Research work carried out by Michał Mańkowski, a Ph.D. student in the KAUST Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, was recently selected for inclusion by the editors of The American Journal of Transplantation (AJT) in their “Top 10 Articles of 2019.
Prediction method could help personalize cancer treatments and reveal new drug targets.
The psychology of human creativity helps artificial intelligence imagine the unseen.
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.
Fourth-year computer science Ph.D. student Jinhui Xiong won the best paper award at this year’s 24th International Symposium on Vision, Modeling, and Visualization (VMV) 2019.
Abeer Almutairi, a student under the supervision of Professor Robert Hoehndorf, defended her Master's thesis "Unsupervised Method for Disease Named Entity Recognition" on November 4, 2019.
Mona Alshahrani a Ph.D. candidate under the supervision of Professor Robert Hoehndorf Defended her Ph.D. thesis "Knowledge Graph Representation Learning: Approaches and Applications in Biomedicine"
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.
Prior to joining the KAUST CEMSE Division earlier this year, Mohamed Elhoseiny received his Ph.D. degree from Rutgers University, New Brunswick in 2016, before spending over two years working as a postdoctoral researcher at Facebook in the company’s AI research wing. Elhoseiny joins the Division as an assistant professor of computer science based in the KAUST Visual Computing Center (VCC). He will also act as the PI of the KAUST Computer Vision, Content AI (Vision-CAIR) Research Group. Outside of his duties at KAUST, he is also acting as an artificial intelligence (AI) research consultant for Baidu Research, Silicon Valley AI Lab.
A deep learning tool could help in structure-based drug discovery.
Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.
Dominik L. Michels, Assistant Professor of Computer Science and Applied Mathematics, and Head of the Computational Sciences Research Group within KAUST's Visual Computing Center, was recently awarded one of the six Artificial Intelligence Grants of the State of North Rhine-Westphalia (NRW), Germany, for his contributions to the simulation of complex physical environments. The grant, amounting to 1.25 million euros, will fund Michels’ research on algorithmic methods to use synthetic data for training of neural networks in Machine Learning.
“Synthetic data are data that were not obtained by direct measurement but were generated by specific algorithms,” Michels explains, “in neural networks, the use of synthetic data is needed whenever the amount of data available is less than required.”
The NANOVIS team has won the 1st Place in Graph Drawing Contest 2019 under the category of "Creative Topic #2 Meal Ingredients" on their graph "Worldmap of Food".