Hi, I’m Matt 👋

Collecting data from humans is always a messy time, because humans are inconsistent, which always resulted in challenges when we had to store the data. So, I created and manage a data engineering pipeline to extract, process, and load data from wearable sensors. This Python forward pipeline is 3+ years old and has been used on 4+ projects since, capable of processing 5+ datasets either locally or on AWS.

Several projects I’m on had needs for Reinforcement Learning and/or Game Theory modeling, with different pain points, and yet, similar needs. As a result, on my own initiative, I created and manage a RL/GT pipeline that is Python and Docker forward. This pipeline leverages the core OpenAI API, but adds scaling capabilities via Docker and custom gaming support, so that a game can transfer learn from other types of games.

We were creating models that predict a person’s activity when I realized these deep learning models were tripping over a particular activity. In its defense, this activity could easily be mistaken for something else and we also had very little data for that activity. So to do my due diligence, I independently collected data of me doing that activity, ran that through the models, and was able to suggest that our models could be capable of predicting that activity if we had more data from more users of this activity.

A common theme in my workplace is they want to use cloud but for one reason or another aren’t always able to get access to the complete power of the cloud. In my work, I’ve deconstructed how cloud technologies work so I can deploy it in-house using the (often restricted) tools available to me. One project had access to AWS S3 but not its databases, so I had to deploy the databases on S3 myself. And on another project, there was no internet at all, so I had to creatively pair multiple machines together to create a makeshift cluster.

Many of our newer employees were being tripped up by my Corporation’s security restrictions for IT, which blocked many of the steps they are asked to do to get onboarded onto their projects. So I took initiative to document my onboarding & technical notes on a team wiki. Since then, the entire team has pitched in and it has went from a 1 page wiki to a 12 page wiki, now used and updated by several teams across the corporation.

During the pandemic, there was a lot of consternation about the direction and unity of our team. Even though I was an engineer, I independently began thinking of ways the team could be re-aligned so that everyone has a clear business and technical objective, thus providing redundancy should one of those areas become volatile. I proposed my plan to our leadership team and it is now the new organization scheme for the group.

Also during the pandemic it became clear that people were losing touch with each other and started feeling more isolated. In response, I created an after hours mailing list where I’d host virtual events and outdoor in person gatherings, to try to keep the team together. This helped built a new office culture where our remote members feel more included even before the pandemic and our in-person members feel more unified, even as we enter a more hybrid schedule.