Bridging the gap between
physical and digital worlds
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What are Digital Assets?
Ownable, transferable digital items with recognized value. From everyday content to blockchain-based assets such as NFTs and digital currencies, the Center for Digital Assets (CDA) helps define, study, and advance these technologies through research and real-world pilots.
Our Mission, at a glance
We leverage blockchain and digital twins to reimagine how assets are valued, verified, and exchanged. By building transparent and efficient systems, we aim to foster greater trust in global markets.
What are Digital Twins?
Dynamic, data-rich virtual replicas of physical objects, systems, or processes used to simulate scenarios, monitor performance, and optimize outcomes in real time.
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Our Strategic Focus
Research & Development
We leverage UC Berkeley’s expertise to accelerate scalable, secure, and practical digitization technologies.
Student Opportunities
We prepare the next-generation of leaders in AI, blockchain, and digital modeling through hands-on projects and industry-relevant coursework.
Community & Ecosystem
We convene global partners, shape policy conversations, and catalyze new markets for secure digital assets.
Discover what’s new at CDA!
A machine-learning enabled digital-twin framework for next generation precision agriculture and forestry
This work utilizes the modern synergy between flexible, rapid, simulations and quick assimilation of data in order to develop next-generation tools for precise biomass management of large-scale agricultural and forestry systems. Additionally, when integrated with satellite and drone-based digital elevation technologies, the results lead to digital replicas of physical systems, or so-called digital-twins, which offer a powerful framework by which to optimally manage agricultural and forestry assets. Specifically, this, enables the investigation of inverse problems seeking to ascertain ideal parameter combinations, such as the number of plants/trees, plant/tree spacing, light intensity, water availability, soil resources, available planting surface area, initial seedling size, genetic variation, etc. to obtain optimal system performance. Towards this goal, a digital-twin framework is developed, consisting of a rapid computational physics engine to simulate an agricultural installation, containing thousands of growing, interacting, plants/trees. This model is then driven by a machine-learning algorithm to obtain optimal parameter sets that match observed statistical representations of a time series of growing agricultural canopy surfaces, measured by digital elevation models. Model simulations are provided to illustrate the approach and to show how such a tool can be used for large-scale biomass management.
Energy-Aware Tokenization of Data Center Assets: A Digital Twin Framework for Valuation and Distributed Footprint Accounting
The explosive growth of the AI economy subjects data centers to immense stress on energy and cooling resources, resulting in transient asset changes that severely complicate accurate, real-time valuation. This project proposes an integrated energy-aware tokenization framework to address this challenge by linking continuous physical performance data to a secure digital asset record. We will develop an exergy-centric digital twin model (using commercial CFD software) of a modular data center to move beyond power usage effectiveness (PUE) metrics, identifying hidden opportunities for high-grade waste heat exergy recovery and characterizing system aging. Concurrently, we will leverage our existing efforts on prototyping and testing a high-exergy rack-level cooling system to validate performance empirically. The central innovation is establishing a proof-of-concept blockchain framework (e.g., XRP Ledger) that utilizes a signed oracle service to securely transmit validated digital twin data (exergy score, aging factor, and PUE) to the ledger. Crucially, the system will record the energy required to compute the twin and to complete the tokenization process, enabling distributed footprint accounting. This creates a digital backpack paradigm for transparent, continuous asset valuation and robust, auditable energy management, directly supporting the digitization of complex industrial assets.
Securing the Rust Blockchain Ecosystem with cargo scan
We propose to build cargo scan, the first interactive program analysis tool designed to help developers audit third-party Rust code. Rust is critical to multiple blockchain ecosystems (e.g., Solana, Stellar, and XRP Ledger) both because Rust is used to implement these systems and because blockchain developers are writing smart contracts in Rust (e.g., before compiling them to WebAssembly). While Rust has become the language of choice because of its performance and safety, third-party code written in Rust is as dangerous as code written in unsafe languages---and auditing this code today is similarly manual and just as painstaking. But with Rust this is not fundamental. We propose to take advantage of this in cargo scan by developing new side effects analysis, specialized to Rust's type system, and tailored to composing human audits across crate and module boundaries. Our approach, if successful, will automatically reduce the manual audit burden to only inspecting the parts of functions with potentially dangerous side-effects, and those functions' calling contexts. This, in turn, has the potential to make it easier for developers to rely on third-party code without the risk of this code resulting in millions (to billions) of dollars in losses.
Digital Twins and Collective Intelligence Metrics in DAOs: Designing Signals of Effective Cooperation for Intelligent Decision Support Systems
This project develops a machine-learning digital-twin governance framework to model and improve decision-making in Decentralized Autonomous Organizations (DAOs). DAOs use blockchain-based voting and communication systems to coordinate collective decisions. They produce detailed, dynamic records of how communities deliberate and reach consensus. These datasets offer an exceptional opportunity to study and simulate collective intelligence in digital environments. Building on prior work that designed and tested new metrics for measuring disagreement and coordination in DAO voting data, this project expands in two directions: First, it analyzes DAO discussion platforms using natural language processing and knowledge-graph methods to link communication dynamics with voting outcomes. Second, it integrates these insights into agent-based simulations, or governance digital twins, co-developed with industry partners through participatory design. Inspired by recent machine-learning digital twin frameworks (Zohdi, 2025), these twins will iteratively calibrate governance “actuation parameters” through machine-learning optimization to converge toward empirically observed patterns of effective decentralized governance—those signaled by measurable indicators of collective intelligence in decentralized governance. By combining advances in digital-twin design with social-scientific research on collective intelligence, the project reframes DAOs as dynamic information systems that not only archive collective reasoning, but also have the potential to model and enhance it. Through the development of new governance metrics and digital-twin frameworks, the research provides both theoretical and practical foundations for intelligent governance infrastructures capable of representing and optimizing the full spectrum of consensus, disagreement, and collaboration that drives decentralized decision-making.
