Arsam Aryandoust

(he/him)

I am a Postdoctoral Researcher at the MIT LIDS | Laboratory of Information and Decision Systems, where my focus lies at the intersection of Artificial Intelligence (AI) and Climate Change. Before joining MIT, I completed my doctoral studies on AI for the renewable energy transition at ETH Zurich, a topic that continues to inform my current research endeavors.

Addressing Climate Change isn't just a scientific challenge but a profound moral obligation. It's about preserving billions of lives, safeguarding our ecosystems, and ensuring the continuity and flourishing of human society in its rich complexity. I firmly believe that AI must be harnessed if we want to solve Climate Change rapidly. Nevertheless, the number of experts currently engaged in this vital work compared with other application domains is alarmingly low. I am therefore passionate about encouraging more talented engineers and scientists to join this global effort.

My approach is to develop safe and secure AI software that can have an immediate impact in our battle against Climate Change. I believe that these solutions require a holistic and interdisciplinary approach, which makes me excited to be located at the new MIT Schwarzman College of Computing.

CV  /  Scholar  /  Github  /  Python  /  Docker  /  Dataverse

Research

My research interest covers the broad field of developing and enhancing AI for tackling Climate Change. My focus, however, is currently on renewable power systems due to their urgency for mitigating greenhouse gas emissions. Recently, I have also started to learn more about the applications of AI in sustainable material discovery and negative emission technologies.

Artificial Intelligence and its subfield of Machine Learning are excellent tools for expressing the world in numbers, discovering their patterns, and designing solutions based on these in higher dimensions than visible to our human eyes and for higher complexity than understandable for us humans otherwise. We show that this also holds for transforming energy and tackling climate change.

We develop an active deep learning algorithm that makes better spatio-temporal predictions of electric load with less data compared to traditional passive deep learning algorithms.

We develop a Hidden Markov Model that is able to infer accurate city-scale car traffic and parking density maps from Origin-Destination travel changes.

We demonstrate why Demand Response technology has remarkable potentials for providing electricity system services on short time-scales, but not on long time-scales.

Credits for the design of this website go to Jon Barron for making the source code of his website publicly available.