This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) (CFDA 47.070) Project Grant award of $108,000 to the Georgia Tech Research Corp, Office of Sponsored Programs, will fund collaborative research to develop a unified framework for analyzing adaptive stochastic optimization methods for machine learning applications. The research aims to produce self-tuning optimization algorithms with rigorous guarantees to reduce wasteful computation required by current...
This Project Grant award of $160,673 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to combine algorithms and machine learning to improve decision-making under uncertainty. The project, led by New York University (NYU), will explore incorporating machine-learned predictions into algorithm design as well as developing learning models optimized for specific algorithmic objectives. This work aims to create a...
The National Science Foundation (NSF) awarded a $444,679 Project Grant to Stanford University under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports a collaborative research project focused on developing "statistical and algorithmic foundations for distributionally robust policy learning in unknown environments, under a possibly misspecified generative model." The key objectives are to study the fundamental learning limits for...
The National Science Foundation (NSF) awarded a $237,028 Project Grant to New York University (NYU) under the Computer and Information Science and Engineering program (CFDA 47.070) to develop statistical and algorithmic foundations for robust policy learning in uncertain environments. The goal is to create provably efficient techniques for learning optimization policies that can be deployed in practical settings where the training and operational environments differ, such as when using digital...
This Project Grant award of $101,476 from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop reliable machine learning algorithms for decision-making in complex feedback systems. The award, running from April 1, 2025 to March 31, 2030, will fund research on leveraging potentially unreliable machine learning predictions while ensuring safety and performance, learning long-term impact models from non-stationary...
This $100,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program supports research on machine learning-augmented algorithms that can operate on weak and sparse predictions. The project aims to study the design considerations and performance tradeoffs of such algorithms, which can be useful in real-world scenarios where obtaining abundant and accurate training data is challenging. The research seeks to...
This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $948,000 in funding to the Georgia Tech Research Corp to develop new methods for data-efficient decision-focused learning. The research aims to create a general framework for tailoring predictive models to address uncertainty factors and integrate them into decision-making problems across domains such as public health, environmental...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $271,343 to Arizona State University to conduct collaborative research on developing robust learning and inference methods. The project aims to design machine learning and inference techniques that are resilient to distributional uncertainty and data corruption, with potential applications in healthcare, transportation, smart cities, and...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant, awarded under CFDA 47.070, supports the University of Utah's research project titled "SLES: HIGH-CONFIDENCE GUARANTEES FOR SAFE REWARD AND POLICY LEARNING UNDER UNCERTAINTY." The $439,425 award, effective August 15, 2024 through July 31, 2027, aims to develop scalable learning methods that are robust to uncertainty, enable self-assessment, and provide test cases for assessing...
This $299,886 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program aims to develop new big data algorithms that are robust to adversarial input. The award supports research to address emerging vulnerabilities in areas such as black-box streaming algorithms, white-box streaming algorithms, and adaptive data analysis with bounded space. This work will focus on improving the reliability, security, and trustworthiness of...