Erfan Loghmani

• Doctor of Philosophy
• University of Washington

Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains...

This study documents the impact of different forms of advertising on the demand for cigarettes and their substitutes. We examine the effects of educational public service announcements (PSAs), e-cigarette advertising, direct-to-consumer advertising (DTCA) for tobacco cessation prescription drugs, and advertising by nicotine replacement therapies (N...

Representation learning methods have revolutionized machine learning on networks by converting discrete network structures into continuous domains. However, dynamic networks that evolve over time pose new challenges. To address this, dynamic representation learning methods have gained attention, offering benefits like reduced learning time and impr...

Portfolio optimization is one of the essential fields of focus in finance. There has been an increasing demand for novel computational methods in this area to compute portfolios with better returns and lower risks in recent years. We present a novel computational method called Representation Portfolio Selection (RPS) by redefining the distance matr...

Graphs are a common language in modeling several problems, from social and economic networks to interactions in cells and brain neurons. According to the availability of an enormous amount of data from graphs, Machine Learning algorithms gained lots of attention in this area. But the main challenge is how to represent and encode nodes so that such...