Research & Education

CityFlow is an open-source multi-agent reinforcement learning environment specifically designed for large-scale city traffic scenarios. It features a microscopic traffic simulator that models the behavior of individual vehicles, offering a high level of detail for traffic evolution. The tool supports flexible definitions for road networks and traffic flow, making it adaptable to various urban layouts. With its friendly Python interface, CityFlow is well-suited for reinforcement learning applications in traffic management. It boasts fast simulation capabilities due to elaborately designed data structures and multithreading, allowing it to simulate city-wide traffic efficiently. This makes it a valuable resource for researchers and engineers working on urban traffic management and planning, enabling them to test and develop advanced traffic control algorithms.

Model Medicines is an AI-driven company dedicated to building better medicines by innovating at the intersection of data science, biology, and drug development. The platform utilizes AI to model chemistry and human biology, accelerating the discovery and development of life-changing drugs. With 192 compounds and 67 validated assets in disease-relevant cellular models across 12 therapeutic targets, Model Medicines focuses on areas such as virology, oncology, inflammation, and longevity. Their proprietary GALILEO™ and AmesNet™ technologies enable ultra-large virtual screening and agentic AI breakthroughs, leading to the identification of best-in-class potential therapeutics, such as MDL-001, a direct-acting, broad-spectrum antiviral.

PINNpapers is a comprehensive, open-source repository maintained by the IDRL lab, dedicated to curating essential research papers on Physics-Informed Neural Networks (PINNs). Since PINNs have gained significant traction in scientific computing, this resource serves as a valuable collection of representative works in the field. The repository categorizes papers across various aspects of PINNs, including foundational models, parallel computing approaches, acceleration techniques, model transfer and meta-learning, probabilistic PINNs, uncertainty quantification, and diverse applications. It also lists relevant software libraries like DeepXDE and SciANN, providing links to papers and code where available. Researchers and practitioners can use this resource to stay updated on the latest advancements and foundational concepts in PINN research.

Hearbitz leverages AI to convert news articles into natural-sounding audio summaries, enabling users to stay informed on the go. It offers an audio-first experience, perfect for multitasking during commutes or workouts. Users can choose from different news personas, including Neutral, Progressive, or Conservative, to align with their worldview. The platform also provides personalized curation, allowing users to select topics of interest for a tailored news feed. With adjustable playback speeds and skip controls, Hearbitz helps users consume more news efficiently, saving time while staying informed.

Introduction to Machine Learning with Python is a comprehensive open-source repository designed to accompany the book of the same name by Andreas Mueller and Sarah Guido. It provides all the notebooks and code examples used in the book, making it an invaluable resource for students and practitioners looking to learn machine learning with Python. The repository includes helper functions from the `mglearn` library for creating figures and datasets, and all necessary datasets are included, with the exception of `aclImdb`. Users can set up their environment using `conda` or `pip` to install required packages like `numpy`, `scipy`, `scikit-learn`, `matplotlib`, `pandas`, `pillow`, and `graphviz`. It also supports `nltk` and `spacy` for text processing chapters.

pysc2-examples offers a collection of Deep Reinforcement Learning examples specifically designed for StarCraft II. Built upon Deepmind's pysc2, OpenAI's baselines, and Blizzard's s2client-proto, it provides a robust framework for developers and researchers. The project leverages TensorFlow 1.3 and includes examples for tasks like 'CollectMineralShards' using Deep Q Networks and A2C algorithms. Users can quickly set up the environment, install necessary libraries like pysc2 and baselines, download StarCraft II maps, and then train and enjoy their AI agents. It supports various parameters for training, including algorithm choice (deepq, a2c), total timesteps, exploration fraction, and options for prioritized replay or dueling networks.

FLUX.2 Klein LoRA Studio is a Hugging Face Space that provides a demo collection of FLUX.2-Klein Model LoRAs. This tool enables users to upload one or two images, select a specific style from the available LoRAs (or a face-swap adapter), and then input a brief text prompt. The system processes these inputs to generate a new, edited image that adheres to the chosen style while preserving key elements from the original picture(s). It's designed for experimentation with image generation and style transfer using advanced AI models, offering a hands-on experience with LoRA technology.

d2l-tvm is an open-source project dedicated to deep learning compilers, offering comprehensive resources for those looking to understand and optimize deep learning models. Hosted on GitHub, it provides a platform for learning about the TVM deep learning compiler stack. The project includes detailed documentation, practical examples, and guides on how to contribute, making it a valuable resource for developers and researchers. It covers various aspects of deep learning compilation, from common operators and CPU/GPU schedules to deployment strategies, enabling users to dive deep into the technical intricacies of optimizing AI models.

