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  <h2 id="about">About</h2>
  <p>
    Computer Scientist, Mathematician, and Software Engineer interested in
    Machine Learning and pushing the boundaries of understanding in Computer
    Science. I have been programming, tinkering, and constantly curious for over
    a decade and I consider it to truly be my lifelong passion. I am
    mathematically obsessed and still exploring the realm of academia &amp;
    research, but I have found the intersection of Computer Science and
    Mathematics to be fascinating. <i>As of July 2024 I have left my role as a Machine
    Learning Scientist at Hinalea Imaging in order to pursue PhD applications.</i>
  </p>
  <p>
    <b>
      I am looking to apply for Applied Mathematics and/or Computer Science Ph.D
      programs this Fall (2024), and I am hoping to start Fall 2025 or sooner.</b
    >
    My <i>primary</i> research interests lie in the rigorous mathematical
    analysis of Deep Learning, including "Deep Linear Learning", as well as the
    reliable and efficient application of the "toolbox" endowed by deep learning
    theory to other scientific disciplines. I am also acutely interested in
    learning more about algorithmic information &amp; complexity theory and its
    application to Machine Learning/AI Theory. I am eager to meet
    collaborators and mentors as I venture forth on this path.
    <br /><br />
    If you would like to have a chat, feel free to reach out to me at my email
    "<code>me&lt;at&gt;freddy.us</code>" or on any of the platforms
    <a href="#contact">below</a>.
  </p>
  <h2 id="experience">Experience</h2>
  <h3>
    Machine Learning Scientist & Software Engineer – Hinalea Imaging (<i
      >Apr 2022 - July 2024</i
    >)
  </h3>
  <div class="exp_desc">
    <p>
      I led the research and development effort of state-of-the-art
      hyperspectral machine learning algorithms at Hinalea Imaging. In addition
      to the research and implementation of novel models for client
      applications, I built the foundation libraries, code, and infrastructure
      necessary to support the burgeoning Data Science team.
    </p>
    Contributions:
    <ol>
      <li>
        Conducted literature reviews and research on current methods and theory
        as well as disseminated knowledge to colleagues through internal
        documentation &amp; "crash courses" on machine learning and optimization
        theory.
      </li>
      <li>
        Built the foundational deep learning and data processing libraries
        necessary for research and development of custom Machine Learning models
        for use in a collaborative business setting.
      </li>
      <li>
        Developed algorithms for high dimensional data reconstruction,
        calibration, and processing to assist in development of our underlying
        hyperspectral data reconstruction from our hardware.
      </li>
      <li>
        Employed self-supervised pretraining of convolution and attention-based
        model to improve the generalization power of models in low-data
        environments.
      </li>
      <li>
        Designed and developed Siamese network-based object tracking and
        homography deep learning models for the real-time co-registration of
        objects to improve spectral stability under motion.
      </li>
      <li>
        Developed regression and classification Deep Learning-based vision
        models for agriculture, laboratory, and other customer application
        settings.
      </li>
    </ol>
  </div>
  <h3>Full Stack Engineer – Simeon Cloud LLC (<i>Mar 2021 - Dec 2021</i>)</h3>
  <div class="exp_desc">
    <p>
      Built a bi-directional sync engine for "configuration as code" in .NET C#
      for the Microsoft 365 platform -- running completely on serverless
      architecture.
    </p>
    <p>
      Developed automatic version control for detection and resolution of state
      changes detected in users’ Microsoft infrastructure configuration.
    </p>
    <p>
      Was also responsible for building out a user interface on the web in Vue,
      Typescript, and SASS.
    </p>
  </div>
  <h3>
    Machine Learning Research Assistant – NYU Shanghai (<i
      >Dec 2019 - Dec 2020</i
    >)
  </h3>
  <div class="exp_desc">
    <p>
      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.
    </p>
    <p>
      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.
    </p>
  </div>
  <h3>
    Cryptography Research Assistant – NYU Shanghai (<i>Aug 2019 - Dec 2019</i>)
  </h3>
  <div class="exp_desc">
    <p>
      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.
    </p>
    <p>
      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.
    </p>
  </div>
  <h2 id="education">Education</h2>
  <table>
    <tr>
      <th>Institution</th>
      <th>Degree</th>
      <th>Dates Attended</th>
    </tr>
    <tr>
      <td>NYU Shanghai</td>
      <td>B.S. Computer Science</td>
      <td>Sept 2016 - Dec 2020</td>
    </tr>
    <tr>
      <td>NYU Shanghai</td>
      <td>B.S. Mathematics</td>
      <td>Sept 2016 - Dec 2020</td>
    </tr>
  </table>
  <h2 id="recent">Recent Work</h2>
  <p>
    Until GitHub provides a better API for showcasing repositories, you can find
    my recent and pinned repositories on
    <a
      href="https://github.com/FrederickGeek8"
      rel="me external"
      class="link"
      target="_blank"
      >my GitHub</a
    >.
