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Tianlin Liu

PhD candidate

University of Basel, Switzerland

I’m seeking full-time positions starting Fall or Winter 2024. If my experience would be a good fit for your organization, please feel free to contact me.

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.

In the fall of 2023, I interned at Google DeepMind in Paris. In the summer of 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.

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  • 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