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Tianlin Liu

PhD candidate

University of Basel, Switzerland

I am a PhD candidate in machine learning, currently pursuing my studies at the University of Basel in Switzerland. My PhD advisor is Prof. Ivan Dokmanić.

I’m currently interning at Google DeepMind in Paris. In the summer 2022, I interned at Google Brain in Zurich.

I received BSc (2016) and MSc (2019) both from Jacobs University Bremen. During my time there, I completed my master’s thesis under the guidance of Prof. Herbert Jaeger.

CVScholar GitHub Twitter


  • Oct 2023: Check out our blog post (with Costa Huang and Leandro von Werra) for a detailed breakdown of OpenAI’s first RLHF paper, featuring results replicated in PyTorch and Jax.

  • Jan 2023: Our paper (with Joan Puigcerver and Mathieu Blondel) has been accepted by ICLR 2023 as a spotlight presentation.

  • May 2022: Our paper (with Anadi Chaman, David Belius, and Ivan Dokmanić) has been accepted by the IEEE Transactions on Computational Imaging.

  • Jan 2022: Our paper (with Anastasis Kratsios, Behnoosh Zamanlooy, and Ivan Dokmanić) has been accepted by ICLR 2022 and selected for a spotlight presentation.

  • June 2020: Our paper (with Friedemann Zenke) on training sparse neural networks has been accepted by ICML 2020.

  • November 2019: Our paper (with Zekun Yang) on word vector denoising has been accepted by AAAI 2020.

  • August 2019: My master’s thesis has been awarded a Deans Prize for outstanding thesis by Jacobs University Bremen.

  • March 2019: Our paper (with Xu He, Fatemeh Hadaeghi, and Herbert Jaeger) on training spiking neural networks for neuromorphic hardware won a best paper finalist award at IEEE NER-2019