Research & Education

The University of Tartu is the leading university in the Baltics, recognized among the top 1.2% globally. It provides a world-class education with a wide selection of over 140 study programs and 1200 continuing education courses. The university emphasizes a practical approach, creative thinking, and the use of new technologies in its teaching. As a significant research hub, it offers science-based solutions to global challenges and supports the advancement of Estonian society and economy through collaboration between top scientists and entrepreneurs. With over 13,600 students and nearly 100,000 alumni worldwide, it is a center for academic spirit and lifelong learning.

Coursiv provides beginner-friendly courses designed to help users master AI productivity tools and acquire practical automation skills. The platform focuses on making AI accessible, even for those without a technical background. Learners can expect to gain proficiency in tools like ChatGPT and integrate AI into their work processes. With a reported 800,000+ learners, Coursiv aims to boost productivity and enable individuals to leverage AI effectively in various professional contexts. The courses are structured to guide users step-by-step through AI concepts and applications.

Geoskop is an advanced climate intelligence platform designed to help renewable energy companies and other industries manage climate-related risks and optimize operations. It utilizes proprietary algorithms and AI to generate highly accurate, validated long-range climate predictions, enabling confident, climate-ready investment decisions. The platform assists in assessing long-term climate impacts on renewable assets, improving day-to-day performance through accurate seasonal forecasts, and anticipating extreme climate events. Geoskop also supports regulatory compliance with standards like the EU taxonomy and IFRS S2 through its Sustax tool, providing factual and fair-priced climate insights for reporting.

Guru AI: Study App is a comprehensive platform designed to assist students with their academic tasks. Users can submit their assignments, questions, or activities and receive detailed, step-by-step solutions. The platform offers two primary methods: an AI-powered solver that boasts over 97% accuracy for instant answers, and a network of expert human teachers who can resolve more complex tasks, provide live sessions, and offer a 7-day guarantee against errors. This dual approach ensures students can get quick help for routine problems or in-depth assistance for challenging coursework, making it suitable for various subjects and educational levels. The app aims to improve grades and save students time by providing reliable and explained solutions.

Koshima.ai offers practical AI training and consulting services designed to help organizations in the UAE and UK identify AI use cases, upskill their teams, and improve business processes. The platform focuses on unlocking operational capacity by redesigning everyday work tasks with AI and training teams to adopt new workflows. Services include identifying time-consuming tasks, designing AI tool workflows, and training teams using their own real work. Koshima.ai also provides consulting for organizations to build shared understanding, identify use cases, and create AI roadmaps. They offer team and department AI productivity training, as well as individual programs for professionals to apply AI to research, writing, analysis, and communication.

FOURIER-Robotics GR-2 is a cutting-edge humanoid robot designed to push the boundaries of agility, precision, and perception. Built upon customer feedback, GR-2 integrates advanced hardware, design, and software enhancements. Its next-level hardware design includes integrated cabling for power and communication, resulting in concealed wires and a more compact form factor. The improved joint configuration simplifies debugging, reduces manufacturing costs, and enhances the robot's ability to transition from AI simulation to real-world applications. GR-2 features 12-DoF dexterous hands, doubling the dexterity of previous models, and is equipped with six array-type tactile sensors for real-time force sensing and object manipulation. Powered by seven types of distinct FSA actuators, including FSA 2.0 with peak torques exceeding 380 N.m, GR-2 achieves dynamic mobility and precise movements. The Fourier Toolkit provides developers with an upgraded Software Development Kit, offering easy access to pre-optimized modules via intuitive APIs and supporting frameworks like NVIDIA Isaac Lab, ROS, and Mujoco.

AI4ANKI is a specialized tool designed for Anki users to effortlessly create language flashcards with integrated audio. It leverages AI to generate high-quality sentence decks in seconds, significantly reducing the time spent on manual flashcard creation. Users can select their target language and preferred difficulty level, with support for languages like English, Spanish, French, German, Japanese, Korean, and Mandarin Chinese. The tool provides translations and natural-sounding audio generated by advanced AI text-to-speech technology. Decks can be easily downloaded and imported directly into Anki, making it ideal for language learners from A1 to B2 proficiency levels who want to accelerate their learning process.

MAGES Studio is a Singapore-based company specializing in custom Augmented Reality (AR), Virtual Reality (VR), and Gamification solutions. They cater to various industries, aiming to transform training, learning, and engagement through impact-driven technology. Their offerings include AR and VR development, applied games, and 3D simulation for immersive learning and corporate training. MAGES Studio focuses on bridging the gap between physical and virtual realities, helping businesses innovate and adapt. They also incorporate data analytics and impact analysis to ensure their solutions deliver measurable results, making them a comprehensive partner for digital transformation.

