Constantin PhilippenkoPhD, École polytechniqueFormer Inria researcher

Lead ML Scientist at AFP - Building production AI systems for one of the world's largest news agencies.

AI Leadership · ML Systems · Research

Building reliable AI systems from research to production.

I work at the intersection of AI research, production systems and organizational transformation.

AI researchFederated LearningRAG SystemsLLM EvaluationML DeploymentTechnical LeadershipLectures and conferences
Always available for consulting & advisory

Experience

Timeline

Industry / Leadership·2025Present

Lead ML Scientist

Agence France-Presse

Leading AI strategy and production machine learning for editorial and investigative workflows across AFP.

Selected engagements
  • Production ML(2025–Present)

    Production ownership of classification, metadata enrichment and content understanding systems used across editorial workflows.

  • RAG & Investigative AI(2025–Present)

    Development of RAG systems, evaluation pipelines and AI quality frameworks. Designed ad hoc RAG workflows on Snowflake to accelerate exploration and analysis of large multimodale database (for example, the Epstein files).

  • Information extraction and structuration for AI agents(2025–Present)

    Exploring how to extract, structure and standardize the informational signal of AFP dispatches into reusable information atoms, with the goal of making news content more actionable for AI agents, chatbots and downstream knowledge systems. Contributing to early discussions on emerging standards with IPTC organisation (Information Interchange Model).

Consulting / Research·2025Present

AI Consultant

Philippenko AI Conseil

Independent AI consulting.

Selected engagements
  • SigmaNova(2025)

    Advised a scientific AI startup on training foundation models using federated learning and knowledge integration. Designded AI architectures, product positioning and monetization strategy.

  • Legomnia(2025)

    Interim CTO / AI Lead. Led OCR and RAG systems for legal documents, defined technical roadmap, supervised AI development and established engineering priorities.

Leadership·2020Present

Founder & Artistic Project Lead

Phantasio

Founded and led a multidisciplinary collective producing music, opera and theatre projects. Directed Barbe-Noire with 100+ artists and technicians.

Research·20232025

Postdoctoral Researcher

Inria Paris · DI ENS

Decentralized algorithms for unsupervised learning under communication and heterogeneity constraints. Collaboration with CEA Saclay.

Research·20192023

Ph.D. in Applied Mathematics

École polytechnique · CMAP

Federated learning, bidirectional compression, distributed optimization, heterogeneous clients, convergence guarantees. Supervised by Aymeric Dieuleveut and Éric Moulines.

Leadership / Research·20212021

Founder & Organizer

CJC-MA

Founded the Congress for Young Researchers in Applied Mathematics. Led funding, partnerships, organization and scientific program design.

Industry / Consulting·20182019

Java Consultant

Global Market Solutions · HSBC · Société Générale

Built data-intensive tools for financial use cases across banking and analytics environments.

Doctoral research

Ph.D. manuscript, slides and distinctions

My Ph.D. work focused on federated learning, distributed optimization, bidirectional compression, client heterogeneity and convergence guarantees.

Research impact

Selected publications

Google Scholar profile
431
Citations
4
h-index
3
i10-index

Adaptive collaboration for online personalized distributed learning with heterogeneous clients

arXiv · 2025

In-depth analysis of low-rank matrix factorisation in a federated setting

AAAI · 2025

Convergence rates for distributed, compressed and averaged least-squares regression: application to federated learning

JMLR · 2024

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings

NeurIPS Datasets · 2022

Preserved central model for faster bidirectional compression in distributed settings

NeurIPS · 2021

Selected talks

2025

AAAI Conference

Philadelphia, USA

2024

21st EUROPT Conference on Advances in Continuous Optimization

Lund University, Sweden

2023

54èmes Journées de Statistique

Brussels, Belgium

2022

DYOGENE Reading Group

Inria, Paris, France

2022

Learning and Optimization in Luminy

CIRM, Marseille, France

2022

StatMathAppli

Fréjus, France

2022

Math for Machine Learning Summer School

EMINES-UM6P, Morocco

2021

NeurIPS

Online

Scientific and artistic trajectory

Biography

Constantin Philippenko is a Ph.D. in Applied Mathematics from École polytechnique, specialized in artificial intelligence, federated learning and distributed optimization. His trajectory combines scientific research, production-grade machine learning, technical leadership and entrepreneurship.

He is now Lead ML Scientist at Agence France-Presse, where he works on hierarchical text classification, RAG systems, metadata enrichment, MLOps, evaluation protocols and AI quality for editorial workflows. His work sits at the intersection of research, robust deployment, model evaluation, bias mitigation and AI transformation.

After an engineering degree from Ensimag in Applied Mathematics and Computer Science, he worked as a Java consultant for financial institutions including HSBC and Société Générale. This early industrial experience gave him a pragmatic view of software engineering, data-intensive systems and delivery constraints in demanding environments.

He then completed a Ph.D. at École polytechnique on federated learning, focusing on communication-efficient distributed optimization under client heterogeneity. His research addressed bidirectional compression, convergence guarantees and low-cost training, with publications in venues such as NeurIPS, JMLR and AAAI. He later pursued postdoctoral research at Inria Paris and DI ENS on decentralized learning and unsupervised methods under communication constraints.

After the COVID period, aware of the need to rebuild scientific links among young researchers, he founded the Congress for Young Researchers in Applied Mathematics. Attached to teaching and scientific outreach, he participated in Ma thèse en 180 secondes and taught the mathematical foundations of AI at École polytechnique from 2020 to 2025.

In parallel, Constantin leads artistic and collective projects. He founded Phantasio in 2020 to make classical music more accessible, support young artists on their path to professionalization and contribute to new musical creation.

As producer of the opera Barbe-Noire, his ambition is to bring a score from paper to life. He structures, finances and pilots the project, brings artistic teams together and builds partnerships. His role is to create the conditions in which artists can fully deploy their creativity and talent, with the aim of bringing to life a work that can endure and inspire future generations.

He also produced the operetta L’Enlèvement consentant, written by composer Ambroise Divaret, and created an annual lyrical singing gala in Paris. Through collaborations with the City of Paris and social housing organizations, he develops initiatives to bring opera and lyrical music into priority neighborhoods, driven by a constant commitment to openness, transmission and access to culture.

Across science, engineering and the arts, his work follows the same principle: transforming ambitious abstract ideas into living systems — algorithms that work in production, institutions that create collective momentum, and artistic projects that create beauty for people together.