Research & Education

Altair offers a comprehensive suite of AI-powered software and cloud solutions designed to tackle the toughest challenges in simulation, high-performance computing (HPC), data science, and artificial intelligence. The platform includes Altair HPCWorks for maximizing compute resource utilization, Altair RapidMiner for low- and no-code data analytics, and Altair HyperWorks for AI-powered design and simulation. These tools enable users to streamline workflows, optimize designs, and make data-driven decisions. Altair serves a wide range of industries, from aerospace and automotive to healthcare and financial services, providing an open and programmable architecture for dynamic, collaborative access to resources.

Cell2Sentence (C2S) is an open-source framework designed for applying Large Language Models (LLMs) to single-cell transcriptomics. It implements the C2S-Scale framework, which transforms expression vectors into "cell sentences"—space-separated gene names ordered by descending expression. This innovative approach allows LLMs to natively model scRNA-seq data using natural language, unifying transcriptomic and textual data. The tool enables advanced single-cell tasks such as perturbation prediction, dataset summarization, cluster captioning, and biological question answering. C2S-Scale models, including those based on Pythia and Gemma-2 architectures, are available on Huggingface, with support for finetuning on custom prompt templates and multi-cell prompt formatting.

clinicalBERT is an open-source repository offering publicly available Clinical BERT embeddings, designed to advance clinical Natural Language Processing (NLP) research. It enables users to leverage pre-trained models like Bio+Clinical BERT and Bio+Discharge Summary BERT, which are finetuned from BioBERT or the cased version of BERT. The tool provides clear instructions for direct integration via the Hugging Face transformers library, simplifying access for researchers and developers. Additionally, it outlines steps to reproduce the pretraining process using MIMIC data and offers examples for downstream tasks such as Med NLI and NER, making it a comprehensive resource for those working with clinical text data.

Deep-Learning-Experiments is an open-source GitHub repository designed to help users understand deep learning through a combination of videos, detailed notes, and practical experiments. It offers comprehensive lecture notes covering fundamental deep learning topics such as Supervised Learning, Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformers. The repository also includes code implementations for many of these concepts, allowing users to run and experiment with models like Mamba, Autoencoders (AE), Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Diffusion Models. Additionally, it provides resources for setting up development environments, including Python, Numpy, PyTorch, and Hugging Face, making it a valuable resource for both theoretical understanding and practical application in deep learning.

DL4Proteins-notebooks offers a comprehensive series of Colab notebooks designed to democratize deep learning for protein design and prediction. This resource provides an accessible, hands-on introduction to the tools and methodologies that have revolutionized computational protein science, including AlphaFold, RFDiffusion, and ProteinMPNN. By blending foundational machine learning principles with state-of-the-art approaches, DL4Proteins equips researchers, educators, and students with the knowledge to contribute to the future of protein engineering. The open-source notebooks bridge the gap between cutting-edge research and classroom learning, fostering innovation in synthetic biology and therapeutics. It covers topics from neural networks with NumPy and PyTorch to graph neural networks and diffusion models.

gpt-fast is a highly efficient PyTorch-native transformer text generation tool, designed for minimal latency and a compact codebase of under 1000 lines of Python. It supports advanced features like int8/int4 quantization, speculative decoding, and tensor parallelism, making it suitable for high-performance applications. The tool is compatible with both Nvidia and AMD GPUs and is intended to showcase optimal performance achievable with native PyTorch, rather than serving as a comprehensive framework. Developers are encouraged to copy, paste, and fork the codebase for their specific needs, leveraging its efficiency for various LLM inference tasks.

Machine-Learning-Notebooks is a comprehensive GitHub repository offering a curated collection of Jupyter notebooks for individuals looking to refresh or learn machine learning and deep learning concepts. The notebooks cover a wide array of topics, including fundamental NumPy operations, various data preprocessing techniques, different regression and classification algorithms, clustering methods, model evaluation metrics, and advanced areas like reinforcement learning, natural language processing, and neural networks. It also includes specialized notebooks on dimensionality reduction and model selection. The resource is compiled from various online sources, making it a valuable, centralized hub for structured learning and practical application of ML/DL concepts.

TextClassificationBenchmark provides a comprehensive open-source benchmark for text classification tasks using PyTorch. It aims to include a wide range of text classification datasets, covering sentiment and topic classification in multiple languages like English and Chinese. The benchmark also offers basic word embeddings and implements numerous popular and state-of-the-art deep neural network models, including FastText, BasicCNN (KimCNN, MultiLayerCNN, Multi-perspective CNN), InceptionCNN, LSTM variants (BILSTM, StackLSTM), LSTM with Attention, Hybrids between CNN and RNN (RCNN, C-LSTM), Transformer, ConS2S, Capsule, and Quantum-inspired NN. This tool is ideal for researchers and developers looking to compare the performance of different text classification models on various datasets.