The Ledger of Making: Exploring Blockchain Provenance in Creative Engineering Design
This project investigates how blockchain technologies can be used to document and authenticate the creative processes that underlie engineering design and fabrication. Using the XRP Ledger (XRPL) as a secure and energy-efficient backbone, the work will prototype and evaluate methods for logging design events, fabrication data, and digitized artifacts. The research will begin with exploratory development of tools for capturing CAD activity, machine data, and 3D scans of prototypes, then culminate in a structured “Ledger of Making” design-a-thon at UC Berkeley’s Jacobs Institute for Design Innovation. The event will serve as a testbed for understanding the technical feasibility and interpretive value of recording creative activity on a blockchain. The anticipated outcomes include a functional proof-of-concept system linking design and fabrication data to blockchain records, and new insight into which elements of creative practice are most meaningful and sustainable to digitize as verifiable digital assets.
Digitizing Infrastructure Assets: UC Berkeley CAV Test Track
This project proposes to develop a prototype digital twin of the UC Berkeley Connected and Automated Vehicle (CAV) Test Track, establishing a foundation for scalable, trustworthy digital infrastructure assets. By integrating LiDAR, drone imagery, dashcam video, AI-based 3D reconstruction, and decentralized data technologies, the project will create a fast, flexible, and cost-efficient pipeline for digitizing and updating roadway environments. This work directly supports the mission of Berkeley’s new Center for Digital Assets, funded by Ripple, by exploring how blockchain-enabled coordination and contribution tracking can sustain a shared digital asset ecosystem. The resulting digital twin will enable mixed-reality testing, real-time synchronization with physical systems, and reuse across simulation, safety validation, and AV development tools. Funding this project will accelerate the creation of interoperable digital infrastructure standards, strengthen Berkeley’s leadership in CAV research, and deliver practical tools that industry can leverage for safer and more intelligent transportation systems.
Multispectral Computed Tomography Digital Asset Creation
To digitize assets superficial scans can provide shape, thermal distributions and the like, but tell nothing about the composition - the value within. For raw materials such as mining ore to e-waste the elemental composition is highly valuable data. Multispectral Computed Tomography (CT) can quantify materials elemental composition, and hence its value. The digital asset thus generated is a rich dataset revealing e.g., mass fraction of copper, lithium, and other rare earths in ore or e/mining-waste.
Physics-Aware Parametric Digitization and Updating of Civil Structures from Automatic LiDAR Scanning
This project proposes a physics-aware parametric framework for the automatic digitization and updating of civil infrastructure using LiDAR-based scanning. The research integrates autonomous drone/rover platforms employing LiDAR-SLAM to efficiently acquire 3D point clouds of buildings and bridges, reducing manual surveying effort. A deep learning model (PointNet++) will segment the point clouds into structural components, which are then fitted to a library of physics-aware parametric models that embed deformation modes as intrinsic parameters. This formulation enables translation of geometric deformations into mechanical state variables such as stress, strain, and damage, thereby linking geometric and structural information. The resulting digital twins will be continuously updated to reflect the true structural state and can be securely registered as blockchain-based digital assets. The outcome will be an automated end-to-end workflow demonstrated on structures at UC Berkeley’s Richmond Field Station, establishing a foundation for intelligent and trustworthy asset management.
Decentralized, Real-time Asset Monitoring Using Oracle Networks
Modern supply chains rely on distributed sensor telemetry—such as GPS, acceleration, and inertial data—to ensure asset integrity, regulatory compliance, and tamper detection. Yet this data is noisy and vulnerable to manipulation, making it difficult to produce tamper-proof evidence for chain-of-custody (CoC) monitoring and automated insurance recovery. Traditional Byzantine fault-tolerance protocols attempt to address this by replicating sensors and using quorum mechanisms that, under bounded adversarial conditions, ensure agreement among non-faulty nodes. However, these protocols are ill-suited for CoC monitoring: exact consensus is not suitable if non-faulty replicas disagree, while approximate Byzantine agreement (ABA) prioritizes convergence over accuracy and scales poorly with high-dimensional telemetry. When accuracy is critical, Byzantine replicas can exploit noisy replicas to skew decisions, making a statistical approach—robust to noise and adversarial influence—essential. Our work demonstrates such an approach is feasible. We propose an oracle network architecture that integrates Trusted Execution Environments (TEEs) with Proximal Byzantine Agreement (PBA)—a coordination-free, multi-dimensional ABA protocol that uses statistical inference via quorum-based probability maximization to achieve robust and accurate agreement despite noisy or Byzantine data. The design builds on EdgeLake, a Linux Foundation project, to manage verified sensor data within TEEs. Through EdgeLake, monitoring processes query distributed data as if it were centralized, receiving a unified, verifiable result set of estimations. These estimations are processed by the PBA engine to produce a tamper-resistant agreement value and a region bound guaranteed to contain the true output, enabling smart contracts and validators to transparently enforce SLA compliance in noisy and adversarial settings—establishing a decentralized, trustless foundation for digitized CoC monitoring in global supply chains.
