Clean Technology Meets Data Science: News and Articles

We've been in "building mode" here at Ecoformatics for the last few months and are delighted to announce our new products! First, welcome to our new, redesigned website. We've consolidated all our products and services under a single platform with Teachable to make it easier for our members to access all our content. As a reminder, your existing account for courses and webinars will still work on our new website. Second, our live events will be back starting January!   Our events will continue to be primarily virtual, and we're adding a new networking aspect to all our events. We are looking forward to talking about data science, machine learning and sensors in different clean technology sectors with professionals from around the world! And finally, we’re excited to announce our new products - the WaterTech track and the AgTech track . These are our first tracks where we focus on data science in a particular clean tech sector - specifically the water and wastew

  One of the most interesting aspects of working in clean technology is the interaction between different sectors in the space - the Food-Energy-Water nexus, for example.   What is the food-energy-water nexus? Well, not being in the Star Trek universe or any other science fiction arena, we’re definitely not talking about black holes of energy where food and water go to die :)! What we are talking about when we talk about the nexus between different clean tech sectors is - how do these sectors interact with each other? How complex are the interactions and can the relationships between them be described? In the case of the energy-water-food nexus, we’re exploring the interactions between the energy, water and food sectors. For example, we need water to grow food and produce energy, but energy is also needed to pump out groundwater and to process food.   Just for fun, let’s take a look at some numbers on the food-energy-water nexus from the UN and FAO. Let’s start with the biggest one - a

  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

  It’s always interesting to take a look at how trends and predictions about new technologies and their ramifications for different sectors pan out. And that’s no different when it come to data science and clean technology.   A graph that often comes up when discussing how new technologies develop is the “Gartner cycle of hype”. This is the idea that all new ideas, concepts and technologies invariably go through several stages in their development - they all start with excitement as the promise of new technology opens up possibilities that seem limitless, followed by a crash course in reality when what’s actually possible collides with dreams, and finally a steady look at immediate solutions that can build towards the dream. The last stage is when startups gain traction or are acquired and larger companies start building teams to work with the new technology. So, how has agriculture and data science - or AgTech worked out?     It’s definitely been an interesting ride since the concept

  This month, let’s talk about agriculture, crops and all things related to food!   If there’s one thing that a global pandemic has shown us - it’s how interconnected our supply chains are, especially in the food sector. For most people these days, getting groceries means going to a well-stocked market or food cart and getting fruits, vegetables and other standard supplies from there. We seldom go to the field or orchards or farms to get our food directly from the suppliers. And in general, the supply chains are so well oiled that we rarely run into issues about food not being available - as long as you’re able to pay for it! The pandemic revealed several aspects of our food system - where our favorite foods come from, how crops are grown, how animals are raised, who harvests and processes our food - and how these systems are so closely connected to each other that impacts on any part of the chain have an effect on the availability of food many miles away.     Pre-pandemic, there was a

I'm excited to have been invited by the organization "Women in Big Data" to talk about data science, agriculture, the environment and all the associated challenges. The symposium is on Wednesday, September 18th at Sacramento, CA and you can expect a lively discussion with me and three other experts on the panel! If you're interested, event details are at https://www.eventbrite.com/e/symposium-using-data-to-sustainably-feed-the-world-tickets-72188915991 . For all of you who are curious,   Women in Big Data   is a grassroots organization that started in Silicon Valley with women from different organizations who work in data science coming together to talk about the technical challenges, career pathways and entrepreneurship and funding among other fun topics. The group started with 5 women and now has close to 14,000 members all over the world - which is an amazing growth rate over the last 3 years! They hold conferences, regular meetups and training sessions and the one

  In today’s post, we’ll take a look at a few problems in Agriculture and Food that are being solved using machine learning, computer vision, social networks and satellite data among other data science tools. What do social networks, sensors, food and farms have in common? Social media immediately makes us think about Facebook, Snapchat, Instagram, Whatsapp, Google and all the different ways in which we human beings connect with each other today. All these apps use Graph and Network Theory to understand how people may be connected to each other, the links between them and how strong or weak those connections are. Reid Hoffman of LinkedIn famously said that “we’re all six weak connections apart from each other” and in today’s connected world, that number looks like it’s getting smaller and smaller. So what would social media have in common with agriculture? First, there’s always the way in which farmers and workers interact and connect with each other.   Several startups like the Farmer