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Zecheng Zhang

Founding Engineer @ Kumo.AI | Stanford CS MS | Ex-AWS AI

San Francisco Bay Area

Overview 

Zecheng Zhang is the Founding Engineer at Kumo.AI, with previous experience at Amazon Web Services (AWS) as a Software Development Engineer. He holds a Master's degree in Computer Science from Stanford University and a Bachelor's degree from the University of Illinois Urbana-Champaign, showcasing a strong academic background and hands-on industry experience in software development and AI technologies.

Work Experience 

  • Founding Engineer

    2022 - Current

    Query the future with AI models for relational data. Graph Neural Networks (GNN), XAI, Tabular Deep Learning and AutoML Co-creator of PyTorch Frame, and contributor to PyTorch Geometric and RelBench

Kumo.AI is a SaaS AI platform that allows anyone in an organization to tackle predictive problems in business.

Raised $36,500,000.00 from Michael Ovitz, SV Angel, Sequoia Capital, A.Capital Ventures, Ronald Conway, Clément Delangue, Michael Stoppelman, Frank Slootman and Kevin Hartz.

  • Contributor

    2023

    Exploring LLMs as communicative agents

  • Software Development Engineer

    2021 - 2022

    AWS AI Amazon Transcribe

Amazon Web Services provides information technology infrastructure services to businesses in the form of web services.

  • Research Assistant

    2020 - 2021

    Stanford SNAP Group Co-creator of DeepSNAP, a Python library assists deep learning on graphs

  • Software Engineer Intern

    2020 - 2020

ServiceNow Venture invests in a diverse range of innovative technologies across enterprise software.

  • Software Engineer Intern

    2019 - 2019

ServiceNow Venture invests in a diverse range of innovative technologies across enterprise software.

  • Undergraduate Research Assistant

    2017 - 2019

    Worked on an open source numerical library, the Cyclops Tensor Framework

  • Software Engineer Intern

    2018 - 2018

ServiceNow Venture invests in a diverse range of innovative technologies across enterprise software.

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