Experienced Software Engineer and Computer Scientist interested in Machine Learning and pushing the boundaries of Computer Science. I am still exploring the field, but I find extreme interest in all the areas I visit. My current research interests lie in solidying the theoretical underpinnings of neural networks and hyperspectral machine learning.
I currently work as a Software & Machine Learning Engineer at HinaLea Imaging, a TruTag Technologies subsidiary focused on the development of hyperspectral imaging and sensing. I lead the effort into developing state-of-the-art hyperspectral machine learning algorithms for sensing on our HYSI data.
Built out a bi-directional sync engine for configuration as code in .NET C# for the Microsoft 365 platform that runs on serverless architecture. Was also responsible for building out a user interface on the web in Vue, Typescript, and SASS.
Researching the mathematical aspects of theoretical machine learning
under Professor Shuyang Ling of NYU Shanghai. Currently focusing on
graph embedding, sparsification, and coarsening, the goal of which is to
allow large-scale graph networks to be computed on with a greatly
reduced computational footprint and minimal deviations from computations
on the original.
Conducted research under Professor Ling for a senior thesis in specifying the limits of popular dimensional reduction techniques and visualization techniques, such as t-SNE, all of which were implemented in PyTorch and Numpy.
Researched emerging post-quantum, lattice-based cryptography schemes
under the guidance of Professor Siyao Guo of NYU Shanghai, as well as
delved into the inner workings of quantum computing theory. The goal of
the research was to investigate some of the many open questions in
post-quantum cryptography and work towards answering them.
Gained experience in writing proofs for proving the security of cryptographic protocols, which are critical in ensuring the security of existing and emerging schemes.
|NYU Shanghai||B.S. Computer Science||Sept 2016 - Dec 2020|
|NYU Shanghai||B.S. Mathematics||Sept 2016 - Dec 2020|
Until GitHub provides a better API for showcasing repositories, you can find my recent and pinned repositories on my GitHub.
Morlock, Frederick, and Dingsu Wang. "MAD-VAE: Manifold Awareness Defense Variational Autoencoder." arXiv preprint arXiv:2011.01755 (2020). Available Here.
You can reach me on any of the following social media platforms. Additionally, you can find my email in my resume.