VideoTuna is a powerful and comprehensive open-source codebase designed for text-to-video applications, integrating multiple AI video generation models for both inference and finetuning. It supports a wide array of functionalities including text-to-video (T2V), image-to-video (I2V), text-to-image (T2I), and video-to-video (V2V) generation. The platform offers comprehensive pipelines covering fine-tuning, pre-training, continuous training, and post-training (alignment) processes. Key features include an all-in-one framework for various pre-trained models, continuous training capabilities, human preference alignment using RLHF, and post-processing for video enhancement. It supports models like HunyuanVideo, WanVideo, StepVideo, Mochi, CogVideoX, Open Sora, VideoCrafter, and Flux.

Transformers.jl offers a Julia implementation of transformer-based models, built upon the Flux.jl deep learning library. This tool is designed for machine learning researchers and developers working within the Julia ecosystem, facilitating the implementation of Natural Language Processing (NLP) tasks. It provides functionalities for using pretrained models, such as BERT, and includes utilities for text encoding, tokenization, and processing. The library supports various transformer architectures, enabling users to experiment with and deploy advanced AI models directly in Julia. It is actively maintained with ongoing updates and community support through GitHub issues and Julia's Slack/Discourse channels.

NordAxon provides comprehensive AI consulting, custom machine learning solutions, and specialized training services from Malmö, Sweden. They assist organizations in navigating transformative technologies like Artificial Intelligence and Machine Learning, focusing on both proof-of-concepts and ambitious, disruptive ideas. Their end-to-end delivery covers everything from initial use case investigation to the deployment of AI solutions. Additionally, NordAxon offers AI education, including courses, seminars, and workshops, tailored for leaders and employees to build knowledge, experience, and confidence in AI. They also provide AI advisory services to analyze organizational AI/ML maturity and embed AI strategy across business units.

Perceptive Space is building an AI-powered space weather platform designed to provide critical predictions and decision intelligence for safe and reliable operations in harsh space environments. Leveraging artificial intelligence, the platform offers hyperlocal space weather predictions and asset-specific insights into space weather impact. This enables satellite operators and launch providers to significantly enhance mission lifetimes and minimize operational downtimes and service interruptions. Unlike traditional models, Perceptive Space's AI-driven approach excels in accuracy, resolution, and real-time updates, offering probabilistic predictions, near real-time updates, and tailored forecasts specific to orbit and mission design. It provides comprehensive space weather risk management from design through deorbit, delivering actionable insights and seamless integrations via user-friendly APIs and self-serve dashboards.

Bosphorus AI offers an Automated Machine Learning (Auto ML) Platform designed to support all stages of the machine learning lifecycle, from data preparation to model deployment and maintenance. This platform is tailored for data-driven organizations seeking trustworthy and accountable AI solutions. Beyond the Auto ML platform, Bosphorus AI provides a range of solutions for various industries including Energy & Utilities, Oil & Gas, Fintech, Metaverse, Manufacturing, Commodity, Telco, and Retail. They also offer AI and Digital Academies for professional development, along with consultancy services in R&D and EU projects, making it a comprehensive partner for AI adoption and implementation.

WindBorne Systems designs, builds, and operates a constellation of long-duration smart weather balloons, known as Atlas, to collect critical atmospheric data globally, including over oceans and remote areas. This proprietary data is then fused with advanced AI models to produce highly accurate weather forecasts through their WeatherMesh deep learning model. The technology allows for complete global access, from the surface to the stratosphere, with each balloon autonomously flying for weeks and controlling its altitude. WindBorne Live provides access to their fleet of AI-powered balloons and real-time weather data, offering solutions for industries like energy, trading, and utilities. The system is designed for safety and compliance with aviation regulations, providing transparent tracking for government partners.

PromeAI is an all-in-one AI image toolset designed to empower users to become powerful image artists. It provides a comprehensive suite of AI tools for both image generation and editing, significantly enhancing workflow efficiency. Key features include Sketch Rendering, AI Image Generator, Erase & Replace, Outpainting, Photo to Sketch, and AI Background Generator. The platform also offers specialized tools for various industries like architectural design, interior design, product development, and game design. Users can train consistency models with just a single image to generate AI images with cohesive styles. PromeAI is crafted for user-friendliness, allowing both beginners and experts to quickly edit images and generate stunning visuals from text descriptions.

PharosBio is an AI-powered decision-support framework designed to accelerate scientific decisions in life sciences. It unifies experimental data, publications, and internal documents, transforming them into fast, reliable, and fully traceable actionable insights. The platform addresses challenges in drug development such as data silos and incomplete pipeline oversight by connecting insights from early discovery, through preclinical stages, to clinical outcomes. It supports hypothesis generation, target selection, and prioritization, and helps predict translatability of in vivo results. PharosBio emphasizes enterprise security and compliance, adhering to industry best practices for data protection, encryption, role-based access controls, and supporting certifications like GDPR, SOC2, and HIPAA.

