Merge pull request #10 from aidos-lab/es-dev

Fixing Toolbox Boxes

content/people.md

@@ -35,7 +35,7 @@ starting a collaboration with us.
     {{< person "MA" >}}
 </div>
 
-Your name is missing here! Learn more about [joining us]({{< relref "/contact/#join-us" >}}).
+Your name is missing here! Learn more about [joining us]({{< relref "/contact" >}}).
 
 # Alumni & Alumnae
 

content/theses.md

@@ -6,7 +6,7 @@ title: "Theses"
 
 This list of theses, sorted in reverse chronological order, provides
 just a brief glimpse into some of the research topics that we are
-interested in. Read on if you are interested in [joining us]({{< relref "/contact/#join-us" >}}).
+interested in. Read on if you are interested in [joining us]({{< relref "/contact" >}}).
 
 # 2025
 

data/people/MA.toml

@@ -1,5 +1,4 @@
 name    = "Mathieu Alain"
-job     = "Collaborator"
 image   = "ma.jpg"
 bio     = """\
 Mathieu seeks to extend machine learning to topological and

data/tools/magnipy.toml

@@ -1,5 +1,5 @@
 name = "magnipy"
-paper   = "Metric Space Magnitude Computations"
+paper   = "Metric Space Magnitude for Evaluating the Diversity of Latent Representations"
 image   = "magnipy.svg"
 venue   = "NeurIPS 2024"
 description = """\

data/tools/presto.toml

@@ -2,7 +2,7 @@ name    = "PRESTO"
 paper   = "Mapping the Multiverse of Latent Representations"
 image   = "presto.svg"
 description     = """\
-            The world of machine learning research is riddled with small decisions, from data collection, cleaning, into model selection and parameter tuning 🎶. Each combination of data, implementation, and modeling decisions leads to a potential universe where we can analyze and interpret results. Together, these form a multiverse! 🌌 With PRESTO (Projected Embedding Similarity based on Topological Overlays), we offer topological tools to efficiently measure the structural variation between representations that arise from different choices in a machine learning workflow.
+            The world of machine learning research is riddled with small decisions, from data collection, cleaning, into model selection and parameter tuning 🎶. Each choice leads to a potential universe where we can analyze and interpret results. PRESTO offers topological tools to efficiently measure the structural variation between representations that arise from different choices in a machine learning workflow.
           """
 repo    = "https://github.com/aidos-lab/Presto"
 pub     = "https://doi.org/10.48550/arXiv.2402.01514"

data/tools/scott.toml

@@ -2,7 +2,7 @@ name    = "SCOTT"
 paper   = "Curvature Filtrations for Graph Generative Model Evaluation"
 image   = "scott.jpg"
 description     = """\
-          SCOTT (Synthesizing Curvature Operations and Topological Tools) is a Python package for computing **curvature filtrations** for graphs and graph distributions. Our method introduces a novel way to compare graph distributions by combining discrete curvature on graphs with persistent homology, providing descriptors of graph sets that are: *robust*, *stable*, *expressive*, and *compatible with statistical testing*. The package is highly adaptable, offering several options for user customization, including different curvature computation methods and diverse metrics for comparing persistent homology outputs.
+          SCOTT is a Python package for computing **curvature filtrations** for graphs and graph distributions. Our method introduces a novel way to compare graph distributions by combining discrete curvature on graphs with persistent homology, providing descriptors of graph sets that are: *robust*, *stable*, *expressive*, and *compatible with statistical testing*.
           """
 repo    = "https://github.com/aidos-lab/curvature-filtrations"
 pub     = "https://doi.org/10.48550/arXiv.2301.12906"

data/tools/tardis.toml

@@ -0,0 +1,13 @@
+name = "TARDIS"
+paper   = "Topological Algorithms for Robust DIscovery of Singularities"
+image   = "TARDIS.svg"
+venue   = "ICML 2023"
+description = """\
+The manifold hypothesis is a staple of modern machine learning research. However, real-world data exhibits 
+distinct **non-manifold structures, i.e. singularities**, that can lead to erroneous findings. 
+We provide a topological framework that quantifies the local intrinsic dimension, and yields a 
+**Euclidicity score** for assessing the 'manifoldness' of a point along multiple scales.
+"""
+arXiv   = "2210.00069"
+pub     = "https://arxiv.org/abs/2210.00069"
+repo    = "https://github.com/aidos-lab/TARDIS"

layouts/shortcodes/tool.html

@@ -1,21 +1,21 @@
-<div class="card">
+<div class="card-tool">
     {{ $tool := .Get 0 }}
     {{ $tool :=  index .Site.Data.tools $tool }}
 
-    <div class="card-left">
+    <div class="tool-left">
         <figure>
             <img src="/tools/{{ $tool.image }}" width="180" alt="{{ $tool.name }}" />
         </figure>
     </div>
 
-    <div class="card-right">
+    <div class="tool-right">
       <div class="tool-title-container">
         <div class="tool-title">{{ $tool.name }}</div>
         <div class="tool-icons">{{ partial "icons.html" (dict "paper" $tool.pub "repo" $tool.repo) }}</div>
       </div>
-      <div class="card-bio">
+      <span >
         {{ $tool.description | markdownify }}
-      </div>
+      </span>
     </div>
 </div>
-
+