A Brief Personal History

Some brief background and history about myself: Starting at around 11 or 12 years old, I became absolutely obssessed with the idea of becoming a theoretical physicist. Being able to not only develop a deep intution about how the world around me is ordered, but also being able to impart this knowledge onto others and see the joy and wonder it could provide them, just as it did for me, was something I could not ever really get out of my head.

While this dream now exists in the modified form of my career in machine learning applications, I still have a deep love for the field of theoretical physics, and have started to spend a lot more of my free time reading about the latest developments in the field.

My educational and personal background led me through many twists and turns, from performing in extreme metal bands and highly competitive drumlines, to living for 3 months in Europe as a student intern at the Large Hadron Collider, to working on cutting edge high energy theoretical particle physics alongside some of the smartest people I could ever hope to meet.

More recently, for the past 4 years, I have focused on building my career in the field of artifical intelligence. My experience in this field now ranges from training my own custom neural network from scratch (my self-taught introduction to the field), all the way to designing, building, deploying, and monitoring LLM-based pipelines in a production setting.

If you want to hear me speak on this latest experience in particular, you can check out this talk I gave for Jon Snow Labs last year.

I will spare you the details from each of my experiences, but I want to share enough information for you to be able to decide for yourself if I am someone who is even worth listening to, so as to not waste your time, which I think is the worst sin one can commit against themselves.

Experience

I graduated from Angelo State University with 2 degrees; Mathematics and Physics. During my undergraduate education, I participated in 2 pivotal internships that significantly influenced the trajectory of my life:

  • In 2015, I participated in the University of Michigan’s Semester Abroad pilot program, during which I got the opportunity to live in Swizterland for 3 months working as a student research intern at CERN.
  • From 2017~2019 I participated in a student research group based out of the University of Maryland and Brown University, led by Sylvester James Gates. During this time, I contributed to the following publication in the field of high energy theoretical particle physics: On the Ubiquity Of Electromagnetic-Duality Rotations in 4D, N = 1 Holoraumy Tensors for On-Shell 4D Supermultiplets

With respect to my professional experience, I have worked full time since summer 2021 at Pieces Technology, an AI startup in Dallas Texas. During this time, I have been involved with the design, development, and implementation of MLOps solution to complex healthcare problems. During this time I have learned a variety of tools and skills, including Tensorflow, PyTorch, Docker, Kuburnetes, Airflow, and others. The most important skill I learned, however, is by far the ability to teach myself anything I could possibly want to learn, from scratch, and have a track record that supports this claim.

What I am Doing Now

I am currently seeking a role involved with deep learning applications and research. In particular, I am seeking roles giving me the opportunity to apply all of the skills I have developed over my education and career, including my experience working in collaborative research in an extremely technical space, and my more recent experience serving AI solutions at scale in a production environment.

Why I am Writing This

Spending the last 2 years away from the modeling and applied science side of things, and more on the software engineering side of things, has left me feeling a bit empty. I have been wanting to get back into the world of deep learning hands for quite some time now, but could never really find the right time to begin toying around with models again, since there was so much to learn and so much work to be done on the other side of things.

I would call it fortunate that life has found a way to provide me the time I need to finally get back to what I love doing again, which is learning about and applying deep learning to solve real world problems.

In order to do this, and put myself in a position where I can speak with confidence on the subject, there are a few things in my mind which I need to do first:

  1. Review the underlying mathematics behind what makes deep learning work. If I cannot understand the mathematical operations that are being performed, then I cannot understand the models themselves (at least not in a way that I would be satisfied with).
  2. Get back up to speed with the last few years of research and development in the field, which I have admittedly been out of touch with for too long now
  3. Build models from scratch, and build applications from pretrained base models, and apply them to some real world problems in a novel way.

I am hoping that by writing about my journey through these three steps, I will be able to not only help myself learn and grow, but also help others who are in a similar position as myself, or maybe those who are themselves just starting out in the field.

I know there are a wide range of people who are starting to realize the current value and the upcoming importance and impact of these technologies, especially people coming from the parts of the software engineering world that were until now not really exposed to these ideas, or if they were then it was only at the surface level.

The amount of information that people in this position have to sift through in order to get up to speed is staggering, and I hope that by writing about my own journey, I can help to make that process a little bit easier for them.

What I Will Be Writing About First

To start, I will be writing about the first step in my journey, which is to review the underlying mathematics behind what makes deep learning work. I will be doing this in a way that is hopefully accessible to anyone who is interested in learning about the field, but who may not have the mathematical background that is typically required to understand the models themselves. Following this, I will be reviewing the latest research and development in the field, and then finally I will be building models from scratch and applying them to some real world problems. After this, I will dive into building these base models from scratch, and walk through the process of building applications from them.

To summarize some of the high level topics I will be covering in this series, I will be writing about:

  • The mathematics behind deep learning
  • The latest research and development in the field
  • Building models from scratch
  • Building applications from pretrained base models
  • Applying these models to some real world problems in a novel way
  • And more!

Note that this list is not exhaustive, and I will be adding to it as I go along. I will also be adding links to each of these sections as I write about them, so that you can easily navigate to the topics that interest you the most.

With that, I will leave you with one of my favorite quotes of all time, which I like to think can be applied to any situation in life, including this one:

“All we have to decide is what to do with the time that is given us.” ― J.R.R. Tolkien, The Fellowship of the Ring