World Monitor is a comprehensive real-time global intelligence dashboard designed for AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking. It features over 500 curated news feeds across 15 categories, synthesized into briefs by AI. The platform includes a dual map engine with 45 data layers, cross-stream correlation for military, economic, and disaster signals, and a Country Intelligence Index for risk scoring. It also offers a finance radar tracking 92 stock exchanges and commodities. A key differentiator is its local AI capability, allowing users to run everything with Ollama without needing API keys. It supports 21 languages with native-language feeds and is available as a native desktop app for macOS, Windows, and Linux.

squeezeDet is an open-source project providing a TensorFlow implementation of SqueezeDet, a convolutional neural network specifically designed for real-time object detection. This tool is particularly optimized for autonomous driving applications, emphasizing a unified, small, and low-power architecture. It allows users to train and evaluate object detection models using datasets like KITTI, supporting various network backbones such as SqueezeNet, ResNet50, and VGG16. The repository includes scripts for installation, demo execution, training, and validation, making it a comprehensive resource for researchers and developers working on efficient object detection in resource-constrained environments.

spark-py-notebooks is a comprehensive collection of IPython/Jupyter notebooks designed to educate users on various Apache Spark concepts using Python (pySpark). The tutorials range from fundamental to advanced topics, focusing on Big Data Analysis and Machine Learning. Users can learn about RDD creation, basic RDD operations like map, filter, and collect, sampling, set operations, and data aggregations. The collection also delves into working with key/value pair RDDs and introduces MLlib for basic statistics, exploratory data analysis, logistic regression, and decision trees. Additionally, it covers Spark SQL for structured processing with DataFrames and includes applications like building a movie recommendation web service.

knowledge-graph-from-GPT is an open-source program designed to create an external memory module for language models, enhancing their ability to organize, access, and generate information. It functions as a wrapper for a language model in Python, allowing for the categorization and structuring of information, identification of knowledge gaps, and generation of questions. The tool addresses key language model shortcomings such as memory, logic, and interpretability by creating a human-interpretable knowledge graph. It supports various long-term applications including database generation, question answering, summarizing research, identifying conflicting information, and serving as an educational tool or flashcard assistant. The program also aims to facilitate hypothesis generation for scientific research by processing vast amounts of information and proposing novel ideas.

Stanford-CS-221-Artificial-Intelligence is a comprehensive resource offering VIP cheatsheets for Stanford's CS 221 Artificial Intelligence course. This repository aims to consolidate all crucial notions covered in the course, including cheatsheets for each artificial intelligence field and an ultimate compilation of concepts. The material is accessible on a dedicated website, ensuring readability across various devices. Authored by Afshine Amidi and Shervine Amidi, it serves as an invaluable study aid for students and anyone interested in understanding core AI principles. The cheatsheets are available in English, French, and Turkish, making it accessible to a broader audience.

tf-gnn-samples is a GitHub repository offering TensorFlow implementations of various Graph Neural Network (GNN) architectures. It serves as the code release for an article introducing GNNs with feature-wise linear modulation (GNN-FiLM). The repository includes implementations for Gated Graph Neural Networks (GGNN), Relational Graph Convolutional Networks (RGCN), Relational Graph Attention Networks (RGAT), Relational Graph Isomorphism Networks (RGIN), GNN-Edge-MLP, and Relational Graph Dynamic Convolution Networks (RGDCN). It provides scripts for training and evaluating models on tasks such as citation networks (Cora, Pubmed, Citeseer), protein-protein interaction (PPI), quantum chemistry prediction (QM9), and variable misuse detection (VarMisuse). The code allows users to reproduce experimental results presented in the accompanying research paper, making it a valuable resource for researchers and developers working with GNNs.

A-dapt brings Emotion AI into LegalTech, providing lawyers with human-centered tools for scalable, privacy-first witness preparation and emotionally intelligent litigation training. Its TestMyWitness platform uses Emotional AI to prepare confident and credible witnesses by focusing on people, not paperwork. Key features include viewer emotion analysis, real-time emotional feedback during witness preparation, dynamic emotion labels, and "move the dot" coaching to improve composure. The platform also offers a transcript and annotation workspace with auto-generated Q&A, emotion tags, and sharable notes for follow-up coaching. It flags risk signals like hostility or low confidence, supporting legal teams in enhancing witness credibility before court or interviews. The system is designed for privacy, reduced bias, and eco-friendliness.

Abzu is a biotechnology company leveraging explainable AI to innovate in the field of RNA therapeutics. The company specializes in developing best-in-class RNA drugs, including siRNAs, ASOs, and anti-miRs, for significant medical needs. Their AI-guided design platform, powered by the QLattice®, allows for the in silico exploration and prioritization of vast sequence spaces, evaluating over 100,000 design variants to predict efficacy and developability properties. This approach significantly reduces experimental cycles, lowers costs, and shortens the time to candidate selection. Abzu also focuses on RNA-based delivery systems, developing targeted aptamers for cell-specific uptake of therapeutic RNA, offering a modular platform for precision delivery beyond the liver. The team combines deep RNA biology, AI-driven design, and drug development experience to create a closed learning loop where data refines models and models improve molecules.