Pluto Bio offers a collaborative multi-omics platform designed to accelerate research and drug discovery. It provides a unified workspace for preclinical and translational strategy, enabling multi-site, interdisciplinary collaboration in real-time. The platform centralizes data visualization with a no-code canvas, allowing users to explore data and test scientific hypotheses quickly while maintaining end-to-end traceability. Pluto Bio supports a wide range of biological assays, including scRNA-seq, RNA-Seq, ChIP-seq, ATAC-seq, and Spatial Transcriptomics, with pipelines for custom assays. It helps organize experiments, plots, data, and files in a secure cloud environment, facilitating target identification, biomarker discovery, and mechanism tracking.

Parentof is the world's first AI-based cognitive intelligence platform, offering live AI-based skill mentor assistants to measure and develop abilities in learners. Utilizing its award-winning Cognitive intelligence technology and ARC Engine, Parentof decodes brain architecture and identifies over 1500 micro-abilities across cognitive, emotional, social, and physical domains. The platform provides personalized ability workouts for 15 minutes a day, aiming to strengthen skills in deficit. Parentof also features specialized AI mentors like the AI Mental Health Assistant for Kids, AI Drawing Mentor (Drawgogo), AI Handwriting Mentor (Handwritezy), and AI Math Ability Mentor (Math Lemon), each designed to address specific developmental and learning challenges. Its technology is certified by NIMHANS, a leading Neuroscience Research Organization, and has shown significant growth in learners.

Numberz.ai develops domain-intelligent AI systems designed for regulated and high-consequence environments where correctness and trust are paramount. The platform helps experts analyze complex documents, integrate with live enterprise systems, and process fragmented data to make critical decisions. It employs agentic intelligence, selective reasoning with large models, and domain-specific small language models (SLMs) for precision. Key features include human-in-the-loop validation, deterministic engines for grounded logic, and continuous evaluation. Built on Google Cloud, Numberz.ai offers a robust, scalable, and secure infrastructure for enterprise-grade intelligence, bridging the gap between probability and certainty in AI applications.

LatentMAS is a multi-agent reasoning framework designed to enhance the efficiency and stability of multi-agent systems. Unlike traditional methods that rely on lengthy textual reasoning traces, LatentMAS facilitates agent collaboration by passing latent thoughts directly through their working memory within the model's latent space. This innovative approach significantly reduces token usage by 50-80% and achieves major wall-clock speedups of 3-7 times compared to standard Text-MAS or chain-of-thought baselines. The framework is compatible with any HuggingFace model and optionally supports vLLM backends for faster inference. It also features training-free latent-space alignment for stable generation, making it a general and powerful technique for developing advanced multi-agent AI applications.

Listening is an AI text-to-speech tool designed specifically for academic papers and research. It converts PDFs, Word documents, MOBI & EPUB files, and even scanned physical pages into natural-sounding audio, allowing users to listen to complex material on the go. Key features include the ability to automatically skip citations, references, and footnotes, and to select specific sections of a paper to listen to. The tool also offers adjustable playback speeds and a one-click note-taking function that captures the last two sentences heard, timestamped and synced across devices. This helps students and researchers manage heavy reading loads, improve retention, and study more efficiently.

Deep-active-learning is an open-source Python library designed for implementing and experimenting with various active learning algorithms. It provides a collection of methods such as Random Sampling, Least Confidence, Margin Sampling, Entropy Sampling, Uncertainty Sampling with Dropout Estimation, Bayesian Active Learning Disagreement, Cluster-Based Selection, and Adversarial Margin. This library is particularly useful for researchers and developers in the field of machine learning who aim to reduce the amount of labeled data required for training models while maintaining or improving performance. The repository includes prerequisites and a demo script for easy setup and experimentation, making it a practical tool for exploring active learning strategies.

Data-Science-Projects is an open-source GitHub repository offering a comprehensive collection of data science projects. Each project is meticulously organized within its own directory, containing all necessary code, relevant datasets, detailed documentation, and additional resources. The repository covers a wide array of topics, including various prediction models such as Breast Cancer Prediction, Red Wine Quality Prediction, Heart Stroke Prediction, House Price Prediction, and many more. It serves as an excellent resource for students and developers looking to explore practical applications of machine learning, data analysis, and visualization techniques, providing concrete examples and results for each project.

daclip-uir provides an official PyTorch implementation for controlling vision-language models, specifically designed for universal image restoration tasks. This tool can address various image degradations such as motion blur, haze, JPEG compression, low-light conditions, noise, raindrops, rain, shadows, snow, and uncompleted images (inpainting). It offers pretrained models for degradation-aware CLIP and universal image restoration, along with a Gradio app for easy testing of custom images. The project also includes a follow-up work focusing on photo-realistic image restoration and handling real-world mixed-degradation images, demonstrating its continuous development and robust capabilities in the field.

