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Last week, while the Denver Metro Area experienced climate crisis conditions with four wildfires burning throughout Colorado, inside the air-conditioned Colorado Convention Center (in two of the last presentations at SIGGRAPH 2024), Professor Mike Pritchard shared his latest research (some of which is in collaboration with NVIDIA’s Earth-2″ Initiative) on the future machine learning, earth system science, computer simulation and visualization and NASA shared their approach to “Visual Data Stories for Climate Action.”

https://oodaloop.com/archive/2024/04/26/earth-2-ai-plus-climate-data-equals-the-ultimate-digital-twin-project/

Frontier Talk: Frontiers in AI for Weather and Climate Prediction

Machine learning methods are proving enabling across the climate simulation research pipeline. In the first part of his talk, Prof. Pritchard discussed his experience from academia using ML in a hybrid multi-scale simulation context to parameterize sub-grid processes of clouds, radiation, and turbulence. In the second part, he provided some emerging perspectives from his time spent at NVIDIA on increasingly ambitious uses of ML to replace entire components of Earth System Models via autoregressive global emulation and for generative super-resolution to replace traditional dynamical downscaling. 

Biography – Professor Mike Pritchard

Mike Pritchard is an Associate Professor in the Department of Earth System Sciences at the University of California, Irvine, where he joined the faculty in 2013. Since 2022, he joined NVIDIA as a 50% Director of Research exploring the use of various AI methods for climate and weather simulation. On Mike’s academic side he has studied tropical climate dynamics, cloud feedbacks, and the planetary water cycle, and experimented with emerging algorithms to improve climate simulations, including ML parameterization. In industry, he has collaborated on whole atmosphere autoregressive ML emulation and generative AI for downscaling weather and climate as part of NVIDIA’s “Earth-2” Initiative. Beyond that, he serves on the Executive Committee of an NSF Science and Technology Center at Columbia University focused on Learning about the Earth with AI and Physics.

Professor Mike Pritchard, in collaboration with NVIDIA, has been at the forefront of applying AI technologies to address significant challenges in weather prediction, wildfire management, and climate modeling.  Through these efforts, Professor Mike Pritchard and NVIDIA are making significant strides in leveraging AI to address some of the most pressing environmental challenges, contributing to advancements in weather forecasting, wildfire management, and climate science.  Here are the key areas of their work:

  1. AI for Weather Prediction:
    • Objective: Improve the accuracy and efficiency of weather forecasting models.
    • Approach: Utilize advanced AI algorithms and machine learning techniques to process large volumes of meteorological data.
    • Outcome: Enhanced predictive capabilities, allowing for more accurate and timely weather forecasts, which are crucial for various sectors, including agriculture, disaster management, and daily planning.
  2. Fighting Wildfires:
    • Objective: Develop AI-driven tools to predict, monitor, and manage wildfires more effectively.
    • Approach: Integrate AI models with satellite imagery, climate data, and real-time sensor inputs to track wildfire behavior and predict spread patterns.
    • Outcome: Improved early warning systems and response strategies, potentially reducing the impact of wildfires on communities and ecosystems.
  3. Climate Prediction:
    • Objective: Enhance long-term climate modeling and prediction to better understand and mitigate climate change.
    • Approach: Employ AI to analyze complex climate data, identify patterns, and simulate future climate scenarios.
    • Outcome: More accurate climate models that can inform policy decisions and climate action plans, aiding in global efforts to combat climate change.
  4. Optimally Calibrated AI Models:
    • Objective: Ensure AI models used in these applications are precisely calibrated for maximum accuracy and reliability.
    • Approach: Develop and implement advanced calibration techniques to fine-tune AI models, leveraging large datasets and robust validation methods.
    • Outcome: AI models that are not only powerful but also trustworthy, providing reliable insights for decision-making.
  5. Ensemble Models and Visualization:
    • Objective: Combine multiple AI models to enhance prediction accuracy and provide comprehensive insights.
    • Approach: Use ensemble modeling techniques to integrate predictions from various AI models, complemented by sophisticated visualization tools to interpret and communicate results.
    • Outcome: More robust and reliable predictive systems with visualizations that make complex data more accessible and actionable for scientists, policymakers, and the public.

What Next?

