Added Ruben's graduation announcement.

Author: moorepants

content/msc-graduation-ruben-terwint.rst

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+=================================================
+Ruben Terwint Successfully Defends His MSc Thesis
+=================================================
+
+:date: 2025-11-19 13:43:00
+:tags: vibration, power, cycling
+:category: news
+:authors: Jason K. Moore
+:thumbnail: https://mechmotum.s3.us-east-005.dream.io/thesis-cover-terwint.png
+
+.. list-table::
+   :class: borderless
+   :width: 60%
+   :align: center
+
+   * - |headshot|
+     - |cover|
+
+.. |cover| image:: https://mechmotum.s3.us-east-005.dream.io/thesis-cover-terwint.png
+   :height: 400px
+   :target: https://repository.tudelft.nl/record/uuid:4642837e-d0a9-44ed-871d-17bc4e903ddf
+
+.. |headshot| image:: https://mechmotum.s3.us-east-005.dream.io/headshot-terwint-ruben.jpg
+   :height: 400px
+
+Ruben Terwint successfully defended his MSc thesis titled, `Linking Traffic
+Condition Metrics to a Cyclist's Workload
+<https://repository.tudelft.nl/record/uuid:4642837e-d0a9-44ed-871d-17bc4e903ddf>`_
+on September 15, 2025.
+
+Ruben extracted metrics from video captured from glasses worn by a cyclist
+while they rode through the city of Delft and corresponding verbal workload
+levels reported by the cyclist. He then developed a regression model based on
+the traffic metrics to predict the subjective workload reported by the rider.
+He found that ego cyclist speed was a main predictor of workload and concluded
+that cyclists modulate their speed when in high workload areas. This result is
+consistent with self regulation to reduce the task difficulty in prior general
+driver behavior findings.
+
+.. figure:: https://mechmotum.s3.us-east-005.dream.io/terwint-video-still.png
+   :width: 600px
+   :align: center
+
+   Example of cyclists being detected and the distance estimate to each
+   cyclist.
+
+|
+
+Ruben was supervised by Jules Ronné, Holger Caesar, and Jason K. Moore.
+Everyone at the bicycle lab is very proud of Ruben and wishes him the best in
+his next adventures in studying machine learning in the UK.