Eyal Pasha
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Academic

Academic AI research in Israel with international conference visibility and applied product follow-through.

A closer look at the publication record, conference work in Vienna and Israel, the interdisciplinary thesis, and the way the academic side connects to shipped systems.

By Eyal Pasha (אייל פשה) · Last updated April 9, 2026

The academic work is not separate from the production work. It gives Europe and international teams a public technical record of published research, interdisciplinary business framing, and full-stack system delivery in one place.

The formal academic base is The College of Management Academic Studies (COLMAN) in Rishon LeZion, with a B.Sc. in Computer Science and an MBA built around an interdisciplinary thesis connecting deep learning and business administration. That combination shapes the work itself: not just model development, but commercialization, deployment, and adoption.

The clearest public research signal is WMSM, a real-time Hebrew Sign Language framework later published by IEEE. It was presented internationally, documented across IEEE, ResearchGate, and Google Scholar, and then carried forward into Handibur as a live iOS beta.

At a glance

  • B.Sc. in Computer Science focused on machine learning, deep learning, and real-time systems.
  • MBA with an interdisciplinary thesis in Computer Science and Business Administration.
  • Lead author of WMSM, a Hebrew-language machine learning publication later published by IEEE.
  • Conference presentations in Vienna and Israel with product follow-through and public research visibility.

Focus areas

  • Hebrew Sign Language recognition and sentence-level translation.
  • Efficient real-time ML instead of compute-heavy academic excess.
  • Computer vision, NLP, and applied machine learning with deployment context.
  • Academic work that feeds back into products rather than stopping at publication.
  • Public research signals that international teams can verify across IEEE, Scholar, ResearchGate, and ORCID.

How it fits together

The research, education, and commercialization pieces reinforce each other.

This is what ties the degrees, publication, and product work together.

Education

COLMAN foundation

The academic base in Rishon LeZion connects computer science training with a business-facing graduate track.

Publication

Published record

WMSM can be followed across IEEE, Google Scholar, and ResearchGate as part of the published academic work.

Thesis

Interdisciplinary framing

The MBA thesis keeps the academic work grounded in business adoption and deep learning commercialization.

Outcome

Research to deployment

The strongest point is not only that the work was published, but that it moved into a live product.

More to read

More on the research, product work, and broader AI systems.

A few deeper reads across the rest of the work.

Israel

AI/ML engineer in Israel focused on Hebrew NLP and applied ML systems.

A closer look at Israel-based AI/ML work across Hebrew AI, applied machine learning, and production systems.

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LegalTech AI

Hebrew NLP and LegalTech AI in production legal workflows.

A closer look at enterprise document intelligence, multi-model orchestration, and Hebrew-English AI delivery for cross-border legal workflows.

Read more

Research

WMSM research on Hebrew Sign Language recognition.

A closer look at the WMSM publication, the key results, and the international research-to-product path into Handibur.

Read more

Questions

A few things people usually want to know about the academic side.

The short version of the publication trail, educational path, and research-to-product thread.

What parts of the academic background stand out most?

Eyal Pasha (אייל פשה) completed a B.Sc. in Computer Science at The College of Management Academic Studies (המסלול האקדמי המכללה למינהל) in Rishon LeZion, Israel, with a focus on machine learning, deep learning, and real-time systems. He is completing an MBA at the same institution with an interdisciplinary thesis connecting deep learning commercialization and strategic business management. His lead-authored paper WMSM was published at IEEE FLLM 2025 in Vienna and is indexed on IEEE Xplore, Google Scholar, ResearchGate, and ORCID. The research was presented internationally in both Vienna and Israel.

Is the academic work separate from the production work?

No — the two are deliberately connected. The WMSM research on Hebrew Sign Language recognition produced a framework that moved from academic paper into Handibur, a live iOS video chat beta delivering real-time sign language translation at 40 frames per second. The MBA thesis specifically examines how deep learning research translates into commercially viable products. This same research-to-production mindset shapes the enterprise LegalTech AI work at Sensemaking Israel, where academic rigor in model design directly informs production deployment decisions around Hebrew NLP, multi-model orchestration, and cost-efficient training pipelines.

What does the academic background add to the broader work?

The academic foundation provides methodological rigor that separates production AI work from ad-hoc engineering. The B.Sc. provided depth in machine learning architectures, algorithm design, and real-time systems. The MBA adds strategic thinking about market viability, cost structures, and technology adoption — directly relevant to building AI products that law firms and enterprises actually adopt. The IEEE publication demonstrates peer-reviewed research capability, while the WMSM framework's 99.93% training cost reduction shows how academic work can produce commercially meaningful efficiency gains. This combination of computer science depth and business acumen is what enables building AI systems that are both technically sound and commercially viable.