Joseph Gonzalez

Associate Professor

Biography

I am an associate professor in the EECS department at UC Berkeley and a founding member of the new UC Berkeley RISE Lab. My research interests are at the intersection of machine learning and data systems and my students are working on a wide range of projects including: real-time model serving; machine learning life-cycle management; accelerated deep learning for computer vision; new cryptographic primitives for federated learning; frameworks for deep reinforcement learning and parameter tuning; model based cloud resource management; software platforms for autonomous vehicles research; computational efficient representations for asynchronous time series; smf frameworks for graph query processing.

Co-founder: I am also co-founder of Turi Inc. (formerly GraphLab), which was originally based on my thesis work on the GraphLab and PowerGraph Systems. Turi was recently acquired by Apple Inc.

Background: Before joining UC Berkeley as an assistant professor, I was a post-doc in the UC Berkeley AMPLab working on several projects including GraphX (now part of Apache Spark), early versions of MLbase, Velox, and concurrency control for ML. I obtained my PhD from the Machine Learning Department at Carnegie Mellon University where I worked with on Parallel and Distributed Systems for Probabilistic Reasoning.

Education

  • 2012, Ph.D., Machine Learning, Carnegie Mellon University

Selected Publications

  • Z. Wu, P. Jain, M. A. Wright, A. Mirhoseini, J. Gonzalez, I. Stoica, Z. Wu, P. Jain, M. A. Wright, A. Mirhoseini, J. Gonzalez, and I. Stoica, "Representing Long-Range Context for Graph Neural Networks with Global Attention," in Advances in Neural Information Processing Systems (NeurIPS), 2021.
  • Z. Wu, P. Jain, M. A. Wright, A. Mirhoseini, J. Gonzalez, I. Stoica, Z. Wu, P. Jain, M. A. Wright, A. Mirhoseini, J. Gonzalez, and I. Stoica, "Representing Long-Range Context for Graph Neural Networks with Global Attention," in Advances in Neural Information Processing Systems (NeurIPS), 2021.
  • E. Liang*, Z. Wu*, M. Luo, S. Mika, J. Gonzalez, and I. Stoica, "RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem," in Advances in Neural Information Processing Systems (NeurIPS), 2021.
  • R. Garcia, E. Liu, V. Sreekanti, B. Yan, A. Dandamudi, J. Gonzalez, J. M. Hellerstein, and K. Sen, "Hindsight Logging for Model Training," 2020.
  • P. Jain, A. Jain, A. Nrusimha, A. Gholami, P. Abbeel, K. Keutzer, I. Stoica, and J. Gonzalez, "Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization," in Proceedings of Machine Learning and Systems 2020, Machine Learning and Systems, 2020, pp. 497--511.
  • X. Pan, S. Jegelka, J. E. Gonzalez, J. K. Bradley, and M. Jordan, "Parallel Double Greedy Submodular Maximization," in Advances in Neural Information Processing Systems 27, 2014.
  • X. Pan, J. E. Gonzalez, S. Jegelka, T. Broderick, and M. Jordan, "Optimistic concurrency control for distributed unsupervised learning," in Advances in Neural Information Processing Systems 26, 2013, pp. 1403--1411.

Awards, Memberships and Fellowships