Additive Catchments is dedicated to restoring river health by providing advanced infrastructure for water quality monitoring. The platform utilizes sensor networks to deliver real-time data, offering transparent insights and actionable intelligence crucial for effective water management. It aims to give rivers a voice by integrating environmental data, civic infrastructure, and pollution monitoring to create a comprehensive river health index. This tool is designed to support water governance and catchment management, enabling stakeholders to make informed decisions and build a sustainable future where rivers, communities, and society can thrive.

pyRiemann is an open-source Python machine learning package designed for processing and classifying real or complex-valued multivariate data. It leverages the Riemannian geometry of symmetric or Hermitian positive definite matrices, offering a high-level interface that mimics the scikit-learn API. While generic for multivariate data analysis, it's specifically tailored for biosignals like EEG, MEG, or EMG in brain-computer interface (BCI) applications, including motor imagery, event-related potentials, and steady-state visually evoked potentials. It also supports multisource transfer learning and remote sensing applications, such as processing radar images. The package provides functionalities for estimating covariance matrices and classifying them, making it a powerful tool for researchers and developers in these fields. It can be easily integrated into scikit-learn pipelines for comprehensive data analysis workflows.

MineRL is a Python package designed for sample-efficient reinforcement learning research, primarily within the Minecraft environment. It provides easy-to-use Gym environments and data access, making it suitable for training AI agents. The package has evolved through several versions, with v1.0 supporting OpenAI VPT models and the MineRL BASALT 2022 competition, featuring a new Minecraft version (1.12 -> 1.16.5), larger default resolution (64x64 -> 640x360), and a near-human action-space focused on GUI and mouse control. It requires Java JDK 8 for installation and can be integrated into projects much like any standard Gym environment for developing and testing AI models.

Autopilot-Notes is a comprehensive open-source knowledge base designed for systematic learning and mastery of autonomous driving technology. It covers a wide array of topics including foundational theories, hardware components, perception algorithms, localization techniques, planning strategies, and control systems. The repository also features in-depth analyses of solutions from leading manufacturers like Tesla, Baidu Apollo, and Huawei ADS. With daily updates on industry news and technical advancements, Autopilot-Notes serves as an invaluable resource for students and developers looking to stay current with the rapidly evolving field of autonomous vehicles. It emphasizes practical application with content on simulation, deployment, and optimization.

TorchDrug is a robust, PyTorch-based machine learning platform specifically designed for drug discovery. It simplifies the implementation of graph operations in a PyTorchic style with GPU support, making it accessible even for practitioners with minimal drug discovery knowledge. The platform facilitates rapid prototyping of machine learning research by providing a wide range of common datasets and building blocks. Users can easily work with graph-structured data and molecules, extracting properties without deep domain expertise. TorchDrug also accelerates training and inference across multiple CPUs or GPUs, offering seamless scalability for complex experiments. It supports integration with Weights & Biases for experiment tracking and management.

Aikreate is an AI literacy platform designed to help middle and high school students understand artificial intelligence through active creation and experimentation. Instead of passively consuming AI, students build AI literacy by engaging in hands-on projects, games, and guided challenges. The platform offers two main products: the Kreate App for families, providing an interactive learning experience for at-home use, and Kreate Academy for schools and teachers, which delivers a classroom-ready AI curriculum. Developed by experienced educators and professors with affiliations to MIT and Babson College, Aikreate focuses on teaching how AI works, its limits, and its impact, fostering critical thinking and ethical understanding in young learners.

HappyChat AI is designed to support educators by streamlining the often time-consuming processes of student evaluation. Leveraging artificial intelligence, the tool automates the generation of personalized feedback for students, allowing teachers to provide more tailored and constructive input efficiently. Beyond feedback, HappyChat AI also assists in developing a diverse range of assessment questions, which can enhance the quality and variety of instructional support. This automation helps teachers save significant time, enabling them to focus more on direct student interaction and curriculum development rather than administrative tasks. The platform aims to improve the overall quality of educational assessment and feedback, making it a valuable asset for academic professionals.

Setup-NVIDIA-GPU-for-Deep-Learning is a comprehensive, open-source guide designed to assist users in setting up their NVIDIA GPUs for deep learning tasks. It outlines a clear, step-by-step process, starting with the installation of the latest NVIDIA GPU drivers. The guide then proceeds to cover essential software components such as Visual Studio with C++ support, Anaconda/Miniconda for package management, the CUDA Toolkit, and cuDNN. Finally, it provides instructions for installing PyTorch and includes a script to test the GPU setup, ensuring all components are correctly configured for optimal deep learning performance. This resource is invaluable for deep learning practitioners and AI researchers looking to streamline their development environment setup.