Fix formatting of research projects

_data/research.yml

@@ -21,7 +21,7 @@
   date: "Fall 2020 & Spring 2021"
   projs:
     - title: Student misconceptions in dynamic programming
-      citation: <a href="https://doi.org/10.1080/08993408.2022.2079865">"https://doi.org/10.1080/08993408.2022.2079865"</a>
+      citation: <a href="https://doi.org/10.1080/08993408.2022.2079865">https://doi.org/10.1080/08993408.2022.2079865</a>
       description: >-
         Dynamic programming is a fundamental paradigm in undergraduate algorithms courses and is infamous for its difficulty. We replicated a 
         <a href="https://dl.acm.org/doi/abs/10.1145/3159450.3159528">previous study</a> which sought to understand
@@ -43,7 +43,7 @@
         These two projects also made up my honors undergraduate thesis.
 
     - title: <a href="https://inklab.usc.edu/rockner/">Adversarial Data Augmentation for Named Entity Recognition</a>
-      citation: <a href="https://doi.org/10.18653/v1/2021.emnlp-main.302">"https://doi.org/10.18653/v1/2021.emnlp-main.302"</a>
+      citation: <a href="https://doi.org/10.18653/v1/2021.emnlp-main.302">https://doi.org/10.18653/v1/2021.emnlp-main.302</a>
       description: >-
         I explored automated methods to develop adversarial data, test data which is created by perturbing a correctly classified example,
         causing the model to misclassify the adversarial example. Adversarial examples are useful because they demonstrate the limits
@@ -52,7 +52,7 @@
         adversarial training.
 
     - title: <a href="https://arxiv.org/abs/2004.07493">TriggerNER&#58; Learning with entity triggers as explanations for named entity recognition</a>
-      citation: <a href="https://doi.org/10.18653/v1/2020.acl-main.752">"https://doi.org/10.18653/v1/2020.acl-main.752"</a>
+      citation: <a href="https://doi.org/10.18653/v1/2020.acl-main.752">https://doi.org/10.18653/v1/2020.acl-main.752</a>
       description: >-
         My team used distantly-supervised machine learning to develop a novel approach to named entity recognition.
         The basic idea is that we use a small amount of human-annotated data consisting of sentences with their words categorically labeled

projects.md

@@ -9,7 +9,7 @@ permalink: /projects/
 <ul class="posts">
   {% for proj in site.data.research %}
     {% if proj.date %}
-      <h4><li>{{ proj.workplace }} &raquo; {{ proj.date }}</small></li></h4>
+      <h4><li>{{ proj.workplace }} &raquo; <small>{{ proj.date }}</small></li></h4>
     {% else %}
       <h4><li>{{ proj.workplace }}</li></h4>
     {% endif %}