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index.html
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---
layout: none
---
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<main role="main" class="inner cover">
<h2>2024</h2>
<ul>
<li>November 2024 (Edinburgh): <a href="2024_edinburgh">Artificial Intelligence, Measured for Safety -- Towards an actionable science of AI metrology</a></li>
<li>November 2024 (<a href="https://sigarra.up.pt/fep/pt/noticias_geral.ver_noticia?p_nr=48229">Data Science and Statistics Webinar, Porto</a>): <a href="Obsidian/export/2024_porto">Explainable Artificial Intelligence, Explained</a></li>
<li>September 2024 (<a href="https://www.datamakersfest.com/agenda#sz-tab-45560">Data Makers Fest 2024, Porto</a>): <a href="2024_dmf">From data mining processes to data science trajectories</a></li>
<li>June 2024 (<a href="https://www.euads.org/fjkdlasjdiglsmdgkcxjhvckh/euads-summer-school-913-487/">Sabine Krolak Schwerdt lecture, Luxembourg</a>): <a href="2024_sks">Data Science in the time of AI: why AI isn't solved, and how Data Science can help</a></li>
<li>April 2024 (National Physical Laboratory): <a href="2024_npl">Artificial Intelligence, Measured for Safety -- Towards an actionable science of AI metrology</a></li>
<li>February 2024 (<a href="https://www.mfo.de/occasion/2407a/www_view">ELLIS Workshop: Semantic, Symbolic and Interpretable Machine Learning</a>): <a href="2024_ellis">Towards interpretable performance evaluation in machine learning</a></li>
</ul>
<h2>2023</h2>
<ul>
<li>November 2023 (<a href="https://icdsai.cs.ait.ac.th/keynotes/">Keynote at 1st International Conference on Data Science and Artificial Intelligence</a>): <a href="Obsidian/export/2023_dsai">Explainable Artificial Intelligence, Explained</a></li>
<li>November 2023 (<a href="https://direc.dk/workshop-on-verifiable-and-robust-ai-2/">Workshop on Verifiable and Robust AI</a>): <a href="2023_vrai">Thoughts on Verifiable and Robust Performance Evaluation</a></li>
<li>July 2023 (<a href="https://xaiworldconference.com/invited-speakers/">Keynote at 1st World Conference on eXplainable Artificial Intelligence</a>): <a href="Obsidian/export/2023_xai">Thoughts on explainability</a></li>
<li>May 2023 (<a href="https://www.universiteitleiden.nl/en/events/2023/05/florence-nightingale-colloquium-peter-flach">Nightingale Colloquium, Leiden</a>): <a href="2023_nightingale">AI in the time of chatGPT -- is AI solved?</a></li>
<li>February 2023 (<a href=https://munichlectures.ai/peter-flach/>Munich AI Lectures</a>): <a href="2023_munich">The Highs and Lows of Performance Evaluation</a></li>
</ul>
<h2>2022</h2>
<ul>
<li><a href="2022_monash">December 2022 (Monash): The Highs and Lows of Performance Evaluation</a></li>
<li><a href="2022_leuven">October 2022 (Leuven): The Highs and Lows of Performance Evaluation</a></li>
<li>April 2022 (<a href="https://ecir2022.org/keynote-talks/">ECIR'22 keynote</a>): <a href="2022_ecir">Empirical Evaluation of Predictive Models: A Matter of Scales and Means
</a></li>
</ul>
<h2>2021</h2>
<ul>
<li>October 2021 (<a href="https://www.universiteitleiden.nl/en/events/2021/11/florence-nightingale-colloquium-presents-peter-flach">Nightingale Colloquium, Leiden</a>): <a href="2021_nightingale">The Highs and Lows of Performance Evaluation</a></li>
<li><a href="2021_jgi">October 2021 (Jean Golding Institute, Bristol): Towards Measurement Theory for Artificial Intelligence and Data Science</a></li>
</ul>
<h2>2020</h2>
<ul>
<li>October 2020 (<a href="https://ds2020.csd.auth.gr/invited-speakers/">Discovery Science '20 invited talk</a>): <a href="2020_ds">The Highs and Lows of Performance Evaluation</a></li>
<li><a href="2020_turing">January 2020 (Turing Institute): More Transparency through Better Performance Measurement</a></li>
</ul>
<h2>2019</h2>
<ul>
<li><a href="2019_isl">November 2019 (Intelligent Systems Lab, Bristol): Performance Evaluation in Machine Learning</a></li>
<li><a href="2019_euads">September 2019 (European Association for Data Science): From Data Mining Processes to Data Science Trajectories</a></li>
<li>January 2019 (<a href="https://ojs.aaai.org/index.php/AAAI/article/view/5055">AAAI '19 Senior Member Summary Talk</a>): <a href="2019_aaai">Performance Evaluation in Machine Learning</a></li>
</ul>
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