Investigation of Regulatory Convergence Between Stablecoins and Central Bank Digital Currencies
This interdisciplinary research project examines how current regulatory frameworks may be transforming privately-issued stablecoins into functional equivalents of Central Bank Digital Currencies (CBDCs) through compliance mandates and government control mechanisms. While policymakers continue to debate whether the United States should launch an official digital dollar, regulators are already exercising substantial surveillance and asset-freezing capabilities over stablecoins. This quiet convergence raises critical questions about financial privacy, due process, and the future of permissionless value.
Towards a UAV-Based Multi-Sensor Framework for Infrastructure-scale Asset Digitization
Drones are uniquely suited for quickly capturing data of infrastructure, allowing asset managers to maintain clear records of the state of the asset, as well as catching issues for maintenance. This project works towards creating an easy-to-use system that uses existing knowledge of an asset with data captured autonomously by a drone to create up-to-date digitizations of physical assets. Such records will increase the asset value, and improved records will facilitate transfer of ownership of such assets, aiding in their liquidity. As a concrete outcome, we will digitize a building at the Richmond Field Station.
The Rise of Stablecoins as a Medium of Payment: Drivers, Adoption, and Implications for Global Finance
Stablecoins, digital tokens pegged to traditional currencies such as the U.S. dollar, are rapidly emerging as a key innovation in payment systems. Unlike volatile cryptocurrencies such as Bitcoin or Ether, stablecoins promise price stability, programmability, and 24/7 global transferability. They have gained increasing attention from policymakers, central banks, and private institutions as a potential bridge between traditional finance and decentralized technologies. Despite this growing attention, our understanding of how stablecoins are actually used as a payment mechanism remains limited. Much of the existing literature focuses on their role in crypto trading and DeFi markets rather than their function in real economic transactions. Yet, recent developments suggest that multinational firms, fintech platforms, and cross-border merchants are beginning to use stablecoins for settlements and supply-chain payments, motivated by lower transaction costs, faster processing, and reduced exchange-rate risks. This project aims to fill this gap by systematically studying the rise of stablecoins as a means of payment rather than an investment asset. It will examine who adopts them, under what conditions, and with what consequences for liquidity, risk management, and financial stability.
Multi-Robot Mapping for Scalable Digitization of Large Environments
In this project, we will develop a multi-robot framework for scalable and adaptive digitization of large environments where we seek to enable autonomous teams of robots to efficiently construct high-fidelity digital twins of large assets. By leveraging recent advances in Neural Radiance Fields (NeRFs), we will achieve high-resolution 3D reconstruction of assets and scene using lightweight and compact representations. Robots will coordinate exploration through distributed optimization, share summarized map data to reduce communication load, and adapt sensing fidelity based on user-defined requirements. We will develop a centralized fusion back-end to integrate local maps into a unified digital twin that can be incrementally updated as the environment evolves. Our proposed pipeline will provide a cost-effective and flexible solution for digitizing large assets such as aircraft, factories, or infrastructure. This research directly supports the CDA’s mission by advancing the technologies needed for autonomous, scalable, and secure digitization of physical assets.
Events →
B@B Hacks (Blockchain at Berkeley)
Blockchain at Berkeley is proud to present B@BHacks 2026, a one-day blockchain technology themed hackathon with $20k+ in cash prizes for winning teams. Expected attendance is 200-250 people. Presentations and food provided. Registration details to be added soon - please check back!
The Digital Frontier at Berkeley (co-host Haas Blockchain Club)
The Digital Frontier at Berkeley is a one-day, high-signal conference hosted by the Haas Blockchain Club that brings together builders, investors, and institutions shaping the next phase of crypto. Featuring curated panels on stablecoins, real-world asset tokenization, regulation, AI × crypto, fundraising, and institutional market trends, the event prioritizes thoughtful discussion over hype and convenes an engaged audience of MBA students, engineers, founders, and alumni. Sponsors gain direct access to emerging talent, senior industry voices, and the broader Berkeley ecosystem at the intersection of blockchain, finance, and AI.
A machine-learning enabled digital-twin framework for next generation precision agriculture and forestry
This work utilizes the modern synergy between flexible, rapid, simulations and quick assimilation of data in order to develop next-generation tools for precise biomass management of large-scale agricultural and forestry systems. Additionally, when integrated with satellite and drone-based digital elevation technologies, the results lead to digital replicas of physical systems, or so-called digital-twins, which offer a powerful framework by which to optimally manage agricultural and forestry assets. Specifically, this, enables the investigation of inverse problems seeking to ascertain ideal parameter combinations, such as the number of plants/trees, plant/tree spacing, light intensity, water availability, soil resources, available planting surface area, initial seedling size, genetic variation, etc. to obtain optimal system performance. Towards this goal, a digital-twin framework is developed, consisting of a rapid computational physics engine to simulate an agricultural installation, containing thousands of growing, interacting, plants/trees. This model is then driven by a machine-learning algorithm to obtain optimal parameter sets that match observed statistical representations of a time series of growing agricultural canopy surfaces, measured by digital elevation models. Model simulations are provided to illustrate the approach and to show how such a tool can be used for large-scale biomass management.
