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Madeleine Udell

Assistant Professor
Cornell
Picture
Madeleine Udell is Assistant Professor of Operations Research and
Information Engineering and Richard and Sybil Smith Sesquicentennial
Fellow at Cornell University. She studies optimization and machine
learning for large scale data analysis and control, with applications
in marketing, demographic modeling, medical informatics, engineering
system design, and automated machine learning. She has received
several awards, including a National Science Foundation CAREER award
(2020), an Office of Naval Research (ONR) Young Investigator Award
(2020), a Cornell Engineering Research Excellence Award (2020), an
INFORMS Optimization Society Best Student Paper Award (as advisor)
(2019), and INFORMS Doing Good with Good OR (2018). Her work is
supported by grants from the NSF, ONR, DARPA, the Canadian Institutes
of Health, and Capital One.

 

Workshop: Automating Machine Learning
Prerequisite: Supervised learning (classification, regression); cross-validation

How should we choose a machine learning model? What kind of model (linear, tree ensemble, deep neural network) would work best? What values should you pick for model hyperparameters - will the defaults work, or should you tweak them? Have you wrung all the value out of your data, or could you predict better if only you found the right model? In this workshop we'll survey some interesting strategies for automated machine learning that can answer these questions for you.

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  • Home
  • About
    • Blog
    • WiDStory
    • News
    • Research
    • Sponsors
    • Collaborators
    • Contact
    • Donate
  • Conferences
    • WiDS Stanford 2023 Agenda
    • WiDS Stanford 2023 Speakers
    • WiDS Regional Events 2023
    • Ambassadors 2023 >
      • Ambassador Advisory Council
    • WiDS Ambassador Program
    • Past Conferences >
      • WiDS 2022
      • WiDS 2021
      • WiDS 2020
      • WiDS 2019
      • WiDS 2018
      • WiDS 2017
      • WiDS 2015
    • Conference Committee
  • Datathon
    • Datathon Details
    • Datathon Resources >
      • Datathon Press Release
    • WiDS Datathon Workshops 2023
    • Datathon News
    • Datathon Collaborators
    • Datathon Committee
  • Podcast
    • Podcast Committee
  • Education
    • Workshops >
      • Workshop Instructors
      • Workhop Committee
    • Next Gen >
      • Next Gen Resources
      • Next Gen Committee