Machine Learning, Deep Learning, and the Future of a “Luxurious Amount of Ensemble Models”

Earth system scientists and data scientists can leverage ML and DL to develop highly calibrated, diverse, and powerful ensemble models.  Machine learning (ML) and deep learning (DL) have significant potential to optimize the calibration of ensemble models and enhance their use in climate science.  These advancements will significantly enhance the ability to understand, predict, and mitigate the impacts of climate change.  Here’s how:

Optimization of Calibration

  1. Hyperparameter Tuning:
    • Method: Techniques like grid search, random search, and more sophisticated methods such as Bayesian optimization and genetic algorithms can systematically tune the hyperparameters of individual models in the ensemble.
    • Impact: Improved model performance and accuracy by finding the optimal configuration for each model.
  2. Automated Machine Learning (AutoML):
    • Method: AutoML platforms can automatically select, train, and tune multiple models, combining them into an optimized ensemble.
    • Impact: There is a reduced need for manual intervention, allowing earth system scientists to focus on interpreting results rather than model tuning.
  3. Cross-Validation and Bootstrapping:
    • Method: Techniques like k-fold cross-validation and bootstrapping provide robust estimates of model performance, ensuring that calibration is based on reliable metrics.
    • Impact: Enhanced model reliability and generalization to new data.

Providing a Luxurious Amount of Ensemble Models

  1. Model Diversity:
    • Method: Incorporating various ML and DL algorithms (e.g., decision trees, support vector machines, neural networks) into the ensemble increases diversity.
    • Impact: Improved robustness and accuracy through diverse perspectives on the data.
  2. Adaptive Ensemble Methods:
    • Method: Adaptive boosting (e.g., AdaBoost) and gradient boosting (e.g., XGBoost, LightGBM) iteratively adjust model weights to focus on harder-to-predict instances.
    • Impact: Enhanced model accuracy by continuously refining predictions.
  3. Neural Architecture Search (NAS):
    • Method: NAS algorithms automatically search for the best neural network architectures tailored to specific tasks.
    • Impact: Optimized deep learning models that contribute to more effective ensembles.

Specific Benefits for Climate Science

  1. High-Resolution Climate Modeling:
    • Application: ML and DL models can process and integrate high-resolution climate data, improving the accuracy of regional climate predictions.
    • Benefit: Better understanding of local climate impacts, aiding in targeted adaptation and mitigation strategies.
  2. Predictive Analytics:
    • Application: Advanced ensemble models can predict extreme weather events and long-term climate trends with greater precision.
    • Benefit: Improved preparedness and response to climate-related hazards.
  3. Data Integration and Fusion:
    • Application: ML and DL techniques can integrate diverse data sources (e.g., satellite data, climate models, historical climate records) into a cohesive ensemble framework.
    • Benefit: Comprehensive and holistic climate models that leverage all available information.
  4. Uncertainty Quantification:
    • Application: Ensemble models can provide probabilistic predictions and uncertainty estimates.
    • Benefit: Better risk assessment and decision-making support for policymakers and stakeholders.
  5. Real-Time Monitoring and Prediction:
    • Application: ML and DL models can process real-time data streams, offering up-to-date climate predictions and alerts.
    • Benefit: Timely information for immediate action, enhancing resilience to climate impacts.
  6. Visualization and Interpretability:
    • Application: Advanced visualization tools can render complex ensemble model outputs in accessible formats, such as interactive maps and graphs.
    • Benefit: Improved communication of climate risks and model results to a broad audience, including non-experts.

Visual Data Stories for Climate Action: The Making of NASA’s Earth Information Center Public Exhibits

