Jose Picado

jpicado [AT] gmail [DOT] com


Hi, I'm Jose. I'm a software engineer at Afterpay 🚀. My team builds systems to support machine learning use cases at Afterpay. This includes serving machine learning models in production, building data pipelines, building a feature store, etc. We also built the search engine in Afterpay's shop directory.

🎓 Before Afterpay, I did a PhD (machine learning/data management), MS (machine learning), and BS, all in computer science. You can find a list of my publications below.

💻 In between my many years in academia, I worked as a data scientist (intern) at Microsoft, as a research scientist (intern) at Intel, and as a software engineer at Avantica.

I enjoy playing tennis 🎾, football (soccer) ⚽, running 🏃, music 🎶, and traveling 🌎.

Publications

  • Representationally Robust and Scalable Learning over Relational Databases    [thesis]
    Jose Picado (advised by Arash Termehchy)
    PhD Thesis, 2019
  • Logical Scalability and Efficiency of Relational Learning Algorithms     [paper]
    Jose Picado, Arash Termehchy, Alan Fern, Parisa Ataei
    VLDB Journal, 2018
  • Learning Efficiently Over Heterogeneous Databases     [paper]
    Jose Picado, Arash Termehchy, Sudhanshu Pathak
    VLDB, 2018
  • Survivability of Cloud Databases - Factors and Prediction     [paper, slides]
    Jose Picado, Willis Lang, Edward C. Thayer
    SIGMOD, 2018
  • Learning Efficiently Over Heterogeneous Databases: Sampling and Constraints to the Rescue    [paper]
    Jose Picado, Arash Termehchy, Sudhanshu Pathak
    DEEM Workshop at SIGMOD, 2018
  • AutoMode: Relational Learning With Less Black Magic    [paper]
    Jose Picado, Sudhanshu Pathak, Arash Termehchy, Alan Fern
    ICDE, 2018
  • Schema Independent Relational Learning    [paper, technical report, slides, code]
    Jose Picado, Arash Termehchy, Alan Fern, Parisa Ataei
    SIGMOD, 2017
  • Schema Independent and Scalable Relational Learning By Castor    [paper]
    Jose Picado, Parisa Ataei, Arash Termehchy, Alan Fern
    VLDB, 2016
  • Markov Logic Networks for Adverse Drug Event Extraction from Text    [paper]
    Sriraam Natarajan, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vitor Santos Costa, David Page, Michael Caldwell
    KAIS, 2016
  • Effectively Creating Weakly Labeled Training Examples Via Approximate Domain Knowledge    [paper]
    Sriraam Nataranan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Re, Jude Shavlik
    ILP, 2014
  • Efficient Information Extraction Using Statistical Relational Learning    [thesis]
    Jose Picado (advised by Sriraam Narajan)
    Master's Thesis, 2013
  • Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text    [paper]
    Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Re, Jude Shavlik
    StarAI Workshop at AAAI, 2013

Updated October 2020.
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