Adrián Bazaga's Homepage
AI Research Scientist @ Microsoft

Welcome to my website!
Background:
I’m an AI Scientist at Microsoft, where I work on empowering agentic AI experiences on Copilot+ devices for millions of users worldwide, leveraging Large Language Models (LLMs).
I hold a Ph.D. in Machine Learning from the University of Cambridge, where I conducted research under the supervision of Prof. Pietro Liò and Prof. Gos Micklem. My research has been published in leading Machine Learning conferences such as ICLR, ICML, and EMNLP, as well as Nature journals. Previously, I gained research experience through internships at Microsoft Research and Amazon AGI, where I explored novel training schemes to enhance few-step generation in diffusion models, and test-time scaling for temporal reasoning with LLMs. Prior to that, I spent ~5 years in various startups, working at the intersection of AI and biology.
Research Interests:
My research is centered around advancing the capabilities of AI, particularly in the areas of multimodality and LLMs. I enjoy developing innovative architectures that can seamlessly integrate diverse data modalities to address both fundamental challenges and real-world problems 🧪. My current focus is on enhancing generative models, expanding their multimodal capabilities, refining new training methodologies, improving inference efficiency, and ensuring robustness and alignment. I’m passionate about using AI to drive impactful projects at a global scale and am always open to collaborations that push the boundaries of what AI can achieve 👐.
Beyond Research:
In addition to my core research, I’m interested in how advancements in natural language processing, particularly generative models, can revolutionize education and governance. I’m deeply committed to conducting research with a significant impact and am eager to engage in discussions and collaborations that align with these goals.
News
Jan 3, 2025 | I joined Microsoft as an AI Scientist. Excited to work on LLMs, Multimodality and Agents at global scale, in London, UK. ⭐ |
---|---|
Sep 20, 2024 | [Paper] Our paper “HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs” has been accepted at EMNLP 2024 🎉 |
Aug 15, 2024 | I joined Amazon Science AGI team as a Research Scientist Intern. Excited to work on reasoning with LLMs with Bill Byrne, Rexhina Blloshmi and Adrià de Gispert, in Berlin, Germany. ⭐ |
Aug 13, 2024 | I have accepted to serve as Reviewer for the International Conference on Learning Representations (ICLR). 👍 |
Jun 16, 2024 | Our paper “FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models” is now on arXiv. 📋 |
Jun 5, 2024 | [Paper] Our paper “TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting” has been accepted at ICML 2024 🎉 |
Jun 1, 2024 | I received a PhD Student Award by the Cambridge Society for the Application of Research in recognition of outstanding research with real world application, for my work on language-graph modality alignment for evidence retrieval. 🏅 |
May 1, 2024 | I joined Microsoft Research as a Research Scientist Intern. Thrilled to work on generative modeling (improved few-step generation for diffusion models) with Javier Zazo, Richard Turner and Ted Meeds, in Cambridge, UK. ⭐ |
Mar 16, 2024 | [Paper] Our paper “Language Model Knowledge Distillation for Efficient Question Answering in Spanish” has been accepted at ICLR 2024 🎉 |
Mar 6, 2024 | [Paper] Our paper “Unsupervised Pretraining for Fact Verification by Language Model Distillation” has been accepted at ICLR 2024 🎉 |