Academic

In today’s data-driven landscape, harnessing vast and varied datasets provides unparalleled opportunities for knowledge extraction and informed decision-making. Yet, within this abundance, a pivotal concern emerges: the quality and origin of the data, a challenge pervasive across diverse fields such as health sciences, epidemiology, economics, and beyond.  The presence of noisy data or measurement error can obscure patterns, introduce biases, and undermine the reliability of analyses. Such an issue has attracted extensive attention in both statistical and machine learning communities. In this talk, I will briefly discuss the complexities arising from dealing with noisy data and their potential to impede statistical inference or machine learning procedures. Through this exploration, we aim to shed light on the importance of addressing data quality issues and developing strategies to mitigate their adverse effects on decision-making processes.