Research, Inclusion, Skills & Empowerment

A research community where women lead and contribute to impactful AI and language technology research for low-resource languages.

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Research Focus Areas

Conducting impactful AI and language technology research across three core focus areas.

Education →

Researching higher education gaps, digital access barriers, and resilient learning pathways for students across Myanmar.

Women and Technology →

Empowering women to lead in AI and technology through hands-on workshops, research mentorship, and workplace studies.

Low Resource Languages →

Building open-source NLP tools, tokenizers, and language models to ensure Burmese and regional dialects thrive in modern AI.

MMDT-RISE in Action

About Us →

Learn the skills. Research the problems. Build the solutions. Lead the change.

Learn

Build foundational skills in AI, research methods, and responsible research through workshops, training, and mentorship.

Build

Turn research into working AI tools, prototypes, and deployed applications.

Research

Apply those skills to real AI research problems, particularly those shaped by limited data, compute, and resources.

Lead

Publish and present research, share knowledge, mentor others, and lead future AI research initiatives.

Recent Publications & Products

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Open-source tools, language models, and applied research papers developed by our team.

MMDT NER

MMDT NER is a foundational language technology project that transforms unstructured Burmese text into structured, usable data by identifying people, organizations, locations, dates, times, and numerical expressions. Built on a 2.14-million-token annotated corpus, it combines transformer-based modeling and responsible evaluation to advance context-aware AI for Burmese—an underrepresented, low-resource language—and support research, journalism, digital archives, humanitarian initiatives, and other public-interest applications.

Products · Low Resource Languages View Product →

mmdt-tokenizer

"mmdt-tokenizer" is an open-source Python toolkit for Myanmar-language text segmentation. It uses a transparent, grammar-informed pipeline to protect URLs, emails, dates, phone numbers, and numeric expressions before segmenting text into syllable- and word-level units. Curated linguistic lexicons and structural rules recognize postpositions, conjunctions, classifiers, verbal particles, and sentence-final particles, while merging related predicate constructions into meaningful tokens. Designed for low-resource settings, the system is lightweight, auditable, and extensible without model retraining, providing essential language infrastructure for Myanmar NLP research, corpus development, search, information extraction, and other language technologies.

Products · Low Resource Languages View Product →

Gender Equality in STEM Workforce: A Survey-Based Approach

This study examines the challenges faced by women in Myanmar’s technology sector and explores their experiences and aspirations. The findings reveal that these women encounter various obstacles, including limited mentorship support, gender discrimination, and unique difficulties related to pursuing job opportunities overseas. Safety concerns amidst the country’s current situation also serve as a significant motivator for seeking employment abroad. Social networks play a crucial role in job acquisition, but women face greater challenges in accessing professional networks compared to men. The male-dominated environment in the industry results in a lack of workplace mentors for female participants, hindering their professional growth. The study underscores the importance of implementing comprehensive measures to support women in the tech industry, including promoting gender equity, improving workplace support, addressing safety concerns, and providing access to mentorship and training programs. Considering the impact of Myanmar’s current situation, providing stability, security, and opportunities for professional growth are crucial for women’s career decisions and aspirations.

Publications · Women and Technology Read Paper →