Professional Experience

Beyond the stylized information on this page, you may find my current, PDF version of my resume at this link.

Table of Contents

Education

Institution Degree Dates Attended
NYU Shanghai B.S. Computer Science Sept 2016 - Dec 2020
NYU Shanghai B.S. Mathematics Sept 2016 - Dec 2020

Experience

Industry Experience

Machine Learning Scientist & Software Engineer – Hinalea Imaging (Apr 2022 - July 2024)

  • Led the research and development effort of state-of-the-art hyperspectral machine learning algorithms from the research phase into deployment in novel client applications. Additionally, I built the foundational libraries, code, and infrastructure necessary to support the burgeoning Data Science team.
  • Collaborated with leadership to plan the company’s approach to establishing ourselves in the ever-evolving Machine Learning & AI market. Mentored new hires in research-based skills and processes upon joining my team.
  • Conducted literature reviews and research on current methods and theory as well as disseminated knowledge to colleagues through internal documentation & "crash courses" on machine learning and optimization theory.
  • Extended algorithms for the high dimensional data reconstruction, calibration, and optimization of our hyperspectral sensors and raw data readouts to greatly improve the quality and robustness of the processed results.

Full Stack Developer – Simeon Cloud LLC (Mar 2021 - Dec 2021)

  • Worked alongside the founder to build a bi-directional “configuration as code” engine in C# for managing companies’ Microsoft 365 infrastructure. Designed to be run on serverless architecture, the engine employed automatic version control through Git to track changes configurations across time.
  • Led the design and development of the user-facing web portal which enabled clients to view, modify, and deploy complex configurations to their cloud infrastructure. The Vue/Typescript/SASS web portal would control the serverless backend APIs.

Research Experience

Machine Learning Research Assistant – NYU Shanghai (Dec 2019 - Dec 2020)

  • Studied embedding and visualization of high-dimensional and non-Euclidean data using unsupervised and supervised Deep Learning networks under the supervision of Professor Shuyang Ling. Careful consideration was made in understanding the mathematics that underpin these techniques.
  • Authored the Senior Capstone Thesis "Exploring the Limitations of t-SNE" under the supervision of Professor Shuyang Ling. This work aimed to find of the limits of popular dimensional reduction and visualization techniques, such as the t-SNE dimensionality reduction algorithm. The manuscript specifically explored degenerate embeddings of high-dimensional data, applications to latent geometric processes in image sequences, and unscrambling randomized Radon Transform tomography data.

Cryptography Research Assistant – NYU Shanghai (Aug 2019 - Dec 2019)

  • Researched emerging post-quantum, lattice-based cryptography schemes under the guidance of Professor Siyao Guo of NYU Shanghai, as well as delving 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 toward 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. Left to resume pursuing interests in Machine Learning.

Papers

Unpublished Manuscripts

Unfortunately, all I have to show here right now are papers I wrote in my naive undergraduate research phase.

Morlock, Frederick. "Exploring the Limitations of t-SNE" NYU Shanghai Senior Capstone Thesis (2020). Available Here .

Some Personal Notes
I wrote my undergraduate Senior Thesis paper "Exploring the Limitations of t-SNE" under the supervision of Professor Shuyang Ling of NYU Shanghai. The paper experimentally analyzed 3 problems using the t-SNE algorithm to test both its limitations and possible novel applications. The paper explored the effects of the "crowding problem" when embedding high-dimensional data in 2D, the preservation of latent geometric structure in embeddings, and the application of unscrambling the "Scrambled Radon Transform" in tomography data. As much as I regret it, while the paper was theoretically motivated, there was not a rigious analysis of the phenomenon, but rather just empirical studies.

Morlock, Frederick. "Graph Embedding and Visualization Using t-SNE" Deans' Undergraduate Research Fund -- NYU Shanghai (2020). Available Here.

Some Personal Notes
I received funding through the Deans' Undergraduate Research Fund at NYU Shanghai, where I conducted research under the supervision of Shuyang Ling. In "Graph Embedding and Visualization Using t-SNE", I furthered some of the research I did as part of my Thesis and explored the application of t-SNE to graph embedding. I primarily looked at 3 datasets: Facebook's "Egonet" dataset, the "JAGMESH" mesh network dataset, and a simple "Stochastic Block Model" network. While t-SNE showed promising embeddings, the computational cost was high. There were 3 approaches taken to improve the efficiency: graph sparsification via "effective resistance" approximations, geodesic distance approximations via the Nystrom Approximation, and graph coarsening via Kron Reduction.
Note: Kron Reduction was viable in reducing the computational complexity of the problem, given that we wanted to preserve all of the nodes in the original data in our embedding, graph coarsening (removing/combining graph nodes) was not included in the results.

It may be noted that this paper was written and submitted in 2020, despite the linked paper header reading 2021. The header there contains the date when I compiled that version of the paper since I had my LaTeX heading set to read the current date...

Morlock, Frederick, and Dingsu Wang. "MAD-VAE: Manifold Awareness Defense Variational Autoencoder." arXiv preprint arXiv:2011.01755 (2020). Available Here.

Some Personal Notes
Personally, I'm not sure that anyone should read this paper. It was from a period of my life when I was naive about research, and it was published long after it was written. Not only was I a naive young aspiring researcher, but the study of adversarial examples was young at the time as well. While I haven't studied this area for a while, if you are curious about adversarial examples, then you might want to read the paper "Adversarial Examples Are Not Bugs, They Are Features" by Ilyas et. al.