Amir Barati Farimani received his Ph.D. in 2015 in Mechanical Science and Engineering from the University of Illinois at Urbana-Champaign. His Ph.D. thesis was titled “Detecting and Sensing Biological Molecules using Nanopores.” He extensively used atomistic simulations to shed light on the DNA sensing and detection physics of biological and solid state nanopores. Right after that, he joined Professor Vijay Pande’s lab at Stanford. During his post-doc, he combined machine learning and molecular dynamics to elucidate the conformational changes of G-Protein Coupled Receptors (GPCRs). He specifically was focused on Mu-Opioid Receptors to elucidate their free energy landscape and their activation mechanism and pathway.
The Barati Farimani’s lab, the Mechanical and Artificial Intelligence laboratory (MAIL), at Carnegie Mellon University is broadly interested in the application of machine learning, data science, and molecular dynamics simulations to health and bio-engineering problems. The lab is inherently a multidisciplinary group bringing together researchers with different backgrounds and interests, including mechanical, computer science, bio-engineering, physics, material, and chemical engineering. The mission is to bring the state-of-the-art machine learning algorithm to mechanical engineering. Traditional mechanical engineering paradigms use only physics-based rules and principles to model the world, which does not include the intrinsic noise/stochastic nature of the system. To this end, the lab is developing the algorithms that can infer, learn, and predict the mechanical systems based on data. These data-driven models incorporate the physics into learning algorithms to build more accurate predictive models. They use multi-scale simulation (CFD, MD, DFT) to generate the data.
2015 Ph.D., Mechanical Science and Engineering, University of Illinois at Urbana-Champaign
- additive manufacturing
- advanced manufacturing
- autonomous vehicles
- computational engineering
- computational fluid dynamics
- computational modeling
- human health
- machine learning
- materials for energy efficiency
- renewable energy
- smart cities
- transport phenomena
A more efficient way to turn saltwater into drinking water
Researchers are working on a way to transform seawater into fresh drinking water with a new, honeycombed-patterned membrane—only a few atoms thick—that uses less energy than existing methods.
Six things you should know about AI from experts in the field
Researchers from Carnegie Mellon University’s College of Engineering share what they have learned about artificial intelligence while working in the field.
AI for Engineering Summer School 2019
Amir Barati Farimani, an assistant professor of mechanical engineering, was an instructor at Autodesk's Artificial Intelligence (AI) for Engineering Summer School 2019, held August 12 to 23 in Toronto, Canada. The event brought together a talented group of engineering graduate students and industry professionals to acquire expertise in state-of-the-art AI methods and techniques, with the focus on deep learning and reinforcement learning.
Where mechanics meets artificial intelligence
Amir Barati Farimani is bringing state of the art machine learning algorithms to mechanical engineering and exploring research topics in physical phenomena, materials discovery, robotics, bioengineering, and more.