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.
A new material for water desalination
The Role of AI and Machine Learning in Mechanical Engineering
The intersection of AI & Mechanical Engineering
- 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
Outsmarting a virus
Can machine learning help us to accelerate the antibody discovery process to fight highly infectious viral diseases like COVID-19 and save thousands of lives?
Capturing physiological signals with an app and a tap
For someone suffering from an acute illness or a medical emergency, the nearest healthcare facility can be several hours away in some parts of the world. Researchers are working to bridge this gap in patient care with machine learning.
Barati Farimani develops new water-desalination material
MechE’s Amir Barati Farimani was featured in Popular Mechanics for developing a new material to improve the process of water desalination. The team’s metal organic framework is micro-thin and was shown in simulations to perform water desalination better than the traditional membrane method.
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.