Internal Research Domains
ValoResearch, Inc. conducts internal research and experimental analysis across a limited number of high-complexity fields at the intersection of computing, cryptography, and systems architecture. Each research stream is developed under a non-commercial, internal-use-only model and is embedded within closed-loop exploratory workflows designed to support technical validation and hypothesis testing.
All research initiatives are governed by internal control policies, and no components, data, models, or tools are distributed or exposed externally.
Machine Learning and Algorithmic Systems
We develop and analyze advanced learning algorithms and system-level algorithmic methods. In particular, we design statistical learning models (supervised, unsupervised, and reinforcement learning) that infer patterns from data. Our work also emphasizes algorithmic analysis and optimization – for example, proving complexity bounds and constructing efficient data structures – to ensure scalable, reliable implementations on large datasets. This domain includes research on neural network architectures, probabilistic inference, optimization (e.g. stochastic gradient and convex optimization), and theoretical learning guarantees. All models are rigorously tested in simulated environments; we do not release any datasets or trained models externally.
Zero-Knowledge and Cryptographic Proofs
This area covers cryptographic proof systems – chiefly zero-knowledge protocols and succinct proofs – for verifiable computation and privacy. Zero-knowledge proofs (ZKPs) are protocols that let one party (the prover) convince another (the verifier) of a statement’s truth without revealing any additional information. We design both interactive and non-interactive ZK schemes (e.g. zk‑SNARKs and zk‑STARKs) with provable soundness. For example, recent work on zk‑SNARKs provides succinct, non-interactive proofs whose verification time is independent of the statement size. Our lab studies the underlying mathematics (polynomial commitment schemes, hash-based argument systems, elliptic-curve cryptography, etc.), optimizing prover efficiency and post-quantum security. Applications include private blockchain transactions and confidential computation, but no actual ZKP instances or secret data are shared outside the lab.
Decentralized Protocol Engineering
We engineer fault-tolerant, peer-to-peer protocols for distributed consensus and messaging. Historically, Bitcoin was the first system to achieve global-scale consensus via a permissionless blockchain. Building on such foundations, we analyze consensus algorithms (byzantine fault-tolerant protocols, proof‑of‑stake chains, permissioned ledger protocols, etc.) to optimize security and performance. A central concern is the fundamental trade-off between liveness (availability) and safety (consistency); for example, the CAP theorem implies no blockchain protocol can guarantee both continuous progress under arbitrary participation and consistency under network partitions. We formally specify and test new consensus designs (often via nested-ledger or hybrid protocols) to reconcile these trade-offs. Research also encompasses peer-to-peer network design, gossip protocols, and cross-chain messaging. All protocol designs are validated in controlled simulations and code bases maintained internally; protocol specifications and source code are confidential and not publicly released.
Smart Contract Architecture
This domain focuses on the formal design and secure implementation of blockchain smart contracts. A smart contract is essentially an on-chain program that automatically enforces contractual logic. We study the full contract lifecycle: writing modular contract architectures, defining precise formal semantics (e.g. for Ethereum’s EVM bytecode or for alternative chains like Move or Michelson), and applying static/dynamic analysis tools to verify correctness. In particular, we employ formal methods (model checking, symbolic execution, theorem proving) to detect common vulnerabilities. For instance, Ethereum smart contracts can suffer from reentrancy or integer-overflow bugs; our work includes developing design patterns and automated checks (e.g. the “checks-effects-interactions” pattern) to mitigate such issues. Gas and resource accounting is analyzed to ensure efficiency and upgradability (proxies, module patterns).
Streaming Data Systems
We develop high-performance systems for real-time data stream processing. Streaming systems ingest continuous, unbounded flows of events and process them with very low latency. Our research addresses stateful stream processing with fault tolerance, out-of-order data handling, and exactly-once semantics. As one survey notes, modern stream processors have become “sophisticated and scalable engines, producing correct results in the presence of failures”. We build on these principles to design engines that automatically checkpoint state and replay events to ensure consistency under node crashes. Work includes optimizing windowed aggregations, dynamic load balancing, and state management for multi-stage dataflows. These systems are applied (in lab simulations) to scenarios like live analytics for fraud detection, monitoring, and control feedback loops. Cloud and edge deployment strategies (containerized microservices, autoscaling) are explored internally.
Hybrid Systems Simulation
This research area targets the modeling and simulation of hybrid dynamical systems, which integrate continuous-time physical processes with discrete-event logic. A hybrid system typically involves differential equations (continuous dynamics) controlled by a discrete controller or mode-switching logic. For example, a heating system’s continuous temperature dynamics are managed by a discrete thermostat controller. We construct formal hybrid automata that capture such interactions and use dedicated simulation tools to validate behavior. In practice, we leverage environments like MATLAB/Simulink/Stateflow and Modelica-based tools, which natively support continuous-time, discrete-time, and event-driven dynamics. Our work develops custom simulation frameworks and numerical methods to handle stiff ODEs, event-triggered switching, and aliasing between continuous and discrete domains. We verify properties (stability, reachability) via co-simulation and formal analysis in internal toolchains. All models of cyber-physical systems, robotics, networked control) are internally maintained.