Jonas Mueller

Jonas Mueller

Chief Scientist and Co-Founder at Cleanlab

    Jonas Mueller is Chief Scientist and Co-Founder at Cleanlab, a software company providing data-centric AI tools to efficiently improve ML datasets. Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms which now power ML applications at hundreds of the world's largest companies. In 2018, he completed his PhD in Machine Learning at MIT, also doing research in NLP, Statistics, and Computational Biology. Jonas has published over 30 papers in top ML and Data Science venues (NeurIPS, ICML, ICLR, AAAI, JASA, Annals of Statistics, etc). This research has been featured in Wired, VentureBeat, Technology Review, World Economic Forum, and other media. He has also contributed open-source software, including the fastest-growing open-source libraries for AutoML (https://github.com/awslabs/autogluon) and Data-Centric AI (https://github.com/cleanlab/cleanlab).

    All Sessions by Jonas Mueller

    Day 1 04/23/2024
    12:00 pm - 1:00 pm

    How to Practice Data-Centric AI and Have AI Improve its Own Dataset

    <span class="etn-schedule-location"> <span class="firstfocus">Deep Learning</span>

    DE Tutorial: In Machine Learning projects, one starts by exploring the data and training an initial baseline model. While it’s tempting to experiment with different modeling techniques right after that, an emerging science of data-centric AI introduces systematic techniques to utilize the baseline model to find and fix dataset issues. Improving the dataset in this manner, one can drastically improve the initial model’s performance without any change to the modeling code at all! These techniques work with any ML model and the improved dataset can be used to train any type of model (allowing modeling improvements to be stacked on top of dataset improvements). Such automated data curation has been instrumental to the success of AI organizations like OpenAI and Tesla. While data scientists have long been improving data through manual labor, data-centric AI studies algorithms to do this automatically. This tutorial will teach you how to operationalize fundamental ideas from data-centric AI across a wide variety of datasets (image, text, tabular, etc). We will cover recent algorithms to automatically identify common issues in real-world data (label errors, bad data annotators, outliers, low-quality examples, and other dataset problems that once identified can be easily addressed to significantly improve trained models). Open-source code to easily run these algorithms within end-to-end Data Science projects will also be demonstrated. After this tutorial, you will know how to use models to improve your data, in order to immediately retrain better models (and iterate this data/model improvement in a virtuous cycle).

    Day 1 04/23/2024
    12:00 pm - 1:00 pm

    How to Practice Data-Centric AI and Have AI Improve its Own Dataset

    <span class="etn-schedule-location"> <span class="firstfocus">Deep Learning</span> </span>

    DE Tutorial: In Machine Learning projects, one starts by exploring the data and training an initial baseline model. While it’s tempting to experiment with different modeling techniques right after that, an emerging science of data-centric AI introduces systematic techniques to utilize the baseline model to find and fix dataset issues. Improving the dataset in this manner, one can drastically improve the initial model’s performance without any change to the modeling code at all! These techniques work with any ML model and the improved dataset can be used to train any type of model (allowing modeling improvements to be stacked on top of dataset improvements). Such automated data curation has been instrumental to the success of AI organizations like OpenAI and Tesla. While data scientists have long been improving data through manual labor, data-centric AI studies algorithms to do this automatically. This tutorial will teach you how to operationalize fundamental ideas from data-centric AI across a wide variety of datasets (image, text, tabular, etc). We will cover recent algorithms to automatically identify common issues in real-world data (label errors, bad data annotators, outliers, low-quality examples, and other dataset problems that once identified can be easily addressed to significantly improve trained models). Open-source code to easily run these algorithms within end-to-end Data Science projects will also be demonstrated. After this tutorial, you will know how to use models to improve your data, in order to immediately retrain better models (and iterate this data/model improvement in a virtuous cycle).

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