| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | March 18, 2019 |
| Latest Amendment Date: | July 20, 2021 |
| Award Number: | 1907412 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Yuliya Gorb
ygorb@nsf.gov (703)292-2113 DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | April 1, 2019 |
| End Date: | August 31, 2022 (Estimated) |
| Total Intended Award Amount: | $17,680.00 |
| Total Awarded Amount to Date: | $17,680.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4400 UNIVERSITY DR FAIRFAX VA US 22030-4422 (703)993-2295 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4400 University Drive, MS: 3F2 Fairfax VA US 22030-4422 |
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Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | COMPUTATIONAL MATHEMATICS |
| 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): | 47.049 |
ABSTRACT
The award provides participant support to the East Coast Optimization Meeting (ECOM) to be held at George Mason University, Fairfax, VA. Date: April 4-5, 2019. The goal of ECOM is to introduce students and early-career researchers to current trends in optimization as well as to provide a strong networking environment between academia, industry, and the national laboratories. The focus of first meeting is Stochastic Optimization. Stochastic optimization problems arise in virtually all science and engineering fields. Common examples of stochastic optimization problems are: (i) determining an allocation of financial assets that minimize the potential for loss subject to market variability; (ii) controlling injection wells in second-stage oil recovery to maximize the net present value of a reservoir in which the subsurface rock properties are unknown; and (iii) designing a photonic meta-material to maximize light absorption subject to uncertain operating environments. For each of these problems, a decision maker must choose an allocation/control/design prior to observing the uncertain outcome (i.e., decisions are deterministic). As a result, one must properly quantify the risks associated with each decision in order to control the outcome variability. This need has led to the modern theory of stochastic and in particular risk-averse optimization. The meeting will provide a unique opportunity for graduate students, postdocs and other early career scientists to take courses from two of the best researchers in stochastic optimization and thus help train next generation of scientists. In addition, there will be four invited talks from the experts in the field and the students and postdocs will have an opportunity to share their work via contributed presentations. The knowledge gained during the meeting will be of relevance to fields such as finance, physics, biology, data science, machine (deep learning), and engineering. The meeting has an affiliation from Association of Women in Mathematics (AWM).
Accurately representing the uncertainty when solving stochastic optimization problems often requires an enormous number of samples, which traditionally resulted in intractable nonlinear optimization problem. However, owing to the recent advances in high-performance computing, computational simulation and numerical optimization, the numerical solution of such problems has become computationally feasible. Additionally, this past year, four stochastic optimization researchers received prestigious awards including two Dantzig Award winners (one of our keynote speaker was among the two), a Khachiyan Prize winner and a Farkas Prize winner. For these reasons, the topic of stochastic optimization is very timely for the inaugural East Coast Optimization Meeting. The proposed meeting has the potential to advance knowledge and understanding in modeling, optimization, numerical analysis, implementation and software development. Stochastic optimization encompasses numerous aspects from statistics, probability theory, optimization and variational analysis, convex analysis, and applied mathematics. The meeting will stimulate new developments in these important areas of mathematics. The tutorials and invited talks will focus on real life problems and will discuss new optimization solvers to handle these problems. Thus the attendees can tackle new set of challenging problems. The variety of topics discussed in the meeting, stochastic optimization, modeling, partial differential equations, risk averse optimization is of much wider interest. For instance, these are relevant in finance, physics, biology, data science, and engineering. Participation from all these fields is expected. The ideas created in the meeting will be actively disseminated. We will upload the lecture notes on the conference website. These resources will help create new graduate courses. More details about the meeting are available at http://math.gmu.edu/~hantil/ECOM/2019/.
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.
The annual East Coast Optimization Meeting (ECOM) is an initiative to introduce students and early-career researchers to the current trends in optimization, numerical analysis, scientific computing as well as to create a strong networking environment among academia, industry, and the national laboratories. Three workshops took place in 2019, 2021, and 2022. First one of them was in person and the last two took place in a virtual format due to the COVID-19 pandemic. The latter also led to postponement of year 2020 workshop. The meeting has seen tremendous interest from the community, for instance, 2021 meeting saw over 400 registrations from 45 countries. This includes more than 60% students and postdocs.
Each workshop respectively had a theme: (i) Stochastic Optimization; (ii) Optimization for Machine Learning; (iii) Nonsmooth Optimization. More details are available on the workshop websites
https://math.gmu.edu/~hantil/ECOM/2019/
https://math.gmu.edu/~hantil/ECOM/2020/
https://math.gmu.edu/~hantil/ECOM/2021/
https://math.gmu.edu/~hantil/ECOM/2022/
Researchers from all optimization areas were encouraged to participate. The meeting comprised of mini courses by two distinguished speakers targeted toward students and early career researchers, and public lectures. There were four invited talks each year and around 20 contributed talks (each year) from early career researchers. The websites listed above provide the detailed program, including all the material (slides, videos, etc.) from the workshops.
The workshops provided unique opportunities for graduate students, postdocs and other early career scientists to take courses from best researchers in stochastic optimization, machine learning, and nonsmooth optimization. The meeting allowed the young researchers to interact with participants from academia, national labs and industry. The variety of topics discussed in the meeting, stochastic optimization, modeling, partial differential equations, risk averse optimization, machine learning is of much wider interest. For instance, these are relevant in finance, physics, biology, data science, and engineering. Participation from all these fields took place. The material shared using the websites is helping in creating new courses. Especial attention was paid in recruiting researchers from underrepresented groups.
Last Modified: 12/20/2022
Modified by: Harbir Antil
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