Hello and welcome to my personal website! My name is Yasmin, and I am currently working as a machine learning (ML) researcher at McGill University under the supervision of Prof. Giannacopoulos.
My current research involves developing novel physics-informed ML algorithms. Generally, my interests lie in graph representation learning, natural language processing (NLP), physics-informed ML, and application of ML in healthcare. I have recently published my work as the first author at Neural Information Processing Systems (NeurIPS2022) and Empirical Methods in Natural Language Processing (EMNLP2020) conferences.
I have previously earned a bachelor's and a master's degree in Electrical and Computer Engineering as well as two minors in Biomedical and Software Engineering from McGill University.
TLDR: PhysGNN is a novel physics-driven graph neural network based model capable of accurately and efficiently approximating tissue deformation caused by applied forces by which 94-97% of errors are shown to be less than 1 mm—the precision in neurosurgery.
PublicationTLDR: SANS is a novel negative sampling method which generates negative samples from the k-hop neighborhood of nodes. SANS was shown to be competitive with SOTA methods and outperform sophisticated Generative Adversarial Network approaches.
Publication