Ivan Perez Avellaneda
Ivan Perez Avellaneda is a researcher specializing in nonlinear systems, currently serving as a business analyst at Monaghan Medical Corporation. During his doctoral studies, he focused on data-driven reachability of nonlinear systems, a field with wide-ranging applications across various scientific and engineering domains, including economics. He brings extensive experience co-teaching in higher education of numerous mathematics-related courses.
He holds a Ph.D. in electrical engineering (2023) from the University of Vermont in the US, an M.Sc in economics (2018), and a B.Sc in mathematics (2016), both obtained from the Pontifical Catholic University of Peru. Alongside his academic pursuits, he has working experience in the education, financial, and business sectors, where he leveraged his skills in data science.
In academia, his interests are vast, but currently, his focus is on specific branches of optimization such as nonconvex, constrained, calculus of variation, optimal control, and the applications of these.
Session
The problem of address matching arrives when the address of one physical place is written in two or more different ways. This situation is very common in companies that receive records of customers from different sources. The differences can be classified as syntactic and semantic. In the first type, the meaning is the same but the way they are written is different. For example, one can find "Street" vs "St". In the second type, the meaning is not exactly the same. For example, one can find "Road" instead of "Street". To solve this problem and match addresses, we have a couple of approaches. The first and simple is by using similarity metrics. The second uses natural language and transformers. This is a hands-on talk and is intended for data process analyst. We are going to go through these solutions implemented in a Jupyter notebook using Python.