Refine layout research

.github/workflows/hugo.yml

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+name: Deploy Hugo site to Pages
+
+on:
+  push:
+    branches: ["main"]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    env:
+      HUGO_VERSION: "0.147.2"
+    steps:
+      - name: Install Hugo
+        run: |
+          wget -O "$RUNNER_TEMP/hugo.deb" "https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_extended_${HUGO_VERSION}_linux-amd64.deb"
+          sudo dpkg -i "$RUNNER_TEMP/hugo.deb"
+      - name: Checkout
+        uses: actions/checkout@v5
+        with:
+          submodules: recursive
+      - name: Configure Pages
+        id: pages
+        uses: actions/configure-pages@v6
+      - name: Build
+        env:
+          HUGO_CACHEDIR: "${{ runner.temp }}/hugo_cache"
+          HUGO_ENVIRONMENT: production
+        run: hugo --minify
+      - name: Upload Pages artifact
+        uses: actions/upload-pages-artifact@v5
+        with:
+          path: ./public
+
+  deploy:
+    needs: build
+    runs-on: ubuntu-latest
+    environment:
+      name: github-pages
+      url: "${{ steps.deployment.outputs.page_url }}"
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v5

.gitignore

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+/public/
+/resources/
+.hugo_build.lock

.gitmodules

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+[submodule "themes/PaperMod"]
+	path = themes/PaperMod
+	url = https://github.com/adityatelange/hugo-PaperMod.git

README.md

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-# Data Scientist
+# Joonho (Phil) Hwang — website
 
-#### Technical Skills: Python, SQL, AWS, Snowflake, MATLAB
-
-## Education
-- Ph.D., Physics | The University of Texas at Dallas (_May 2022_)								       		
-- M.S., Physics	| The University of Texas at Dallas (_December 2019_)	 			        		
-- B.S., Physics | The University of Texas at Dallas (_May 2017_)
-
-## Work Experience
-**Data Scientist @ Toyota Financial Services (_June 2022 - Present_)**
-- Uncovered and corrected missing step in production data pipeline which impacted over 70% of active accounts
-- Redeveloped loan originations model which resulted in 50% improvement in model performance and saving 1 million dollars in potential losses
-
-**Data Science Consultant @ Shawhin Talebi Ventures LLC (_December 2020 - Present_)**
-- Conducted data collection, processing, and analysis for novel study evaluating the impact of over 300 biometrics variables on human performance in hyper-realistic, live-fire training scenarios
-- Applied unsupervised deep learning approaches to longitudinal ICU data to discover novel sepsis sub-phenotypes
-
-## Projects
-### Data-Driven EEG Band Discovery with Decision Trees
-[Publication](https://www.mdpi.com/1424-8220/22/8/3048)
-
-Developed objective strategy for discovering optimal EEG bands based on signal power spectra using **Python**. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.
-
-![EEG Band Discovery](/assets/img/eeg_band_discovery.jpeg)
-
-### Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales
-[Publication](https://www.mdpi.com/1424-8220/22/11/4240)
-
-Used **Matlab** to train over 100 machine learning models which estimated particulate matter concentrations based on a suite of over 300 biometric variables. We found biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body.
-
-![Bike Study](/assets/img/bike_study.jpeg)
-
-## Talks & Lectures
-- Causality: The new science of an old question - GSP Seminar, Fall 2021
-- Guest Lecture: Dimensionality Reduction - Big Data and Machine Learning for Scientific Discovery (PHYS 5336), Spring 2021
-- Guest Lecture: Fourier and Wavelet Transforms - Scientific Computing (PHYS 5315), Fall 2020
-- A Brief Introduction to Optimization - GSP Seminar, Fall 2019
-- Weeks of Welcome Poster Competition - UTD, Fall 2019
-- A Brief Introduction to Networks - GSP Seminar, Spring 2019
-
-- [Data Science YouTube](https://www.youtube.com/channel/UCa9gErQ9AE5jT2DZLjXBIdA)
-
-## Publications
-1. Talebi S., Lary D.J., Wijeratne L. OH., and Lary, T. Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning (2019). DOI: 10.26717/BJSTR.2019.20.003446
-2. Wijeratne, L.O.; Kiv, D.R.; Aker, A.R.; Talebi, S.; Lary, D.J. Using Machine Learning for the Calibration of Airborne Particulate Sensors. Sensors 2020, 20, 99.
-3. Lary, D.J.; Schaefer, D.; Waczak, J.; Aker, A.; Barbosa, A.; Wijeratne, L.O.H.; Talebi, S.; Fernando, B.; Sadler, J.; Lary, T.; Lary, M.D. Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning. Sensors 2021, 21, 2240. https://doi.org/10.3390/s21062240
-4. Zhang, Y.; Wijeratne, L.O.H.; Talebi, S.; Lary, D.J. Machine Learning for Light Sensor Calibration. Sensors 2021, 21, 6259. https://doi.org/10.3390/s21186259
-5. Talebi, S.; Waczak, J.; Fernando, B.; Sridhar, A.; Lary, D.J. Data-Driven EEG Band Discovery with Decision Trees. Preprints 2022, 2022030145 (doi: 10.20944/preprints202203.0145.v1).
-6. Fernando, B.A.; Sridhar, A.; Talebi, S.; Waczak, J.; Lary, D.J. Unsupervised Blink Detection Using Eye Aspect Ratio Values. Preprints 2022, 2022030200 (doi: 10.20944/preprints202203.0200.v1).
-7. Talebi, S. et al. Decoding Physical and Cognitive Impacts of PM Concentrations at Ultra-fine Scales, 29 March 2022, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-1499191/v1]
-8. Lary, D.J. et al. (2022). Machine Learning, Big Data, and Spatial Tools: A Combination to Reveal Complex Facts That Impact Environmental Health. In: Faruque, F.S. (eds) Geospatial Technology for Human Well-Being and Health. Springer, Cham. https://doi.org/10.1007/978-3-030-71377-5_12
-9. Wijerante, L.O.H. et al. (2022). Advancement in Airborne Particulate Estimation Using Machine Learning. In: Faruque, F.S. (eds) Geospatial Technology for Human Well-Being and Health. Springer, Cham. https://doi.org/10.1007/978-3-030-71377-5_13
-
-- [Data Science Blog](https://medium.com/@shawhin)
+This repository powers <https://joonho-phil-hwang.github.io/> using Hugo and the PaperMod theme. Academic content is maintained in [content/_index.md](content/_index.md); GitHub Pages deployment is defined in [.github/workflows/hugo.yml](.github/workflows/hugo.yml).

