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My work
portfolio

Project: Journal Hub

Tech Stack

  • Backend: Flask, PostgreSQL, ChromaDB
  • Frontend: TailwindCSS, DaisyUI, jQuery, MathJax
  • Deployment: AWS, Docker, Cloudflare, nginx

Summary

Journal Hub is a prototype online literature discussion platform, focused on publicly and privately curated collections of research publications.

    <div class="flex flex-col md:flex-row gap-4">
        <div class="w-xl m-auto rounded-full">
            <a class="cboxElement" href="{{site.baseurl}}/assets/img/journal-hub/home.png"><img class="rounded-lg"
                src="{{site.baseurl}}/assets/img/journal-hub/home.png" alt="Home page of the Journal Hub alpha.">
            </a>
            <figcaption class="text-center"><b>Home page of the Journal Hub alpha</b></figcaption>
        </div>
        <div class="w-xs m-auto rounded-full">
            <a class="cboxElement" href="{{site.baseurl}}/assets/img/journal-hub/showcase.png">
                <img class="rounded-lg" src="{{site.baseurl}}/assets/img/journal-hub/showcase.png"
                alt="An article showcase in Journal Hub alpha.">
            </a>
            <figcaption class="text-center"><b>An article showcase in Journal Hub alpha</b></figcaption>
        </div>
    </div>
    
    <p>As a platform, the vision of Journal Hub is to be an open space for discussing scientific research, and be a tool
        for
        research groups to collaborate on their own scientific scopes.</p>
    </div>

⚙️ Alpha development

Journal Hub is in its alpha development stage, however its core features have already been implemented. Along with a few close-circle volunteers, we are currently rolling out environments for interested groups, and we welcome all your feedback and ideas!

If you're interested in contributing or using Journal Hub, please send me an email: [email protected]

Research

Applying multivariate methods in Experimental Particle Physics Analysis

Libraries

  • numpy, pandas, scikit-learn, scipy
  • py-$$\mathtt{ROOT}$$
  • Keras

Summary

During my doctorate research, I worked as an author of the ATLAS experiment under an analysis group focusing on a special category of particle interactions associated with di-boson (specifically $W^{\pm}Z$) production and scattering. The culmination of my thesis was to work with LHC data from the period between 2015 and 2018 to validate the observation of a purely electroweak signal and set experimental limits for New Physics, based on an extension of terms of the Standard Model Lagrangian of Particle Physics.

Likelihood-ratio for the observation of the purely electroweak $W^{\pm}Z$ signal Likelihood-ratio for the observation of the purely electroweak $W^{\pm}Z$ signal

The statistical analysis compared single and multi-variate methods (traditionally Boosted Decision Trees) to an application of Neural Network trained utilizing a selection of topological features discriminating the various exepected theoretical signal signatures from the Standard Model background, with the resulting limits on novel interaction being far more constrained in the latter case. Limits on the New Physics (EFT) model parameters were extracted using the profile-likelihood ratio test statistic at a 68% and 95% C.L., being close to $0$ as no significant deviations in terms of previously unobserved interactions have been found.

Two-dimensional contour plot with limits on EFT model parameters Two-dimensional contour plot with limits on EFT model parameters
High-level topological kinematic features used in Neural Network training High-level topological kinematic features used in Neural Network training

You may find a full presentation of my thesis here!

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