User-Centric Gender Rewriting

Bashar Alhafni 
| October 25th 2022 – 11 am, NYUAD C1 (ERB) -045 |

This presentation introduces I-FENN, an integrated finite element neural network framework. The objective of this framework is to decrease the computational expense of nonlinear computational solid mechanics problems, with the use of deep neural networks.

Speaker’s Bio

Bashar is a computer science Ph.D. student at NYU and a graduate research assistant at the CAMeL Lab. Bashar’s research interests lie in the fields of natural language processing and machine learning. Particularly, he is interested in multilingual natural language processing and language generation tasks such as grammatical error correction and machine translation. He is also interested in interpretability to understand how bias manifests in natural language processing models and in creating more robust and fair systems.

Bashar received a Master of Science in Computer Science from the University of Southern California (USC) where he worked at the USC Information Sciences Institute (ISI) on low-resource machine translation, reinforcement learning, event relations extraction, knowledge representation, and semantic modeling. He also has a Bachelor of Science in Computer Science and Mathematics from the University of Bridgeport. Prior to his graduate studies, Bashar also had the chance to work in industry as a software engineer for multiple companies such as Morgan Stanley.