Research Overview
My research bridges two worlds: the formal study of language structure and the applied challenges of multilingual and culturally responsive AI.
On one side, I investigate the architecture of grammar: the primitive relations underlying complex predicates, morphology, event structure, and meaning. On the other, I develop computational approaches to multilingual NLP, narrative and event analysis, social and scientific knowledge discovery, and AI-assisted language learning, with particular attention to Persian and other underrepresented languages. Across these directions runs a common question: how does language organize meaning, and what would it take for artificial systems to represent that complexity more faithfully?
This broader research direction is deeply influenced by the work of Jean-Roger Vergnaud, whose view of grammar as a system built from abstract primitives, structural relations, and event representations continues to shape my thinking. His ideas raise a fundamental question that guides my work: what is the underlying architecture of language, and how might it inform the future of linguistically grounded AI?
My research often crosses disciplinary boundaries, drawing on linguistics, AI, cognition, culture, social systems, and history to explore questions that no single field fully addresses.
What I Work On
Linguistic Structure and Meaning
My theoretical work focuses on complex predicates, event structure, morphology, and the syntax–semantics interface, particularly in Persian and Armenian. I examine how the components of a predicate contribute to meaning and argument structure, and how similar meanings are expressed through different grammatical forms across languages. These questions inform my broader interest in the primitive relations underlying linguistic structure.
Multilingual & Culturally Responsive AI
I work on language technologies for Persian and other underrepresented languages, including multilingual NLP, AI evaluation, and AI-assisted language learning. I’m interested in how linguistic structure and cultural context affect what systems can interpret and where their performance breaks down. This includes evaluating how language models handle pragmatic conventions, indirect meaning, and differences in social expectations across cultures.
Narrative & Social Analysis
I use computational methods to identify events, participants, relationships, and timelines in text, and to examine how they combine into narratives. My work includes multilingual narrative analysis, social media discourse, and the relationship between language and online communities. I’m particularly interested in how different accounts organize the same events and convey different perspectives.
Scientific Knowledge and Research Discovery
I have done some work in scientometrics examining connections among publications, researchers, and ideas. I use NLP and network analysis to explore how research topics develop and intersect, and how those relationships can help people navigate scientific literature. This also connects to my interest in toos for organizing research and synthesizing knowledge.
Language Learning & Education
Applying linguistic theory and AI to heritage language analysis, multilingual pedagogy, adaptive instruction, curriculum development, and culturally informed educational technologies.
Selected Research
& Applications
AI Adoption & Workflow Transformation for the U.S. Federal Judiciary
MITRE, 2017-2023
Led the discovery, design, and deployment of AI-powered workflows for the U.S. Courts, partnering with judicial leadership to identify operational challenges, redesign business processes, and integrate AI into existing workflows. Solutions reduced manual effort by up to 80%, improved accuracy, and enabled large-scale analysis that was previously impractical through traditional human review.
Projects ranged from intelligent document processing for bankruptcy courts to narrative analytics for nearly 100,000 individuals, supporting data-driven planning and operational decision-making across the federal judiciary.
Cultural-Pragmatic Alignment Diagnostics for LLMs
Zoorna Institute, 2025
Co-authored research demonstrating systematic failure modes in mainstream LLMs on Persian cultural-pragmatic norms — published at EMNLP 2025. The diagnostic approach surfaces a class of alignment failures invisible to standard multilingual benchmarks, directly relevant to product teams deploying LLM-based interfaces in global, multilingual, or culturally diverse markets.
STEM-Focused LLM Evaluation Framework
Zoorna AI, 2025
Designed a research-grade evaluation framework for LLM reasoning systems, including rubric development, AI judge calibration, and structured-output validation across multiple frontier models.
AI Workflow Automation for Language Learning
Zoorna AI, 2025-2026
Designed and implemented an AI workflow for generating CEFR-aligned language-learning content, combining LLMs with deterministic validation to ensure pedagogical quality across multiple languages. The system automated curriculum generation while preserving linguistic constraints, difficulty progression, and structured content delivery.