I'm currently Sr. Data Scientist at Booz Allen Hamilton. Most of what is on this Github site is related to my Ph.D. work, including my work in SA-PINNs and TensorDiffEq, which have a few hundred stars between them. The SA-PINNs paper has over 450 citations at the time of this writing.

• Deep Learning - specifically Physics-Informed Machine Learning or Scientific Machine Learning. I'm fascinated by the intersection of Deep Learning and Physics. Recently I have been working on algorithmic methodologies to improve training of PINNS, as well as an open source software suite for multi-GPU training of large problem domains for PINNS
• Materials Informatics - A lot of my research is centered around materials applications. This is the main application domain of TensorDiffEq as it stands, although it is applicable to any field of science.

I'm interested in a swath of areas of computer science that aren't directly related to my research, notable examples are:

• Distributed computing - I love working in distributed environments. The Army Research Lab has granted me an Nvidia DGX Station for my research, which is a platform I use heavily to experiment and build in distributed environments
• Compilers/IRs - I just find the intersection of hardware and software fascinating. Recently I have been digging into LLVM, MLIR, as well as implementation of assembly in various architectures to include MIPS, x86, and ARM. I'm mostly a high-level programming language guy (Python, R, etc) but I love learning more about the hardware interface, as well as how to take deep-learning implementations to the edge on 'micro' computing devices. Oftentimes these implementations must be executed eithout operating system oversight, and require creative programming
• Computer Vision - early in my PhD I worked heavily in computer vision with applications in materials informatics, and learned a lot about CV.
• Writing - Recently, I have been contributing to d2l.ai with this knowledge of CV, Tensorflow, Distributed Computing, etc. The d2l.ai project is an open-source general purpose deep learning textbook covering topics from MLPs to attention mechanisms and online video processing. The textbook is being used at 140+ universities worldwide and is endorsed by high-profiles individuals to include Jensen Huang, CEO of Nvidia. Specifically, I have been contributing code snippets to the Tensorflow implementation of the concepts in the text, as well as contributing technical content on differences between code implementation in Tensorflow, Pytorch, and MXNet.
• Reviewing - I have taken part in many technical reviews of Manning Publishing books, to include texts on distributed computing and tensorflow, amongst others. Many are in early-stage writing, and technical reviews are anonymous, so I forgo listing specific texts for now.