Yarco Hayduk
Distributed Systems • Quantitative Research • Web3 Protocol Research

Yarco Hayduk, PhD

Researcher and systems builder working across blockchain infrastructure, distributed systems, quantitative research, and AI-native systems.

Based across Lisbon and Seoul. Citizenship: Ukraine and Canada.
Expertise
Distributed Systems Quantitative Research Web3 x AI Web3 Protocol Research AI Systems Consensus Protocols Blockchain Infrastructure Algorithmic Trading Technical Due Diligence Concurrent & Parallel Computing GPU & Heterogeneous Compute
Publications

Research across distributed systems, concurrent computing, blockchain systems, and lock-free order book designs.

DAIS · OPODIS · ManLang · NETYS · DATE · DASFAA · DSD
Experience
06/2021 – Present
Partner, Research
Pragma Ventures pragma.ooo Lisbon / Seoul

A private, research-first Web3 fund focused on blockchain infrastructure, tokenization, stablecoins, and AI-native systems.

  • Led technical diligence on prospective investments through deep reviews of whitepapers, protocol design, and systems architecture.
  • Conducted founder interviews and technical deep dives to surface execution risk, architectural tradeoffs, and whether technical claims held up under scrutiny.
  • Built AI-assisted research workflows to accelerate diligence and internal decision-making.
  • Prepared internal research reports and diligence memos on technical architecture, protocol risk, and market structure.
  • Supported portfolio companies on systems challenges spanning indexing, parallel execution, infrastructure reliability, and on-chain data.
Selected research writing: Monad: A Technical Review.
11/2020 – 02/2021
Principal Blockchain Architect
CovalentVancouver, Canada

Blockchain indexing infrastructure spanning multiple EVM networks with differing reorg and finality behavior.

  • Oversaw architecture of the decentralized indexing pipeline, structuring multi-chain EVM data in relational databases for reliable querying through client-facing APIs.
  • Refined architectural approaches for multi-chain data ingestion, consistency, and query performance across supported EVM networks.
  • Contributed to architectural approaches for handling reorgs, finality, and chain-specific indexing behavior across supported networks.
Stack:
ElixirErlangPythonJavaSQLPostgreSQL
06/2019 – 09/2020
Quantitative Engineer
Hex CapitalVancouver, Canada

A proprietary trading firm building quantitative strategies and high-frequency trading infrastructure across crypto markets, with live multi-venue execution spanning both DeFi and CeFi.

  • Drove the design and implementation of proprietary market-neutral strategies and high-frequency trading systems across crypto venues, with strong focus on execution quality, arbitrage capture, and execution risk management.
  • Built resilient trading infrastructure spanning exchange connectivity, order routing, monitoring, and failure handling for live multi-venue deployment.
  • Developed the observability stack for real-time latency profiling, execution validation, and strategy performance analysis.
Stack:
GoPythonPrometheusPostgreSQL
03/2019 – 06/2019
Polyglot Developer
Bluzelle NetworksVancouver, Canada

Decentralized censorship-resistant storage network with multi-language client libraries.

  • Led implementation and support for Go, C#, and Ruby client libraries while preserving strict protocol consistency across runtimes.
  • Defined and implemented cross-language client behavior with consistent semantics and predictable error handling.
  • Translated asynchronous and synchronous protocol semantics into the C++ client implementation.
Stack:
GoC#RubyC++
11/2017 – 03/2019
Principal Developer
HQS ConsultingWinnipeg, Canada

Enterprise software and infrastructure consulting across JVM systems, cloud platforms, and production web applications.

  • Designed and built enterprise applications in Scala and Java.
  • Built backend services and web platforms using Spring-based stacks, relational databases, and containerized deployment workflows for client-facing products.
  • Owned deployment and operational environments on GCP, covering infrastructure administration, monitoring, release workflows, and ongoing production maintenance.
Stack:
ScalaJavaSpringDockerGCP
10/2012 – 04/2017
Lead Research Engineer
ParaDIME Project, EU FP7 Switzerland / Belgium / Germany

EU-funded consortium (Neuchâtel, TU Dresden, BSC Barcelona, IMEC Belgium) targeting energy optimization across the full data center stack, with production integration work for IMEC Belgium and Cloud & Heat Technologies in Germany. Ran in parallel with the PhD and produced the majority of the publications.

  • Designed a distributed message-passing framework on top of Akka (Actor Model) to accelerate execution across available CPU, GPU, and distributed resources.
  • Developed a compile-time code rewriting framework using Scala Macros for transparent energy-aware optimizations, requiring no application-level changes.
  • Integrated agent-based coordination into existing systems at IMEC Belgium and Cloud & Heat Technologies to support seamless production operation.
  • Received a best paper award at DAIS 2016 for work on energy-efficient actor execution on heterogeneous architectures.
Stack:
JavaScalaAkkaCLLVMCUDASparkASM
01/2009 – 12/2009
Java Developer
Zilliant via SoftServeRemote

B2B pricing optimization and revenue management software for large product catalogs, pricing workflows, and data-driven commercial decision-making.

