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NEC Laboratories Europe


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NEC Student Research Fellowship Interview with Dr. Sergi Abadal

Dr. Sergi Abadal completed his NEC Student Research Fellowship with NEC Laboratories Europe on the topic, iGNNspector: Graph-Driven Acceleration of Graph Neural Networks. He recently received a 2022 European Research Council Starting Grant to continue his research.

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Advancing Disease Prevention with NEC MicrobiomePredict: Human Gut Microbiome Disease Prediction

Recent studies show that human gut microbiome plays a key role in regulating human health. Yet despite this, there are no clinical diagnostic tools that use microbiota to identify imbalances within our gut that can lead to serious disease, or tools for identifying existing disease. To help overcome this NEC have developed the machine learning model and system, NEC MicrobiomePredict, which analyzes a person’s gut microbiome to predict whether they are suffering from a disease.

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5G is specifically designed to accelerate the digital transformation of industries

NEC Laboratories Europe is helping lead the way for NEC with its core and applied research. In this recent article, Dr. Xavier Costa, Head of 5G &6G Networks at NEC Laboratories, recently discussed some of our latest research with the Spanish newspaper, Elmundo, including our open RAN solutions, cross-border autonomous driving edge approach, automated search and rescue drone technology (SARDO) and the future of smart surfaces.

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A Study on Ensemble Learning for Time Series Forecasting and the Need for Meta-Learning

Time series forecasting estimates how a sequence of observations continues into the future. In this blog post, we discuss the performance of ensemble methods for time series forecasting. We obtained our insights from conducting an experiment that compared a collection of 12 ensemble methods for time series forecasting, their hyperparameters and the different strategies used to select forecasting models. Furthermore, we will describe our developed meta-learning approach that automatically selects a subset of these ensemble methods (plus their hyperparameter configurations) to run for any given time series dataset.

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