Christoforos Anagnostopoulos
Tech Fellow Partner at Imperial College London
London, England, Greece
Summary
👤
Senior🎓
Top SchoolChristoforos Anagnostopoulos is a Chief Causal AI Officer and seasoned innovation leader with a decade of experience bridging academia, consultancies, tech unicorns and biopharma to accelerate scientific discovery with causal AI. With a PhD in Mathematics and roles from Imperial College to QuantumBlack and Valo Health, he combines deep theoretical expertise in statistics and real-time ML with hands-on product and engineering leadership. He co-founded Mentat Innovations, building privacy-respecting real-time data analysis and an open-source anomaly detection framework (datastream.io), and previously led research on digital twins and synthetic environments at Improbable. Known for translating rigorous mathematical ideas into production-grade systems, he also teaches ethics in AI and data science at Imperial College, underscoring his commitment to responsible deployment. Based in London with Greek roots, he blends academic rigor, entrepreneurial grit and practical engineering to push causal methods into industry-scale impact.
10 years of coding experience
7 years of employment as a software developer
MSc Informatics and Learning from Data (distinction), MSc Informatics and Learning from Data (distinction) at The University of EdinburghPhD Mathematics, PhD Mathematics at Imperial College LondonMSc Mathematical Logic and Theory of Algorithms (distinction), MSc Mathematical Logic and Theory of Algorithms (distinction) at Ethnikon kai Kapodistriakon Panepistimion AthinonBAHons Mathematics (2:1), BAHons Mathematics (2:1) at University of Cambridge
English, French, Greek
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
pandas10
object-oriented-programming10
anomaly-detection10
python10
data-analysis10
scipy-stats9
machine-learning8
data-structure7
data-structures7
algorithm7
algorithms7
Programming languages (2)
Github contributions (5)
MentatInnovations/datastream.io
Aug 2017 - Feb 2018
An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:42 commits, 5 pushes, 8 branches in 5 months
Contributions summary:Christoforos primarily contributed to the development of an anomaly detection framework. Their work involved implementing object-oriented anomaly detectors in Python, particularly Gaussian and Percentile based methods. Key contributions included the design and implementation of update and scoring functions for the detectors, along with testing and debugging to ensure accurate anomaly detection. The user also worked on integrating the detectors with the framework's data processing pipeline.
anomalypythonanomaly-detectionmachine-learningkibana
canagnos/hmeasure
Feb 2017 - Feb 2019
Contributions:15 commits, 9 pushes, 4 branches in 2 years 1 month
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