Announcements

Slides from the UIUC Information Session on February 15

Second Call for Proposals on Digital Transformation and AI for Energy and Climate Security announced

The energy industry is being digitally transformed by investment at all levels of production, generation, transmission, and distribution: sensors, data analytics, new privacy-aware markets, and usage of smart meters in homes are all part of this transformation. However, the transformation of energy to be resilient to large environmental changes, faults (including maintenance errors), and cyber-attacks is still a work in progress. The early lead of energy operators in embracing digital transformation has enabled those systems to use digital transformation not only to enhance energy efficiency but also to lead the way to a lower-carbon, higher-efficiency economy that will enhance both energy and climate security.

This C3DTI Second Call for Proposals addresses the challenges for AI and Digital Transformation for Energy and Climate Security.

Areas of interest include but are not restricted to:

Sustainability: Applying AI/ML and advanced analytic techniques to support sustainability initiatives. Areas of focus may include scalable and trustworthy techniques for energy consumption analysis, supply chain and Scope 3 energy and emissions analyses (see EPA Scope 3 Inventory Guidance), tracking water use across full-stream operations, and optimizing energy and water intensity of hydrocarbon production, storage, and transportation.
AI for Carbon Sequestration: Applying AI/ML techniques to increase the scale and reduce the cost of carbon sequestration. Areas of focus may include AI for advanced materials research to build better extraction of carbon dioxide (CO2) from the atmosphere, petrochemical process optimization for carbon capture, fossil fuel decarbonization, site-specific modeling of geophysical sequestration and emissions containment, and new sequestration technologies.
AI for Leaks and Emissions Detection: Applying advanced AI/ML techniques for large scale emissions detection, facility-level data reconciliation and gap analysis for emissions sensors, prediction of emissions risk, and analysis and optimization of flaring intensity across upstream and downstream operations.
Safe Hydrocarbon Production and Transportation Infrastructure: Applying analytic and AI/ML modeling techniques to increase the safety and reduce emissions from oil and gas extraction, petrochemical production, and hydrocarbon transportation. Areas of focus may include AI-based video and imaging algorithms to detect potential hazards and reduce accidents, with particular application in multi-modal sensing and drone-based real-time detection of methane and CO2 leaks, AI-based predictive maintenance, AI-enabled corrosion detection, AI-supported augmented reality systems for maintenance support, and next-generation AI-based digital twinning to support the modeling of hydrocarbon systems.
AI for Advanced Energy and Carbon Markets: Enabling dynamic, automated, and real-time pricing of energy generation sources. Areas of focus may include distributed resources, spinning reserve and voltage support, renewables, peer-to-peer energy transactions, improved energy and carbon price forecasting, and mechanism design to positively incentivize energy and carbon markets and prevent free riding and adverse selection.
Cybersecurity of Power and Energy Infrastructure: Leveraging AI/ML techniques to improve the cybersecurity of our critical power and energy assets, as well as smart connected factories and homes. Areas of focus may include AI/ML for distributed hardware and network management, detection of anomalous network activity and log monitoring, and the cohesive analysis of hybrid Information Technology (IT) and Operational Technology (OT) systems.
Smart Grid Analytics: Applying AI and other analytic approaches to improve the efficiency and effectiveness of grid transmission and distribution operations. Areas of focus may include Volt/VAR optimization, non-technical loss reduction, predictive maintenance for the grid, accelerated outage detection and recovery, automated power routing, grid management given load profiles of Electric Vehicles, and improved control and operation of microgrids.
Distributed Energy Resource Management: Applying AI to increase the penetration and use of distributed renewables. Areas of focus may include improving grid efficiency with renewables, granular load forecasting, automated demand-response, appropriate policy design, and the optimal dispatching of distributed resources.
AI for Energy-Efficient Buildings and Factories: Leveraging AI techniques for advanced building and factory control to improve energy efficiency. Areas of focus may include AI-based motor control systems, advanced load disaggregation analyses, direct load control, and optimal pre-cooling or heating to minimize costs and stress on energy networks.
AI for Improved Natural Catastrophe Risk Assessment: Applying AI to improve modeling of natural catastrophe risks from future weather-related events (e.g., tropical storms, wildfires, floods). Areas of focus may include advanced asset vulnerability models, the prioritization of climate adaptation measures to enable rapid and more effective disaster recovery, and the appropriate portfolio and pricing of risk transfer solutions.
Resilient Energy Systems: Addressing how the use of AI/ML techniques and markets for energy and carbon introduce new vulnerabilities. Areas of focus may include detecting cyber-attacks, including Advanced Persistent Threats (APTs), mitigating the risks from such attacks, and operating resiliently through such attacks.
AI for Improved Climate Change Modeling: Using AI/ML to address climate change modeling and adaptation. Areas of focus may include deep-learning based fine-scale cloud models to enhance larger-scale climate models, circulation models of the stratosphere and troposphere, multi-scale modeling of weather phenomena, processes that govern climate variability and change, and methods to predict climate variations, extended weather, and climate predictability.

All proposals should be submitted online via EasyChair at:

https://easychair.org/conferences/?conf=c3dticfp2

Proposals must be submitted to EasyChair before 11:59 pm PDT March 29, 2021.

Awards will be announced in late May 2021, with start dates of June 1, 2021.