Energy-Aware Tokenization of Data Center Assets: A Digital Twin Framework for Valuation and Distributed Footprint Accounting
The explosive growth of the AI economy subjects data centers to immense stress on energy and cooling resources, resulting in transient asset changes that severely complicate accurate, real-time valuation. This project proposes an integrated energy-aware tokenization framework to address this challenge by linking continuous physical performance data to a secure digital asset record. We will develop an exergy-centric digital twin model (using commercial CFD software) of a modular data center to move beyond power usage effectiveness (PUE) metrics, identifying hidden opportunities for high-grade waste heat exergy recovery and characterizing system aging. Concurrently, we will leverage our existing efforts on prototyping and testing a high-exergy rack-level cooling system to validate performance empirically. The central innovation is establishing a proof-of-concept blockchain framework (e.g., XRP Ledger) that utilizes a signed oracle service to securely transmit validated digital twin data (exergy score, aging factor, and PUE) to the ledger. Crucially, the system will record the energy required to compute the twin and to complete the tokenization process, enabling distributed footprint accounting. This creates a digital backpack paradigm for transparent, continuous asset valuation and robust, auditable energy management, directly supporting the digitization of complex industrial assets.
Securing the Rust Blockchain Ecosystem with cargo scan
We propose to build cargo scan, the first interactive program analysis tool designed to help developers audit third-party Rust code. Rust is critical to multiple blockchain ecosystems (e.g., Solana, Stellar, and XRP Ledger) both because Rust is used to implement these systems and because blockchain developers are writing smart contracts in Rust (e.g., before compiling them to WebAssembly). While Rust has become the language of choice because of its performance and safety, third-party code written in Rust is as dangerous as code written in unsafe languages---and auditing this code today is similarly manual and just as painstaking. But with Rust this is not fundamental. We propose to take advantage of this in cargo scan by developing new side effects analysis, specialized to Rust's type system, and tailored to composing human audits across crate and module boundaries. Our approach, if successful, will automatically reduce the manual audit burden to only inspecting the parts of functions with potentially dangerous side-effects, and those functions' calling contexts. This, in turn, has the potential to make it easier for developers to rely on third-party code without the risk of this code resulting in millions (to billions) of dollars in losses.
Digital Twins and Collective Intelligence Metrics in DAOs: Designing Signals of Effective Cooperation for Intelligent Decision Support Systems
This project develops a machine-learning digital-twin governance framework to model and improve decision-making in Decentralized Autonomous Organizations (DAOs). DAOs use blockchain-based voting and communication systems to coordinate collective decisions. They produce detailed, dynamic records of how communities deliberate and reach consensus. These datasets offer an exceptional opportunity to study and simulate collective intelligence in digital environments. Building on prior work that designed and tested new metrics for measuring disagreement and coordination in DAO voting data, this project expands in two directions: First, it analyzes DAO discussion platforms using natural language processing and knowledge-graph methods to link communication dynamics with voting outcomes. Second, it integrates these insights into agent-based simulations, or governance digital twins, co-developed with industry partners through participatory design. Inspired by recent machine-learning digital twin frameworks (Zohdi, 2025), these twins will iteratively calibrate governance “actuation parameters” through machine-learning optimization to converge toward empirically observed patterns of effective decentralized governance—those signaled by measurable indicators of collective intelligence in decentralized governance. By combining advances in digital-twin design with social-scientific research on collective intelligence, the project reframes DAOs as dynamic information systems that not only archive collective reasoning, but also have the potential to model and enhance it. Through the development of new governance metrics and digital-twin frameworks, the research provides both theoretical and practical foundations for intelligent governance infrastructures capable of representing and optimizing the full spectrum of consensus, disagreement, and collaboration that drives decentralized decision-making.
The Ledger of Making: Exploring Blockchain Provenance in Creative Engineering Design
This project investigates how blockchain technologies can be used to document and authenticate the creative processes that underlie engineering design and fabrication. Using the XRP Ledger (XRPL) as a secure and energy-efficient backbone, the work will prototype and evaluate methods for logging design events, fabrication data, and digitized artifacts. The research will begin with exploratory development of tools for capturing CAD activity, machine data, and 3D scans of prototypes, then culminate in a structured “Ledger of Making” design-a-thon at UC Berkeley’s Jacobs Institute for Design Innovation. The event will serve as a testbed for understanding the technical feasibility and interpretive value of recording creative activity on a blockchain. The anticipated outcomes include a functional proof-of-concept system linking design and fabrication data to blockchain records, and new insight into which elements of creative practice are most meaningful and sustainable to digitize as verifiable digital assets.
Digitizing Infrastructure Assets: UC Berkeley CAV Test Track
This project proposes to develop a prototype digital twin of the UC Berkeley Connected and Automated Vehicle (CAV) Test Track, establishing a foundation for scalable, trustworthy digital infrastructure assets. By integrating LiDAR, drone imagery, dashcam video, AI-based 3D reconstruction, and decentralized data technologies, the project will create a fast, flexible, and cost-efficient pipeline for digitizing and updating roadway environments. This work directly supports the mission of Berkeley’s new Center for Digital Assets, funded by Ripple, by exploring how blockchain-enabled coordination and contribution tracking can sustain a shared digital asset ecosystem. The resulting digital twin will enable mixed-reality testing, real-time synchronization with physical systems, and reuse across simulation, safety validation, and AV development tools. Funding this project will accelerate the creation of interoperable digital infrastructure standards, strengthen Berkeley’s leadership in CAV research, and deliver practical tools that industry can leverage for safer and more intelligent transportation systems.