  </p>
  <h2 id="publications">Papers</h2>
  <h3>Unpublished Manuscripts</h3>
  <p class="note grey tight" style="border: 1px black solid">
    <i
      >Unfortunately, all I have to show here right now are papers I wrote in my
      naive undergraduate research phase.</i
    >
  </p>
  <div class="pub">
    <p>
      Morlock, Frederick. "Exploring the Limitations of t-SNE"
      <i>NYU Shanghai Senior Capstone Thesis</i> (2020).
      <a
        href="https://drive.google.com/file/d/12C-xS0Q8_QXlKUVdpexMhyEIyhmMwXgU/view?usp=sharing"
        rel="external author"
        target="_blank"
        class="link">
        Available Here </a
      >.
    </p>
    <p class="note">
      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.
    </p>
  </div>

  <div class="pub">
    <p>
      Morlock, Frederick. "Graph Embedding and Visualization Using t-SNE"
      <i>Deans' Undergraduate Research Fund -- NYU Shanghai</i> (2020).
      <a
        href="https://drive.google.com/file/d/12GawzvvX-YWl45SsYs7lx663cWwOt3co/view?usp=drive_link"
        rel="external author"
        target="_blank"
        class="link"
        >Available Here</a
      >.
    </p>
    <p class="note">
      I received funding through the Deans' Undergraduate Research Fund at NYU
      Shanghai, where I conducted research under the supervision of Shuyang
      Ling. In the manuscript "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 and visualization. In the paper, I
      studied applying t-SNE for use in graph embeddings. 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
      possesses the ability to be able to perform graph embeddings, one of its
      limitations is the computational complexity required to compute it. There
      were 3 approaches that were taken to overcome this limitation: graph
      sparsification via "effective resistance" approximations, geodesic
      distance approximations via the Nystrom Approximation, and graph
      coarsening via Kron Reduction. While Kron Reduction was viable in some
      sense for reducing the computational complexity of the problem, given that
      we wanted to preserve all of the nodes in the original data in our
      embedding, I did not include results using the Kron Reduction as graph
      coarsening (removing/combining graph nodes) as it was not in the spirit of
      the paper's objective.
      <br />
      <br />
      <i
        >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...</i
      >
    </p>
  </div>
  <div class="pub">
    <p>
      Morlock, Frederick, and Dingsu Wang. "MAD-VAE: Manifold Awareness Defense
      Variational Autoencoder."
      <i>arXiv preprint arXiv:2011.01755</i> (2020).
      <a
        href="https://arxiv.org/abs/2011.01755"
        rel="external author"
        target="_blank"
        class="link"
        >Available Here</a
      >.
    </p>
    <p class="note">
      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
      <a
        href="https://arxiv.org/abs/1905.02175"
        rel="external"
        target="_blank"
        class="link"
        >"Adversarial Examples Are Not Bugs, They Are Features" by Ilyas et.
        al.</a
      >
    </p>
  </div>
  <h2 id="resume">
    <a
      href="{{ site.resume }}"
      rel="external author"
      class="link"
      target="_blank"
      >Resume/CV</a
    >
  </h2>
  <h2 id="contact">Contact</h2>
  <p>
    You can reach me on any of the following social media platforms.
    Additionally, you can find my email in my
    <a
      href="{{ site.resume }}"
      rel="external author"
      class="link"
      target="_blank"
      >resume</a
    >.
  </p>
  <ul id="links">
    <li>
      <a
        href="https://www.linkedin.com/in/frederickmorlock"
        rel="me external author"
        class="link"
        target="_blank"
        >LinkedIn</a
      >
    </li>
    <li>
      <a
        href="https://github.com/FrederickGeek8"
        rel="me external author"
        class="link"
        target="_blank"
        >GitHub</a
      >
    </li>
    <li>
      <a
        href="https://social.nerd.net/@fooderick"
        rel="me external author"
        class="link"
        target="_blank"
        >Mastodon</a
      >
    </li>
    <!-- <li>
      <a
        href="https://twitter.com/FrederickGeek8"
        rel="me"
        class="link"
        target="_blank"
        >Twitter</a
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    <li>
      <a
        href="https://letterboxd.com/frederickgeek8/"
        rel="me external author"
        class="link"
        target="_blank"
        >Letterboxd</a
      >
    </li>
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