Research

Argues that progress on the SDGs is bottlenecked by data discoverability, and sets out the design and ethics of a global catalog to fix it.

Whether a clinical AI should explain itself depends on validation level, deployment context and who is deciding — explainability is not always the right answer.

A post-hoc trustworthiness assessment of a deployed chest-X-ray severity model in Brescia, applying the EU HLEG framework under pandemic pressure.

Presents the SDG Data Catalog system: NLP over open-access literature to build a global database of datasets, metadata and research networks.

Shows how an ethically-aligned co-design process shapes a skin-lesion classifier from the earliest design phase, with a multidisciplinary team.

Applies the EU trustworthy-AI guidelines to a live emergency-call cardiac-arrest detector in Copenhagen and surfaces the trade-offs an interdisciplinary team actually faces.

Describes mining millions of open-access papers to build an open, extensible catalogue of SDG-relevant datasets.

Led the UN SDG Data Catalog at the AI for Good Foundation: an NLP extraction pipeline (BiLSTM-CRF, fine-tuned BERT) over millions of academic papers that maps datasets, research, and organizations across the Sustainable Development Goals, reaching over 92% recall on dataset discovery. Secured $300K+ in Microsoft funding and was presented at SIGKDD 2019.

Co-developed a process, grounded in applied ethics and the EU High-Level Expert Group definition of trustworthy AI, for assessing whether an AI system can be trusted in practice. Applied it to four production healthcare AI systems with clinicians, ethicists, and legal scholars; the assessments became four peer-reviewed publications.

Fine-tuned Mistral and LLaMA on k-mer DNA sequences for metagenomic classification, reaching 82% family-level taxonomic accuracy by treating genomic sequences as a language.

Trained LSTM and Transformer architectures from scratch on tokenized MIDI corpora at UC Berkeley, with sparse attention for compute efficiency. The accompanying write-up became one of the most-read Towards Data Science pieces on music generation.

Monthly public tracking of workforce diversity across 1.3M+ US organizations, built with the AI4Good Foundation.

An interactive tool that adapts student learning to cognitive preferences and learning styles. Presented at SIGKDD 2019.