Wenbo Zhang

Machine Learning Engineer at Riskfuel Analytics

Wenbo Zhang has five years of professional experience. In 2020, they began working at Ansys as a Mitacs Accelerate Intern, where they built a web application with React, Django, and PostgreSQL serving the reduced order model of plasma electrical conductivity. Wenbo also created a dataset of 110k records and trained a lightweight neural network to reduce the CPU time of conductivity evaluation for large scale 3D simulations from hours to seconds. In the same year, they became a Postdoctoral Research Fellow at McGill University, where they were a core software developer of an innovative all-Mach number finite-element 3D solver with a focus on DSMC, mesh optimization, hybrid CFD-DSMC modelling, ionization, gas-surface interactions, surface ablation, and bulk viscosity concept. Wenbo also participated in a successful MITACS grant application. In 2020, they also worked on a Machine Learning Project: Lookalike Audience Model, where they built a lookalike audience expansion model to improve click rate in online advertising. In 2019, they worked on a Web Development Project: A Real-time Chat Website, where they built and deployed a real-time chat website following the Django Model-Template-View framework with WebSockets and Django Channels. Wenbo is currently working at Riskfuel as an AI Research Engineer since 2022.

Wenbo Zhang obtained their Bachelor of Engineering - BE in Mechanical Engineering and Automation from Shanghai Jiao Tong University from 2007 to 2011. Wenbo then went on to pursue their Master of Science (M.S.) in Aerospace, Aeronautical and Astronautical Engineering from the University of Illinois Urbana-Champaign from 2013 to 2015. Finally, they completed their Doctor of Philosophy - PhD in Computational Fluid Dynamics from McGill University from 2015 to 2020. In addition to their formal education, Wenbo Zhang has obtained several certifications, including Django for Everybody by University of Michigan on Coursera from the University of Michigan | Coursera in October 2021, AWS Cloud Technical Essentials from Coursera in August 2021, Deep Learning from Coursera in June 2018, Probabilistic Graphical Models 1: Representation from Coursera in May 2017, Algorithms on Graphs from Coursera in October 2016, Data Structures from Coursera in September 2016, and edX Verified Certificate for Introduction to Apache Spark from edX in August 2016.

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Timeline

  • Machine Learning Engineer

    January, 2022 - present

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