Xiaozhe Yao (姚晓哲)

Xiaozhe Yao is a first-year doctoral student advised by Prof. Dr. Ce Zhang at DS3Lab, Systems Group, Department of Computer Science, ETH Zurich. With interests spanning from MLOps algorithms and systems to application of machine learning in existing systems, his long-term goal is to make machine learning automatic and accessible.

Prior to ETH Zurich, Xiaozhe Yao gained his Master’s degree at the University of Zurich in Data Science, advised by Prof. Dr. Michael Böhlen and Qing Chen. Before that, he completed his Bachelor’s study at Shenzhen University in Computer Science, advised by Prof. Dr. Shiqi Yu. He interned at Shenzhen Institute of Advanced Technology in 2016 where he investigated recommendation systems for food nutrition data. Since then, he has been working on machine learning and computer vision systems aiming to reduce the barriers to applying algorithms.

Between 2021 and 2022, he worked on the project AID as an Innovator Fellow at the Library Lab, ETH Zurich. The objective of AID is to support the application of machine learning algorithms by imitating a real library. It provides a unified programming interface to access and manage machine learning models, a digital library for searching, filtering and inspecting machine learning models.


In inverse chronological order:

  1. Mazumder, Mark, Colby Banbury, Xiaozhe Yao, Bojan Karlaš, William Gaviria Rojas, Sudnya Diamos, Greg Diamos, et al. “DataPerf: Benchmarks for Data-Centric AI Development”. ICML workshop (Benchmarking Data for Data-Centric AI).
  2. Cedric Renggli, Xiaozhe Yao, Luka Kolar, Luka Rimanic, Ana Klimovic, Ce Zhang. “SHiFT: An Efficient, Flexible Search Engine for Transfer Learning”. To appear in International Conference on Very Large Data Bases (VLDB 2023).
  3. Yao, Xiaozhe. “MLPM: Machine Learning Package Manager” Workshop on MLOps, MLSys, 2020.
  4. Chen, Yingying, and Xiaozhe Yao. “CVTron Web: A Versatile Framework for Online Computer Vision Services” World Congress on Services. Springer, Cham, 2018.
  5. Yao, Xiaozhe, et al. “Face Based Advertisement Recommendation with Deep Learning: A Case Study” International Conference on Smart Computing and Communication. Springer, Cham, 2017.

Technical Reports

In inverse chronological order:

  1. Yao, Xiaozhe, Neeraj Kumar and Nivedita Nivedita. Slides/Implementing learned indexes on 1 and 2 dimensional data (Master Project 2021).
  2. Yao, Xiaozhe. ModelDB: Machine Learning Model Management (2020).
  3. Yao, Xiaozhe. Implementation of Naive Bayes Classifier (2020).
  4. Yao, Xiaozhe. Implementing Deconvolution to Visualize and Understand Convolutional Neural Networks, supervised by Prof. Dr. Michael Böhlen and Qing Chen (2020).
  5. Chen, Yingying, and Xiaozhe Yao. Knowledge Graph Embedding and OpenKE (2019).
  6. Chen, Yingying, Weijie Niu and Xiaozhe Yao. Diversity in Open Source Software Community and its Impact on Software Quality (2019).


  1. Master Thesis: Implementation of Learned Cardinality Estimation in Database Contexts, supervised by Prof. Dr. Michael H. Bohlen, Prof. Dr. Anton Dignös, Qing Chen.
  2. Bachelor Thesis: Face Detection with Multi-Block Local Binary Pattern in OpenCV, supervised by Prof. Dr. Shiqi Yu.

Work Experiences



I served as the teaching assistant at both Shenzhen University and Universität Zürich, for the following courses:


If you are interested in the slides of the following talks, please contact me.

Entrepreneurship Experience

Awards, Scholarships and Projects Grants.

Community Service