This National Science Foundation (NSF) Project Grant award, provided under the Computer and Information Science and Engineering (CFDA 47.070) program, aims to conduct research on retrieval-enhanced machine learning (REML) from an information retrieval (IR) perspective. The $612,970 award, spanning October 2024 to September 2027, will focus on three key research thrusts: Developing novel architectures and optimization solutions that enable multiple machine learning models to access information...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $100,000 to the University of California, Merced to study machine learning-augmented algorithms that utilize weak and sparse predictions. The research aims to understand the tradeoffs between prediction quality and performance guarantees when designing such algorithms, with the goal of expanding the applicability of machine...
This federal Project Grant award for $101,476, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports research aimed at developing reliable machine learning algorithms for decision-making in complex feedback systems. The project, led by Cornell University, focuses on three key thrusts: 1) Reliably leveraging unreliable machine learning predictions to ensure performance and safety, 2) Learning accurate models of...
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 $299,993 Project Grant under the Computer and Information Science and Engineering program (CFDA 47.070) to the University of Chicago. The grant supports a collaborative research project on the "Foundations of Few-Round Active Learning" in supervised machine learning. The key objectives are to advance active learning algorithms and improve understanding of their capabilities in scenarios with limited interaction rounds. The research aims...
The National Science Foundation (NSF) awarded a $599,410 Project Grant to Carnegie Mellon University (CMU) to develop a collaborative learning framework for dynamic and diverse computing environments, particularly focused on edge devices. The grant, under NSF's Computer and Information Science and Engineering program (CFDA 47.070), aims to innovate model-parallel collaborative learning by designing unique model architectures and efficient algorithms, facilitate practical on-device training and...
The National Science Foundation (NSF) awarded a $184,208 Project Grant under its Computer and Information Science and Engineering (CFDA 47.070) program to the University of California, Santa Barbara (UCSB) to develop a "data preparation framework for end-to-end equitable machine learning." The project aims to investigate how biases can be mitigated when handling prevalent dataset issues such as missing values, heterogeneity, and data imbalance, and devise fairness-aware data...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) grant, awarded under CFDA 47.070, provides $123,859 to the University of California, Santa Barbara (UCSB) to develop a closed-loop machine learning (ML) pipeline that iteratively refines training data collection to improve the generalizability of ML models for network operations. The project aims to design a programmable data-collection platform that enables flexible and scalable training data...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant award of $600,000 to the Massachusetts Institute of Technology (MIT) supports research into developing better algorithms for machine learning problems that involve sequential data with rich dependency structures. The project will explore learning methods for linear dynamical systems, graphical models, and hidden Markov models, with the goal of proving rigorous theoretical...
This Project Grant award, funded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041), supports fundamental research and advanced algorithm development for continual learning systems. The $544,381 award, made on September 1, 2024, aims to establish a principled framework for continual learning to enable intelligent engineering systems to learn more efficiently and effectively in dynamic environments. The research focuses on three key areas: 1) developing mathematical...