Climate Change AI Directory

Grounded in cultural analysis I specialise in creating accessible human centered models that bring core alignment across the enterprise to unlock value, in other words, I help decolonize processes, organizations and policies.

Seeking problems to solve and organizations to advance.

Project management, operations, Financial modelling, Monitoring and evaluation. Assessment of infrastructure and energy supply and demand, Productivity study.

Volunteering, Research opportunities in buildings and cities.

Interested in using novel data science methods to extract climate change signals in large ensemble simulations

Well established Japanese Canadian in Africa providing solutions on a number of sectors: energy, education, tourism, youth, climate change, etc

GlobalGrow - considered to be the largest, most cost effective and the best NBS - Nature Based Solution to remove billions (not millions) of tons of CO2 from our atmosphere beyond the IPCC recommendations and timelines seeks AI intervention.

I am an Agricultural Engineer by profession, and would love to develop capacity in applications of ML in agriculture and environmental issues.

I am interest in collaboration that will enhance my research capacity.

I seek new knowledge, partnerships,and collaborations

chair of https://www.snap4city.org platform and solution fully open source

Recently, I am building a carbon accounting SaaS for buildings

Engineering & workforce development

Seeking collaborations with Engineers for a Sustainable World...our members are on the launchpad of their engineering study with a focus on sustainability.

I have a background in ML and been working on ML for oceanography since my PhD (2014-2017). Recently I started working on ML for climate modeling, you can learn more by visiting my personal website.

Self-taught AI hobbyist looking to learn more. Equally interested in the mathematical underpinnings of AI algorithms and their practical implementation.

Unsupervised learning applied to remote sensing. Surrogate models for accelerated simulation.

My goal is to build an automatic e-waste recycling system.

I used to be a quant trader, have built statistical models for stock price predictions, have a patent involving some NLP methods, and am overall technical. I have not touched much of modern machine learning (deep learning and such).

In the context of climate, I'm especially interested in developing methods and tools to integrate AI simulations. My recent work includes developing machine-learning emulators to replace expensive parts of simulations. I am also interested in methods to incorporate domain knowledge in machine learning and representation learning.

PhD dissertation focuses on understanding OOD generalisation in the context of remote sensing

Check out my paper on interpretable computer vision for damage assessment and disaster relief using satellite imagery at the NeurIPS 2020 workshop!

On my way to becoming an AI researcher in the environmental science field.

Provide knowledge and teach programming (python!) to as many people as possible.

With 8 years of work experience, I have worked with the Apple Inc and IBM research team on advanced machine learning, neural network, NLP & Deep Learning projects. I have good knowledge on Convolutional Neural Networks, Recurrent Neural network, LSTM, Text analytics, Information Retrieval, Natural Language Processing, Scientific Spacy, Gensim, Named Entity Recognition, architecting Gene Regulatory Network Architecture designing for identifying candidate driver genes for cancer for Multiple Myeloma from MMRF, and currently developing a search engine to develop established AI products in the Healthcare. I have used C, Java, objective-C, Python, R programming languages. I got an award of full free registration to attend the conference and workshop track in ICML 2021, attended the Google Developer’s program, serving as a reviewer of a journal on AI & Deep Learning, attended the RegML summer program, Italy, and presented Attention is all you need paper to Portland State University, USA. I have contributed to real-world projects from multiple domain horizons & could transfer these skills in my work.

Marine Science Intelligent Sensors Edge AI Data stream mining

Big data analytics, stream data analytics

Earth system modeling, marine biogeochemistry

Head of the Biophysical Ecology lab, studying the impacts of climate on ecological systems and forecasting possible effects of climate change using Artificial Intelligence, Individual-based simulations, and empirical observations.

20+ years experience in the weather/climate industry.

R&D Scientist & Process Specialist, Experienced in Industrial Design & Scale-up, Modelling & Simulation, TEA, Data Analysis, Machine Learning, and AI.

Much of my research focuses on the assessment of 'embodied impacts' of buildings, i.e. environmental impacts (GHG emissions, etc) related to construction material processing (production, transport, end-of-life treatment). Embodied GHG emission are a hidden challenge for effective climate change mitigation in buildings and cities, as we could show in earlier research.

I also have extensive experience in Semantic Web, Knowledge Graphs and Ontology Modelling and Engineering.

I have over a decade long R&D experience post PhD in computational fields spanning from computational EM to semiconductors and from ML to carbon capture.

I am primarily interested in using ML/AI to do record linkage, entity resolution, outlier detection, missing value imputation, and automated data labeling. We have a lot of messy data to wrangle and rule-based data data cleaning is too tedious and fragile.

Experience of leading infra projects focussing on asset inspection (Computer Vision), anomaly detection (Unsupervised Time Series Analysis), and operational forecasting (Bayesian Hierarchical Modelling).

Researcher at the University of Huddersfield on intelligent system with self-rectifying capability for autonomous farming powered by solar energy.

My experience and interests are primarily in food systems, especially in food waste reduction. I'm currently doing ML engineering at Afresh Technologies!

I'm a graduate physicsist who works in the automotive industry as a software engineer for advanced driver assictance systems. One of my hobbies is deep learning. I have experience with deep learning for image classification, time series datas, Bayesien classification methods, web-scraping and willing to learn mor about other deepl learning areas with a special interest in reinforcement learning.

deeeper.technology gathers large datasets for land cover and its change down to submeter resolution.

Experience includes building platforms streaming high volume IoT data from energy storage devices to data collection for agroforestry. Looking to build my ML expertise - and share my existing SaaS expertise.

Primarily focused on computer vision for remote sensing

Thinking Machines specializes in leading-edge data platforms and custom cloud AI solutions to match various business needs and social sector use cases. Our GeoAI solution uses remote sensing techniques, geospatial data, and machine learning to leverage location intelligence for grounded decision-making, while our DocAI solution uses NLP to turn unstructured documents into structured data and rapidly extract valuable insights. Lastly, our Customer Intelligence solution brings together terabytes of siloed data to fast-track customer data insights.

Industrial experience on deep learning, CV, SSL Research experience on OOD, generative models, bayesian "Kaggle" experience on remote sensing

I've worked at NASA on using ML onboard Earth observation satellites, previously at Scale AI doing sensor fusion of lidar+rgb cameras for self-driving vehicle data and assisting human-in-the-loop labelling. Currently using satellite imagery to predict future PV yield.

My goal in research is to contribute towards understanding the data and techniques used for developing Machine Learning approaches, as well as to contribute to the growing field of ‘AI for Good’, applying AI to high-impact societal problems like climate change, health, education, and humanitarian response. Most recently, I have been working on analyzing the data used for training large language models, as well as quantifying the carbon footprint of AI algorithms

Geospatial machine learning, spatio-temporal machine learning, GeoAI, Urban informatics