Kasper Piskorski
Lead AI Researcher at Technology Innovation Institute
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolKasper Piskorski is a Lead AI Researcher based in London with a decade of experience building production-grade AI and knowledge-graph systems. He blends deep academic training (PhD in Scientific Computing, Cambridge) with hands-on engineering leadership across graph reasoning, foundation models and multi-agent systems, having led projects from Grakn’s reasoning engine to greenfield Marketing Knowledge Graphs and predictive recruitment platforms. Kasper’s work spans backend systems, graph databases, and modern ML stacks (PyTorch, HuggingFace, Spark, BigQuery) and includes open-source contributions to TypeDB/Grakn where he improved reasoning, type unification and bug fixes. Known for turning formal knowledge representation and automated reasoning research into practical products, he repeatedly bridges R&D and delivery to serve real-time inference and explainable predictions. He brings a rare combination of declarative reasoning expertise and pragmatic ML deployment experience, often working at the intersection of semantic models and large language models.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Scientific Computing, Doctor of Philosophy (Ph.D.) Scientific Computing at University of CambridgeBachelor’s Degree Theoretical Physics, Bachelor’s Degree Theoretical Physics at University of Birmingham
Github Skills (7)
type-system10
javas10
java10
rule-engine9
data-structure9
data-structures9
algorithms9
Programming languages (7)
Github contributions (5)
typedb/typedb
Aug 2016 - Apr 2021
TypeDB: the power of programming, in your database
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 reviews, 614 commits, 935 PRs in 4 years 9 months
Contributions summary:Kasper's commits primarily focused on implementing changes to the Grakn reasoning engine, including adding type playability checks and refining the process of attribute and relation type conversion. Their work involved modifying the implementation for various unifiers and ensuring that the correct type hierarchies were being taken into account. The user also addressed inconsistencies in resource representation and implemented fixes to several bugs.
hyper-relationalknowledge-baseknowledge-graphagdagraph-theory
kasper-piskorski/grakn
Aug 2016 - Apr 2021
A Knowledge Graph Platform
Contributions:3 PRs, 3025 pushes, 1748 branches in 4 years 8 months
dataminingknowledgememgraphsemanticsknowledge-graph
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.