Hjalmar Turesson holds a Ph.D. in Neuroscience and Psychology from Princeton University, an M.Sc in Behavioral and Neural Sciences from the International Max Planck Research School at the University of Tübingen, and a B.Sc. in Biology from Lund University. His research straddles the line between natural and artificial intelligence, investigating the neural implementations and computational principles behind intelligence. Using methods spanning from in vivo to in silica, he has conducted basic research on semi-supervised learning, applied research in deep learning-based object tracking, machine learning applications for acoustic signals, and, more recently, blockchain and privacy-preserving machine learning on encrypted data. His research is published in top-tier scientific journals.
Kim, Henry M., Marek Laskowski, Michael Zargham, Hjalmar Turesson, Matt Barlin, and Danil Kabanov (2021), "Token Economics in Real-Life: Cryptocurrency and Incentives Design for Insolar Blockchain Network", IEEE Computer, 24(1), 72-80.
The study of setting up cryptocurrency incentive mechanisms and operationalizing governance is called token economics. Given the US$250 billion market cap for cryptocurrencies, there is compelling need to investigate it. In this article, we present facets of the token engineering process for a Swiss blockchain startup.
Turesson, Hjalmar, Henry M. Kim, Marek Laskowski, and Alexandra Roatis (2021), "Privacy Preserving Data Mining as Proof of Useful Work: Exploring an AI/Blockchain Design", Journal of Database Management, 20(1), 69-85.
Blockchains rely on a consensus among participants to achieve decentralization and security. However, reaching consensus in an online, digital world where identities are not tied to physical users is a challenging problem. Proof-of-work provides a solution by linking representation to a valuable, physical resource. While this has worked well, it uses a tremendous amount of specialized hardware and energy, with no utility beyond blockchain security. Here, the authors propose an alternative consensus scheme that directs the computational resources to the optimization of machine learning (ML) models – a task with more general utility. This is achieved by a hybrid consensus scheme relying on three parties: data providers, miners, and a committee. The data provider makes data available and provides payment in return for the best model, miners compete about the payment and access to the committee by producing ML optimized models, and the committee controls the ML competition.
Saxena, Shivam, Hany Farag, Aidan Brookson, Hjalmar Turesson, and Henry M. Kim (2020), "A Permissioned Blockchain System to Reduce Peak Demand in Residential Communities via Energy Trading: A Real-World Case Study", IEEE Access, 9, 5517-5530.
Residential energy trading systems (RETS) enable homeowners with distributed energy resources (DERs) to participate in virtualized energy markets that have the potential to reduce the peak demand of residential communities. Blockchains are key enablers of RETS, by virtue of providing a decentralized, self-governed network that mitigates concerns regarding privacy and transparency. However, more real-world case studies are needed to evaluate the techno-economic viability of blockchain-based RETS to improve their positive uptake. Thus, this article develops a permissioned blockchain-based RETS, which enables homeowners to select bidding strategies that consider the individual preferences of their DERs, and further evaluates the impact of the bidding strategies on reducing the peak demand of the community. The proposed system is implemented on the permissioned Hyperledger Fabric platform, where a decentralized ledger is used to store all energy bids, and a smart contract is used to execute a double auction mechanism and dispatch the homeowner DERs. The proposed system is validated by conducting simulations on a 8-home community using real-world data, and also by deploying the system to a Canadian microgrid, where the smart contract execution time is benchmarked. Simulation results demonstrate the efficacy of the proposed system by achieving a peak demand reduction of up to 48 kW (62%), which leads to an average savings of $1.02 M for the distribution system operator by avoiding transformer upgrades. Also, the simulation results show that the execution time of the proposed smart contract is 17.12 seconds across 12 nodes, which is sufficient for RETS.
Saxena, Shivam, Hany Farag, Hjalmar Turesson, and Henry M. Kim (2020), "Blockchain Based Transactive Energy Systems for Voltage Regulation in Active Distribution Networks", IET Smart Grid, 3(5), 646-56.
Transactive energy systems (TES) are modern mechanisms in electric power systems that allow disparate control agents to utilise distributed generation units to engage in energy transactions and provide ancillary services to the grid. Although voltage regulation is a crucial ancillary grid service within active distribution networks (ADNs), previous work has not adequately explored how this service can be offered in terms of its incentivisation, contract auditability, and enforcement. Blockchain technology shows promise in being a key enabler of TES, allowing agents to engage in trustless, persistent transactions that are both enforceable and auditable. To that end, this study proposes a blockchain based TES that enables agents to receive incentives for providing voltage regulation services by (i) maintaining an auditable reputation rating for each agent that is increased proportionately with each mitigation of a voltage violation, (ii) utilising smart contracts to enforce the validity of each transaction and penalise reputation ratings in case of a mitigation failure, and (iii) automating the negotiation and bidding of agent services by implementing the contract net protocol as a smart contract. Experimental results on both simulated and real-world ADNs are executed to demonstrate the efficacy of the proposed system.