Danilo S. Carvalho, Ph.D. Research Associate - Artificial Intelligence / NLP

Vision & Research Focus

My research work deals with understanding and filling the gap between the realm of human though, and in particular human language and the realm of computer machinery, which is the key component the next generation of intelligent systems that will be able to automatically understand and process the meaning of information at scale.
I have been involved with Artificial Intelligence (AI) and Natural Language Processing work for over a decade, in which this field has seen several big advancements in technology and practical applications, from the generation and organization of massive textual corpora to Neural-based Machine Translation. Now, the application of AI in many areas hangs in the ability to explain the answers it provides, with the analysis of information from healthcare, energy and transportation sectors, and of the propagation of misinformation on online social media posing as challenging, but necessary testing grounds for the design of explainable AI, wherein lies my current efforts.

Profile

Danilo Carvalho is a Research Associate at the Department of Computer Science at the University of Manchester, working on Safe and Explainable Artificial Intelligence (AI) architectures.
[About]

Current Research

◈ Theoretical research on verification and interpretability of neural AI architectures.

◈ Application of explainable AI systems into safety-critical tasks in healthcare and energy.

◈ Applied research in the fields of Natural Language Processing, Knowledge Representation, on the analysis of patent, bibliographical, and biotechnology databases.

◈ Technological development project, in the scope of data analysis automation and strategic monitoring on healthcare innovation.

◈ Online social media analysis and media literacy.

Areas of Interest

◈ General

  • Computational Linguistics / Natural Language Processing
  • Artificial Intelligence
  • Data Science
  • Software Engineering

◈ Specific (summary)

  • Explainable AI
  • Open Information Extraction
  • Semantic Representation
  • Patent / Bibliographical Databases
  • Language Models

Current Teaching


Projects

TDV: Word vector representation based on Wiktionary meanings [more]

  • Morpheme to phrase representation
  • NLP features: Muilti-language, sense polarity, sense disambiguation by POS

EasyESA: Easy Semantic Approximation with Explicit Semantic Analysis [more]

  • Provides concept vectors and a semantic relatedness measure
  • Query explanations can give insights on relatedness results

Graphia: Extraction of Structured Discourse Graphs from text [more]

  • Performs Named Entity Resolution to DBpedia entities and co-reference resolution
  • Serialization of discourse graphs as RDF