Hi there! My name is Christian and I am currently a grad student in Computer Science @ University of Illinois at Urbana-Champaign.
I am an introverted guy who enjoys adventures, dogs, food, art, math, computing, and a random assortment of other things! Like this one time I put together an algorithm to make some abstract art via Delaunay Triangulations...
While I started out as an Aerospace Engineer, I transitioned to developing myself as a Computer Scientist after some time in industry because the basis of my interests in Aerospace Engineering were in Computer Science. As a grad student in Computer Science, my advisor is Jeff Erickson and my current research revolves around adaptive meshing under convex constraints for a niche computational physics application.
In terms of academic interests, I have a few. I have a strong interest in math in a broad sense but specifically enjoy more theoretical areas of Artificial Intelligence, Scientific Computing, and Computer Science. Practical implementations can be fun too, but I do have a biased interest towards the theoretical side. That said, I have a strong background in practical implementations in software and I am continuously working on my theoretical skills.
For anyone that wants to reach out, feel free to contact me through my gmail handle choward1491.
One of my last projects while I worked in defense was when I built a framework for distributed optimization, performed using heuristic optimization schemes like Particle Swarm Optimization, that can be used to tackle optimization problems with computation heavy and time consuming cost functions. With this framework, I integrated our Six Degree of Freedom (6DOF) simulation, that models weapon engagement scenarios, to create an optimally tuned set of Bayesian estimators for estimating target motion. What once took months for a person to hand tune has been turned into a job that takes up to a couple days to achieve performance superior to that of the human tuned baseline.
I have also used this framework to begin progress in crafting an optimal guidance algorithm, based on a parametric formulation using Neural Networks, for producing a weapon that can hit a target at some designated time. This latter task was never completed, but my work provides a foundation upon which other GNC engineers can work upon to building a superior guidance algorithm.
Another project I built while working in defense was a Domain Specific Language, named tesl, used to compile scattered datasets on our cluster together and fuse them into a single dataset that could be integrated into other codebases we used (like a library for producing high fidelity Launch Acceptability Regions). This codebase modeled the scattered datasets as a hyperrectangle tessellation and used this assumption to find hyperrectangles that enclosed a desired data point using a fast data structure I designed. Other selectors were also written in case various subsets had different results for the same high dimensional coordinates, allowing us to fuse the results using various filtering techniques. Under the hood, the overall set of coordinates would be represented as a dense graph and would be traversed based on a pattern set in the input script.
Using this interpreted language, written in C++, we were able to efficiently compile many datasets together and greatly reduce time needed to get from generating data to using it in our libraries.