“I’m smart so I can figure it out” is a very good way to live your life. Too many engineers don’t want to delve into unfamiliar topics, however I really enjoy expanding my wheelhouse and challenging myself where possible.
While my thesis is in the cogs of being published, because I still wanted to post about it and talk about it on my blog. This is a cut up version that touches on the major keystones of my work. Originally I was doing a PhD, but decided to master out, so my research is more than a typical master’s thesis in terms of scope.
PGQueuer takes a PostgreSQL database and can turn it into a reliable background job processor. The problem becomes how on the face unwieldy the technology is. Because it’s powerful, it requires a lot to get into, especially for people new to backend development.
An interesting phenomenon in data is that of missing values. What’s even more interesting are the ways missing values mean different things. They can be missing completely at random (MCAR), but more likely there is a cause for why that data doesn’t exist. If we have other pieces of data that suggest why a value might be missing (missing at random: MAR), then not having a value is a value in itself…
This blog post provides a recent exploration into multimodal models related to health. Especially in the case of diagnosis, we hardly ever rely on a single modality to inform our decisions, so we must rely on the multimodal nature of human beings to come up with a comprehensive view of what is going on. This won’t mean that we truly use “everything” in every paper, as in feature selection only…
In my research and understanding of multimodal models in health, I came across a modality that I dreaded; unidimensional data. How do you deal with such a modality? What’s tricky is that unlike other modalities it isn’t discussed as much and oftentimes feels underdeveloped in the literature. With no formal training in how to deal with this topic, I went back and read the first seven chapters of…