David Vazquez
Director of AI Research
AI Research Management
Leadership team
David Vázquez is Director of AI Research at ServiceNow Research, where he leads the Fundamental AI Research group. His current work focuses on multimodal learning, vision–language models, reasoning for enterprise applications, web agents, and data-efficient learning. He has published extensively in top venues such as NeurIPS, ICLR, ICML, CVPR, ICCV, and ACL, contributing to advances in multimodal document understanding (BigDocs), chart reasoning (BigCharts), visual content–to–code generation (StarFlow, StarVector), and alignment techniques for VLMs (AlignVLM). David received degrees in Software Engineering from Universidade da Coruña and in Computer Science from the Universitat Autònoma de Barcelona (UAB), including a PhD in Computer Vision and AI. He completed postdoctoral fellowships at the Computer Vision Center (CVC) and at MILA under Aaron Courville, funded by a Marie Curie Fellowship. Earlier in his career, he worked on autonomous driving technologies, creating the SYNTHIA dataset, an autonomous driving simulator, and contributing to real vehicle prototypes with a focus on perception (object detection, semantic segmentation, 3D reconstruction, SLAM). He is also an Adjunct Professor at UAB, where he continues to teach and supervise graduate research.
Intérêts
- Machine Learning
- Computer Vision
- Natural Language Processing
- Intent Classification
- Low Supervision
Publications
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding.
Neural Information Processing Systems (NeurIPS),
2025.
Rendering-Aware Reinforcement Learning for Vector Graphics Generation.
Neural Information Processing Systems (NeurIPS),
2025.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation.
Conference on Empirical Methods in Natural Language Processing (EMNLP),
2025.
BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning.
Conference on Language Modeling (COLM),
2025.
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery.
Workshop at the Annual Meeting of the Association for Computational Linguistics (ACL),
2025.
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction.
International Conference on Machine Learning (ICML),
2025.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation.
Workshop at the Computer Vision and Pattern Recognition Conference (CVPR),
2025.
StarVector: Generating Scalable Vector Graphics Code from Images and Text.
Computer Vision and Pattern Recognition (CVPR),
2025.
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding.
Workshop at the International Conference of Learning Representation (ICLR),
2025.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation.
Workshop at the International Conference of Learning Representation (ICLR),
2025.
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks.
International Conference of Learning Representations (ICLR),
2025.
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation.
International Conference of Learning Representations (ICLR),
2025.
StarVector: Generating Scalable Vector Graphics Code from Images and Text.
AAAI Demos,
2025.
BigDocs: A Permissively-Licensed Dataset for Training Vision-Language Models on Document and Code Tasks.
Workshop at the Neural Information Processing Systems (NeurIPS),
2024.
Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection.
NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets),
2024.
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content.
NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets),
2024.
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference.
Workshop at the Neural Information Processing Systems (NeurIPS),
2024.
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference.
Conference on Empirical Methods in Natural Language Processing (EMNLP),
2024.
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?.
International Conference on Machine Learning (ICML),
2024.
Exploring validation metrics for offline model-based optimisation with diffusion models.
Transactions on Machine Learning Research (TMLR),
2024.
IntentGPT: Few-shot Intent Discovery with Large Language Models.
Workshop at the International Conference of Learning Representation (ICLR),
2024.
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?.
Workshop at the International Conference of Learning Representation (ICLR),
2024.
Workflow discovery in low data regimes.
International Conference of Learning Representations (ICLR),
2024.
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding.
ArXiv,
2024.
Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images.
ArXiv,
2024.
Language Decision Transformers with Exponential Tilt for Interactive Text Environments.
ArXiv,
2024.
Capture the Flag: Uncovering Data Insights with Large Language Models.
Workshop at the Neural Information Processing Systems (NeurIPS),
2023.
The Unsolved Challenges of LLMs in Open-Ended Web Tasks: A Case Study.
Workshop at the Neural Information Processing Systems (NeurIPS),
2023.
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification.
Conference on Empirical Methods in Natural Language Processing (EMNLP),
2023.
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning.
Conference on Neural Information Processing Systems (NeurIPS),
2023.
OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning.
Workshop at the International Conference on Machine Learning (ICML),
2023.
Workflow discovery in low data regimes.
Workshop at the International Conference on Machine Learning (ICML),
2023.
GEO-Bench: Toward Foundation Models for Earth Monitoring.
NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets),
2023.
Constraining Representations Yields Models That Know What They Don't Know.
International Conference of Learning Representations (ICLR),
2023.
FigGen: Text to Scientific Figure Generation.
International Conference of Learning Representations (ICLR),
2023.
Multilingual Code Retrieval Without Paired Data: A New Benchmark and Experiments.
Workshop at the International Conference on Learning Representations (ICLR),
2023.
Knowledge Hypergraph Embedding Meets Relational Algebra.
Journal of Machine Learning Research (JMLR),
2023.
Leveraging Human Preferences to Master Poetry.
AAAI Workshops,
2023.
Workflow discovery in low data regimes.
Transactions on Machine Learning Research,
2023.
Flaky Performances when Pretraining on Relational Databases.
