diff --git a/docs/papers/joss/paper.bib b/docs/papers/joss/paper.bib
index da9eee7cb..a9c3de1ad 100644
--- a/docs/papers/joss/paper.bib
+++ b/docs/papers/joss/paper.bib
@@ -80,7 +80,7 @@ @techreport{ParMOODesign23
 
 @article{PhysRevAccelBeams.26.084601,
   title = {Bayesian optimization of laser-plasma accelerators assisted by reduced physical models},
-  author = {Ferran Pousa, A. and Jalas, S. and Kirchen, M. and Martinez de la Ossa, A. and Th\'evenet, M. and Hudson, S. and Larson, J. and Huebl, A. and Vay, J.-L. and Lehe, R.},
+  author = {{Ferran Pousa}, A. and Jalas, S. and Kirchen, M. and Martinez de la Ossa, A. and Th\'evenet, M. and Hudson, S. and Larson, J. and Huebl, A. and Vay, J.-L. and Lehe, R.},
 	journal = {Physical Review Accelerators and Beams},
   volume = {26},
   issue = {8},
@@ -95,7 +95,7 @@ @article{PhysRevAccelBeams.26.084601
 
 @article{Pousa22,
 year = {2022},
-    author = {Ferran Pousa, A and Jalas, S. and Kirchen, M. and Martinez de la Ossa, A. and Thévenet, M. and Hudson, S. and Larson, J.  and Huebl, A. and Vay, J.-L. and Lehe, R.},
+    author = {{Ferran Pousa}, A and Jalas, S. and Kirchen, M. and Martinez de la Ossa, A. and Thévenet, M. and Hudson, S. and Larson, J.  and Huebl, A. and Vay, J.-L. and Lehe, R.},
     title = {Multitask Optimization of Laser-Plasma Accelerators Using Simulation Codes with Different Fidelities},
     pages = {1761--1764},
     paper = {WEPOST030},
@@ -205,4 +205,4 @@ @Article{harris2020array
  doi           = {10.1038/s41586-020-2649-2},
  publisher     = {Springer Science and Business Media {LLC}},
  url           = {https://doi.org/10.1038/s41586-020-2649-2}
-}
\ No newline at end of file
+}
diff --git a/docs/papers/joss/paper.md b/docs/papers/joss/paper.md
index 37418defb..9ea91108d 100644
--- a/docs/papers/joss/paper.md
+++ b/docs/papers/joss/paper.md
@@ -54,7 +54,7 @@ Examples include determining simulation parameters using numerical optimization
 methods, machine learning techniques, or statistical calibration tools. In each of
 these examples, the ensemble members are typically simulations that use different
 parameters or data. Additional examples of applications that have used libEnsemble are
-surveyed in [Representative libEnsemble Use Cases](#Representative-libEnsemble-Use-Cases).
+surveyed in the [Representative libEnsemble Use Cases](#markdown-header-representative-libensemble-use-cases) section below.
 
 The target audience for libEnsemble includes scientists, engineers, and other researchers
 who stand to benefit from such dynamic workflows.