Hector Garcia Rodriguez

I am a ELLIS PhD student at Marcus and Anna Rohrbach's Multimodal AI Lab. I am co-advised by Hervé Jégou.

Previously, I was a research engineer on efficient deep learning at Huawei Zurich. I completed an MSc Machine Learning at University College London, graduating on the Dean's List (top 5%). During my MSc thesis, I was advised by Timoleon Moraitis and Pontus Stenetorp. Previously, I interned as a Software Development Engineer in Amazon Web Services, and obtained a BSc Theoretical Physics from UCL with first-class honours.

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I'm interested in multimodal representation learning: improving efficiency and reliability using adaptable networks with adjustable compute budgets, and using more contextualised representations for sequential decision making tasks.

We enable MLLMs to understand time in videos by timestamping. This achieves state-of-the-art on moment-retrieval (Charades-STA, QVHighlights, ActivityNet Captions) and grounded video QA (NExT-GQA), in both zero-shot (GPT-4o) and fine-tuned (BLIP-2) settings.

We train deep ConvNets with an unsupervised Hebbian soft winner-take-all algorithm, multilayer SoftHebb. It sets SOTA results in image classification in CIFAR-10, STL-10 and ImageNet for other biologically plausible networks. SoftHebb increases biological compatibility, parallelisation and performance of state-of-the-art bio-plausible learning.

STPN is a recurrent neural network that improves Supervised and Reinforcement Learning by meta-learning to adapt its weights to the recent context, inspired by computational neuroscience. Additionally, STPN shows higher energy efficiency in a simulated neuromorphic implementation, due to its optimised explicit forgetting mechanism.

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