Moshe Mash, Ph.D.

AI Researcher & Machine Learning Expert

Conducting cutting-edge research at the intersection of computational linguistics and Jewish studies, with expertise in NLP, multi-agent systems, and human-centric AI. Founder of MASHINNOVATEAI, providing research and development solutions for technology companies.

moshikmash@gmail.com
+1-412-626-1676
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Moshe Mash, Ph.D.

Research Focus

Natural Language Processing Machine Learning Multi-Agent Systems Game Theory Human-Centric AI Data Science Workflows

Professional Experience

AI Researcher

Dicta 2022–Present
  • Conduct research at the intersection of computational linguistics and Jewish studies, focusing on rabbinical and modern Hebrew
  • Developed NLP pipelines for authorship attribution, including models predicting the rabbi/author of texts where attribution is disputed
  • Designed AI-driven systems for automatic citation generation and cross-referencing of classical sources
  • Built advanced machine learning and LLM-based tools to support scholarly research in Hebrew texts

Postdoctoral Researcher

Carnegie Mellon University, Robotics Institute 2019–2022
  • Conducted research on the behavior and cognitive processes of machine learning practitioners as they develop AI models
  • Designed frameworks to capture, model, and analyze workflows, with emphasis on decision-making and iteration cycles
  • Developed predictive methods to identify critical moments of difficulty ("stuckness") in ML development
  • Mentored graduate students in AI and human-computer interaction research

AI Researcher

Diagnostic Robotics 2019
  • Developed machine learning models for the healthcare domain, with applications in risk prediction
  • Collaborated with clinicians and researchers to refine AI-driven predictive healthcare decision-support systems

AI Researcher

Amdocs 2018–2019
  • Led the development of an innovative system predicting at-risk maintenance contract renewals, saving the company millions of dollars
  • Designed, built, and deployed the system from scratch, including problem definition, algorithm design, evaluation, and deployment pipeline

MASHINNOVATEAI

Research & Development for Technology Companies

As the founder of MASHINNOVATEAI, I lead a company dedicated to unlocking the power of data and AI for technology companies. We provide expert analysis, custom AI tools, and ongoing support to help businesses transform complex information into actionable insights and drive real results.

Deep Data Analysis

Unlock insights with our deep data analysis, turning complex information into clear, actionable strategies.

Custom AI Tools

Tailored AI solutions built for your needs, enhancing efficiency and driving growth effortlessly.

Ongoing Support

Dedicated assistance to ensure your success, keeping your AI tools running smoothly and effectively.

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Education

Ph.D.

Software and Information Systems Engineering

Ben-Gurion University of the Negev

2014–2018

Dissertation: Reaching Fair Agreements in Group Settings
Advisor: Prof. Ya'akov (Kobi) Gal

M.Sc.

Computer Science (AI specialization)

Bar-Ilan University

2011–2013

Thesis: Joint Exploration with Self-Interested Agents
Advisor: Prof. David Sarne

B.Sc.

Software Engineering

Shenkar College of Engineering and Design

2007–2011

Awards & Recognition

Best Paper Award

ACM Conference on Economics and Computation (2016)

Excellence in Teaching Award

Ben-Gurion University (2016, 2017)

Ph.D. Scholarship for Academic Excellence

Ben-Gurion University (2014)

Selected Publications

The T cell receptor landscape of childhood brain tumors

Raphael I, Xiong Z, Sneiderman CT, Raphael RA, Mash M., et al.

Science Translational Medicine. 2025

Gendered Words and Grant Rates: A Textual Analysis of Disparate Outcomes in the Patent System

Gerhardt D., Marcowitz-Bitton M., Schuster W.M., Elmalech A., Suissa O., Mash M.

Northwestern Journal of Technology and Intellectual Property. 2025 (Accepted)

How to Form Winning Coalitions in Mixed Human-Computer Settings

Mash, M., Zick, Y., Bachrach, Y., & Gal, K.

ACM Transactions on Intelligent Systems and Technology (TIST). 2017

Which Is the Fairest (Rent Division) of Them All?

Gal, K., Mash, M., Procaccia, A., & Zick, Y.

Journal of the ACM (JACM). 2017

Predicting Data Scientist Stuckness During the Development of Machine Learning Classifiers

Mash, M., Oryol, S., Rosenthal, S., & Simmons, R.

VL/HCC. 2022

DSWorkFlow: A Framework for Capturing Data Scientists' Workflows

Mash, M., Rosenthal, S., & Simmons, R.

CHI. 2021

Which Is the Fairest (Rent Division) of Them All? Best Paper Award

Gal, K., Mash, M., Procaccia, A., & Zick, Y.

EC. 2016

Peer-Designed Agents for Evaluating Distribution of Outcomes in Human Environments

Mash, M., Lin, R., & Sarne, D.

AAMAS. 2014