Advanced AI-Based Techniques Scale-up Solving Complex Combinatorial Optimization Problems

A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a study led by engineers at the University of California San Diego. In the paper, which was published May 30 in Nature Machine Intelligence, researchers present HypOp, a framework that uses unsupervised learning and hypergraph neural networks.

New $10M NSF-Funded Institute Will Get to the CORE of Data Science

A new National Science Foundation initiative has created a $10 million dollar institute led by computer and data scientists at University of California San DIego that aims to transform the core fundamentals of the rapidly emerging field of Data Science.

NUS AI platform enables doctors to optimise personalised chemotherapy dose

A team of researchers from National University of Singapore, in collaboration with clinicians from the National University Cancer Institute, Singapore which is part of the National University Health System, has reported promising results in using CURATE.AI, an artificial intelligence tool that identifies and better allows clinicians to make optimal and personalised doses of chemotherapy for patients.

Quantum, Classical Computing Combine to Tackle Tough Optimization Problems

A research team led by the Georgia Tech Research Institute (GTRI) was recently selected for second-phase funding of a $9.2 million project aimed at demonstrating a hybrid computing system that will combine the advantages of classical computing with those of quantum computing to tackle some of the world’s most difficult optimization problems.

NSF makes $20 Million investment in Optimization-focused AI Research Institute led by UC San Diego

The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego.

Sensor Data Identifies Turbine Wake Clustering, Improves Wind Farm Productivity Via Yaw Control

In the Journal of Renewable and Sustainable Energy, researchers describe a real-time method for potentially helping turbine farms realize additional power from the clustering of their turbines. Their method requires no new sensors to identify which turbines at any given time could increase power production if yaw control is applied, and validation studies showed an increase of 1%-3% in overall power gain.

Scientists use reinforcement learning to train quantum algorithm

Scientists are investigating how to equip quantum computers with artificial intelligence and machine learning approaches.

Six Argonne researchers receive DOE Early Career Research Program awards

Argonne scientists Michael Bishof, Maria Chan, Marco Govini, Alessandro Lovato, Bogdan Nicolae and Stefan Wild have received funding for their research as part of DOE’s Early Career Research Program.

Inverse Design Software Automates Design Process for Optical, Nanophotonic Structures

Stanford University researchers created an inverse design codebase called SPINS that can help researchers explore different design methodologies to find fabricable optical and nanophotonic structures. In the journal Applied Physics Reviews, Logan Su and colleagues review inverse design’s potential for optical and nanophotonic structures, as well as present and explain how to use their own inverse design codebase.