Multispectral Computed Tomography Digital Asset Creation
To digitize assets superficial scans can provide shape, thermal distributions and the like, but tell nothing about the composition - the value within. For raw materials such as mining ore to e-waste the elemental composition is highly valuable data. Multispectral Computed Tomography (CT) can quantify materials elemental composition, and hence its value. The digital asset thus generated is a rich dataset revealing e.g., mass fraction of copper, lithium, and other rare earths in ore or e/mining-waste.
Physics-Aware Parametric Digitization and Updating of Civil Structures from Automatic LiDAR Scanning
This project proposes a physics-aware parametric framework for the automatic digitization and updating of civil infrastructure using LiDAR-based scanning. The research integrates autonomous drone/rover platforms employing LiDAR-SLAM to efficiently acquire 3D point clouds of buildings and bridges, reducing manual surveying effort. A deep learning model (PointNet++) will segment the point clouds into structural components, which are then fitted to a library of physics-aware parametric models that embed deformation modes as intrinsic parameters. This formulation enables translation of geometric deformations into mechanical state variables such as stress, strain, and damage, thereby linking geometric and structural information. The resulting digital twins will be continuously updated to reflect the true structural state and can be securely registered as blockchain-based digital assets. The outcome will be an automated end-to-end workflow demonstrated on structures at UC Berkeley’s Richmond Field Station, establishing a foundation for intelligent and trustworthy asset management.
Decentralized, Real-time Asset Monitoring Using Oracle Networks
Modern supply chains rely on distributed sensor telemetry—such as GPS, acceleration, and inertial data—to ensure asset integrity, regulatory compliance, and tamper detection. Yet this data is noisy and vulnerable to manipulation, making it difficult to produce tamper-proof evidence for chain-of-custody (CoC) monitoring and automated insurance recovery. Traditional Byzantine fault-tolerance protocols attempt to address this by replicating sensors and using quorum mechanisms that, under bounded adversarial conditions, ensure agreement among non-faulty nodes. However, these protocols are ill-suited for CoC monitoring: exact consensus is not suitable if non-faulty replicas disagree, while approximate Byzantine agreement (ABA) prioritizes convergence over accuracy and scales poorly with high-dimensional telemetry. When accuracy is critical, Byzantine replicas can exploit noisy replicas to skew decisions, making a statistical approach—robust to noise and adversarial influence—essential. Our work demonstrates such an approach is feasible. We propose an oracle network architecture that integrates Trusted Execution Environments (TEEs) with Proximal Byzantine Agreement (PBA)—a coordination-free, multi-dimensional ABA protocol that uses statistical inference via quorum-based probability maximization to achieve robust and accurate agreement despite noisy or Byzantine data. The design builds on EdgeLake, a Linux Foundation project, to manage verified sensor data within TEEs. Through EdgeLake, monitoring processes query distributed data as if it were centralized, receiving a unified, verifiable result set of estimations. These estimations are processed by the PBA engine to produce a tamper-resistant agreement value and a region bound guaranteed to contain the true output, enabling smart contracts and validators to transparently enforce SLA compliance in noisy and adversarial settings—establishing a decentralized, trustless foundation for digitized CoC monitoring in global supply chains.
Investigation of Regulatory Convergence Between Stablecoins and Central Bank Digital Currencies
This interdisciplinary research project examines how current regulatory frameworks may be transforming privately-issued stablecoins into functional equivalents of Central Bank Digital Currencies (CBDCs) through compliance mandates and government control mechanisms. While policymakers continue to debate whether the United States should launch an official digital dollar, regulators are already exercising substantial surveillance and asset-freezing capabilities over stablecoins. This quiet convergence raises critical questions about financial privacy, due process, and the future of permissionless value.
Towards a UAV-Based Multi-Sensor Framework for Infrastructure-scale Asset Digitization
Drones are uniquely suited for quickly capturing data of infrastructure, allowing asset managers to maintain clear records of the state of the asset, as well as catching issues for maintenance. This project works towards creating an easy-to-use system that uses existing knowledge of an asset with data captured autonomously by a drone to create up-to-date digitizations of physical assets. Such records will increase the asset value, and improved records will facilitate transfer of ownership of such assets, aiding in their liquidity. As a concrete outcome, we will digitize a building at the Richmond Field Station.
The Rise of Stablecoins as a Medium of Payment: Drivers, Adoption, and Implications for Global Finance
Stablecoins, digital tokens pegged to traditional currencies such as the U.S. dollar, are rapidly emerging as a key innovation in payment systems. Unlike volatile cryptocurrencies such as Bitcoin or Ether, stablecoins promise price stability, programmability, and 24/7 global transferability. They have gained increasing attention from policymakers, central banks, and private institutions as a potential bridge between traditional finance and decentralized technologies. Despite this growing attention, our understanding of how stablecoins are actually used as a payment mechanism remains limited. Much of the existing literature focuses on their role in crypto trading and DeFi markets rather than their function in real economic transactions. Yet, recent developments suggest that multinational firms, fintech platforms, and cross-border merchants are beginning to use stablecoins for settlements and supply-chain payments, motivated by lower transaction costs, faster processing, and reduced exchange-rate risks. This project aims to fill this gap by systematically studying the rise of stablecoins as a means of payment rather than an investment asset. It will examine who adopts them, under what conditions, and with what consequences for liquidity, risk management, and financial stability.
