PeopleofSuperintelligence

Researcher

Julian McAuley

Also known as Julian John McAuley

Julian McAuley is a computer scientist whose research on recommender systems and personalized machine learning reaches from images of clothes and Dance Dance Revolution step charts to large language models. After a PhD at the Australian National University in 2011 and a postdoc at Stanford University from 2011 to 2014, he joined UC San Diego as an assistant professor in 2014. With Wang-Cheng Kang, he proposed the SASRec model in 2018, and Cambridge University Press published his textbook Personalized Machine Learning in 2022. In 2025, his 2015 SIGIR paper on image-based recommendations of styles and substitutes won the SIGIR Test of Time Award. He is a professor in the Department of Computer Science and Engineering at UC San Diego.

Timeline

  1. 2011–2014

    From Stanford to UC San Diego

    He earns a PhD at the Australian National University in 2011 and works as a postdoctoral researcher at Stanford University from 2011 to 2014. In 2014, he becomes an assistant professor in the Computer Science Department at the University of California, San Diego.

  2. 2015

    Image-based style recommendations

    In 2015, he coauthors the SIGIR paper "Image-based recommendations on styles and substitutes" with Christopher Targett, Qinfeng Shi and Anton van den Hengel; its system recommends which clothes and accessories go well together. He also coauthors the KDD paper "Inferring networks of substitutable and complementary products" with Rahul Pandey and Jure Leskovec.

  3. 2017

    Dance Dance Convolution

    In 2017, he coauthors "Dance Dance Convolution" with Chris Donahue and Zachary C. Lipton, published at ICML 2017. The paper introduces the task of learning to choreograph, which turns a raw audio track into a Dance Dance Revolution step chart, and uses a conditional LSTM for step selection.

  4. 2018

    The SASRec model

    In 2018, he and Wang-Cheng Kang propose SASRec, a self-attention based sequential model for recommendation, in a long paper accepted at ICDM 2018. The model captures long-term semantics like an RNN and bases its predictions on relatively few actions like a Markov chain.

  5. 2022

    A Cambridge textbook on personalization

    In 2022, Cambridge University Press publishes his textbook Personalized Machine Learning. The book starts from traditional machine learning models and builds toward matrix factorization, deep learning and generative modeling for personalized predictions.

  6. 2023–2024

    LLM recommenders and Amazon Reviews 2023

    In 2023, he and his team build demo systems that merge large language models with traditional recommender systems, with funding from Netflix, so that users can talk with a recommender. In 2024, he coauthors "Bridging Language and Items for Retrieval and Recommendation" with Yupeng Hou and others, which contributes an Amazon Reviews 2023 dataset with over 570 million reviews and 48 million items.

  7. 2025

    A SIGIR Test of Time Award

    In 2025, his SIGIR 2015 paper "Image-based recommendations on styles and substitutes" receives the SIGIR Test of Time Award.

  8. As of October 2026

    Professor at UC San Diego

    He is a professor in the Department of Computer Science and Engineering at UC San Diego. His current PhD students work on large language models, recommender systems, computer music and agentic AI.

Career

  1. Since 2014University of California, San DiegoAssistant professor
  2. 2011–2014Stanford UniversityPostdoctoral researcher

Education

  1. 2011Australian National UniversityPhD

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of California, San Diego. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes University of California, San Diego. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  3. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of California, San Diego. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  4. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Stanford University. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Julian McAuley yet. Send us a free-licensed photo.

  1. Faculty Profiles | Jacobs School of Engineering jacobsschool.ucsd.edu. Checked 21 October 2026.
  2. Talks at the Network Science Institute | Julian McAuley networkscienceinstitute.org. Checked 21 October 2026.
  3. [1506.04757] Image-based Recommendations on Styles and Substitutes arxiv.org. Checked 21 October 2026.
  4. Julian McAuley cseweb.ucsd.edu. Checked 21 October 2026.
  5. [1703.06891] Dance Dance Convolution arxiv.org. Checked 21 October 2026.
  6. Julian McAuley mds.ucsd.edu. Checked 21 October 2026.
  7. [1808.09781] Self-Attentive Sequential Recommendation arxiv.org. Checked 21 October 2026.
  8. Preface - Personalized Machine Learning cambridge.org. Checked 21 October 2026.
  9. Personalized Machine Learning cseweb.ucsd.edu. Checked 21 October 2026.
  10. A chatbot that gives movie recommendations | Artificial Intelligence ai.ucsd.edu. Checked 21 October 2026.
  11. [2403.03952] Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders arxiv.org. Checked 21 October 2026.
  12. Test of Time Award Winners | SIGIR sigir.org. Checked 21 October 2026.