The Research Lab | About Me & The Lab
Intro:
Hello and welcome to The Research Lab! My name is Aaron Tellis and to start I should probably warn you now that I am currently not a published researcher or anything like that. You may be wondering why call it “The Research Lab” then? Besides being an awesome name I think of my articles on Medium and Code in my Github Repository as fulfilling a childhood dream of having my own Dexter’s Laboratory, although I am no boy genius. While that reference may only be relevant to the other 90’s kids I believe our goals are similar, and that is simply to experiment and innovate.
Background:
In modeling there’s the idea of model drift that describes a situation where the model is no longer predictive, because the underlying processes of which the model was trained has changed. I bring this up because my biggest pet peeve is when people operate heavily on heuristics despite the data suggesting that the model be updated. Not trying to go into full rant, but I believe that is important context to have when trying to understand my desires for The Research Lab.
Education/Interest
- M.S. in Data Science w/ Specialization in Analytics & Modeling
- B.B.A in Management Information Systems & Finance
- Guitar/Piano
- Wrestling, Kickboxing, MMA
- Philanthropy
- GOD
- NBA Basketball (Go Pistons!)
- And Many More!
Despite this specific article I am really not one that likes to talk about themselves, so let me give you a high-level synopsis and feel free to ask me if you have more questions.
Generalist & Systems Thinker
Perspective is important, and I understand we all have trouble truly taking the time to develop a full perspective because life is busy and it’s easier to use our mental models to label things and move on. As a response I chose to identify as a Generalist and Systems Thinker to help those of us that feel the need to label.
- Generalist :A Generalist is a person with a wide array of knowledge on a variety of subjects, useful or not.
- Systems Thinking : Systems thinking is a way of making sense of the complexity of the world by looking at it in terms of wholes and relationships rather than by splitting it down into its parts.
There are two strategies every living organism must choose throughout their lifetime. These strategies are to go deep or go wide ie. develop a skillset based on being generalized or specialized. My decision is based on the analogy of there being a field of knowledge.
Imagine that there is a field of knowledge with different areas representing different skills that one can build expertise in. We all have the choice to dig where we want and we can dig one hole or we can dig many holes. As you dig you find that at first the hole is hard to dig, perhaps the ground is hard or you may not have the proper tools to dig. As you continue you begin to understand how to optimize your digging and you’re able to dig deeper faster. Over a longer period of time digging becomes harder again as you can’t dig forever and eventually you may hit a different type of material in the ground requiring a more specialized tool to dig deeper therefore you begin to experience the same issues as in the beginning. At this point you have a choice to continue or to start a different hole. I might add that some holes contain precious metals while others may be home to poisonous creatures or be at risk of natural disaster. Now and lets compare two scenarios.
The first scenario being you chose to dig many holes. In this scenario lets say you chose some homogenous holes to ones you’ve dug in the past and others are not so similar. With the homogenous holes you were able to use the skills and tools built from previous holes to dig new ones. This allowed you to dig deeper faster from the beginning as the experience required has largely already been developed. Other heterogenous holes gave you new tools, but the learning was not as quickly gained. Then one day an earthquake and tsunami hits. Some of your holes filled with precious metals are now filled with earth or water and some of your tools are destroyed. You still have holes left, but the event does hit hard.
The second scenario represents the strategy of digging a few deep holes and the holes are fairly similar to each other. Having dug so deep during calm periods you were able to build great wealth having dug in an area filled with highly valuable metals and minerals. However during the same earthquake tsunami combo you lost some of your most valuable holes and specialized tools leaving you nearly at an identical position to when you first started.
I could drag this analogy out much more however, the point I am making is that when evaluating the history of economies while making professional decisions I found that digging many holes help develop the skills to make it easier to bounce back from disaster even if the gain from the many holes are marginal compared to those that dug deeply.
Philanthropy
Having had experienced healthcare insecurity and watching my parent’s financial woes growing up through the 2008 Housing Crisis I have grown to understand what it means to not have and also understood that a lot of the hardship we experience in this existence is due to not having. This reality has driven my interest in philanthropic work. I subscribe to the idea that “if I can be the person that I needed growing up then maybe I can make this life better even if only for one person”.
The Research Lab:
The goal of the research is to use my education and interest in the pursuit of experimentation and innovation. This will mainly be expressed through Technology, Data Science, and other Computational Statistics explorations.
Technology
Having experience in data visualization, engineering, and modeling we will spend a lot of time just reviewing technology used in these verticals to understand their strengths, weaknesses, and similarities to other technologies.
Data Science
There’s a new algorithm or enhancement to old algorithms every week. In addition old ideas are applied to new use cases all the time. Trying to keep up feels impossible, but we will try. You can expect exploration in models & analytic methods to uncover their behaviors and the intuition behind them.
Computational Statistics
Overtime it became apparent to me that a lot of information is offered in terms of exploring algorithms, and we may have lost sight of how their applications can be integrated into our world. Our goal is to not only find an “accurate” way to model, but also implement those models given our uncertain reality. You can expect topics such as backtesting, stress testing, and optimization to realize this goal.
Others
I won’t promise everything will be exactly on topic as this will probably be a space where I also make commentary on industry or specific situations that I have experienced. For example, conversation on how the above is actually being implemented in organizations or other patterns occurring in “data driven” organizations.
Using a systems thinking perspective we will explore the above facets in an attempt to create a wholistic picture of we can use our tools too experiment and innovate.
Conclusion:
If you’re not bored to death yet then I hope you are excited to join me in this journey. I love receiving feedback, whether positive or constructive, so please feel free to contact me. Also please feel free to browse my reading list as I hope to not only share my own ideas, but highlight others working in these spaces. So without further ado, kick back, relax, and enjoy the show!
