| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | August 3, 2018 |
| Latest Amendment Date: | August 3, 2018 |
| Award Number: | 1763000 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Georgia-Ann Klutke
gaklutke@nsf.gov (703)292-2443 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | August 15, 2018 |
| End Date: | July 31, 2023 (Estimated) |
| Total Intended Award Amount: | $314,206.00 |
| Total Awarded Amount to Date: | $314,206.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
615 W 131ST ST NEW YORK NY US 10027-7922 (212)854-6851 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NY US 10027-6623 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | OE Operations Engineering |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
This award will contribute to the Nation's prosperity and welfare by capitalizing on the increased availability and accessibility of data to improve operational decision making. Operational decisions are ubiquitous in all aspects of the commercial economy, and even incremental improvements in operations can have major impacts in the competitiveness of such sectors as transportation, logistics, healthcare delivery, supply chain management. Similarly, public sector service operations involve decision making to wisely invest limited public resources. The ongoing data revolution has created great opportunities for leveraging large scale data to improve operational decision making. This award will support research in new techniques to make effective use of these data in the management of operations. This project provides a broadly applicable framework for addressing operational decisions and will result in improved performance and efficiency in practice. The project will involve outreach engagements with diverse organizations, including a nonprofit foster care agency.
Current operational decision-making often involve two significant challenges: prediction and optimization. These tasks are usually addressed sequentially: key parameters are first predicted using modern statistical machine learning tools, and then planning decisions are made using these predictions within a complex optimization model. This project advances a new, broadly applicable framework, called Smart "Predict, then Optimize" (SPO), that effectively addresses the prediction and optimization challenges in tandem. In this new framework, operational performance is measured by the true objective value of the solutions generated from the predicted parameters. This project investigates the statistical and computational properties of novel loss functions in the SPO framework, including convex surrogates as well as non-convex formulations. The project will also develop new algorithms for training machine learning models, such as linear models, logistic models, and decision trees, using the new loss functions, and will extend the SPO framework to handle regularization, robustness, different data primitives, and dynamic data collection with exploration-exploitation tradeoffs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project developed foundational frameworks for the design and analysis of operations-driven machine learning tools that effectively address prediction and decision-making challenges in tandem. Correspondingly, new techniques and computational methods for building operations-driven machine learning models were developed.
One of the principal outcomes was the development of the Smart Predict-then-Optimize (SPO) framework for quantifying the error of a machine learning model for predicting key quantities associated with a downstream optimization problem that is used for decision-making. Importantly, the loss function in this framework quantifies error with respect to the excess cost of making a suboptimal decision for the optimization problem. The use and effectiveness of this framework and associated computational methodologies were theoretically and empirically justified using appropriate performance metrics. In particular, this project developed new theoretical statistical properties in the predict-then-optimize setting, including consistency and generalization guarantees, that relate the performance on past data to future outcomes. The project has applications in many problem settings, such as vehicle routing and portfolio optimization, and in industries, such as e-commerce and finance, where organizations aim to effectively use machine learning tools in their decision-making processes.
Another principal outcome was to consider fairness issues that arise in the context of integrating machine learning and decision-making. In particular, the project focus on the setting of personalized pricing where users received different prices based on their characteristics. One important setting is that of auto-lending, where users get different interest rates based on their data and it is important to make sure that those rates are fair with respect to protected attributes. The project helped formalize the notion of price and demand fairness in these settings, and quantify the positive and negative effects on society by imposing such fairness constraints when doing personalized pricing.
This project directly involved the mentoring of graduate students at master's and doctoral level. The research was integrated into undergraduate/masters level courses in analytics that helps brings students close to the research frontier. Open-source code has also been provided for all experimental results, allowing other researchers to build off of the frameworks we developed.
Last Modified: 08/27/2023
Modified by: Adam N Elmachtoub
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