Multi-Robot Mapping for Scalable Digitization of Large Environments
In this project, we will develop a multi-robot framework for scalable and adaptive digitization of large environments where we seek to enable autonomous teams of robots to efficiently construct high-fidelity digital twins of large assets. By leveraging recent advances in Neural Radiance Fields (NeRFs), we will achieve high-resolution 3D reconstruction of assets and scene using lightweight and compact representations. Robots will coordinate exploration through distributed optimization, share summarized map data to reduce communication load, and adapt sensing fidelity based on user-defined requirements. We will develop a centralized fusion back-end to integrate local maps into a unified digital twin that can be incrementally updated as the environment evolves. Our proposed pipeline will provide a cost-effective and flexible solution for digitizing large assets such as aircraft, factories, or infrastructure. This research directly supports the CDA’s mission by advancing the technologies needed for autonomous, scalable, and secure digitization of physical assets.
Events →
B@B Hacks (Blockchain at Berkeley)
Blockchain at Berkeley is proud to present B@BHacks 2026, a one-day blockchain technology themed hackathon with $20k+ in cash prizes for winning teams. Expected attendance is 200-250 people. Presentations and food provided. Registration details to be added soon - please check back!
The Digital Frontier at Berkeley (co-host Haas Blockchain Club)
The Digital Frontier at Berkeley is a one-day, high-signal conference hosted by the Haas Blockchain Club that brings together builders, investors, and institutions shaping the next phase of crypto. Featuring curated panels on stablecoins, real-world asset tokenization, regulation, AI × crypto, fundraising, and institutional market trends, the event prioritizes thoughtful discussion over hype and convenes an engaged audience of MBA students, engineers, founders, and alumni. Sponsors gain direct access to emerging talent, senior industry voices, and the broader Berkeley ecosystem at the intersection of blockchain, finance, and AI.
A machine-learning enabled digital-twin framework for next generation precision agriculture and forestry
This work utilizes the modern synergy between flexible, rapid, simulations and quick assimilation of data in order to develop next-generation tools for precise biomass management of large-scale agricultural and forestry systems. Additionally, when integrated with satellite and drone-based digital elevation technologies, the results lead to digital replicas of physical systems, or so-called digital-twins, which offer a powerful framework by which to optimally manage agricultural and forestry assets. Specifically, this, enables the investigation of inverse problems seeking to ascertain ideal parameter combinations, such as the number of plants/trees, plant/tree spacing, light intensity, water availability, soil resources, available planting surface area, initial seedling size, genetic variation, etc. to obtain optimal system performance. Towards this goal, a digital-twin framework is developed, consisting of a rapid computational physics engine to simulate an agricultural installation, containing thousands of growing, interacting, plants/trees. This model is then driven by a machine-learning algorithm to obtain optimal parameter sets that match observed statistical representations of a time series of growing agricultural canopy surfaces, measured by digital elevation models. Model simulations are provided to illustrate the approach and to show how such a tool can be used for large-scale biomass management.
Energy-Aware Tokenization of Data Center Assets: A Digital Twin Framework for Valuation and Distributed Footprint Accounting
The explosive growth of the AI economy subjects data centers to immense stress on energy and cooling resources, resulting in transient asset changes that severely complicate accurate, real-time valuation. This project proposes an integrated energy-aware tokenization framework to address this challenge by linking continuous physical performance data to a secure digital asset record. We will develop an exergy-centric digital twin model (using commercial CFD software) of a modular data center to move beyond power usage effectiveness (PUE) metrics, identifying hidden opportunities for high-grade waste heat exergy recovery and characterizing system aging. Concurrently, we will leverage our existing efforts on prototyping and testing a high-exergy rack-level cooling system to validate performance empirically. The central innovation is establishing a proof-of-concept blockchain framework (e.g., XRP Ledger) that utilizes a signed oracle service to securely transmit validated digital twin data (exergy score, aging factor, and PUE) to the ledger. Crucially, the system will record the energy required to compute the twin and to complete the tokenization process, enabling distributed footprint accounting. This creates a digital backpack paradigm for transparent, continuous asset valuation and robust, auditable energy management, directly supporting the digitization of complex industrial assets.
Securing the Rust Blockchain Ecosystem with cargo scan
We propose to build cargo scan, the first interactive program analysis tool designed to help developers audit third-party Rust code. Rust is critical to multiple blockchain ecosystems (e.g., Solana, Stellar, and XRP Ledger) both because Rust is used to implement these systems and because blockchain developers are writing smart contracts in Rust (e.g., before compiling them to WebAssembly). While Rust has become the language of choice because of its performance and safety, third-party code written in Rust is as dangerous as code written in unsafe languages---and auditing this code today is similarly manual and just as painstaking. But with Rust this is not fundamental. We propose to take advantage of this in cargo scan by developing new side effects analysis, specialized to Rust's type system, and tailored to composing human audits across crate and module boundaries. Our approach, if successful, will automatically reduce the manual audit burden to only inspecting the parts of functions with potentially dangerous side-effects, and those functions' calling contexts. This, in turn, has the potential to make it easier for developers to rely on third-party code without the risk of this code resulting in millions (to billions) of dollars in losses.
