Citizens of Silopolis: Vladislav Nenchev

Landscape headshot of Vladislav Nenchev.

Vladislav Nenchev is a Senior AI Engineer, specialized in natural language processing, mathematics, software development, and architectural design. Vladislav is a solid AI expert who comes with two decades of experience, backed up by a PhD in mathematics. Vladislav is specialized in algorithm design and artificial intelligence, with a recent focus on state-of-the-art machine and deep learning.

Prior to joining Silo AI, Vladislav worked as assistant professor at Sofia University in Bulgaria and joined as a visiting lecturer and researched at the Tampere University of Technology in Finland. In addition, Vladislav has been engaged in several roles featuring NLP and software development, including automated analysis of documents in natural language at Selko Technologies where Vladislav was the CTO and Co-Founder.

Vladislav, in your professional life you’ve been an active contributor in both academia and industry. How has your career connected these two?

Throughout the years I’ve wandered on both sides of “the no man’s land” between industry and science, looking for a peaceful resolution. The main theme in my work has always been to find a solution to a complicated problem, usually involving a mathematical challenge. Sometimes I have had to design a high-level custom algorithm for a commercial data problem, other times, I’ve been part of creating novel automation for systems rich in information, like ERPs.

To complement this “IT merrymaking”, I’ve written a doctoral dissertation in the very depths of abstract mathematics, and taken part in academic projects in NLP and computational linguistics. My students from my years as a professor at Sofia University might remember tricky homework I gave just to have a little bit of fun – with the ultimate purpose of learning, of course.

What do you enjoy in your work as an AI engineer?

In general, I enjoy dealing with non-trivial problems and having to relate different topics or domains to one another. I enjoy designing something - be it a software solution, a new algorithm, or just a piece of elegant code.

Perhaps the thing that excites me the most is when I am able to combine techniques or solutions from different fields (especially when those fields are of scientific nature). An example of this would be using pure mathematics in computer programs, or using cognitive studies in creating AI solutions.

As someone with a long track record in data science, how would you describe your take in the field?

Between the words “data” and “scientist”, it is the latter that wins by far. I have spent ample years working in the previous mainstreams of artificial intelligence, like expert knowledge and statistical modelling, and nowadays I explore the new trends in deep learning. In each incarnation of AI, I keep coming back to the same situation: How to use what I know of natural intelligence, in order to recreate it.

I believe that science is the best tool available to answer this fundamental question and in particular those fields that study our minds and behaviour. It is a neat back-and-forth, as those fields were created via natural intelligence in order to study natural intelligence, and now they help to create its artificial reflection. Quite the cycle… hopefully, a virtuous and not a vicious one.

So, when it comes to Data Science, for me Science is what sculpts the solutions. Data is just the clay.

In your past, you worked as a developer on an ERP product, for which you later became one of the leading software architects. Why was this a great learning experience?

It all started as a simple fix for a local problem, which I decided to generalize until it became a meta-system working all over the ERP’s modules. Its job was to automatically create completing executions and reminders for every order document within the ERP, which was not executed according to its plan. This system quickly became a guardian angel for the planning modules in the ERP, and proved impervious to most human errors. Its name – “the difference system” – was a moniker for good planning long after it was rewritten in later stages of the ERP’s development. By the way, that ERP still has a difference system, up to this date.

When you were teaching at Sofia University, you gave a course to show how the most abstract theoretical subjects in the University’s programs can actually be used during everyday programming. Tell us about that experience.

My background in both academia and IT culminated in this course. To my delight, some of the best students circulating through the faculty at the time joined, and the problems they solved during the final exam were more than impressive. It all ended quite personal, as the students presented me with a farewell gift (after they received their marks, of course). It is still at my desk today, inducing feelings of warmth every time my eyes happen on it. … Up to my knowledge, there are not many lecturers in the university that have received something like that. I value this gift and cherish the great memory still!

What do you do in your free time?

My free time is quartered by four major forces:

  • Creative writing. Scripting short pieces of fiction, like stories, plays, poems, and articles, some of which have already seen the light of public eyes. Others are still working their way out of the shadows.
  • Learning about… everything. Greed for money never managed to sink its roots in me, but one of knowledge is running deep. Its hungry gaze cycles through various topics like history, philosophy, psychology, neuroscience, and physics.
  • Nature trekking, tracing and tumbling. Having a trip in the wild is one of my favorite past-times. The main requirement is to use only the chemical energy provided by my own organism, via the medium of shoes, skies, bicycle tires, or a kayak. Now and again, it all ends upside-down, but that is a risk you can never fully avoid.
  • Music. Any second, in which the world does not demand active participation, is spent listening to music. The cacophony of choice usually stems from some sort of blues or rock.

Silo AI has four values, Build Bonds, Keep Learning, Ask Why, and Be Good. Which one is your favorite?

“Be good”. Because it’s good. Because without it, the rest are just senseless tools without the soul behind them. And because… just because.

Silo AI is the largest private AI lab in the Nordics – a unique talent pool of AI Scientists and Engineers – to see our open positions and learn more about our work, go to


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