assets/css/extended/custom.css

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+.main .profile {
+  min-height: 0;
+  padding: 2rem 0 1rem;
+}
+
+.home-intro {
+  max-width: 720px;
+  margin: 0 auto 2rem;
+}
+
+.post-single .post-content h3 {
+  margin-top: 1.5rem;
+  margin-bottom: 0.25rem;
+  font-size: 1.08rem;
+}
+
+.post-single .post-content details {
+  margin: 0.8rem 0 1.5rem;
+  padding: 0.7rem 0.9rem;
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  background: var(--code-bg);
+}
+
+.post-single .post-content summary {
+  cursor: pointer;
+  font-weight: 600;
+}
+
+.post-single .post-content details p:last-child {
+  margin-bottom: 0;
+}

content/_index.md

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+---
+title: "Joonho (Phil) Hwang"
+description: "PhD student in economics at Seoul National University."
+---
+
+Hello. My name is Joonho (Phil) Hwang (황준호; 黃俊晧), and I am a fifth-year PhD student in economics at Seoul National University, where I am fortunate to be advised by Professor [Seojeong Lee](https://sites.google.com/site/misspecifiedjay/). My research interests are in econometrics, especially panel data models and causal inference that exploits panel data structures. I am also interested in network econometrics and the use of machine learning methods in econometric analysis.

