Robert Hoehndorf

About Robert Hoehndorf

Robert Hoehndorf

Associate Professor, Computer Science

artificial intelligence bioinformatics biomedical data knowledge representation Neuro-Symbolic AI neuro-symbolic methods

Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Events

Presented Events

Dec 1 - Dec 7, 2024

Neuro-symbolic methods for Semantic Web ontologies

Robert Hoehndorf, Associate Professor, Computer Science

Dec 2, 12:00 - 13:00

B9, L2, R2325

Semantic Web ontologies are widely used to provide a conceptual schema for sharing and integrating data and knowledge using a logic-based language. The content of ontologies may also be used to provide background knowledge in machine learning models or provide domain-specific constraints that can be verified automatically and used for zero-shot predictions.

Sep 17 - Sep 23, 2023

Neuro-symbolic methods for Semantic Web ontologies

Robert Hoehndorf, Associate Professor, Computer Science

Sep 18, 11:30 - 12:30

B9 L2 H2 H2

Semantic web Ontologies neuro-symbolic methods

Semantic Web ontologies are widely used to provide a conceptual schema for sharing and integrating data and knowledge using a logic-based language. The content of ontologies may also be used to provide background knowledge in machine learning models or provide domain-specific constraints that can be verified automatically and used for zero-shot predictions. The combination of embedding symbolic representations (such as ontologies) and extracting symbolic representations from the embeddings are two main components of neuro-symbolic AI systems. I will introduce methods for embedding Semantic Web ontologies and outline some of the properties of the embeddings that relate to model and proof theory.

Nov 21 - Nov 27, 2021

Knowledge-based methods in bioinformatics

Robert Hoehndorf, Associate Professor, Computer Science

Nov 22, 12:00 - 13:00

B9 R2322 H1

The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for identification of gene-disease associations, genomic variants that are causative for heritable disorders, and to predict protein functions. The methods I describe are generic and can be applied in other domains in which biomedical ontologies and structured knowledge bases exist.

Jun 27 - Jul 3, 2021

Artificial Intelligence methods for precision health: Applications to rare disease and cancer research

Robert Hoehndorf, Associate Professor, Computer Science

Jun 27, 10:00 - 11:00

B9 L2 H2

artificial intelligence

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for the identification of gene-disease

Mar 1 - Mar 7, 2020

Knowledge-based methods in life sciences

Robert Hoehndorf, Associate Professor, Computer Science

Mar 3, 10:00 - 11:30

B9 L2 H2 R2325

Computer science bioinformatics knowledge representation

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

Aug 25 - Aug 31, 2019

Knowledge-based methods in biomedicine

Robert Hoehndorf, Associate Professor, Computer Science

Aug 26, 12:00 - 13:00

B9 L2 H1

Biostatistics artificial intelligence gene-disease associations drug targets

The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to apply knowledge-based methods for the analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets.

Feb 24 - Mar 2, 2019

Neuro-symbolic systems in the life sciences

Robert Hoehndorf, Associate Professor, Computer Science

Feb 25, 12:00 - 13:00

B9 H1 R2322

biomedical data data analysis symbolic methods

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to generate knowledge graph embeddings for analysis of biological and biomedical data. Brief Biography Robert

Sep 30 - Oct 6, 2018

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

Robert Hoehndorf, Associate Professor, Computer Science

Oct 3, 12:00 - 13:00

B2 R5220

biology biomedicine bioinformatics Ontologies

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

Mar 11 - Mar 17, 2018

Symbolic AI in Computational Biology

Robert Hoehndorf, Associate Professor, Computer Science

Mar 12, 12:00 - 13:00

B9 H1 R2322

About The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to combine symbolic and statistical Artificial Intelligence approaches for the analysis of biological and biomedical

Feb 25 - Mar 3, 2018

Symbolic AI in Computational Biology: Applications to Disease Gene and Drug Target Identification

Robert Hoehndorf, Associate Professor, Computer Science

Feb 26, 16:30 - 17:30

The University of Cambridge in the United Kingdom

Abstract KAUST Assistant Professor Robert Hoehndorf will give a seminar on " Symbolic AI in Computational Biology: Applications to Disease Gene and Drug Target Identification" at the University of Cambridge in the United Kingdom. More Information The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are

Feb 18 - Feb 24, 2018

Keynote Speaker | The 8th BEAR PGR Conference & Users Forum 2018

Robert Hoehndorf, Associate Professor, Computer Science

Feb 23, 09:00 - 16:30

The University of Birmingham in the United Kingdom

High Performance Computing cloud storage data visualisation

KAUST Assistant Professor Robert Hoehndorf will be a keynote speaker at the 8th BEAR PGR Conference & Users Forum at the University of Birmingham in the United Kingdom.