“Get non-Real”: Department of Energy grant funds novel research in High-Performance Algorithms at U-M

By | Feature, Research

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,

Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science
Source: www.energy.gov/science/articles/department-energy-invests-28-million-novel-research-high-performance-algorithms

New research from the University of Michigan will help revolutionize the data processing pipeline with state-of-the-art algorithms to optimize the collection and processing of any kind of data. Algorithms available now are built for real data, meaning real numbers, however, most of the data we see on the internet is non-real, like discrete data, or categorical. This project is part of a $2.8 million grant from the Department of Energy on algorithms research, which is the backbone of predictive modeling and simulation. The research will enable DOE to set new frontiers in physics, chemistry, biology, and other domains. 

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,” said Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science. “Foundational research in algorithms is essential for ensuring their efficiency and reliability in meeting the emerging scientific needs of the DOE and the United States.”

The U-M project, led by associate professor Laura Balzano and assistant professor Hessam Mahdavifar, both of electrical engineering and computer science, is one of six chosen by DOE to cover several topics at the leading-edge of algorithms research. According to the DOE, researchers will explore algorithms for analyzing data from biology, energy storage, and other applications. They will develop fast and efficient algorithms as building blocks for tackling increasingly large data analysis problems from scientific measurements, simulations, and experiments. Projects will also address challenges in solving large-scale computational fluid dynamics and related problems.

Laura Balzano and Hessam Mahdavifar portraits

Laura Balzano, associate professor of electrical engineering and computer science (left); Hessam Mahdavifar assistant professor of electrical engineering and computer science (right)

Balzano and Mahdavifar, both Michigan Institute for Computational Discovery and Engineering (MICDE) affiliated faculty members, will use a $300,000 portion of the overall grant to study randomized sketching and compression for high-dimensional non-real-valued data with low-dimensional structures.

“Randomized sketching and subsampling algorithms are revolutionizing the data processing pipeline by allowing significant compression of redundant information,” said Balzano. “Sketches work well because scientific data are generally highly redundant in nature, often following a perturbed low-dimensional structure. Hence, low-rank models and sketching that preserves those model structures are ubiquitous in many machine learning and signal processing applications.” 

Even though a lot of the data used and processed in scientific and technological applications are best modeled mathematically as discrete, categorical or ordinal data, most state-of-the art randomized sketching algorithms focus on real-valued data. To add to this, in practical applications, treating high-dimensional data can be challenging in terms of computational and memory demands. Thus, the proposed project will significantly expand the applicability of randomized sketching.

“A key to data-driven modeling is to carefully reformulate the computational and data analysis challenges and take full advantage of the underlying mathematical structure that is often common across application areas,” said Krishna Garikipati, MICDE director and professor of mechanical engineering and mathematics.”This research and the work that Laura and Hessam are doing is critically important to the advancement of computational discovery.”

Fall 2021 Information Sessions

By | Educational, Events

Fall 2021 information sessions on graduate programs in computational and data sciences at U-M

U-M graduate students interested in computational and data sciences are invited to learn about joint programs that will prepare them for success in computationally intensive fields. The programs are organized by the Michigan Institute for Computational Discovery & Engineering, and the Michigan Institute for Data Science. Both institutes offer vast training and networking opportunities, including webinar series, symposia and student centered events.

Two sessions are scheduled

The sessions will address:

  • The Graduate Certificate in Computational Discovery and Engineering: trains students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments.

  • The Graduate Certificate in Data Science: focuses on developing core proficiencies in data analytics: modeling, technology and practice.

  • The Graduate Certificate in Computational Neuroscience: provides training in interdisciplinary computational neuroscience to students in experimental neuroscience programs, and to students in quantitative science programs, such as physics, biophysics, mathematics and engineering.

  • The  Ph.D. in Scientific Computing: open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”