content/research.md

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+---
+title: "Research"
+description: "Working papers and work in progress by Joonho (Phil) Hwang."
+---
+
+## Working papers
+
+### Online Updating for Linear Panel Regressions
+
+With [Seojeong Lee](https://sites.google.com/site/misspecifiedjay/)
+
+<details>
+<summary>Abstract</summary>
+
+In this paper, we develop online updating methods for linear panel regression models. Online updating refers to procedures for sequentially updating parameter estimates as new data become available. In practice, the potential size of the dataset or data confidentiality constraints may preclude researchers from storing or accessing the entire dataset. We propose an online updating procedure for widely used linear regression models in panel data, where data expansion can occur through either (1) the arrival of new units or (2) the arrival of additional time periods for existing units. The proposed procedure yields closed-form expressions for updating both the point estimates and associated standard errors in each scenario.
+
+**Award:** Best Third-Year Paper, Department of Economics, Seoul National University.
+
+**Presentations:** SNU Econometrics Workshop, SETA 2025 (University of Macau), University of Sydney, KERIC 2025, SNU Workshop on Recent Advances in Econometrics, SETA 2026 (University of Tokyo).
+</details>
+
+### On the Bracketing Relationship in Staggered Treatment Designs
+
+*Under review*<br>
+[PDF](https://drive.google.com/file/d/1OywpSJyjLC3es5_pC6MGTUEEu11rkCPJ/view?usp=sharing) · [SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6683845)
+
+<details>
+<summary>Abstract</summary>
+
+Researchers often use fixed-effects and lagged-dependent-variable (LDV) estimates as upper and lower bounds on a treatment effect. This paper shows that this bracketing relationship can fail in staggered treatment designs. In staggered two-way fixed effects settings, neither estimator necessarily bounds the true group-time average treatment effect. Monte Carlo simulations show that such failures are common. The results suggest caution in using fixed-effects and LDV estimates as informal bounds in staggered-adoption settings.
+</details>
+
+## Work in progress
+
+### Deep Panel Quantile Regression
+
+With [Chencheng Fang](https://ccfang2.github.io/) and [Gayeon Hong](https://sites.google.com/view/gayeon-hong/)
+
+### Synthetic Difference-in-Differences with Missing Post-Treatment Outcomes
+
+With [Chencheng Fang](https://ccfang2.github.io/)

hugo.yaml

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+baseURL: "https://joonho-phil-hwang.github.io/"
+languageCode: "en-us"
+title: "Joonho (Phil) Hwang"
+theme: "PaperMod"
+enableRobotsTXT: false
+
+params:
+  description: "Joonho (Phil) Hwang is a PhD student in economics at Seoul National University."
+  author: "Joonho (Phil) Hwang"
+  keywords: ["Joonho Hwang", "econometrics", "panel data", "causal inference", "Seoul National University"]
+  defaultTheme: light
+  disableThemeToggle: false
+  disableScrollToTop: true
+  ShowReadingTime: false
+  ShowShareButtons: false
+  ShowPostNavLinks: false
+  disableSpecial1stPost: true
+  disableAnchoredHeadings: true
+  hideFooter: false
+  analytics:
+    google:
+      SiteVerificationTag: "nJZS8P1pi_xAjH-fMItjcU3Ek_wyatmWAhKJQ20Kj-U"
+  profileMode:
+    enabled: true
+    title: "Joonho (Phil) Hwang"
+    subtitle: "PhD student in Economics, Seoul National University"
+    imageUrl: "/assets/img/profile2.png"
+    imageTitle: "Joonho (Phil) Hwang"
+    imageWidth: 180
+    imageHeight: 180
+    buttons:
+      - name: Research
+        url: "/research/"
+      - name: CV
+        url: "https://drive.google.com/file/d/13QDqi6l7taFGNiiQqLaE9hcR6tynHDkJ/view?usp=sharing"
+  socialIcons:
+    - name: Email
+      url: "mailto:jhhwang24@snu.ac.kr"
+
+markup:
+  goldmark:
+    renderer:
+      unsafe: true

layouts/index.html

@@ -0,0 +1,8 @@
+{{ define "main" }}
+  {{ partial "index_profile.html" . }}
+  <article class="post-single home-intro">
+    <div class="post-content">
+      {{ .Content }}
+    </div>
+  </article>
+{{ end }}

layouts/research/single.html

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+{{ define "main" }}
+  <article class="post-single research-page">
+    <div class="post-content md-content">
+      {{ .Content }}
+    </div>
+  </article>
+{{ end }}

static/robots.txt

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+User-agent: *
+Allow: /
+Sitemap: https://joonho-phil-hwang.github.io/sitemap.xml