Interview with Women in Clean Technology and Sustainability

 It was an honor and a pleasure be interviewed by Women in Clean Technology and Sustainability (WCS) as their member of the month. WCS is a non-profit organization headquartered in California that fosters community and networking among professionals in the green economy and has members from all over the United States and the world.

We had a wide ranging discussion that covered the inspiration behind Ecoformatics, the opportunities for jobs and startups at the intersection of data science and clean technology and where we as a company can help.

Read excerpts of the interview below or check out the entire interview at WCS' website here!

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ECOFORMATICS IS YOUR BRAINCHILD, WHAT DOES IT DO?

Ecoformatics was born out of an unmet need that I discovered while working as a data scientist and environmental engineer for several entities. The planet is facing so many challenges. At the same time, we’ve seen an explosion of new tools and technologies in machine learning and artificial intelligence that can be used to aid in building a sustainable society. However, there are very few university programs and educational platforms that help clean technology professionals learn all these new skills and apply them successfully to problems in agriculture, water, energy, climate, biodiversity and sustainability. Ecoformatics was launched in 2018 to meet this need. Our educational platform has online courses to help clean technology professionals at all levels master data science and AI skills and apply them successfully to solve problems in different cleantech sectors.

CAN YOU GIVE US A CASE STUDY ON HOW AI FOR THE PLANET WORKS?  

AI has several applications for the planet. Classic examples include wildfire detection from space; discovering outbreaks of Covid-19 via wastewater; identifying air pollution sources; monitoring floods and building better disaster management systems. ..........................

DO YOU SEE A SHORTAGE OF QUALIFIED PEOPLE TO FILL JOBS IN THIS AREA? I NOTICED THAT YOUR WEBSITE HAS A JOBS BOARD.

There’s definitely a shortage of people with the skills in both data science/machine learning and the domain knowledge of different clean technology sectors in order to build useful applications. ............

HOW DO YOUR COURSES HELP PREPARE PEOPLE FOR JOBS?

The biggest challenge with existing online courses and platforms is that they focus on data science in the high-tech sector! Building a recommendation engine for a website is very different from using drones to monitor power lines and predicting energy usage by consumers. Data in clean technology is sparse and at different scales. Natural systems and outcomes are rarely in our control. So people who try and solve problems in clean tech without understanding the sector and the challenges with the data in the sector run into issues.

Our courses are focused only on the clean technology sector; we teach professionals the ins and outs of applying data science and machine learning in clean technology. Each course has real-world, hands-on problems such as detecting wildfires from space – where students are guided through the process of building actual solutions and work through the concepts using real data – messy, unwieldy, non-standard data. 

The goal is to help professionals in clean technology go from “I’ve never coded, I hate statistics and I’m not sure how useful machine learning and AI are to my problems” to “I can now build useful machine learning models, I understand when and how to apply data science, and I can successfully build and manage a team that can use these new technologies in my company and sector”.

What our community are reading

Moonshots, Models, IoT and Machine Learning in Agriculture

  What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems?     Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively -     scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
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Our online community space is now live!

 Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !
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How much water should an email consume? Data centers and water use

  A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
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