machine-learning-cheat-sheet offers a comprehensive collection of classical equations and diagrams essential for understanding machine learning concepts. This resource is designed to help users quickly recall fundamental knowledge and ideas, making it particularly useful for students, professionals, and anyone preparing for job interviews in the machine learning field. The cheat sheet is available as a downloadable PDF, providing a convenient and accessible reference. It also includes instructions for compiling the LaTeX source on various platforms, catering to users who prefer to customize or build the document themselves.

deep-image-retrieval is an open-source project from Naver Labs Europe focused on advancing image retrieval through deep learning. It offers models and evaluation scripts implemented in Python3 and PyTorch 1.0+, enabling researchers and developers to learn deep visual representations for image retrieval tasks. The tool supports training image retrieval systems using various loss functions, including triplet loss and a novel Average Precision (AP) loss, which directly optimizes for retrieval performance. It includes pre-trained models based on Resnet architectures with different pooling mechanisms (MAC, GeM) and provides scripts for evaluating these models on standard benchmarks like Oxford5K and Paris6K, as well as for extracting features from custom image datasets.

Deep-Learning-Approach-for-Surface-Defect-Detection is an open-source project offering a Tensorflow implementation of a segmentation-based deep learning approach for surface defect detection. This tool is designed for automated visual inspection and quality control, particularly relevant in manufacturing processes. It allows users to train a deep learning model on datasets like KolektorSDD to identify and classify surface imperfections. The implementation supports independent training of segmentation and decision networks, providing flexibility for model optimization. It includes scripts for testing, training, and visualization of results, making it a practical resource for researchers and developers working on computer vision applications for industrial quality assurance.

Deep-Learning-Book-Chapter-Summaries is a GitHub repository dedicated to making the comprehensive Deep Learning book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville more accessible. It provides detailed summaries for each chapter, breaking down complex topics into easier-to-understand explanations. The project also includes blog posts for particularly challenging chapters, offering further insights and elaborations. This resource is ideal for students and researchers looking to grasp the core concepts of deep learning without having to delve into every intricate detail of the original textbook, serving as a valuable study aid and reference.

Deep-Learning-for-Medical-Applications is a comprehensive, open-source repository on GitHub, providing a curated list of deep learning papers specifically focused on medical image analysis. This resource is designed to be a valuable starting point for researchers and practitioners in the field of medical AI. It meticulously classifies papers based on their deep learning techniques (e.g., CNN, RNN, GAN) and learning methodologies, along with metadata such as imaging modality, area of interest, and clinical database. The collection includes papers published since 2015 from peer-reviewed journals and high-reputed conferences, as well as recent arXiv preprints. The repository offers shortcuts to understand common deep learning techniques and imaging modalities, making it easier to navigate the extensive list of research.

deep-learning-models is a GitHub repository offering Keras code and pre-trained weights for several widely used deep learning models. This resource includes implementations for VGG16, VGG19, ResNet50, Inception v3, and a CRNN for music tagging. The architectures are designed to be compatible with both TensorFlow and Theano backends, automatically adapting to the image dimension ordering specified in your Keras configuration. Users can easily load pre-trained weights, such as 'imagenet' for image models or 'msd' for the music tagging model, which are automatically downloaded and cached locally. While this repository is deprecated in favor of `keras.applications`, it remains a valuable reference for understanding and utilizing these foundational models.

Deep-Learning-TensorFlow is a GitHub repository offering a collection of pre-built Deep Learning algorithms implemented with the TensorFlow library. This package is designed as a command-line utility, enabling users to quickly train and evaluate popular Deep Learning models. It can also serve as a benchmark or baseline for comparing custom models and datasets. The repository includes implementations for Convolutional Networks, Restricted Boltzmann Machines, Deep Belief Networks, Deep Autoencoders, Denoising Autoencoders, Stacked Denoising Autoencoders, and MultiLayer Perceptrons. It also supports Logistic Regression. The package can be installed via pip as 'yadlt' or by cloning the GitHub repository, and it features a scikit-learn-like interface for ease of use.

Infinilearn is a free fantasy RPG designed to help middle school students in grades 6-8 master math concepts. Students engage in battles by solving Common Core-aligned math problems covering topics like fractions, decimals, equations, geometry, and ratios. The game features adaptive difficulty, pulling problems from a student's weakest subjects for targeted practice. It runs in any modern web browser, requiring no downloads. While the game is completely free for students with no ads or in-app purchases, parents and teachers can opt for a premium subscription to access deeper analytics, Common Core standards tracking, and the ability to manage multiple children or classes. Infinilearn emphasizes a safe environment with no external links or personal information sharing, and uses preset quick-chat options for multiplayer interactions.