Popular GitHub Repositories for Wastewater Treatment

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

A collection of Bayesian data analysis recipes using PyMC3

A General Causal Inference Framework by Encoding Generative Modeling

Machine learning based causal inference/uplift in Python

Code for processing and managing data for EEG-based emotion recognition of individuals with and without Autism. EEG and other clinical data were collected in StonyBrook Social Competence Treatment Lab, for data request evaluation please contact professor Matthew D. Lerner matthew.lerner@stonybrook.edu

A Python project to create specialized LLM-based AI agents that analyze complex medical cases. The system integrates insights from various medical professionals to provide comprehensive assessments and personalized treatment recommendations, showcasing the potential of AI in multidisciplinary medicine.

Launched in March 2020 in response to the coronavirus disease 2019 (COVID-19) pandemic, COVID-Net is a global open source, open access initiative dedicated to accelerating advancement in machine learning to aid front-line healthcare workers and clinical institutions around the world fighting the continuing pandemic. Towards this goal, our global multi-disciplinary team of researchers, developers, and clinicians have made publicly available a suite of tailored deep neural network models for tackling different challenges ranging from screening to risk stratification to treatment planning for patients with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, we have made available fully curated, open access benchmark datasets comprised of some of the largest, most diverse patient cohorts from around the world.

AAAI 22 - Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

An in-development R package and a Bayesian hierarchical model jointly fitting multiple "local" wastewater data streams and "global" case count data to produce nowcasts and forecasts of both observations

Personalized Medicine: Redefining Cancer Treatment with deep?learning

[ICML 2024] Outlier-Efficient Hopfield Layers for Large Transformer-Based Models

Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.

Medical Images processing

Named Entity Recognition in Healthcare data to identify possible diseases and their suggested treatments from a corpus of medical text containing both disease and treatment.

A generative Bayesian estimation framework for individualized treatment effect (ITE) estimation, integrates representation learning, adversarial matching and causal estimation.

Code library for training causal inference deep learning models with automatic hyperparameter optimization written in Tensorflow 2.

Successfully established a machine learning model which can predict whether any given water sample is potable or not, based on its set of various properties, to a considerably high level of accuracy.

Code and notebook for text summarization with BERT along with a simple baseline model. Includes a research-backed treatment on the state of transfer learning, pretrained models, NLP metrics, and summarization dataset resources.