Bringing Fairness in AI to the Forefront of Education
LDAV 2021 Best Paper Honorable Mention
SCI at University of Utah Accelerates Visual Computing via oneAPI
oneAPI cross-architecture programming & Intel® oneAPI Rendering Toolkit to Improve Large-scale Simulations, Data Analytics & Visualization for Scientific Workflows
[Oct. 26, 2021] - The Scientific Computing and Imaging (SCI) Institute at the University of Utah is pleased to announce that it is expanding its Intel Graphics and Visualization Institute of Xellence (Intel GVI) to an Intel oneAPI Center of Excellence (CoE). The oneAPI Center of Excellence will focus on advancing research, development and teaching of the latest visual computing innovations in ray tracing and rendering, and using oneAPI to accelerate compute across heterogeneous architectures (CPUs, GPUs including future upcoming Intel Xe architecture, and other accelerators). Adopting oneAPI’s cross-architecture programming model provides a path to achieve maximum efficiency in multi-architecture deployments supporting CPUs + accelerators. This core approach based on open standards will allow fast, agile development and support new, advanced features without costly management of multiple vendors’ specific proprietary code bases.
Democratizing Data Access
The National Science Foundation (NSF) awarded a $5.6 million project to a team of researchers led by School of Computing professor Valerio Pascucci (pictured), who is also director of the Center for Extreme Data Management in the College of Engineering, to build the critical infrastructure needed to connect large-scale experimental and computational facilities and recruit others to data-driven sciences.
Chuck Hansen Elected to IEEE Board of Governors
Valerio Pascucci Wins a NASA Earth Exchange Award
WIFIRE Commons and BurnPro3D
Manish Parashar Named ACM Fellow
The ACM Fellows program recognizes the top 1% of ACM Members for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community, according to the organization. Fellows are nominated by their peers.
SCALE MoDL: Advancing Theoretical Minimax Deep Learning: Optimization, Resilience, and Interpretability
The past decade has witnessed the great success of deep learning in broad societal and commercial applications. However, conventional deep learning relies on fitting data with neural networks, which is known to produce models that lack resilience.