Active Neuron Academy
Our portfolio
Delivered corporate workshops, university teaching, EU PRR programmes with Europe-recognised qualifications, and the peer-reviewed research behind the curriculum. For current offerings and booking, see Programmes.
Teaching approach
Practical work first when it fits. Sessions mix hands-on exercises with theory, tied to everyday examples so ideas stay easy to grasp. Classes stay open for discussion, not one-way slides.
Corporate training
Same lab-first style for engineering and data teams, and for non-technical groups who need practical literacy without jargon overload. Topics, formats, and booking: Programmes.
Delivered workshops
SQL for non-technical teams
Three-day SQL training designed for participants without a technical background.
From traditional ML to generative AI
Delivered for data science and engineering teams moving from classical ML pipelines to generative AI in production. Covered model selection, evaluation, retrieval design, and deployment patterns grounded in regulated fintech work.
Learning on the Graphs workshop
Hands-on workshop on graph representation learning: from feature engineering on relational data through to graph neural networks, with exercises tied to network and transaction-graph use cases.
Employing agentic AI in industry
Workshop on how to put agentic AI to work in industry: integrating autonomous agents into existing workflows, tool orchestration, governance, and practical patterns teams can deploy for measurable impact.
University teaching
Higher education & continuous training
Undergraduate (BSc)
Continuous training (PRR)
EU-sponsored PRR program: diverse courses and Europe-recognised qualifications, with emphasis on applied skills.
- Databases (theoretical & practical)
- Programming in Python (practical)
Research & publications
Google Scholar profilePeer-reviewed work in graph learning, dynamic networks, and AML that informs programme content. Each card opens the paper.
Deep-Graph-Sprints: accelerated representation learning in continuous-time dynamic graphs
Low-latency deep representation learning on continuous-time dynamic graphs.
ICAIF 2023 · arXivFrom random-walks to graph-sprints: low-latency node embeddings on continuous-time dynamic graphs
Streaming, low-latency approximation to random-walk features for CTDGs.
AAAI WFS 2022 · arXivAnti-money laundering alert optimization using machine learning with graphs
ML triage with entity and graph features on dynamic graphs; also related patent work.
ACM SAC 2017 · proceedingsScalable subgraph counting using MapReduce
MSc-era work; Motif-Discovery plugin reached 10k+ downloads.