpretation of the traditional DevOps culture and methodologies are required to build and maintain a successful production Machine Learning system? 

It’s not so much in my opinion as it is something that is still developing. It’s something called ML ops and machine learning operations. So people have tried to apply DevOps, standard DevOps, conventional DevOps to build machine learning systems and machine learning applications. And it didn’t really give us a day or hope for – I’m talking mainly about companies that are trying to put production machine learning systems out there, and the reason for that is the fact that machine learning systems are unique. You need to change the way you think about machine learning because they are unlike anything we’re doing within conventional software engineering.

So, DevOps for machine learning requires a more targeted approach to apply them to machine learning and that’s what ML Ops is. It basically looks at machine learning and then says and recognises that machine learning is weird. So, what I’m thinking is that machine learning is the quantum mechanics of software engineering. Quantum mechanics is really weird when compared to classical mechanics or Newtonian mechanics. They can behave in all sorts of unexpected ways, they are really opaque. You cannot directly measure it, you cannot actually measure what it’s doing as you do in classical mechanics.

So you don’t really know what’s happening inside. You need to take a lot of measurements, you need to do a lot of experimentation with machine learning models to understand how they behave, and to change them to behave the way that you want them to. So it’s really, really unlike anything that we do with conventional engineering.

What AI and machine learning tools are you familiar with?

My experience with machine learning tools and additional registers comes mostly from what I do with this fusion project.

And, gosh, I wish I had more than 24 hours in a day because there’s like an entire ecosystem of things, tools and ideas that are constantly expanding and evolving. Some more research papers are being published each and every day in the machine learning field. More tools are being able to capture those ideas and I can only scratch the surface with what I’m doing.

But in my experience as an engineer that is working on something like ML ops, that is maybe closer to the tools that you need to successfully put machine learning in production. So I can give some examples there. I have some experience with tools that relate to those that you use to track what you do with machine learning models, to Version Control Data and all these artefacts that are coming from machine learning development. Things like ML flow, that is a very popular tool for doing that. And not just that but also it’s really popular for data science DVC, and that stands for data version control, which is another tool that I briefly worked with.

Well, the tools that are inside a component that can be used to generate predictions are called Prediction Service platforms. And today I’m also dealing with my voice. And, I mean I can go on and on.

You need these pipeline orchestration engines to implement DevOps like workflows and that are specific to machine learning things are tacked on Argo and kept for pipelines. And yeah, I think that there are maybe hundreds of thousands of doors in here. It’s very hard to keep track of all of them.

You have mentioned that you have dealt with a lot of machine learning problems. What kinds of machine learning problems have you tackled, and how did you tackle them?

Yeah, so those again are related to what I’m doing. It’s an orchestration tool for ML ops and because of that machine learning sometimes involves a lot of tools. So sometimes you need several of those tools to glue them together.

Because every tool serves a localised purpose in what you’re doing to create machine learning systems that are production-grade, one challenge that we’ve been trying to do with FuseML is how to integrate all those tools together, how to get open source tools from various open-source projects, unrelated to one another, and create a complete end to end workflow on top of them. How to integrate them in a way that, first of all, they work together, because sometimes they don’t. And how we did that is, we’re featuring all these Extensibility Mechanisms. We fuse them out with a FuseML project. We can integrate all these tools, and use them as components in your automated workflow with minimal friction.

I guess the way that an end-user would see that someone that uses FuseML is through abstractions. So through abstractions, it is a very nice way of bringing this thought again. Extracting simplicity out of complexity and giving everybody a chance to interact with those that can be complicated and really not challenging to interact with otherwise. So that’s one problem that I tackled. And that’s what, not me myself but the whole team from the project does.

Peace of mind is also an automation tool that automates the workflow of machine learning and building machine learning systems. The question was how. So, how do you deal with it? How can you find a balance between automation on one hand and customization?