C3DTI will host a series of online information sessions and to provide an overview of the call for proposals and discuss the computing resources available to Research Award recipients as well as office hours with technical staff. Below are the dates and times for each information session and details about office hours.

General Information Sessions (Online)

Monday, February 15, 11 am – 12 pm PT / 2 – 3 pm ET
Zoom Meeting: https://berkeley.zoom.us/j/92742836523
- This online information session will provide an overview of the C3.ai DTI and the second call for proposals and provide an opportunity for proposers to ask questions or get clarification as they prepare proposals.
Wednesday, February 17, 11 am – 12 pm PT / 2 – 3 pm ET
Zoom Meeting: https://berkeley.zoom.us/j/95629876415
- This online information session will provide an overview of the C3.ai DTI and the second call for proposals and provide an opportunity for proposers to ask questions or get clarification as they prepare proposals.

Computing Resources Information Sessions (Online)

Friday, February 19, 10 – 11 am PT / 1 – 2 pm ET
Zoom Webinar: https://berkeley.zoom.us/j/96277461240
- This online information session will provide an overview of the C3 AI Suite and the available supporting resources.
Tuesday, February 23, 2 – 3 pm PT / 5 – 6 pm ET
Zoom Webinar: https://berkeley.zoom.us/j/95880959454
- This online information session will take a deeper dive into the capabilities of the C3 AI Suite.
Tuesday, March 2, 10 – 11 am PT / 12 – 1 pm ET
Zoom Webinar: https://berkeley.zoom.us/j/99845556376
- This online information session will discuss Ex Machina, a C3 AI tool that enables anyone to develop, scale, and apply AI insights without writing code.

Weekly Office Hours (Online)

Additionally, the C3DTI Development Operations and C3 AI technical support teams will be available every Tuesday from 2 – 3 pm PT / 5 – 6 pm ET between March 2 and March 23.
Zoom Meeting: https://illinois.zoom.com.cn/j/87825348092?pwd=c1k1VWkxRXRQQTRuWllxUnN0Q256Zz09

Questions about general eligibility, proposal preparation, or research awards should be directed to the C3DTI by e-mail at proposals@c3dti.ai.

Colloquium on Digital Transformation Science

March 11, 3 pm CT

Using Data Science to Understand the Heterogeneity of SARS-COV-2 Transmission & COVID-19 Clinical Presentation in Mexico

Stefano Bertozzi, MD, Professor, School of Public Health, University of California, Berkeley

Juan Pablo Gutierrez, Professor at the Center for Policy, Population & Health Research, National Autonomous University of Mexico

REGISTER FOR ZOOM WEBINAR

In 2020, Mexico confirmed 1.5M cases of COVID-19, with 128,000 deaths — an 8.8 percent fatality rate that is among the highest worldwide. The positivity rate for those tested is 42 percent (WHO target = 5 percent). The pandemic is likely to become the main cause of death in 2020, and in 2021— even with the vaccine —mortality is expected to rise. Almost half of the Mexican population receives its medical care from the Mexican Social Security Institute (IMSS). Our team from UCB, IMSS, and UNAM aims to harness the massive patient-level clinical and socio-demographic data from the IMSS to better predict susceptibility to infection and serious complications among those who are infected. The advantages of working with the IMSS are clear – the disadvantage is that it has taken many months to get approval from the relevant human subjects and research committees. The IMSS comprises many poorly integrated data systems, so there is significant work involved in relating the disparate databases to each other. We now have 2.5 years of utilization data (outpatient visits [>300M], hospitalizations, prescriptions [almost 500M], and COVID tests). We will study variability by employer, by state and neighborhood, by household structure, by clinic, by provider (and provider behavior), by current and prior health conditions, by degree of control of chronic health conditions, by any drugs that they have been prescribed, as well as by the usual demographic and socioeconomic characteristics. The priority will be to identify modifiable factors that the IMSS can use to reduce population risk.

Stefano M. Bertozzi is Dean Emeritus and Professor of Health Policy and Management at the University of California, Berkeley School of Public Health, and Interim Director of University of California systemwide programs with Mexico (UC-MEXUS, the UC-Mexico Initiative, and Casa de California). Previously, he worked at the Bill and Melinda Gates Foundation, Mexican National Institute of Public Health, World Health Organization, UNAIDS, World Bank, and the Government of the Democratic Republic of the Congo. He recently co-edited the Disease Control Priorities (DCP3) volume on HIV/AIDS, Malaria & Tuberculosis, has served on governance and advisory boards for the East Bay Community Foundation, HopeLab, UNICEF, WHO, UNAIDS, Global Fund, PEPFAR, NIH, Duke University, University of Washington, and the AMA, has advised NGOs and ministries of health and social welfare in Asia, Africa, and Latin America, and is a member of the National Academy of Medicine.

Juan Pablo Gutierrez is Professor at the Center for Policy, Population & Health Research, National Autonomous University of Mexico (UNAM), Chair of the Technical Committee of the Morelos’ Commission on Evaluation of Social Development, and Member of GAVI Evaluation Advisory Committee. His research focuses on comprehensive evaluation of social programs and policies, universal health coverage and effective access, and social inequalities in health. He has been responsible for the evaluation of social and health programs in Mexico, Ecuador, Guatemala, Dominican Republic, Honduras, and India, as well as several population-based health surveys both in households and facilities. He is a member of the National Observatory on Health Inequalities in Mexico and has authored or co-authored more than 60 papers in peer-reviewed journals.