Render book

pbiecek · Jan 20, 2024 · 905d6ee · 905d6ee
1 parent b725d0d
commit 905d6ee
Show file tree

Hide file tree

Showing 4 changed files with 14 additions and 3 deletions.
diff --git a/docs/images/maciej_chrabaszcz.jpg b/docs/images/maciej_chrabaszcz.jpg
diff --git a/docs/papers.html b/docs/papers.html
@@ -174,6 +174,17 @@ <h2>Papers<a href="papers.html#papers" class="anchor-section" aria-label="Anchor
 document.querySelector(".page-inner section > *:first-child").classList.add("two-columns-layout")
 </script>
 <div>
+<img src="images/survex.png">
+<a href="https://doi.org/10.1093/bioinformatics/btad723">survex: an R package for explaining machine learning survival models</a>
+<p>
+Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein, Hubert Baniecki, Marvin N Wright, Przemysław Biecek
+</p>
+<p>
+<strong>Bioinformatics (2023)</strong>
+</p>
+<p>This paper demonstrates the functionalities of the <a href="https://github.com/ModelOriented/survex">survex package</a>, which provides a comprehensive set of tools for explaining machine learning survival models. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, promoting transparency and responsibility in sensitive areas.</p>
+</div>
+<div>
 <img src="images/consolidated-learning.png">
 <a href="https://doi.org/10.1007/s10994-023-06359-0">Consolidated learning: a domain-specific model-free optimization strategy with validation on metaMIMIC benchmarks</a>
 <p>

diff --git a/docs/search_index.json b/docs/search_index.json
diff --git a/docs/the-team.html b/docs/the-team.html
@@ -205,7 +205,7 @@ <h4>Members<a href="the-team.html#members" class="anchor-section" aria-label="An
 <li><a href="the-team.html#przemysław-biecek">Przemysław Biecek</a>, PhD, DSc (Team Leader)</li>
 <li><a href="the-team.html#hubert-baniecki">Hubert Baniecki</a>, PhD student</li>
 <li><a href="the-team.html#mustafa-cavus">Mustafa Cavus</a>, PhD</li>
-<li><a href="the-team.html#maciej-chrabąszcz">Maciej Chrabąszcz</a>, MSc student</li>
+<li><a href="the-team.html#maciej-chrabąszcz">Maciej Chrabąszcz</a>, PhD student</li>
 <li><a href="the-team.html#mateusz-grzyb">Mateusz Grzyb</a>, MSc student</li>
 <li><a href="the-team.html#stanisław-giziński">Stanisław Giziński</a>, MSc student</li>
 <li><a href="the-team.html#weronika-hryniewska-guzik">Weronika Hryniewska-Guzik</a>, PhD student</li>
@@ -319,7 +319,7 @@ <h3>Julian Sienkiewicz<a href="the-team.html#julian-sienkiewicz" class="anchor-s
 <div id="maciej-chrabąszcz" class="section level3 unnumbered hasAnchor">
 <h3>Maciej Chrabąszcz<a href="the-team.html#maciej-chrabąszcz" class="anchor-section" aria-label="Anchor link to header"></a></h3>
 <p><img src="images/maciej_chrabaszcz.jpg"></p>
-<p>Master’s student in mathematical statisctics at Warsaw University of Technology. Interested in deep learning on text and images, explainable and responsible AI.</p>
+<p>I am pursuing a PhD in Computer Science at Warsaw University of Technology, where I also obtained my MSc in Mathematical Statistics in 2023. My main research interests lie in the fields of responsible and explainable machine learning, with a focus on Red Teaming foundation models.</p>
 <p>GitHub: <a href="https://github.com/maciejchrabaszcz">maciejchrabaszcz</a></p>
 <p>LinkedIn: <a href="https://www.linkedin.com/in/maciej-chrabaszcz/">maciej-chrabaszcz</a></p>
 </div>