Afroditi Papadaki

Generative AI Data Scientist

About Me

I am a Generative AI Data Scientist working at Legal & General and a Visiting Researcher at University College London (UCL), where my research focuses on Generative and Responsible AI. Previously, I was a Lecturer in Data Science and Signal Processing in the Department of Electronic and Electrical Engineering at UCL.

Interests

Generative AI
Federated Learning
Responsible AI (Fairness, Robustness, Safety, Privacy)

Education

PhD in Electronic and Electrical Engineering
University College London
MSc in Internet Engineering
University College London
BSc Digital Systems
University of Piraeus

📚 My Research

At Legal & General, my work focuses on developing generative and agentic AI systems for a range of applications — including question answering, content tagging and classification, text generation, and web search summarisation — applied across financial and enterprise domains.

In parallel, my research at UCL and through independent projects focuses on building efficient and adaptable methods to enhance robustness, predictive performance, and scalability across a broad spectrum of generative AI applications. I also work on approaches that support Responsible AI in distributed and heterogeneous environments, with a particular focus on federated learning and multi-agent systems.

If you’re working on related problems or are interested in collaboration, feel free to reach out 😃

Featured Publications

Large Language Models

Generating Diverse Negations from Affirmative Sentences

In this work, we focus on improving the robustness of LLMs robustness on negated statements by proposingNegVerse, a data augmentation approach capable of generating various types of negations, including verbal, non-verbal, and affixal. We provide new masking rules and propose a filtering mechanism to identify negation cues and remove degenerate examples, producing diverse and in parallel meaningful negated sentences. We experiment with five real-world datasets and that NegVerse outperforms existing methods and generates negations with higher lexical similarity to the original sentences, better syntactic preservation, and greater negation diversity. Our empirical results also highlight that the proposed approach can generate negated sentences without specific guidance on blank placement. However, the resulting sentences exhibit reduced diversity compared to those generated with such guidance.

Nov 19, 2024

Federated Learning

Federated Fairness without Access to Sensitive Groups

Federated learning is crucial to obtaining large, representative datasets across every sensitive group. Ensuring fairness across protected groups is essential for responsible machine learning, but prior knowledge of such groups is not always available, due to privacy constraints and evolving fairness requirements.This is the first work to present a flexible federated learning objective to ensure minimax Pareto fairness with respect to any group of sufficient size. We propose an algorithm that solves a proxy of the proposed objective, providing performance guarantees in the convex setting. Experimentally, our approach surpasses relevant FL baselines, exhibits comparable performance to centralized ML approaches, and demonstrates the ability to achieve a diverse range of solutions. For a single local epoch, FedSRCVaR is robust to data imbalances and heterogeneity across clients, yielding the same solution as centralized ML settings. For multiple local epochs, FedSRCVaR improves communication costs, but might yield a suboptimal solution when data are highly non-iid across clients

Feb 22, 2024

Recent Publications

Darian Rodriguez Vasquez, Afroditi Papadaki (2024). Generating Diverse Negations from Affirmative Sentences. In AFM at NeurIPS 2024.

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Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro, Miguel Rodrigues (2024). Federated Fairness without Access to Sensitive Groups.

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