This production session presented the What, How, and Why of bringing to life the physical installations and data-driven visualizations featured at the Earth Information Center (earth.gov). The session shares with the SIGGRAPH community the challenges of, approaches to, and lessons learned from visualizing data for climate action and sheds light on research agendas and upcoming efforts.  First of its kind and launched in June 2023 at NASA Headquarters in Washington DC, the Earth Information Center (EIC) provides a holistic view of how our planet is changing in ways that affect the lives and livelihoods of individuals and communities across the globe. The EIC, which is spearheaded and coordinated by NASA, brings actionable data to the hands of the public by partnering with U.S. Federal Government agencies, including EPA, FEMA, NOAA, USAID, USDA, and USGS.
The EIC has two major components: 1) the backend cyber infrastructure of providing to the public actionable, easy-to-use data and information about Earth’s changing systems, and 2) physical exhibits located at centers across the US that bring data to life.  The exhibits include hyperwalls and immersive and interactive experiences, showing the latest information about our climate.  
This session was presented by a team of artists, Earth data visualization experts, and visualization researchers. The team shares with the SIGGRAPH community the goals and challenges of, processes involved in, and outcomes of creating and executing such large-scale exhibits for the general public. Specifically, the presentation focuses on the physical installations, the development of data-driven visualization media, the required pipelines, and the open research questions explored by the team. The NASA team had less than 6 months to bring the exhibit to life for the launch of the Center. 
Presenters
Helen-Nicole (Eleni) Kostis leads data dashboards and visualizations for the Earth Information Center hyperwall displays. Based at the Scientific Visualization Studio at NASA, Eleni designs, develops and produces data-driven media, conduits, and experiences with the goal to communicate complex climate phenomena and research findings to the scientific communities and the public. Eleni is a co-author of the Foundations of Data Visualization book (Springer) and the recipient of NASA’s Exceptional Achievement Award for Outreach.
https://svs.gsfc.nasa.gov
https://earth.gov/
Dan Goods leads an extraordinary team of creatives called The Studio at NASA’s Jet Propulsion Laboratory. They transform complex concepts into meaningful stories that can be universally understood. Their work is seen in public spaces, art museums, and outer space.
https://www.jpl.nasa.gov/thestudio
Joe Ardizzone is the Chief Software Engineer for NASA’s Global Modeling Assimilation Office Data Services project. With over 25 years of experience in geophysical validation, assimilation and evaluation of satellite Earth observations for climate studies and improved weather prediction, Joe is a multi-disciplined software developer with expertise in data visualization and analysis, object oriented design and advanced information systems technology.
https://gmao.gsfc.nasa.gov/
Ryan Boller is the Data Visualization Lead for the Earth Science Data and Information System (ESDIS) Project at NASA Goddard Space Flight Center. In this capacity, he leads an effort to facilitate the visual discovery of scientific phenomena, support timely decision-making for natural hazards, and educate the next generation of scientists. During his earlier years at NASA, Ryan developed data visualization tools and science data processing systems for solar, lunar, and Earth-based missions. He holds a bachelor’s degree from Penn State University and a master’s from Brown University, both in computer science.
https://worldview.earthdata.nasa.gov/
Fanny Chevalier is an Assistant Professor in Computer Science and Statistics at the University of Toronto, and Knight of the France’s Order of Academic Palms. Her research lies at the intersection of Human-Computer Interaction and Information Visualization, with a primary focus on interactive visualization for the visual exploration of rich and complex data, visualization education and statistical communication, and computing tools supporting creativity. Her work is consistently recognized with best paper and nomination awards at top-tier venues including ACM CHI and IEEE VIS, and has led to commercial products, such as Autodesk Sketchbook Motion – selected as Apple’s 2016 iPad App of the Year.
http://fannychevalier.net/
Benjamin Bach is a researcher at Inria (France) and an Associate Professor in Visualization at the University of Edinburgh (UK). Before that, Benjamin was a postdoc at Harvard University (Visual Computing Group), Monash University (Immersive Analytics Lab), Microsoft Research, and a visiting scholar at the Brain Imaging Center at the University of Washington. Benjamin was named Significant New Researcher by the IEEE VIS conference (2021) and Eurographics Young Researcher (2019). His PhD received a VGTC Best Thesis honorable mention award (2014).
https://vishub.net/bach

OODA Loop – Further Scenarios:  The Future of Climate Change, the Climate Crisis, Earth System Science, and Computer Simulation and Visualization

NASA

By advancing its capabilities in these areas, NASA will play a crucial role in addressing the climate crisis and advancing our understanding of Earth’s complex systems.