  • Contributed to Java-based pricing optimization software used to support large-scale catalog pricing, price guidance, and margin-focused decision workflows.
  • Worked on data-intensive application components involving pricing logic, statistical analysis, and high-volume product and customer datasets.
  • Overhauled ORM query batching and related data-access paths to improve data-fetching throughput by 40%.
Stack:
JavaSpringHibernateREST
Education
11/2017 – 07/2018
Postdoctoral Researcher
University of British ColumbiaVancouver, Canada
Research on frequent pattern mining for Big Data streams using modern stream processing frameworks and heterogeneous CPU/GPU execution.
Faculty: Faculty of Engineering
Group: Computer and Software Systems
Advisor: Prof. Alexandra (Sasha) Fedorova
Focus: High-throughput stream analytics, latency and throughput benchmarking, and concurrent CPU/GPU execution.
  • Proposed and benchmarked streaming data-mining workflows in Spark, Flink, and Storm, with focus on latency and throughput in real-world settings.
  • Explored concurrent versions of FP-streaming algorithms designed to exploit multi-core CPUs and GPU resources for high-throughput stream analytics.
  • Positioned the work around social data mining and large-scale event streams, including evaluation of production-style workloads on cloud infrastructure.
Stack:
SparkFlinkStormScalaCUDA
10/2012 – 07/2017
PhD, Computer Science
Université de NeuchâtelNeuchâtel, Switzerland
Research spanning concurrency, energy-efficient computing, Big Data systems, GPU computing, and systems-level performance engineering.
Group: Complex Systems and Big Data Lab
Advisor: Prof. Pascal Felber
Thesis: Exploiting Concurrency and Heterogeneity for Energy-efficient Computing: An Actor-based Approach.
  • Built a heterogeneous task scheduler routing work between GPU and CPU resources based on real-time energy cost, reducing consumption by 40–80%.
  • Designed a lock-free concurrent order book for HFT workloads, compatible with LMAX Disruptor, with 15x speedup on a 16-core machine.
  • Modified OpenJDK HotSpot C++ to enable pauseless garbage collection in ParallelNew and CMS using Hardware Transactional Memory.
  • Consulted on concurrency workflow design for robotic arm motion control at Innovation Mining & Incorporated Projects Inc.
TA: Concurrency, Security, Cloud Computing (2012–2016)
01/2010 – 05/2012
MSc, Computer Science
University of ManitobaWinnipeg, Canada
Research focused on distributed data mining and uncertain-data pattern mining using MapReduce and Hadoop.
Group: Database and Data Mining Lab
Advisor: Prof. Carson Leung
Thesis: Distributed frequent pattern mining algorithms for uncertain data with MapReduce.
  • Implemented frequent pattern mining algorithms for regular and uncertain data on Apache Mahout and Hadoop.
  • Used MapReduce and ForkJoin on Amazon EC2 to achieve 9.5x speedup on 11 nodes.
TA: Analysis of Algorithms, Automata Theory, Database Concepts
09/2009 – 06/2012
MSc, Computer Science
Lviv Polytechnic National UniversityLviv, Ukraine
Research focused on parallel frequent pattern mining for Big Data.
Topic: Parallel frequent pattern mining for Big Data.
09/2005 – 06/2009
BSc, Computer Science
Lviv Polytechnic National UniversityLviv, Ukraine
Studies in computer science with an early focus on concurrency middleware and systems programming.
Topic: Comparison of concurrency middleware in C# and Java.
Result: GPA 4.91/5, 2nd of 95.
03/2019 – 04/2019
Blockchain Bootcamp
7 Gate AcademyVancouver, Canada
Blockchain systems training covering protocol architectures, smart contracts, privacy, and decentralized storage.
Coverage: Bitcoin (PoW, UTXO), Ethereum (EVM, Solidity, PoS, Truffle), Zcash (zkSNARKs, bulletproofs), 0x, HyperLedger, IPFS, Filecoin.
Selected Publications
DAIS
Enhanced Energy Efficiency with the Actor Model on Heterogeneous Architectures
Y. Hayduk, A. Sobe, P. Felber
Best Paper Award
NETYS
Exploiting Concurrency in Domain-Specific Data Structures: A Concurrent Order Book and Workload Generator for Online Trading
R. P. Barazzutti, Y. Hayduk, P. Felber, E. Rivière
ManLang
Towards an Efficient Pauseless Java GC with Selective HTM-Based Access Barriers
M. Carpen-Amarie, Y. Hayduk, P. Felber, C. Fetzer, G. Thomas, D. Dice
Full list at dblp.org