{"id":3624,"date":"2024-10-07T08:12:10","date_gmt":"2024-10-07T13:12:10","guid":{"rendered":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/?post_type=tribe_events&p=3624"},"modified":"2024-10-07T08:12:10","modified_gmt":"2024-10-07T13:12:10","slug":"lans-seminar-148","status":"publish","type":"tribe_events","link":"https:\/\/wordpress.cels.anl.gov\/lans-seminars\/event\/lans-seminar-148\/","title":{"rendered":"LANS Seminar"},"content":{"rendered":"
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Seminar Title: <\/strong>Building\u00a0Sustainable\u00a0and Collaborative Data-Ecosystems<\/p>\n

Speaker: Sai Praneeth Karimireddy, Assistant Professor, Thomas Lord Department of Computer Science, USC<\/strong><\/p>\n

Date\/Time:<\/strong> November 13, 2024\/ 10:30 AM-11:30 AM
\nLocation:\u00a0<\/strong>See Meeting URL on the cels-seminars website which will require an Argonne login.<\/em><\/p>\n

Description:\u00a0 <\/strong>Data is the most important factor determining the quality of an AI system. However, The\u00a0data commons<\/a>\u00a0that current AI relies on is fast collapsing.\u00a0This issue is only exacerbated when considering more valuable data (e.g. healthcare) which are firmly locked behind privacy and incentive barriers. We will examine how tools from optimization, statistics, and economics can be combined to reimagine AI infrastructure and build\u00a0sustainable\u00a0data ecosystems. This talk will be largely based on these three papers:<\/p>\n

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  1. SCAFFOLD: Stochastic Controlled Averaging for Federated Learning (arxiv<\/a>)<\/li>\n
  2. Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning (arxiv<\/a>)<\/li>\n
  3. Data Acquisition via Experimental Design for Decentralized Data Markets (arxiv<\/a>)<\/li>\n<\/ol>\n

    Bio:<\/strong> \u00a0Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at USC. Before this, he was a postdoc at UC Berkeley with\u00a0Michael Jordan<\/a>\u00a0and did my PhD among the picturesque mountains of EPFL with\u00a0Martin Jaggi<\/a>. He also co-leads the\u00a0Data Quality and Federated Learning for Health<\/a>. Praneeth\u2019s research studies data challenges in machine learning, including privacy, incentives and data markets, and AI for health. Praneeth\u2019s work has seen widespread adoption both by public health organizations (e.g.,\u00a0Doctors Without Borders<\/a>, the\u00a0Cancer Registry of Norway<\/a>) and by industries such as\u00a0Meta<\/a>,\u00a0Google<\/a>,\u00a0OpenAI<\/a>, and\u00a0Owkin<\/a>. It has also been recognized by numerous awards such as the\u00a0Patrick Denantes Memorial Prize<\/a>\u00a0for the best thesis in computer science, the\u00a0Chorafas Foundation Award<\/a>\u00a0for exceptional applied research, and multiple best paper awards.<\/p>\n

    Please note that the meeting URL for this event can be seen on the cels-seminars website which requires an Argonne login.<\/em><\/p>\n

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    See all upcoming talks at\u00a0https:\/\/www.anl.gov\/mcs\/lans-seminars<\/a><\/p>\n<\/div>\n<\/div>\n

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