Digital Twins and Collective Intelligence Metrics in DAOs: Designing Signals of Effective Cooperation for Intelligent Decision Support Systems
This project develops a machine-learning digital-twin governance framework to model and improve decision-making in Decentralized Autonomous Organizations (DAOs). DAOs use blockchain-based voting and communication systems to coordinate collective decisions. They produce detailed, dynamic records of how communities deliberate and reach consensus. These datasets offer an exceptional opportunity to study and simulate collective intelligence in digital environments. Building on prior work that designed and tested new metrics for measuring disagreement and coordination in DAO voting data, this project expands in two directions: First, it analyzes DAO discussion platforms using natural language processing and knowledge-graph methods to link communication dynamics with voting outcomes. Second, it integrates these insights into agent-based simulations, or governance digital twins, co-developed with industry partners through participatory design. Inspired by recent machine-learning digital twin frameworks (Zohdi, 2025), these twins will iteratively calibrate governance “actuation parameters” through machine-learning optimization to converge toward empirically observed patterns of effective decentralized governance—those signaled by measurable indicators of collective intelligence in decentralized governance. By combining advances in digital-twin design with social-scientific research on collective intelligence, the project reframes DAOs as dynamic information systems that not only archive collective reasoning, but also have the potential to model and enhance it. Through the development of new governance metrics and digital-twin frameworks, the research provides both theoretical and practical foundations for intelligent governance infrastructures capable of representing and optimizing the full spectrum of consensus, disagreement, and collaboration that drives decentralized decision-making.
The Ledger of Making: Exploring Blockchain Provenance in Creative Engineering Design
This project investigates how blockchain technologies can be used to document and authenticate the creative processes that underlie engineering design and fabrication. Using the XRP Ledger (XRPL) as a secure and energy-efficient backbone, the work will prototype and evaluate methods for logging design events, fabrication data, and digitized artifacts. The research will begin with exploratory development of tools for capturing CAD activity, machine data, and 3D scans of prototypes, then culminate in a structured “Ledger of Making” design-a-thon at UC Berkeley’s Jacobs Institute for Design Innovation. The event will serve as a testbed for understanding the technical feasibility and interpretive value of recording creative activity on a blockchain. The anticipated outcomes include a functional proof-of-concept system linking design and fabrication data to blockchain records, and new insight into which elements of creative practice are most meaningful and sustainable to digitize as verifiable digital assets.
Digitizing Infrastructure Assets: UC Berkeley CAV Test Track
This project proposes to develop a prototype digital twin of the UC Berkeley Connected and Automated Vehicle (CAV) Test Track, establishing a foundation for scalable, trustworthy digital infrastructure assets. By integrating LiDAR, drone imagery, dashcam video, AI-based 3D reconstruction, and decentralized data technologies, the project will create a fast, flexible, and cost-efficient pipeline for digitizing and updating roadway environments. This work directly supports the mission of Berkeley’s new Center for Digital Assets, funded by Ripple, by exploring how blockchain-enabled coordination and contribution tracking can sustain a shared digital asset ecosystem. The resulting digital twin will enable mixed-reality testing, real-time synchronization with physical systems, and reuse across simulation, safety validation, and AV development tools. Funding this project will accelerate the creation of interoperable digital infrastructure standards, strengthen Berkeley’s leadership in CAV research, and deliver practical tools that industry can leverage for safer and more intelligent transportation systems.
Multispectral Computed Tomography Digital Asset Creation
To digitize assets superficial scans can provide shape, thermal distributions and the like, but tell nothing about the composition - the value within. For raw materials such as mining ore to e-waste the elemental composition is highly valuable data. Multispectral Computed Tomography (CT) can quantify materials elemental composition, and hence its value. The digital asset thus generated is a rich dataset revealing e.g., mass fraction of copper, lithium, and other rare earths in ore or e/mining-waste.
Physics-Aware Parametric Digitization and Updating of Civil Structures from Automatic LiDAR Scanning
This project proposes a physics-aware parametric framework for the automatic digitization and updating of civil infrastructure using LiDAR-based scanning. The research integrates autonomous drone/rover platforms employing LiDAR-SLAM to efficiently acquire 3D point clouds of buildings and bridges, reducing manual surveying effort. A deep learning model (PointNet++) will segment the point clouds into structural components, which are then fitted to a library of physics-aware parametric models that embed deformation modes as intrinsic parameters. This formulation enables translation of geometric deformations into mechanical state variables such as stress, strain, and damage, thereby linking geometric and structural information. The resulting digital twins will be continuously updated to reflect the true structural state and can be securely registered as blockchain-based digital assets. The outcome will be an automated end-to-end workflow demonstrated on structures at UC Berkeley’s Richmond Field Station, establishing a foundation for intelligent and trustworthy asset management.
