AI Tool Pairs Protein Pathways with Clinical Side Effects, Patient Comorbidities to Suggest Targeted Covid-19 Treatments

Researchers led by Jeffrey Skolnick have designed a new AI-based “decision prioritization tool” that combines data on protein pathways with common Covid-19 side effects and known patient comorbidities. The tool offers possible targeted treatment options with existing FDA-approved drugs to foster better health outcomes for individuals fighting Covid-19.

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.

AI learns physics to optimize particle accelerator performance

Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have demonstrated that they can use machine learning to optimize the performance of particle accelerators by teaching the algorithms the basic physics principles behind accelerator operations – no prior data needed.

Maximizing cancer survival, minimizing treatment side effects with AI

Computer scientists at the University of Illinois Chicago are developing a computational artificial intelligence system they hope will serve as a decision support tool for doctors prescribing treatment for head and neck cancer. The work is supported by a $2.8 million grant from the National Institutes of Health.

On the design of an optimal flexible bus dispatching system with modular bus units: Using the three-dimensional macroscopic fundamental diagram

This project proposes a flexible bus dispatching system using automated modular vehicle technology, and considers multimodal interactions and congestion propagation dynamics.  This study proposes a novel flexible bus dispatching system in which a fleet of fully automated modular bus units, together with conventional…

From Curb to Doorstep: Driving Efficiencies for Delivering Goods

In a collaboration between Pacific Northwest National Laboratory and the University of Washington’s Urban Freight Lab, a prototype webapp has been developed that combines smart sensors and machine learning to predict parking space availability. The prototype is ready for initial testing to help commercial delivery drivers find open spaces without expending fuel and losing time and patience.

Bringing medical AI closer to reality

For AI to continue to transform cancer diagnoses, researchers will have to prove that the success of their machine-learning tools can be reproduced from site to site and among different patient populations. Biomedical engineering researchers at Case Western Reserve University say they doing just that. They say they have demonstrated that their novel algorithms for distinguishing between benign and malignant lung cancer nodules on CT scan images from one site can now be successfully reproduced with patients from other sites and locations.