Here are some possible scenarios:

Climate Change Monitoring and Prediction
  1. Advanced Satellite Missions:
    • Scenario: NASA continues to develop and deploy advanced satellite missions to monitor Earth’s climate systems in unprecedented detail.
    • Impact: Enhanced data collection on greenhouse gases, ice sheet dynamics, sea level rise, and temperature variations.
  2. Improved Climate Models:
    • Scenario: NASA refines its climate models using AI and machine learning to provide more accurate and granular predictions.
    • Impact: Better-informed policy decisions and climate mitigation strategies.
  3. Global Climate Data Integration:
    • Scenario: NASA integrates data from international space agencies and ground-based sensors to create a comprehensive global climate database.
    • Impact: A unified global approach to understanding and combating climate change.
Earth System Science
  1. Interdisciplinary Research:
    • Scenario: NASA fosters collaborations between climate scientists, ecologists, oceanographers, and other specialists to study the Earth system holistically.
    • Impact: Deeper insights into the interconnectedness of Earth’s systems and more effective solutions to environmental challenges.
  2. Real-Time Environmental Monitoring:
    • Scenario: Development of real-time monitoring systems for critical ecosystems, such as the Amazon rainforest, coral reefs, and polar regions.
    • Impact: Immediate detection and response to environmental changes and threats.
  3. Citizen Science Initiatives:
    • Scenario: NASA expands citizen science programs, allowing the public to contribute to climate data collection and analysis.
    • Impact: Increased public engagement and education on climate issues, along with a larger dataset for researchers.
Climate Crisis Response
  1. Disaster Response and Recovery:
    • Scenario: NASA enhances its capabilities to provide critical data and analysis for natural disaster response and recovery efforts.
    • Impact: Improved preparedness and resilience for communities facing climate-induced disasters like hurricanes, floods, and wildfires.
  2. Adaptation and Mitigation Strategies:
    • Scenario: NASA collaborates with governments and organizations to develop and implement adaptation and mitigation strategies.
    • Impact: Practical solutions for reducing vulnerability and enhancing resilience to climate impacts.
  3. Sustainable Technologies:
    • Scenario: NASA advances research in sustainable technologies, such as renewable energy and carbon capture.
    • Impact: Acceleration of the transition to a low-carbon economy.
Computer Simulation and Visualization
  1. High-Resolution Earth System Models:
    • Scenario: NASA develops high-resolution Earth system models that simulate complex interactions between climate, ecosystems, and human activities.
    • Impact: More precise predictions and better understanding of future scenarios.
  2. Virtual Reality (VR) and Augmented Reality (AR):
    • Scenario: NASA uses VR and AR to create immersive climate simulations for educational and research purposes.
    • Impact: Enhanced learning experiences and more effective communication of climate science to the public and policymakers.
  3. Cloud Computing and Big Data Analytics:
    • Scenario: NASA leverages cloud computing and big data analytics to handle the vast amounts of climate data generated.
    • Impact: Faster processing times, more comprehensive analyses, and the ability to run complex simulations that were previously infeasible.
Collaborative Initiatives
  1. International Partnerships:
    • Scenario: NASA strengthens partnerships with international space agencies, research institutions, and NGOs to tackle climate change collectively.
    • Impact: Shared knowledge, resources, and technologies leading to more effective global climate action.
  2. Public-Private Partnerships:
    • Scenario: NASA collaborates with private sector companies to innovate and deploy new technologies for climate monitoring and mitigation.
    • Impact: Accelerated technological advancements and broader implementation of climate solutions.