So how to have a tool that allows you to automate everything that you want to automate but at the same time gives you the control you need to customise the way it’s running all those processes that are needed to implement the workflow that you’re trying to automate, how do we tackle that one?

Well first of all we recognise that there’s a problem that needs to be solved. So, let’s say you have some images, and you want to recognise you do not want to do object detection from those images. You can have an end to end, fully automatic workflow that does that for you – you just deploy it, apply it to those images and it does that for you with us. But you also can get very personal and particular about how you’re doing things. You can split that workflow into PCs and orchestrate them in a more customizable way.

What are the ethical implications of using machine learning?

Yeah, that’s what everybody is thinking about. I guess that doesn’t matter what we are doing with machine learning. I don’t know if I’m prepared enough to answer that question myself. Everybody has their own interpretation of things. It is a big challenge that machine learning in the industrial sector still needs to deal with that and even more. I myself have to admit I’m still struggling to understand what those indications are but probably time will show that. So I think machine learning has the capacity to transform society, on a very deep level. I guess everyone is concerned with things like privacy, surveillance, and, of course, bias and discrimination because machine learning systems can do that if they’re not properly built.

And these are maybe personal things. So how does it affect me and how will it affect me? How will machine learning systems take away my job? Will it, and how will it impact me? Will this benefit me as a person, as an individual?

I think the more pressing question and the more pressing ethical implication is how it will impact us as a society. There’s one example that I figure we can just give a thought of how much we need to how much we lie on a day to day basis to friends, to relatives, to our children, to our employer, employees. So we do that, maybe even to ourselves sometimes because we need some kind of reassurance on different things.

So as a society and as individuals, we really rely on a lot of stuff that is distorting the truth. So imagine that there will be an app out there, some point in the future and it won’t take too long to come up with that kind of thing that can detect whether you’re telling the truth. You can install it on your phone, you can screen your conversations like the conversation that we’re having right now. So, everyone can have such an app on there, or on their laptop installed and they can track the eye movements, the way our lips move the inflexions in our voice. It can tell us whether we’re telling the truth, or whether we’re not really truthful about what we’re saying.

Now imagine the implications of that and how we can deal with that as a society, as individuals. I think that’s the kind of thing that keeps me up at night. And I really wonder whether we as a society, as a human species, were able to cope with those types of changes. Because everyone has their personal interpretation of those occasions.

So let’s move slightly to the OpenFest 2021. At this year’s OpenFest2021 you presented “MLOps: specialized DevOps for Machine Learning”. What is the reason to choose this topic? How important is it?

Yeah, so it all goes back to what I was saying earlier about FuseML. This project that we’ve been working on at SUSE, with the ML team there. So ML is an emerging engineering discipline where there is still a lot of experimentation, a lot of thoughts being gathered, a lot of ideas being exchanged as practices. They are being collected and I think people need to be aware of what’s happening. Because it’s only through collaboration and exchange of healthy ideas. There’s even an ML ops community, where people that are interested in that exchange ideas. And that’s some of the reasons why I think everyone needs to know about ML ops.

What is your message to all beginners and tech newbies? 

Well, if you want to be successful in this industry, that’s really highly competitive, find something that is complicated to use, simplify it in that way so it’s not widely available. Make it accessible to everyone. And I think in that way you’ll not only have to benefit yourself as someone who is passionate about technology but also contribute to the larger picture.

