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Jonas Wieschollek

  • Scholar
  • Germany
  • 2023 PhD Law
  • Queens' College
Jonas Wieschollek

Jonas Wieschollek

  • Scholar
  • Germany
  • 2023 PhD Law
  • Queens' College

How and why do judges quote prior judgments when drafting new ones? This simple question is the driving force of my PhD project that offers a comparative analysis of judicial referencing in England and Germany from a historical perspective. After stumbling upon a little-known but enlightening piece by the Russian linguist Michail Bachtin, I realised the potential of linguistic methods for scrutinizing the language of the law. Building upon the training in Philology and Law I received in Berlin, Freiburg (both Germany), St. Petersburg (Russia), and Krakow (Poland), I suggest an interdisciplinary framework for the study of judicial referencing that will help to address fundamental challenges of the judiciary in the 21st century. When not promoting educational equality in academia or advocating the rights of refugees and migrants, I enjoy the world’s cultural diversity by diving into, among other things, the realm of pop music in various languages.

Previous Education

European University St Petersburg Philology and Art History 2023
Albert-Ludwigs-Universitat Freiburg Russian and German Studies 2021
Albert-Ludwigs-Universitat Freiburg Law 2020

Ping Lin Yeap

  • Scholar
  • Singapore
  • 2023 PhD Oncology
  • Homerton College
Ping Lin Yeap

Ping Lin Yeap

  • Scholar
  • Singapore
  • 2023 PhD Oncology
  • Homerton College

As a medical physicist, I enjoy solving problems at the intersection of disciplines. During my MPhil with the Cambridge computational radiotherapy group, I used deformable image registration to investigate discrepancies between planned and delivered dose to the spinal cord for head-and-neck cancer patients, and correlated delivered dose with Lhermitte’s Sign toxicity. My PhD project will focus on adaptive radiotherapy, which entails adapting cancer treatment plans to patients’ changing anatomies over the course of treatment. I will be developing and evaluating machine learning and deep learning methods to predict and minimise errors between registered CT images. I am also interested in the use of generative models to enhance the quality of cone-beam CT scans, such that they can be used directly for plan adaptation. This research will hopefully enable and enhance adaptive radiotherapy workflows in the clinic, and contribute towards personalised and precision medicine. Having worked in the education and public policy sectors in Singapore, I am also passionate about democratising STEM education and improving access to career guidance for youths. Outside of work, I can be found exploring far-flung corners of the world with my camera.