Editor-in-Chief · Journal of iShareHow Labs

Jamel El Eliyah

iShareHow Labs LLC

Researching · building · studying — technology, operations, and business innovation

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I'm Editor-in-Chief of the Journal of iShareHow Labs. I research, build, and study at once — a data analyzer, event-and-trigger detector, and optimization-focused problem solver working at the intersection of technology, operations, and business innovation.

Through agents.isharehow.app, I'm pioneering large-scale choice models that combine operations data with customer data to solve operations problems. More broadly, I use mathematical optimization, machine learning, and statistics to ship solutions that scale to large, practical operations and marketing problems — including data analysis tools, AIs, and LLMs that companies can run, not only demo.

My passion extends beyond academia: entrepreneurial ventures at iShareHow Labs LLC, contracts we have won, and agency advisory roles. I measure success when industry notes the impact of those tools, AIs, LLMs, and machine learning on real company outcomes.

The same agenda drives Labs scholarship: machine learning and deep learning, data mining, network science and graph methods (GNNs, Connectivity / STTN), explainable AI, and privacy-preserving / on-device AI — applied to proximity-aware identity, networked infrastructure, and verifiable transactional layers for algorithmic justice. Web3 substrates are treated as infrastructure, not trading or asset pricing.

Research

  • Large-scale choice models for operations

    Pioneering choice models on agents.isharehow.app that combine operations data with customer data to solve practical operations and marketing problems — mathematical optimization, machine learning, and statistics at real company scale, not demo size.

  • Urban LLM data pipelines & Connectivity GraphRAG

    End-to-end LLM pipelines (catalog → preview → filter → codegen → execute+retry) and a live Neo4j Connectivity graph with spatial, temporal, and transactional dimensions — scholarship that ships as Labs product surfaces.

  • CEIN, proximity identity & algorithmic justice

    Secure proximity-aware identity, explainable and privacy-preserving AI, and graph methods applied to networked infrastructure and community economic identity — with Web3 treated as infrastructure, not trading.

  • Builder-researcher ventures & agency advisory

    Contracts won and agency advisory roles as field proof: turning research into deployable Agents/Labs tooling so industry can note the impact of data analysis tools, AIs, LLMs, and machine learning.

  • Trustworthy Agentic Operations

    Research statement: turn every iShareHow Agents deployment into governed evidence — baselines, traces, trust, security/privacy, and persistence over time — for RAG/graph learning and Labs publication for urban partners.

Research areas

  • Large-scale choice models (ops × customer data)
  • Mathematical optimization for practical ops & marketing
  • Machine learning, deep learning & statistics
  • Data analysis tools, AIs & LLMs for industry impact
  • Network science & graph methods (GNNs, STTN / Connectivity)
  • Explainable AI · privacy-preserving / on-device AI
  • Proximity-aware identity & algorithmic justice
  • Trustworthy Agentic Operations (deployment research)

Publications

Open research journal →

From the Journal of iShareHow Labs public shelf (R1 paper view).

2026

News

Programs at the Labs

SaaS Applications Laboratory

Production software research — agent platforms, orchestration layers, and auditable systems that translate field work into deployable services.

Electronics Technology & Devices Laboratory

Device assessment, edge deployment, and hardware literacy programs that connect electronics research to economic opportunity.

Vulnerability Assessment Laboratory

Rigorous measurement of security, social, and economic vulnerabilities affecting communities misrepresented in conventional datasets.