
NSF Org: |
DMS Division Of Mathematical Sciences |
Recipient: |
|
Initial Amendment Date: | September 10, 2018 |
Latest Amendment Date: | July 17, 2020 |
Award Number: | 1839353 |
Award Instrument: | Standard Grant |
Program Manager: |
Tracy Kimbrel
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
Start Date: | October 1, 2018 |
End Date: | September 30, 2023 (Estimated) |
Total Intended Award Amount: | $199,353.00 |
Total Awarded Amount to Date: | $199,353.00 |
Funds Obligated to Date: |
|
History of Investigator: |
|
Recipient Sponsored Research Office: |
526 BRODHEAD AVE BETHLEHEM PA US 18015-3008 (610)758-3021 |
Sponsor Congressional District: |
|
Primary Place of Performance: |
Building C, Mountaintop Campus Bethlehem PA US 18015-0001 |
Primary Place of
Performance Congressional District: |
|
Unique Entity Identifier (UEI): |
|
Parent UEI: |
|
NSF Program(s): |
TRIPODS Transdisciplinary Rese, OFFICE OF MULTIDISCIPLINARY AC |
Primary Program Source: |
|
Program Reference Code(s): |
|
Program Element Code(s): |
|
Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.049 |
ABSTRACT
This project encompasses the planning and organization of several specialized workshops that will bring together top experts in multiple areas to shape new and emerging multidisciplinary fields, tapping the tremendous recent surge in the adoption of machine learning tools in various areas of science and engineering. The premise of this project is the need for sophisticated computational tools to analyze data and improve our ability to understand and harness phenomena associated with complex domains such as chemical processes, autonomous robots operating in open and dynamic environments, supply chain optimization involving large organizations with multiple and competing objectives, and cognitive neuroscience bridging electrical brain impulses and high-level functions such as problem solving. Towards this end it is necessary to foster interdisciplinary collaborations and to promote convergent research and develop fertile space for collaborations among industrial, academic, and governmental partners to attack some of the most pressing problems in technology and society.
Under the umbrella of the new Institute for Data, Intelligent Systems, and Computation (I-DISC) at Lehigh University, which builds upon the foundation of Lehigh research expertise in areas such as machine learning, optimization, and data-driven decision making, four workshops will be organized that will bring together leading researchers from different research communities that otherwise may not interact. All of these workshops are on newly emerging topics which are expected to gain significant traction in the near future. These topics are as follows: (1) Chemistry, chemical engineering, materials science, and related disciplines where machine learning is used to elucidate and design complex processes (chemical/biological, engineered/natural) or material systems with wide ranging applications addressing grand challenges in energy, health, environment, and water. (2) Robotics, where applications of machine learning, also known as robot learning, has been rapidly growing in recent years, where the main focus has been to develop algorithms to assist robots to acquire novel skill or adapt to their environment through sensing. (3) Supply chain management with the specific focus on applying machine learning models for prescriptive analytics, such as optimization, in contrast to already popular use of machine learning (deep learning) models for predictive and descriptive analytics, such as predicting customer demands. (4) Cognitive Neuroscience with the focus on understanding the brain-cognition-behavior interface, which requires expertise in neuroscience as well as computational modeling, machine learning and big data science in order (a) to enable sophisticated analyses of complex patterns in brain data and (b) to provide insight into how hypothesized brain-level implementations could in fact produce observed behavioral outcomes.
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.
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 supported five specialized workshops that brought together top experts in multiple areas to shape new and emerging multidisciplinary fields, tapping the tremendous recent surge in the adoption of machine learning tools in various areas of science and engineering. Under the umbrella of the Institute for Data, Intelligent Systems, and Computation (I-DISC) at Lehigh University, which builds upon the foundation of Lehigh research expertise in areas such as machine learning, optimization, and data-driven decision making, five workshops were organized that brought together leading researchers and practitioners from different communities that otherwise may not interact. In particular, we held workshops under the general theme of ”Machine Learning + X”, including the following:
- Chemistry, chemical engineering, materials science, and related disciplines where machine learning is used to elucidate and design complex processes (chemical/biological, engineered/natural) or material systems with wide-ranging applications addressing grand challenges in energy, health, environment, and water. (This workshop was held in May 2019.)
- Robotics, where applications of machine learning, also known as robot learning, have been rapidly growing in recent years, where the main focus has been to develop algorithms to assist robots to acquire a novel skill or adapt to their environment through sensing. (This workshop was held in October 2019.)
- Supply chain management with the specific focus on applying machine learning models for prescriptive analytics, such as optimization, in contrast to the already popular use of machine learning (deep learning) models for predictive and descriptive analytics, such as predicting customer demands. (This workshop was originally scheduled for June 2021 but was postponed to December 2021 due to the COVID-19 pandemic.)
- Catastrophe modeling & data, with the goal of bringing together stakeholders from private sector, public sector, and academia in this field where siloed data and modeling efforts are hindering progress. The event served also to launch a new research center in this field, and a “coordination network” including other academic institutions. (This workshop was held in March 2023.)
- Data-Driven Urban Tech: How Machine Learning and Optimization Addresses Today’s Urban Challenges. Technological innovations are creating opportunities for cities to rethink the way in which they address pressing and complex challenges. These include: mitigating and adapting to climate change, which has prompted researchers to design innovative prevention tools and disaster relief measures with the goal of making cities more robust; responding to the algorithmic economy (e.g., ride-hailing, bike-sharing systems, delivery systems), which has revolutionized our interactions with each other in the city; innovations in real-time information to help users’ decision-making, including smart infrastructure that aims to create a more sustainable ecosystem (e.g., energy efficient buildings, robust communication networks, smart grids); and other developments across mobility, healthcare and, in general, public and private services, including the adoption of autonomous vehicles, computer-assisted healthcare, analytics for water management, robust supply chains, among others. These developments bring myriad new questions around security, privacy, efficiency, equity, and complexity, alongside old challenges in urban planning. This workshop considered how machine learning and data-driven approaches can be used to improve quality of life in urban environments. (This workshop was held in May 2023.)
Last Modified: 01/09/2024
Modified by: Lawrence V Snyder
Please report errors in award information by writing to: awardsearch@nsf.gov.