CAMERA is an integrated, cross-disciplinary center aimed at inventing, developing, and delivering the fundamental new mathematics required to capitalize on experimental investigations at scientific facilities. Jointly funded by the Office of Advanced Scientific Computing Research (ASCR) and the Office of Basic Energy Sciences (BES) within the US Department of Energy's Office of Science, CAMERA identifi€es areas in experimental science that can be aided by new mathematical insights, develops the needed algorithmic tools, and delivers them as user-friendly so‰ware to the experimental community. Led by James Sethian.

The high data-throughput of scientific instruments has made image recognition one of the most challenging problems in scientific research today. Supported by a U.S. DOE Early Career Award, IDEAL focuses on computer vision and machine learning algorithms and software to enable timely interpretation of experimental data recorded as 2D or multispectral images. Led by Daniela Ushizima.

DAPHNE aims to develop reliable and robust networks with guaranteed high-throughput data transfer and uninterrupted performance for science needs while exploring smart contracts and blockchains as a means of reliable and distributed machine learning communication across distributed nodes. Supported by a DOE Early Career Award, this research couples deep learning methods with software defined networking (SDN) for predicting real-time network behavior and avoiding data traffic congestion or degraded network performance. Led by Mariam Kiran.

ExaLearn is an ECP co-design center working towards exascale machine-learning software for use by ECP applications projects, other ECP co-design centers and U.S. Department of Energy (DOE) experimental facilities and computing facilities. Berkeley Lab is one of eight DOE national laboratories collaborating the R&D process which will produce a scalable and sustainable machine learning software framework that allows application scientists and the applied mathematics and computer science communities to engage in co-design for learning, the center will also collaborate with ECP PathForward vendors on the development of exascale machine-learning software.

Next-generation scientific discoveries rely on the insights we can derive from the large amounts of data that are produced through simulations and experimental and observational facilities. Today however, data is accessed and analyzed primarily by those who generate or produce the data, since it is difficult to search and find relevant data sets. The goal of Science Search is to use machine learning techniques to generate automated metadata that will enable search on a range of scientific datasets. Enabling search on data will accelerate scientific discoveries through virtual experiments, multidisciplinary and multimodal data assimilation. Led by Katie Antypas.

Scientists at the Department of Energy’s Lawrence Berkeley National Laboratory, working with the University of Arkansas and Glennoe Farms, are bringing together molecular biology, biogeochemistry, environmental sensing technologies, and machine learning, to help revolutionize agriculture and create sustainable farming practices that benefit both the environment and farms. If successful, we envision being able to reduce the need for chemical fertilizers and enhance soil carbon uptake, thus improving the long-term viability of the land, while at the same time increasing crop yields.

Funding: Lab Directed Research and Development (LDRD) grant. Led by Ben Brown.

Deep Learning for Science

The DL4SCI LDRD is examining three key CS challenges: handling complex datasets, developing interpretable methods, and improving performance and scaling. This work is being motivated by realistic problems that span a number of Berkeley Lab divisions and science areas: predicting cosmological constants from 3D simulations (cosmology), obtaining sub-pixel accuracy for electron counts (electron microscopy), classification of one vs. two-photon particle showers (nuclear physics).

Funding: Lab Directed Research and Development (LDRD) grant. Led by Prabhat.

Combining Data-driven and Science-based Generative Models

This project investigates the many connections between data-driven and science-driven generative models. When do scientists use physical models to create synthetic data for science applications? When do we supplement them with data driven machine learning models? Conversely, can researchers use physical models to improve on the current data-driven generative models in machine learning?

Funding: Directed Research and Development (LDRD) grant. Led by Uros Seljak.

Machine Learning to Extract Features from Massive Distributed Acoustic Sensing Data

Machine learning has transformed the time-consuming task of developing custom analysis tools into a feasible computational task. As the operator of some of the largest high-performance computing facilities, DOE could significantly improve these machine learning tools and accelerate scientific discoveries. One of the key challenges in this process is the interpretability of the results from the automated learning process. For example, deep neural networks are known to be effective in extracting signals, but their results are notoriously hard to understand. In this work, we plan to extend key ideas from statistical mechanics to improve understanding and guide the design of well-known machine learning algorithms. The new approaches will not only produce more interpretable results but also dramatically increase the convergence rate of the associated learning algorithms. The design of these tools will be guided by the requirements of the on-going Distributed Acoustic Sensing project at Berkeley Lab.

Funding: Lab Directed Research and Development (LDRD) grant. Led by John Wu.

The Chemical Universe through the Eyes of Generative Adversarial Neural Networks

Funding: Lab Directed Research and Development (LDRD) grant. Led by Wibe Albert de Jong

Interactive Machine Learning for Tomogram Segmentation and Annotation

Funding: Lab Directed Research and Development (LDRD) grant. Led by Nicholas K. Sauter.