Justine Toms is a Bulgarian entrepreneur, lecturer and author, who has played a major role in Bulgarian digital marketing since 1998. Justine loves her work and networking, meeting new people, new ideas, and creative projects. Justine tells us that she is still looking for a way to successfully balance her professional and personal life. When she is working from home, her roles are constantly intertwined – mother, wife, teacher, expert, and of course time for herself. She admits that juggling is not an easy task, and although it has been more than 12 months, she is still learning. Justine believes that over time she has managed to gain equal gender rights professionally. When asked if she has encountered any obstacles in her career because she is a woman, she answered that this has happened more than once. Justine adds that she has been involved in business and online entrepreneurship for 23 years – an area seen more like a man’s job, although this has changed significantly over time. She is a big fan of podcasts and follows a few in Bulgarian and a lot in English, out of which she recommends the Ratio and Internet Speaks podcasts. Regarding inspiration, she recommends their project “Creators – the digital optimists of Bulgaria” (blog and podcast). About social networks, Justine says that she has relatively reduced Facebook, but instead she follows Twitter, which gives her enough information. She reads many books and also listens to them in audio format. Justine warmly recommends Harari – Homo Deus to those who have not read it yet, as well as their new book with Marina Stefanova – Leaders for a New Beginning. 

“We’ve always tried to gain a diversity of people on our team and fortunately, we’ve succeeded.”

Justine Toms

Favourite clothes?

In the last year – leggings and a T-shirt. 🙂 

Otherwise – dresses. 🙂

High heels or flat shoes?

Low, comfortable, fast.

Which typical female quality or household responsibility does not apply to you?

Vanity.

What is THE thing that you must have in your bag when going out?

Maybe a phone. 🙂

Favourite lipstick colour?

Sour Cherry.

Do you currently support any social causes?

I currently support several causes:

– Gorichka and eco themes, life with a small ecological footprint, green future

– Several projects related to children from marginalized groups

– Causes for equality, women’s entrepreneurship.

Read More Inspiring Stories for Women in Tech in WRTech here.

Can you share something specific about yourself that we can’t find on LinkedIn?

I don’t know whether it’s obvious how much I love my job. 🙂 Also networking, meeting people with new ideas, with projects with creativity.

Have you encountered any challenges in your career in connection to being a woman?

Yes, not once. Maybe mainly because I have been involved in business and online entrepreneurship for over 23 years – areas that in the past were seen as males’ jobs, but lately the problem is not seen so often.

Do you know how many women work in your position?

There seem to be more men, probably only 15-20% of women.

Do you think that the problem of gender inequality is still relevant in Bulgaria and globally?

Yes, it exists in Bulgaria, but in places around the world, such as Asia and Africa – much more. But yes, the topic is very important.

What policy does your company follow in terms of promoting diversity?

We have always strived to have a variety of people on the team and fortunately,we have succeeded. Not with quotas, but with the search for interesting alternatives. 

Do your male colleagues treat you differently?

I seem to have won equal gender rights. My positions on the topic are clear, communicated and probably due to that. I don’t think this applies to all girls and in all situations.

Do you think that a woman’s appearance and charm affect their professional success? (example: does it make communication easier?)

I’m not sure. It depends on who is standing in front of you and what their attitude is. I strongly believe that the “female vision” will not matter in the future. About charm – it’s up to the person, it’s not so much related to gender or gender identity.

How do you combine personal and professional life? Are there any rules that you follow?

I’m trying to keep the boundaries, however the balance is difficult. It is especially complicated in the last year, when we are at home during the last year, when the roles are constantly intertwined – mother, wife, teacher, expert, time for yourself – juggling turned out to be quite a difficult task and although 12 months have passed I am still learning. Unfortunately, personal time suffers the most.

Which sources do you follow and can you recommend some of them to our audience? (example: podcasts, pages, influencers, vloggers, etc.).

I’m a big fan of podcasts – I watch a few in Bulgarian and many in English. Out of these, I would recommend. Of these, I recommend the Radio and Talk Internet podcasts. Also for inspiration our project Creators – the digital optimists of Bulgaria (blog and podcast). I have reduced Facebook. I mainly follow Twitter which gives me enough information. I also read a lot of books, I also listen to audiobooks. To anyone who has not yet read Harari – Homo Deus, I strongly recommend it. ☺ And our new book with Marina Stefanova – Leaders for a New Beginning.

If You Want to Read More Inspiring Stories for Success and Professional Growth Join the DevStyleR #Tech Influencers Community and Get Access to our Special Editions here.