Uchenna Akujuobi is currently a research scientist with the SonyAI gastronomy team based in Tokyo. His research interests include network embedding, graph mining, information retrieval,text mining, and deep neural networks. Born in Nigeria, he obtained his BS degree in Saint Petersburg Electrotechnical University and his MS and PhD degree in the MINE Laboratory at the King Abdullah University of Science and Technology. He is motivated by the use of AI to augment human abilities for more creativity and improved performance. He believes in pushing the boundary between human possibilities and impossibilities one AI step at a time.
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the use of machine learning to the scientific process of hypothesis generation (HG), which refers …
T-PAIR: Temporal node-pair embedding for automatic biomedical hypothesis generation
In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningfulimplicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted fromdatabases of biomedical publica…
July 9, 2021 | Sony AI
Revolutionizing Hypothesis Generation
In part, scientific research is led by the hypothesis – the supposition or proposal that forms the basis for further investigation. Traditionally, such hypotheses are formed by the researcher and their team, taking into account v…
In part, scientific research is led by the hypothesis – the supposition or proposal that forms the basis for further investigatio…
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