oodaloop.com and the OODA Network Community

By integrating advanced technologies, fostering collaboration, and leveraging its expertise in intelligence and analysis, oodaloop.com and the OODA Network community can play a crucial role in addressing climate change, enhancing earth system science, and improving the tools and strategies used for climate simulation and visualization.
The future role of oodaloop.com and the OODA Network community can be envisioned through several impactful scenarios:
Climate Change and the Climate Crisis
  1. Climate Intelligence and Analysis:
    • Scenario: Oodaloop.com provides cutting-edge climate intelligence and analysis, offering real-time insights and predictions about climate risks and trends.
    • Impact: Decision-makers in government and industry can access timely and actionable information, leading to better-prepared responses and strategies for climate resilience.
  2. Policy Support and Advocacy:
    • Scenario: OODA Network members advocate for robust climate policies, leveraging data-driven insights to influence legislative and regulatory actions.
    • Impact: Stronger, evidence-based policies that effectively address climate challenges and drive significant reductions in greenhouse gas emissions.
Earth System Science
  1. Collaborative Research Platforms:
    • Scenario: Development of collaborative platforms that integrate research from diverse fields, allowing scientists to share data and insights in real time.
    • Impact: Accelerated discovery and innovation in earth system science, leading to a more comprehensive understanding of climate dynamics and environmental impacts.
  2. Open Data Initiatives:
    • Scenario: Launch of open data initiatives that make extensive datasets available to researchers and the public.
    • Impact: Increased transparency and accessibility of climate data, fostering community-driven research and enabling wider participation in climate science.
Computer Simulation and Visualization
  1. Advanced Simulation Tools:
    • Scenario: Creation of sophisticated simulation tools that model complex climate systems and scenarios, incorporating AI and machine learning.
    • Impact: More accurate and detailed climate projections that help stakeholders anticipate and plan for future environmental conditions.
  2. Interactive Visualization Platforms:
    • Scenario: Development of interactive platforms that allow users to visualize climate data and simulations in intuitive and engaging ways.
    • Impact: Enhanced public understanding of climate issues and better communication of scientific findings to non-experts.
Community and Policy Engagement
  1. Community-Driven Climate Action:
    • Scenario: OODA Network fosters community-driven climate action projects, encouraging local initiatives and grassroots movements.
    • Impact: Empowered communities taking active roles in climate mitigation and adaptation, leading to more resilient and sustainable local environments.
  2. Educational Outreach:
    • Scenario: Comprehensive educational outreach programs that utilize the latest in computer simulation and visualization to teach about climate science.
    • Impact: Increased awareness and knowledge about climate change among the general public and young generations, fostering a culture of sustainability.
Strategic Insights and Intelligence
  1. Threat and Risk Assessment:
    • Scenario: OODA Network specializes in threat and risk assessments related to climate impacts, advising organizations on vulnerabilities and strategic responses.
    • Impact: Enhanced organizational resilience and adaptive capacity, minimizing the adverse effects of climate disruptions.
  2. Scenario Planning and Foresight:
    • Scenario: Implementation of scenario planning exercises that explore various future climate conditions and their potential impacts on different sectors.
    • Impact: Better-prepared businesses and governments that can anticipate and navigate future climate challenges effectively.
Technological Innovation
  1. Innovation Hubs and Think Tanks:
    • Scenario: Establishment of innovation hubs and think tanks that focus on developing new technologies and methodologies for climate science and mitigation.
    • Impact: Continuous advancement in tools and strategies to combat climate change, driven by collaborative innovation and thought leadership.
  2. Integration of Emerging Technologies:
    • Scenario: Leveraging emerging technologies such as blockchain for transparent climate data management or IoT for enhanced environmental monitoring.
    • Impact: More robust and efficient systems for managing and utilizing climate data, leading to improved environmental stewardship.
Networking and Collaboration
  1. Global Partnerships:
    • Scenario: Building global partnerships with other organizations and networks focused on climate action, sharing resources, and best practices.
    • Impact: A more unified and coordinated global response to climate change, maximizing the impact of collective efforts.
  2. Interdisciplinary Collaboration:
    • Scenario: Encouraging interdisciplinary collaboration within the OODA Network to tackle climate challenges from multiple perspectives.
    • Impact: Holistic solutions that consider economic, social, and environmental dimensions of climate change.

Association of Computing Machinery (ACM), SIGGRAPH Community

By leveraging their expertise in computer graphics and interactive techniques, the SIGGRAPH community can play a pivotal role in advancing our understanding of climate change and driving innovative solutions to mitigate and adapt to the climate crisis.

The Association of Computing Machinery (ACM) Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) community is poised to make significant contributions to addressing climate change, the climate crisis, earth system science, and computer simulation and visualization. Here are some potential future scenarios for their involvement:

Climate Change and the Climate Crisis
  1. Advanced Visualization Tools:
    • Scenario: SIGGRAPH community develops cutting-edge visualization tools that make complex climate data accessible and understandable to the public, policymakers, and scientists.
    • Impact: Enhanced communication and comprehension of climate change impacts, leading to more informed decision-making and greater public awareness.
  2. Interactive Climate Models:
    • Scenario: Creation of interactive climate models that allow users to explore different scenarios and their potential outcomes.
    • Impact: Improved engagement and understanding of the potential consequences of various climate actions and inactions.
  3. Virtual Reality (VR) and Augmented Reality (AR):
    • Scenario: VR and AR will be used to create immersive experiences that simulate the effects of climate change on different regions and ecosystems.
    • Impact: Increased empathy and urgency among the public and stakeholders to take action against climate change.
Earth System Science
  1. Data Integration and Visualization:
    • Scenario: SIGGRAPH experts work on integrating disparate datasets from various earth system science domains into cohesive visualizations.
    • Impact: A more holistic understanding of earth systems and their interactions, aiding in more comprehensive research and policy development.
  2. Real-Time Monitoring and Analysis:
    • Scenario: Development of real-time data visualization platforms for monitoring environmental changes and anomalies.
    • Impact: Timely insights and responses to environmental changes, improving resilience and adaptive capacity.
  3. Educational Tools:
    • Scenario: Creation of educational tools and simulations that help students and researchers visualize and understand complex earth system processes.
    • Impact: Enhanced education and training in earth system science, fostering a new generation of informed scientists and advocates.
Computer Simulation and Visualization
  1. High-Performance Computing:
    • Scenario: The SIGGRAPH community leverages high-performance computing to run large-scale simulations of climate models and earth systems.
    • Impact: More accurate and detailed simulations that can better predict future climate scenarios and their impacts.
  2. Artificial Intelligence (AI) and Machine Learning:
    • Scenario: Integration of AI and machine learning techniques to improve the accuracy and efficiency of climate simulations.
    • Impact: Accelerated development of predictive models that can offer more reliable forecasts and insights.
  3. Collaborative Visualization Platforms:
    • Scenario: Develop collaborative platforms that allow scientists from different disciplines to collaborate on shared visualizations and simulations.
    • Impact: Enhanced interdisciplinary collaboration and innovation in climate research and solutions.
Community and Policy Engagement
  1. Public Outreach and Education:
    • Scenario: SIGGRAPH community engages in public outreach campaigns using advanced visualization techniques to educate the public about climate change.
    • Impact: Increased public awareness and support for climate action initiatives.
  2. Policy Advocacy:
    • Scenario: SIGGRAPH community partners with policymakers to create visualizations that effectively communicate the urgency and impact of climate policies.
    • Impact: More compelling presentations of scientific data that can influence policy decisions and drive legislative action.
  3. Collaboration with Other Organizations:
    • Scenario: Partnerships with environmental organizations, educational institutions, and industry leaders to develop and disseminate climate-related visualizations and simulations.
    • Impact: Broader reach and impact of SIGGRAPH’s work, leading to more widespread adoption and implementation of climate solutions.
Technological Innovation
  1. Innovative Software and Tools:
    • Scenario: Development of new software and tools that push the boundaries of what’s possible in climate visualization and simulation.
    • Impact: Enhanced capabilities for researchers and practitioners to analyze and communicate complex climate data.
  2. Open Source Projects:
    • Scenario: Creation of open-source projects that allow for broader collaboration and innovation in climate visualization and simulation.
    • Impact: Democratization of advanced visualization tools, enabling more researchers and organizations to contribute to climate solutions.
  3. Workshops and Conferences:
    • Scenario: Hosting workshops and conferences focused on climate visualization and simulation, fostering a community of practice and continuous learning.
    • Impact: Ongoing professional development and knowledge exchange, leading to continuous improvements and innovations in the field.

Additional OODA Loop Resources

For additional OODA Loop News Briefs and Original Analysis on this topic, go to:

Technology Convergence and Market Disruption: Rapid advancements in technology are changing market dynamics and user expectations. See: Disruptive and Exponential Technologies.

The New Tech Trinity: Artificial Intelligence, BioTech, Quantum Tech: Will make monumental shifts in the world. This new Tech Trinity will redefine our economy, both threaten and fortify our national security, and revolutionize our intelligence community. None of us are ready for this. This convergence requires a deepened commitment to foresight and preparation and planning on a level that is not occurring anywhere. The New Tech Trinity.

Benefits of Automation and New Technology: Automation, AI, robotics, and Robotic Process Automation are improving business efficiency. New sensors, especially quantum ones, are revolutionizing sectors like healthcare and national security. Advanced WiFi, cellular, and space-based communication technologies are enhancing distributed work capabilities. See: Advanced Automation and New Technologies

Rise of the Metaverse: The Metaverse, an immersive digital universe, is expected to reshape internet interactions, education, social networking, and entertainment. See Future of the Metaverse.

Daniel Pereira

About the Author

Daniel Pereira

Daniel Pereira is research director at OODA. He is a foresight strategist, creative technologist, and an information communication technology (ICT) and digital media researcher with 20+ years of experience directing public/private partnerships and strategic innovation initiatives.

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