Man

Isel Grau

Assistant Professor

Eindhoven

Eindhoven, Holanda

Líneas de Investigación


machine learning, explainable artificial intelligence, decision?making in industry

Educación

  •  Computer Science, UNIVERSIDAD CENTRAL DE LAS VILLAS. Cuba, 2011
  •  Computer Science, UNIVERSIDAD CENTRAL DE LAS VILLAS. Chile, 2014
  •  Computer Science, VRIJE UNIVERSITEIT BRUSSEL. Bélgica, 2020

Experiencia Académica

  •   Teaching Assistant Full Time

    UNIVERSIDAD CENTRAL DE LAS VILLAS

    Faculty of Mathematics, Physics and Computing

    Santa Clara, Cuba

    2011 - 2017

  •   PhD Candidate Full Time

    VRIJE UNIVERSITEIT BRUSSEL

    Bélgica

    2015 - 2020

  •   Postdoctoral Researcher Full Time

    VRIJE UNIVERSITEIT BRUSSEL

    Brussel, Bélgica

    2020 - 2021

  •   Assistant Professor in Explainable Artificial Intelligence for Decision Making Full Time

    EINDHOVEN UNIVERSITY OF TECHNOLOGY

    Eindhoven, Holanda

    2021 - A la fecha

Experiencia Profesional

  •   Assistant Professor Full Time

    Eindhoven University of Technology

    Eindhoven, Holanda

    2021 - A la fecha

Formación de Capital Humano


Supervision of 4 ongoing PhDs, 1 defended EngD, 30+ master and bachelor defended thesis.
The master thesis “Leveraging Time Aggregated Data and Advanced Machine Learning Techniques to Improve Semiconductor Demand Forecasting” (2nd supervisor) was nominated for the ESCF Student Award and was published as a paper at the BNAIC/Benelearn Conference 2022.


Difusión y Transferencia


CoRDS: Confident Data?Driven Decision Support.HORIZON?TMA?MSCA?DN, Europe.
AI?Anomaly: Condition Assessment of Urban Water Assets through the Detection and Classification of Anomalies based on Artificial Intelligence Methods. CERIS Seed Funding, Portugal.
Driving Collective Data Governance through Smart Engagement Platforms. INNOVIRIS, Belgium.
BridgeIris: Brussels Big Data Platform for Sharing and Discovery in Clinical Genomics. INNOVIRIS, Belgium



 

Article (10)

Estimating the limit state space of quasi-nonlinear Fuzzy Cognitive Maps
Learning of Fuzzy Cognitive Map models without training data
Learning-based aggregation of Quasi-Nonlinear Fuzzy Cognitive Maps
A revised cognitive mapping methodology for modeling and simulation
Backpropagation through time learning for recurrence-aware long-term cognitive networks
Forward Composition Propagation for Explainable Neural Reasoning
Presumably correct decision sets
Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification
Challenges in describing the conformation and dynamics of proteins with ambiguous behavior
Recommender system using Long-term Cognitive Networks

ConferencePaper (1)

Model Selection Using Graph Neural Networks
10
Yamisleydi Salgueiro

ACADÉMICA

CIENCIAS DE LA COMPUTACIÓN

UNIVERSIDAD DE TALCA

Curicó, Chile

11
Isel Grau

Assistant Professor

Information Systems

Eindhoven

Eindhoven, Holanda