Federated Learning from Electronic Health Records Paper Receives Best Paper Honors
Our paper on ``Federated learning of predictive models from federated Electronic Health Records''...
Funding Agency: National Science Foundation, Directorate for Engineering, Division Division of Design, Manufacture and Industrial Innovation (DMII), Manufacturing Enterprise Systems (MES) Program.
Award Number: DMI-0300359.
Principal Investigators: Yannis Paschalidis and Michael Caramanis, Boston University.
In an era of time-based competition, the management and control of supply chains has emerged as a critical component of manufacturing and distribution enterprises. Customers have become more demanding and require customized products delivered in a consistently timely manner. Production speed along all stages of the supply chain and high service rates are key performance measures. Inventory cost reduction and Quality of Service (QoS) provisioning are in the foreground of practitioners’ radar screens. Despite successful decision support tool development to date, significant gaps exist. The primary research objective of this proposal is to develop methodology that enables a practical and effective framework for decentralized, yet coordinated, management and control of supply chains. To that end, it is proposed to address a host of key challenges:
To meet these objectives the proposed work will draw upon the PIs expertise and prior experience with modeling and analysis of stochastic systems, simulation as a design and analysis tool, dynamic optimization, and optimal control. To deal with computational complexities it is proposed to (i) exploit the time scale decomposition among decisions with different scope and functionality, (ii) employ analytical approximations to estimate complex quantities of interest, and (iii) use efficient simulation methodologies. The broader impact of this research stems from its interdisciplinary science base contributions to information and production systems engineering, and their potential to enable significant productivity growth in the manufacturing and distribution industries. On the educational front, plans consist of (i) integrating the proposed research into courses, (ii) training graduate students by exposing them to a balanced mix of relevant theory and systems engineering practice, (iii) involving undergraduate students in the work, (iv) creating interactive, java-based, educational software and demos, and (v) working through the NSF science ambassador award to Boston University (BU) — one of the ambassador fellows is our doctoral advisee — to reach out to high school students. Finally, and in addition to the usual means of disseminating the outcomes of the proposed work (publications, presentations at conferences, invited lectures, etc.), the PIs plan to: (i) leverage their association with the recently established BU Center for Information and Systems Engineering (CISE) to work with affiliated companies including Foresight Systems, Brooks-PRI Automation, Genuity, Sycamore, Nokia, Hewlett-Packard, Solectron, and others; (ii) present findings on the Web using java-based interactive examples; and (iii) leverage their recent NSF IGERT award on Advanced Computing in Engineering and Science to sponsor doctoral student internships at participating companies and support a post doctoral fellow who will focus on applied work in industry.