Distilling the essence of human language into artificial intelligence.

Omni Language AI Research develops scalable and multimodal AI systems tailored for real-world applications.

A natural approach to complex problems.

We are a dedicated research collective operating at the intersection of deep learning, linguistics, and healthcare. Our mission is to bridge the gap between high-resource and low-resource languages, ensuring equitable access to advanced AI.

By blending rigorous academic methodology with modern engineering, we create systems that don't just process text, but comprehend context, nuance, and intent at an unprecedented scale.

More About Our Research

Photo by Emilipothèse on Unsplash

Research Directions & Projects

All Projects

Browse all of our research directions and projects, including archived ones.

View Full List

Selected Publications

arXiv 2026

MemoryATHENA: Adaptive Routing over Latent and Generated Memories

A causal router decides when generated memories should correct direct learned-memory retrieval, and how strongly they intervene.

Read Paper
arXiv 2026

Label-Free Steering of Test-Time Reinforcement Learning

Compressing test-time RL into ~100K bias parameters with majority-vote pseudo-label rewards, matching labeled bias steering across text, vision-language, and audio reasoning.

Read Paper
NeurIPS 2026

Frozen Memory Is Not Enough: Rethinking External Memory as Extraction

We study cross-model frozen-memory transfer, showing that target-side readers can extract and align representations across model architectures and tokenizers.

Read Paper
ACL Findings 2026

Data-Centric Continual Pre-training for 500+ Languages

Compiling the MaLA bilingual translation corpus and evaluating the EMMA Llama 3 suite of models adapted to 500+ languages.

Read Paper
COLM 2026

MaLA: Massively Multilingual Language Adaptation

A comprehensive open-access suite featuring a 74B-token corpus across 939 languages and the EMMA-500 model.

Read Paper
EMNLP 2026 Findings

Reasoning over Grammar

Generating synthetic linguistic reasoning traces from UD treebanks, dictionaries, and grammar rules for low-resource machine translation.

Read Paper
EACL 2026

Test-Time Scaling of Reasoning Models for Machine Translation

Investigating the efficacy of inference-time computation in machine translation across diverse benchmarks and reasoning scenarios.

Read Paper

More Publications

Explore our complete list of publications, search research papers, and download BibTeX citations.

View Full List

Our Team

Researchers

Shaoxiong Ji
Shaoxiong Ji

Principal Investigator

ELLIS Institute Finland & University of Turku

NLP & AI for Health

Zihao Li
Zihao Li

Doctoral Researcher

University of Helsinki (Co-supervision with Jörg Tiedemann)

Multilingual NLP

Renhao Pei
Renhao Pei

Doctoral Researcher

ELLIS Institute Finland & University of Turku

Multilingual NLP

Mingyuan Li
Mingyuan Li

Postdoc Researcher

ELLIS Institute Finland & University of Turku

Machine Learning

Md Mohsinul Kabir
Md Mohsinul Kabir

ELLIS PhD

University of Manchester (co-advised with Sophia Ananiadou)

NLP

Yicong Wu
Yicong Wu

Doctoral Researcher

ELLIS Institute Finland & University of Turku

AI for Health

Naveen Vakada
Naveen Vakada

Doctoral Researcher

ELLIS Institute Finland & University of Turku

Multimodal AI

Zhibo Man
Zhibo Man

Postdoctoral Researcher

ELLIS Institute Finland & University of Turku

NLP

Students

Matti Hölttä
Matti Hölttä

MSc Student

Aalto University

AI for Health

Miikael Mändmets
Miikael Mändmets

MSc Student

University of Turku

Multimodal AI

Verneri Sirva
Verneri Sirva

MSc Student

University of Turku

AI Agents

Our Alumni

See our full list of former members and where they are now.

View Alumni