AAAI-23 Student Abstract and Poster Program,
2023.
OCR-VQGAN: Taming Text-within-Image Generation.
Winter Conference on Applications of Computer Vision (WACV),
2023.
Haptics-based Curiosity for Sparse-reward Tasks.
Conference on Robot Learning (CoRL),
2022.
Constraining Low-level Representations to Define Effective Confidence Scores.
Workshop at the Neural Information Processing Systems (NeurIPS),
2022.
Contrastive Self-supervision Defines General-Purpose Similarity Functions.
Workshop at the Neural Information Processing Systems (NeurIPS),
2022.
Implicit Offline Reinforcement Learning via Supervised Learning.
Workshop at the Neural Information Processing Systems (NeurIPS),
2022.
Consistency-CAM: Towards Improved Weakly Supervised Semantic Segmentation.
British Machine Vision Conference (BMVC),
2022.
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models.
Montreal AI Symposium (MAIS),
2022.
Explainable, Sensible and Virtuous Workplace Chatbots.
Montreal AI Symposium (MAIS),
2022.
Leveraging Activation Patterns to Define Classifiers Able to Detect and Reject Anomalies.
Montreal AI Symposium (MAIS),
2022.
S-LLM: Semi-Supervised Large Language Model for Chat Summarization.
Montreal AI Symposium (MAIS),
2022.
Overcoming challenges in leveraging GANs for few-shot data augmentation.
Workshop at the Conference on Lifelong Learning Agents (CoLLAs),
2022.
Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts.
Workshop at the International Conference on Machine Learning (ICML),
2022.
Multi-label Iterated Learning for Image Classification with Label Ambiguity.
Computer Vision and Pattern Recognition (CVPR),
2022.
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models.
Workshop at the Annual Meetings of the Association for Computational Linguistics (ACL),
2022.
A Probabilistic Perspective on Reinforcement Learning via Supervised Learning.
Workshop at the International Conference on Learning Representations (ICLR),
2022.
Object-centric Compositional Imagination for Visual Abstract Reasoning.
Workshop at the International Conference on Learning Representations (ICLR),
2022.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark.
Workshop at the Neural Information Processing Systems (NeurIPS),
2021.
A Survey of Self-Supervised and Few-Shot Object Detection.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),
2021.
A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images.
IEEE Journal of Biomedical and Health Informatics,
2021.
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations.
International Conference on Computer Vision (ICCV),
2021.
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data.
International Conference on Computer Vision (ICCV),
2021.
SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction.
Workshop at the International Conference on Computer Vision (ICCV),
2021.
3D Perception with Slanted Stixels on GPU.
IEEE Transactions on Parallel and Distributed Systems,
2021.
Weakly Supervised Underwater Fish Segmentation Using Affinity LCFCN.
Nature Scientific Reports,
2021.
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images.
Winter Conference on Applications of Computer Vision (WACV),
2021.
Learning Data Augmentation with Online Bilevel Optimization for Image Classification.
Winter Conference on Applications of Computer Vision (WACV),
2021.
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery.
Workshop at the Neural Information Processing Systems (NeurIPS),
2020.
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
Synbols: Probing Learning Algorithms with Synthetic Datasets.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
LOOC: Localize Overlapping Objects with Count Supervision.
International Conference on Image Processing (ICIP),
2020.
Proposal-based Instance Segmentation with Point Supervision.
International Conference on Image Processing (ICIP),
2020.
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions.
Artificial Intelligence Journal,
2020.
Knowledge Hypergraphs: Prediction Beyond Binary Relations.
International Join Conference on Artificial Intelligence (IJCAI),
2020.
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning.
Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR),
2020.
Knowledge Hypergraphs: Prediction Beyond Binary Relations.
Workshop at the Association for the Advancement of Artificial Intelligence (AAAI),
2020.
A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis.
Nature Scientific Reports,
2020.
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation.
Women in Machine Learning (WiML),
2019.
Knowledge Hypergraphs: Prediction Beyond Binary Relations.
Women in Machine Learning (WiML),
2019.
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation.
Workshop at the International Conference on Computer Vision (ICCV),
2019.
Fourier-CPPNs for Image Synthesis.
Workshop at the International Conference on Computer Vision (ICCV),
2019.
Where are the Masks: Instance Segmentation with Image-level Supervision.
Britsh Machine Vision Conference (BMVC),
2019.
Context-Aware Visual Compatibility Prediction.
Computer Vision and Pattern Recognition (CVPR),
2019.
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation.
International Journal in Computer Vision (IJCV),
2019.
Adversarial Learning of General Transformations for Data Augmentation.
Workshop at the International Conference on Learning Representations (ICLR),
2019.
Where are the Blobs: Counting by Localization with Point Supervision.
European Conference on Computer Vision (ECCV),
2018.
Synbols: Probing Learning Algorithms with Synthetic Datasets.
Montreal AI Symposium (MAIS),
2018.
Slanted Stixels: A way to represent steep streets.
International Journal in Computer Vision (IJCV),
2018.
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation.
Workshop at the International Conference on Learning Representations (ICLR),
2018.
Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data.
International Conference on Computer Vision Theory and Applications (VISIGRAPP),
2018.