CCAI Core Team Profile: Priya Donti

An interview with the Co-founder and Executive Director of CCAI

CCAI Core Team Profile Power & Energy
Photo from Priya Donti

This interview has been edited for clarity and length.

Tell us about yourself.

I’m a co-founder and the Executive Director of Climate Change AI (CCAI), currently funded by the Runway Startup Postdoc Program at Cornell Tech. I’ll start as an Assistant Professor at MIT in Fall 2023.

Recently, I finished my Ph.D. at Carnegie Mellon in Computer Science & Public Policy, working on machine learning to dynamically optimize power grids to better integrate renewable energy. Before that I traveled for a year as a Watson Fellow to interview people about next-generation power systems and did my undergrad at Harvey Mudd.

How do you feel experiences like the Watson Fellowship have interacted with your current academic research?

I went into my undergrad at Harvey Mudd knowing I wanted to work on climate change, but it wasn’t clear to me how I would do that. I initially thought I’d be a materials scientist and develop next-generation solar panels, but I ended up enjoying my computer science classes much more than any others. This created a conundrum, because it wasn’t clear to me then how computer science could play a role in climate change.

I ended up taking a leap of faith — doing a computer science and math major, and pursuing environmental analysis through a minor and my extracurricular activities — and kept looking for ways to bring these interests together.

I eventually found a paper arguing that AI will be a critical component of next-generation power grids that are able to incorporate large amounts of renewable energy.

I got really excited about that topic and applied for the Watson Fellowship to learn more. During my Watson, I conducted interviews in five different countries to better understand what people meant when they used the terms “next-generation power grids” or “smart grids,” as well as what the technical, policy, and social considerations were behind modernizing the grid. I wanted to understand how these differed between countries and contexts. That exploration gave me a lot of perspective as I continued on to do my Ph.D.

Let’s talk about the leap of faith you mentioned. You knew you enjoyed computer science and you knew you wanted to work on climate change, but you didn’t quite see the intersection. You’ve done a great job of building the space that you wanted to inhabit. Do you have any reflections on that, or advice for people starting out?

Addressing climate change is going to take a really wide set of skills, tools, and approaches, and everybody has something that they can contribute. At the same time, it’s important not to approach problems with a hammer looking for a nail — different problems will require different skillsets, and even when a particular skillset is relevant, the way you actually use that skillset often fundamentally needs to change based on the realities of the problems you’re addressing.

For example, many machine learning tools have been developed primarily with large-scale image and text data in mind, and those methods don’t work out-of-the-box when analyzing (e.g.) power grid physics. This means machine learning needs to be approached differently in power grids than in some other settings. Setting up the necessary “feedback loop” between your problems and your skillsets requires actually doing your homework on the climate change areas you care about.

Making your journey publicly available can be a way to keep yourself accountable, and to provide a lot of value to other people as well. Take the “Tackling Climate Change with Machine Learning” paper that launched CCAI. We came in not with the conclusion that AI is necessarily applicable to climate action, but with a hypothesis. We looked through the literature, and we talked to people to understand whether that hypothesis was substantiated, and in what ways. We did not talk about applications where we didn’t think that AI was relevant.

I agree that the feedback loop is important. It’s also hard sometimes. What has helped you build that kind of feedback loop to make sure your work is interacting with the domain you’re working in?

The first step is to pick a domain. What are you interested in and excited about? Pick a domain that reflects that and then do a deep dive - read the literature, understand who the players are, go to the relevant venues where people are discussing these topics, follow people on social media, and generally get integrated into the community.

Many people try to over-optimize this choice of domain to try to find the “objectively best” one, and that decision paralysis prevents them from ever diving in. In reality, we need a diversity of people working on a diversity of problems across sectors - so it’s okay to be guided by your interest, plus a rough understanding of the order-of-magnitude of impact.

What are you excited about right now in CCAI?

I’m really excited by the impact that CCAI has had so far. We’ve brought together thousands of people through our conferences and events, including our inaugural CCAI Summer School in August. We’ve run multi-million dollar grants programs to fund impactful work, provided actionable policy advice through venues such as the Global Partnership on AI, and inspired the creation of many other initiatives through our work. What is sometimes unbelievable is that this work has largely been done by volunteers.

While we’re excited by the impact we’ve been able to have as a predominantly volunteer team, we’re ready to further scale our activities and our professional staff to further unlock the potential of AI for climate action. We’re currently looking for funding to hire staff, scale our existing activities, and launch new programs aimed at further catalyzing impactful work by bringing together the right information, organizations, and people. In particular, we want to foster better pathways to deployment for AI-for-climate work to make sure it has actual impact on the ground.

Working on climate change can be tough. What gives you hope or motivates you in the work?

To me, action is a very empowering thing. The climate is changing. The realities of that are really difficult, but we have the ability to work on creating a better future.

Are there specific people that are inspiring in this space?

Carla Gomes, the founder of the computational sustainability movement, has been a huge inspiration and mentor to me. She’s done a lot to show both that sustainability is an inherently important topic to work on, and that sustainability challenges can push the boundaries of computer science innovation in interesting and novel ways.

What would your superpower be and why?

Teleportation - I would love to be able to connect more easily with family, friends, colleagues, and others from around the world, as well as more easily travel to new places. Or, you know, maybe the ability to suck carbon dioxide out of the atmosphere and turn it into basalt - but perhaps that’s a little bit too on theme.

Are there any important lessons that you’d like to share, either from your academic path or from helping start this organization?

One is the importance of being humble and open to feedback. It can be tempting to sprint as fast as you can with the tools you have. But climate change problems are systems problems; the way you work on them interacts with considerations from many different sectors and fields. Being open to being told you might be thinking about something incorrectly and using that to strengthen your thinking is extremely important.

Relatedly, it’s important to collaborate, meet people, and expand your horizons to learn from those who were trained in different ways than you and who bring in different perspectives from their sectors, geographies, and lived experiences.

This post represents the views of its authors, and does not necessarily represent the views of Climate Change AI.