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About Me.

In my professional life I am responsible for designing A.I. and Data architectures while managing large teams of mostly Data Scientists, Developers and Researchers. At the same time I am pursuing my own personal path of reconciling Perception (Deep Learning) with Cognition (hierarchical/graphical frameworks, e.g. Bayesian Networks), in a unified Neurosymbolic architecture. Learn more about my Research initiatives. 


2024 -

StarBayes Systems


Designing A.I. algorithms, Data strategy and in charge of the company's positioning. StarBayes' three-step probabilistic platform will provide invaluable insights for space agencies, satellite operators, and aerospace companies, enhancing their ability to plan and execute space missions, solving for uncertainty like never before.

2024 -

Collective Action

Cognitive A.I. Director

Designing the first Neurosymbolic architecture of the field, focused on cognition. Collective Action (CA) is a dynamic interaction of self-managed and self-funding teams moving towards the common goals of transitional equanimity, transparency as a basis for governance and raising consciousness, using Collective Intention (CI) IP.

2023 - 2024

SiSaf Ltd

Chief A.I. Officer

Architecting the SiSaf intelligence platform, while building the human body simulation algorithms that will be used for the SiSaf experiments. In charge of Data and ML/DL/A.I. teams. Reconciling internal data operations and building data analytics architectures to support BD and Product strategies.

2022 - 2023


Chief A.I. Officer

In charge of Data and ML/DL/A.I. teams. Reconciling internal data operations and building data analytics architectures to support BD and Product strategies. Designed the company’s economy simulation algorithm, connected to Bayesian Networks and Statistics. Designed the Neurosymbolic architecture of the company combining Deep Learning and Hierarchical Graph computations, used in gamer behaviour predictions.


Intoolab (acquired)

CEO & Co-Founder

In charge of the company’s mission to build the first Bayesian Network in production that was able to simulate human body mechanisms and chemical pathways. Designed “Tzager”, the company’s Neurosymbolic framework as a SaaS platform, that allowed Pharmas and Researchers, to simulate different parts of the human body interacting together, in ways that is impossible with off-the-shelf algorithms. Designed the first Bayesian framework that can incorporate existing knowledge directly from millions of papers and internal research of each customer.

- Read more about Tzager
- Won Digital Science (Holtzbrinck) Catalyst Grant
- Customers Talking about Tzager



Director of Data & Marketing

In charge of redesigning the company's and customer's Data analytics aligning with BD and Product strategies, while at the same time designing the architecture of the company's internal tool for user behaviour prediction and next step simulations.


Aarhus University, DK
Research Assistant, Marketing

Basic and advanced principles of marketing/consumer research, advanced qualitative/quantitative analytical techniques (SPSS, MS Excel) in marketing research, analysis of purchase (panel, scanner) data. Applied Statistics.

Recent Publications

Generative Image Model Benchmark for Reasoning and Representation (GIMBRR)

Jascha AchterbergRon ArelTetiana GrinbergAdel ChaibiJoscha Bach, Nikos Tzagkarakis

March 2023,  AAAI 2023 Spring Symposium Series EDGeS Submission

"Recent developments in generative AI have highlighted how the field is moving to a state where models start developing a large set of skills and can solve a multitude of tasks without having actively been optimized for them. Benchmarks have been a key component driving model development in the past, but as models’ capabilities become more complex, it becomes harder to create benchmarks which can meaningfully capture the skillsets of algorithms to inform future model developments and training pipelines. While in the domain of language we have seen the development of a wide-ranging array of benchmarks, these are currently missing for generative image algorithms. Here we introduce the Generative Image Model Benchmark for Reasoning and Representation (GIMBRR) which is an open-source software package to assess generative image algorithms on 11 cognitive tasks using manual and automated evaluation pipelines. GIMBRR is built with customizability in mind, so that it can easily be updated with new tasks and assessment routines. This way it can be adapted to suit the needs of research teams with specific goals in image generation and to update task difficulty as generative image algorithms progress in general. We used GIMBRR to measure performance of three popular generative image models (DALL-E 2, Midjourney, Stable Diffusion), demonstrating that reasoning and representation tasks pose a considerable challenge to all of them. We have also demonstrated how cognitive theory can be used to perform a systematic analysis of generative and representational capabilities of these models."

Download detailed CV






B.A. in Business

Technological Educational Institute Of Messolonghi, Greece




Canterbury Christ Church University

Canterbury, UK


Ph.D. in Computer Science (part-time)

"Exploring Neurosymbolic Architectures for
Self-Modelling and Inter-Agent Communication"


Open University, London UK
The Faculty of Science, Engineering,
Technology and Mathematics

Nikos Tzagkarakis

London, UK

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