Poka Labs deploys AI agents to automate the full commercial workflow for industrial businesses, from setting prices to closing deals. It features AI agents that track cost trends, flag repricing priorities, and set defensible prices across entire catalogs. The platform automates quoting by parsing messy RFQ emails, applying pricing rules, and drafting quotes in minutes. Additionally, it provides guided selling capabilities, arming sales teams with TCO analysis, margin defense talking points, and cross-sell recommendations. Poka Labs integrates with existing ERP, CRM, and pricing spreadsheets, handling unstructured data and enforcing business rules without requiring a rip-and-replace of current systems.

Giotto.ai is a Swiss AI lab dedicated to advancing reasoning technologies beyond conventional machine learning. Their mission is to develop AI that deeply understands context, leading to breakthroughs that transform how machines learn, adapt, and serve humanity. The technology is designed to bridge the gap towards Artificial General Intelligence (AGI), focusing on AI that can adapt, learn, and reason with human-like flexibility and creativity. Giotto.ai's innovative approach combines cutting-edge methodologies, enabling their AI to generalize across tasks, solve problems elegantly, and offer transformative potential for various industries and society. They have achieved notable results on the ARC-AGI benchmark, outperforming major reasoning systems like Grok4 and GPT-5 with a 200M-parameter system and low inference costs.

IQVIA NLP is an advanced natural language processing platform specifically designed for the life sciences industry. It enables organizations to extract insights at scale from unstructured text, offering fast, accurate, and proven capabilities. This tool is part of IQVIA's broader suite of AI-powered solutions aimed at transforming life sciences through data, technology, and human science. It helps accelerate innovation, improve patient outcomes, and bring treatments to market faster by intelligently connecting data, technology, and analytics. IQVIA NLP is particularly valuable for real-world evidence generation, allowing users to gain critical insights across the product lifecycle and streamline research processes.

The Laboratory for Computational Social Systems (LCS2) is a prominent research group led by Dr. Tanmoy Chakraborty at IIT Delhi. Their work centers on advancing Large Language Models (LLMs) by grounding them in real-world human behavior. The lab pursues four integrated research directions: efficient and controllable LLMs, behavior-aware and human-centric modeling for social phenomena like misinformation, interpretability and diagnostic analysis of LLMs, and AI for critical social applications such as mental health. LCS2 emphasizes rigorous evaluation, focusing on generalization, fidelity, reasoning, and alignment, while developing multimodal, multilingual, and personalized systems that are scalable and deployable in practical settings. They actively seek MS R/PhD candidates and Research Assistants/Postdoctoral fellows.

App-DL is a comprehensive GitHub repository dedicated to deep learning and its applications, particularly within startups, computer vision (CV), and natural language processing (NLP). It serves as a valuable resource for researchers, developers, and students interested in these fields, offering a curated collection of academic papers and references. The repository covers various sub-topics including Deep Reinforcement Learning, Dialogue Systems (Task-Oriented Dialogue), Text Generation, and Text Summarization. Each section lists relevant research papers, providing a structured overview of significant contributions and advancements in these areas. This makes App-DL an excellent starting point for exploring cutting-edge deep learning techniques and their practical implementations.

CSIRO's Data61 Imaging and Computer Vision Group is dedicated to advancing imaging and computer vision science within Australia. The group focuses on analyzing diverse imaging sources, including RGB cameras, infrared, and medical imaging, to extract valuable insights. They leverage advanced AI methods, with a strong emphasis on machine learning techniques like deep learning, to process and interpret visual data. Their work aims to derive useful information and enhance images, ultimately assisting human decision-making across various applications. This research contributes to solving significant national challenges through innovative science and technology.

Numerion Labs offers an AI superplatform designed to revolutionize drug discovery by exploring vast chemical spaces to identify novel, drug-like molecules. Leveraging machine learning, the platform aims to uncover previously unseen potential in molecular structures, accelerating the identification and development of new drugs. The company focuses on delivering first- and best-in-class potential in immune and inflammatory diseases through programs born from its superplatform. Numerion Labs is backed by a world-class team of scientists and engineers dedicated to redefining small-molecule drug discovery, making it a valuable tool for pharmaceutical companies and research institutions.

MSCA SE CardioSCOPE is an EU-funded research project dedicated to advancing the understanding and treatment of cardiovascular diseases (CVDs), particularly acute coronary syndrome (ACS). The project brings together experts from academia and companies across Europe to exchange and expand expertise in interdisciplinary approaches. It employs a comprehensive multiomic strategy, integrating various biological data types, alongside artificial intelligence and machine learning techniques, to develop personalized assessment and predictive models for ACS and major adverse cardiovascular events (MACE). The initiative focuses on enhancing Europe's research and innovation capacity in CVDs, moving beyond traditional risk factors to more patient-oriented and personalized strategies.