Decentralized, Real-time Asset Monitoring Using Oracle Networks
Modern supply chains rely on distributed sensor telemetry—such as GPS, acceleration, and inertial data—to ensure asset integrity, regulatory compliance, and tamper detection. Yet this data is noisy and vulnerable to manipulation, making it difficult to produce tamper-proof evidence for chain-of-custody (CoC) monitoring and automated insurance recovery. Traditional Byzantine fault-tolerance protocols attempt to address this by replicating sensors and using quorum mechanisms that, under bounded adversarial conditions, ensure agreement among non-faulty nodes. However, these protocols are ill-suited for CoC monitoring: exact consensus is not suitable if non-faulty replicas disagree, while approximate Byzantine agreement (ABA) prioritizes convergence over accuracy and scales poorly with high-dimensional telemetry. When accuracy is critical, Byzantine replicas can exploit noisy replicas to skew decisions, making a statistical approach—robust to noise and adversarial influence—essential. Our work demonstrates such an approach is feasible. We propose an oracle network architecture that integrates Trusted Execution Environments (TEEs) with Proximal Byzantine Agreement (PBA)—a coordination-free, multi-dimensional ABA protocol that uses statistical inference via quorum-based probability maximization to achieve robust and accurate agreement despite noisy or Byzantine data. The design builds on EdgeLake, a Linux Foundation project, to manage verified sensor data within TEEs. Through EdgeLake, monitoring processes query distributed data as if it were centralized, receiving a unified, verifiable result set of estimations. These estimations are processed by the PBA engine to produce a tamper-resistant agreement value and a region bound guaranteed to contain the true output, enabling smart contracts and validators to transparently enforce SLA compliance in noisy and adversarial settings—establishing a decentralized, trustless foundation for digitized CoC monitoring in global supply chains.
Investigation of Regulatory Convergence Between Stablecoins and Central Bank Digital Currencies
This interdisciplinary research project examines how current regulatory frameworks may be transforming privately-issued stablecoins into functional equivalents of Central Bank Digital Currencies (CBDCs) through compliance mandates and government control mechanisms. While policymakers continue to debate whether the United States should launch an official digital dollar, regulators are already exercising substantial surveillance and asset-freezing capabilities over stablecoins. This quiet convergence raises critical questions about financial privacy, due process, and the future of permissionless value.
Towards a UAV-Based Multi-Sensor Framework for Infrastructure-scale Asset Digitization
Drones are uniquely suited for quickly capturing data of infrastructure, allowing asset managers to maintain clear records of the state of the asset, as well as catching issues for maintenance. This project works towards creating an easy-to-use system that uses existing knowledge of an asset with data captured autonomously by a drone to create up-to-date digitizations of physical assets. Such records will increase the asset value, and improved records will facilitate transfer of ownership of such assets, aiding in their liquidity. As a concrete outcome, we will digitize a building at the Richmond Field Station.
The Rise of Stablecoins as a Medium of Payment: Drivers, Adoption, and Implications for Global Finance
Stablecoins, digital tokens pegged to traditional currencies such as the U.S. dollar, are rapidly emerging as a key innovation in payment systems. Unlike volatile cryptocurrencies such as Bitcoin or Ether, stablecoins promise price stability, programmability, and 24/7 global transferability. They have gained increasing attention from policymakers, central banks, and private institutions as a potential bridge between traditional finance and decentralized technologies. Despite this growing attention, our understanding of how stablecoins are actually used as a payment mechanism remains limited. Much of the existing literature focuses on their role in crypto trading and DeFi markets rather than their function in real economic transactions. Yet, recent developments suggest that multinational firms, fintech platforms, and cross-border merchants are beginning to use stablecoins for settlements and supply-chain payments, motivated by lower transaction costs, faster processing, and reduced exchange-rate risks. This project aims to fill this gap by systematically studying the rise of stablecoins as a means of payment rather than an investment asset. It will examine who adopts them, under what conditions, and with what consequences for liquidity, risk management, and financial stability.
Multi-Robot Mapping for Scalable Digitization of Large Environments
In this project, we will develop a multi-robot framework for scalable and adaptive digitization of large environments where we seek to enable autonomous teams of robots to efficiently construct high-fidelity digital twins of large assets. By leveraging recent advances in Neural Radiance Fields (NeRFs), we will achieve high-resolution 3D reconstruction of assets and scene using lightweight and compact representations. Robots will coordinate exploration through distributed optimization, share summarized map data to reduce communication load, and adapt sensing fidelity based on user-defined requirements. We will develop a centralized fusion back-end to integrate local maps into a unified digital twin that can be incrementally updated as the environment evolves. Our proposed pipeline will provide a cost-effective and flexible solution for digitizing large assets such as aircraft, factories, or infrastructure. This research directly supports the CDA’s mission by advancing the technologies needed for autonomous, scalable, and secure digitization of physical assets.
Events →
B@B Hacks (Blockchain at Berkeley)
Blockchain at Berkeley is proud to present B@BHacks 2026, a one-day blockchain technology themed hackathon with $20k+ in cash prizes for winning teams. Expected attendance is 200-250 people. Presentations and food provided. Registration details to be added soon - please check back!
The Digital Frontier at Berkeley (co-host Haas Blockchain Club)
The Digital Frontier at Berkeley is a one-day, high-signal conference hosted by the Haas Blockchain Club that brings together builders, investors, and institutions shaping the next phase of crypto. Featuring curated panels on stablecoins, real-world asset tokenization, regulation, AI × crypto, fundraising, and institutional market trends, the event prioritizes thoughtful discussion over hype and convenes an engaged audience of MBA students, engineers, founders, and alumni. Sponsors gain direct access to emerging talent, senior industry voices, and the broader Berkeley ecosystem at the intersection of blockchain, finance, and AI.