{"id":2722,"date":"2023-02-21T19:03:34","date_gmt":"2023-02-21T19:03:34","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/speakers\/daniel-rice-copy\/"},"modified":"2023-02-21T19:21:21","modified_gmt":"2023-02-21T19:21:21","slug":"heping-liu","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/speakers\/heping-liu\/","title":{"rendered":"Heping Liu"},"content":{"rendered":"\n
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Heping Liu<\/span><\/h1>\n

Senior Machine Learning Principal at Workday, Inc.<\/span><\/h1>\n
Heping Liu is a Senior Principal doing machine learning at Workday Inc. In this role, he has been building the forecasting and optimization platforms of infrastructure resources by using machine learning and leading the next-generation platformization architecture design and development within a cross-team organization. Before joining Workday, he co-founded Beijing Unigroup AI Technologies Inc. where he served as Chief Executive Officer, built the team from scratch, and financed more than $7 million USD. Prior to that, he served in various technical leadership roles at either technology start-ups or medium size public companies over the course of his early career in Silicon Valley. Heping has a Ph.D. degree in Industrial Engineering and his research interests are mainly forecasting, optimization, AI\/ML algorithms, big data and analytics. Dr. Liu has published around 20 papers in top academic journals in these research areas and has worked as a reviewer of more than 20 top academic journals.<\/div> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Track: Platforms & SaaS<\/h6> <\/div>\n<\/div>\n\n
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Demand Planning of Infrastructure Resources Based on AutoML Time Series Forecasting and Assignment Optimization Model<\/h2>\n

This talk presents a demand planning framework of infrastructure resources in IT companies. The framework is based on AutoML time series forecasting and assignment optimization model. AutoML time series forecasting is developed under the architecture of big data and distributed computation, and it has a highly generalized forecasting engine powered by AutoML and big data ETL (extract, transform, load). It has the linear scalability and can enable the automatically fitting and forecasting of millions of time series forecasting models. With the forecasting output of resources, the assignment optimization model can determine how to assign infrastructure resources to individual tenants in order to minimize the usage of total infrastructure resources in an IT company for any future time within the forecasting horizon.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n","protected":false},"excerpt":{"rendered":"

Heping Liu Heping Liu Senior Machine Learning Principal at Workday, Inc. Heping Liu is a Senior Principal doing machine learning at Workday Inc. In this role, he has been building the forecasting and optimization platforms of infrastructure resources by using machine learning and leading the next-generation platformization architecture design and development within a cross-team organization. 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In this role, he has been building the forecasting and optimization platforms of infrastructure resources by using machine learning and leading the next-generation platformization architecture design and development within a cross-team organization. Before joining Workday, he co-founded Beijing Unigroup AI Technologies Inc. where he served as Chief Executive Officer, built the team from scratch, and financed more than $7 million USD. Prior to that, he served in various technical leadership roles at either technology start-ups or medium size public companies over the course of his early career in Silicon Valley. Heping has a Ph.D. degree in Industrial Engineering and his research interests are mainly forecasting, optimization, AI\/ML algorithms, big data and analytics. Dr. Liu has published around 20 papers in top academic journals in these research areas and has worked as a reviewer of more than 20 top academic journals.\n<h6>Track: Platforms & SaaS<\/h6>\n<h2>Demand Planning of Infrastructure Resources Based on AutoML Time Series Forecasting and Assignment Optimization Model<\/h2> <p>This talk presents a demand planning framework of infrastructure resources in IT companies. The framework is based on AutoML time series forecasting and assignment optimization model. AutoML time series forecasting is developed under the architecture of big data and distributed computation, and it has a highly generalized forecasting engine powered by AutoML and big data ETL (extract, transform, load). It has the linear scalability and can enable the automatically fitting and forecasting of millions of time series forecasting models. 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