Going from “I’ve never coded, I hate statistics and I’m not sure how AI and machine learning works” to “I can now build useful machine learning models.” Copy
February 2021 Member of the Month: Gayathri Gopalakrishnan
How did you hear about WCS and what prompted you to join?
I was among the first to join once WCS started its membership program. When I moved to the SF Bay Area in 2012, I was looking for a community of folks interested in cleantech and sustainability. I joined a WCS meetup and it was a great experience. I enjoy meeting women who are passionate about the topic and are working in all kinds of roles.
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.
An interesting case study with AI that highlights its usefulness in improving people’s lives is monitoring and warning people in India, Bangladesh, Kenya and other countries about floods and other disasters. In developed countries like the United States and Europe, this process has historically been done using expensive sensors that are placed at specific locations along sources of water. Now, government agencies in developing countries have been partnering with tech companies like Google to build systems that combine low-cost sensors, satellite data from space, climate/water models and local information so that they can predict when areas are likely to flood and warn the area residents in time to evacuate. That’s something that was much harder before the rise of big data, the Internet of Things and AI.
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. For example, in a survey of agricultural firms, about 65% reported a shortage of skilled professionals to deploy the latest tools. Similarly, consultants in the energy sector have warned of a “looming talent shortage” of professionals who are capable of applying data science to solve problems in energy analytics, renewable energy and electric vehicles. And that’s true for the water sector, the conservation sector and other cleantech sectors.
What academic degree would lead to a quality job in AI and data analytics?
There’s a significant need for qualified people and useful programs for AI and data analytics for the clean technology sector! At this point, most universities offer some coursework in data analytics for the Earth Sciences (University of Colorado being one of the pioneers). However, no academic degrees are specific to data science in the cleantech field. Most of the actual hands-on experience is pioneered by people working towards a graduate degree.
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 is your long-term vision for Ecoformatics?
In the long-term, I envision that Ecoformatics will be the online educational hub for applying AI in clean technology sectors. We intend to develop applied data science and machine learning courses for each cleantech sector (we’re currently working in agriculture and water), and partnering with universities, companies and organizations to help professionals upskill easily and efficiently. Our goal is to empower anyone who’s interested in helping the planet with the skills that are needed in the next century!
Interviewed by Jeanne Trombly, Writer in Residence for Women in Cleantech and Sustainability.
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