Clean Technology Meets Data Science: News and Articles

  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 ! ------------------------------------------------------------------------------------------------------------ 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

  Happy 2021 everyone! The year certainly started with a bang - if there’s one thing we can’t say - it’s that January has been a boring month.   With the change in administrations in the United States, we’ve seen several changes in climate and clean technology policy which will have significant impacts on the state of the market, startups in the field, funded research and technology developments. And this is likely to extend to the use of machine learning, AI, smart sensors and the other aspects of data science in clean technology. So, let’s take a quick look at what happened at the intersection of data science and clean technology in 2020 and what the prospects for 2021 look like.   Looking back at 2020 :     2020 of course, is the year like no other - divided between pre-pandemic and what happened as the pandemic took hold globally. So, how did that influence the trends in clean technology and the application of data science in the different sectors?   Pre-pandemic, there were some c

  It's time for our last two events for us in 2020 - what a crazy year it's been!  Continuing our theme of exploring data science in different clean technology sectors, we're doing to be focusing on the energy sector this month.   On Friday, December 18th at 2 pm PT, we'll be holding a free webinar exploring the market and opportunities for data science in the energy sector - with a focus on renewables, the grid and buildings.     And then, on Sunday, December 20th at 11 am PT, we'll host a hands-on workshop where we'll look at the types of problems in the energy sector where data science is being applied, what data sources are useful and where to find them and how do we integrate physics based models and machine learning into our solutions. 

  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 Covid-19, social networks, traffic congestion and floods have in common? On the face of it, the answer would seem to be - nothing. But interestingly enough, all these systems can be represented in similar fashions - as nodes and networks with spatial and temporal effects.   So, scientists and researchers often try to see if models and ideas developed to solve problems in fields with similar representations can be adapted to problems in other fields.   A really interesting study was published in Nature   a couple of months ago, where researchers explored what happened when they adapted models used in understanding how infectious diseases like Covid spread to predicting floods in cities and urban systems.   How have floods and their impacts typically been modeled and why do people care? To start with, people care about flooding because it directly impacts their lives - when do areas have to be evacuated, how long should the evacuation last, and what are potential health consequ

  What comprises the water sector? If you asked ten people working in the sector, you’d probably get ten different opinions! And that’s because there are so many aspects of working with water and water technology.   There’s drinking water - which involves figuring out how much water is available (water resources management), how to treat it, how to deliver it effectively and at an acceptable price to customers. Then there’s wastewater - how do you treat, remove and recycle wastewater, both residential and industrial, to acceptable levels. Next, we come to the interactions between water and other built systems - hydropower and irrigation being the largest. Then, there’s the impact of water-related disasters - floods and tsunamis for example. Finally, we’ve got natural resources management - where we explore, monitor and evaluate the condition of natural water bodies - lakes, rivers, glaciers and the ocean - and how they interact with other systems.   The fun part about working with wate

  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