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The Artificial Intelligence Methods Lab (AIML) is a new research laboratory dedicated to developing innovative methods in artificial intelligence, machine learning, and related fields.

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Artificial Intelligence Methods Lab (AIML)

AIML is the Artificial Intelligence Methods Laboratory at the Institute of Computer Science, Faculty of Mathematics and Informatics, Vilnius University, Lithuania. The lab conducts research and education in machine learning, deep learning, remote sensing, agentic AI, large language models, and AI applications for Earth observation, environment, healthcare, and industry.

Website: https://aiml.lt
Institution: Vilnius University (VU), Faculty of Mathematics and Informatics (MIF), Institute of Computer Science
Location: Didlaukio g. 47, Vilnius, Lithuania
Email: aiml@mif.vu.lt

Official launch: December 2025.

About

The Artificial Intelligence Methods Lab (AIML, lt: Dirbtinio intelekto metodų laboratorija) is a research lab at Vilnius University focused on advancing artificial intelligence methods and their real-world applications.

Areas of focus include:

  • Machine learning and deep learning methods
  • Remote sensing and Earth observation with AI
  • Large language models (LLMs) and small language models (SLMs)
  • Agentic AI workflows and MLOps
  • Self-supervised and generative models for satellite imagery
  • Environmental digital twins, climate resilience, urban and peatland monitoring
  • AI in healthcare and supply chains
  • Trustworthy, explainable, and reproducible AI (XAI, multimodal AI, agentic methods)

Vision: To be a leading Lithuanian research lab advancing artificial intelligence methods and their impactful application to science, industry, and society.

Mission: Conduct high-quality AI research, educate the next generation of AI researchers and practitioners, and collaborate with academia, industry, and public institutions in Lithuania and internationally.

Leadership

Team

Visiting Researchers

  • Sébastien Gadal — Visiting Researcher; Professor of Geography at Aix-Marseille Université (CNRS ESPACE UMR 7300). Specialises in spatial analysis, geomatics, and remote sensing with AI applications in environmental urban planning, agricultural drought modelling, forest fire prediction, and geospatial heterogeneity analysis.
    Email: sebastien.gadal@univ-amu.fr

Postdoctoral Researchers

  • Salah Al-Heejawi — Postdoctoral Fellow in AI for Remote Sensing (started 1 October 2026), supervised by Valentas Gružauskas.
    Email: awad.heejawi@mif.vu.lt

Notable recent collaborators include Thép Đồng (2026 summer intern from Politecnico di Milano / Hanoi University of Science and Technology), supervised by Valentas Gružauskas on agentic workflows for supply chain inventory management with LLMs.

Projects

  • Development of Reasoning-Based Small Language Models for Oncology Data — Joint VU and National Cancer Institute project (Research Council of Lithuania ITP programme, S-ITP-26-11, 2026–2028), led by Linas Petkevičius. Privacy-preserving SLMs with reasoning methods to extract cancer diagnoses, dates, TNM stages, histology, biomarkers, and treatment details from 6 million unstructured electronic health records. Potential deployment in the Lithuanian Cancer Registry on secure NVIDIA SPARK infrastructure (GDPR and EU AI Act compliant).

  • NEUTRINO — New-Generation Multi-Objective Satellite Image Recognition Algorithms for Climate Monitoring (LMT, 2023–2026). Rapid detection of environmental change (floods, droughts, surface-water dynamics) from multi-sensor satellite imagery.
    https://neutrino.aiml.lt/

  • AI-Driven Multimodal Remote Sensing Analysis (terraAI) — Natural-language Earth observation with code-generating AI agents; unified AI pipeline fusing EO imagery and in-situ data, orchestrated by an agentic LLM (LMT, S-TIP-25-3, 2025–2027).

  • Real-Time Mapping of Peat Hydration Using Data Fusion and Deep Learning — SAR–optical fusion to map peatland moisture in (near) real time (ESA RPA, 2025–2026).

  • UrbanClimate — Integrated EO and urban sensor data with AI super-resolution for neighbourhood-scale heat maps and cooling interventions (ESA Malta, 2024–2026).

  • AgeHome: Ageing at home — Understanding housing, social support, and community services for older adults’ care needs, with evidence-based modelling (LMT, 2025–2028).

Selected Publications (2026)

  • Lekavičius J., Kömürcü K., Gružauskas V., Petkevičius L. “Insights from Autoresearch for Solar Panel Segmentation.” AutoML4EO workshop, AutoML 2026.
  • Kömürcü K., Petkevičius L. “Evolutionary Architecture Search for Chlorophyll-a Prediction in Lakes using Sentinel-2.” AutoML4EO workshop, AutoML 2026.
  • Kömürcü K., Petkevičius L. “Sat-JEPA-Diff: Caption-Guided Zero-RGB Satellite Image Forecasting via Self-Supervised Diffusion.” IEEE Geoscience and Remote Sensing Letters, Vol. 23, 2026. DOI: 10.1109/LGRS.2026.3708464
  • Kömürcü K., Petkevičius L. “Sat-JEPA-Diff: Bridging Self-Supervised Learning and Generative Diffusion for Remote Sensing.” 4th ICLR Workshop on Machine Learning for Remote Sensing (ICLR 2026). arXiv:2603.13943
  • Shapovalov E., Hovhannisyan A., Gružauskas V. “Agentic Workflow Architecture for Environmental Remote Sensing Analytics.” ICERS 2026. DOI: 10.5281/zenodo.20407491

Open Source & Models

Recent Hugging Face Releases (AIML-VU)

The lab maintains models and datasets on Hugging Face under the AIML-VU organisation, with recent focus on Lithuanian biomedical typed-decision models:

  • AIML-VU/medgemma-1.5-4b-it-decitions-lt-med
    LoRA fine-tune of MedGemma 1.5 4B IT for Lithuanian MEDLINE typed decisions (choice + yes/no questions on titles/abstracts). Non-generative decision head; strong accuracy and calibration on the held-out test split of MEDLINE-S1-LT.

  • AIML-VU/laya-multilingual-lt-med
    Fine-tune of the Laya multilingual (mmBERT-base, ~0.3B) System 1 decision model on the same Lithuanian MEDLINE data. Single-forward-pass calibrated probabilities; significant accuracy and ECE improvements.

  • AIML-VU/MEDLINE-S1-LT
    Lithuanian translation (NLLB-200) of the MEDLINE-S1 biomedical typed-decision dataset (~46k abstracts). Used for training the above models. Labels derived from NLM MeSH and publication types.

These support privacy-preserving, reasoning-oriented small models for healthcare applications (e.g., oncology data extraction) aligned with the lab’s ongoing projects.

Education

  • Agentic MLOps — New 16-module course starting Autumn 2026 covering MLOps fundamentals, containerisation, cloud platforms, Kubernetes, CI/CD, model serving, GPU optimisation, workflow orchestration, AI agents, observability, scaling, security & compliance, federated learning, and responsible AI governance.
  • 3D Optimization Landscape Explorer (visual educational resource).

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