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.
Using Machine Learning and AI to Scale Up Additive Manufacturing
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
- digital twins
- generative manufacturing
- human health
- machine learning
- manufacturing workforce
- materials for energy efficiency
- renewable energy
- smart cities
- transport phenomena
In a world run by catalysts, why is optimizing them still so tough?
CatBERTa, an energy prediction Transformer model, was developed by researchers in Carnegie Mellon University’s College of Engineering in an approach to tackle molecular property prediction using machine learning.
Chop, chop: Improving food prep with the power of AI
Researchers at CMU combined two vision foundational models—models trained on large visual data sets—to help a robot arm recognize the shape and the type of fruit and vegetable slices.
Dowd Fellowship encourages ambitious student research
Four Ph.D. students in the College of Engineering have received funding to pursue research on valuable, relatively unexplored topics.
CMU College of Engineering
2023 Engineering Faculty Awards announced
The 2023 Engineering Faculty Awards highlight faculty members who have shown outstanding educational, research, and service efforts. Congratulations to all of this year’s awardees!
Training robotic arms with a hands-off approach
Researchers at Carnegie Mellon University recently trained a robotic arm with human movements generated by artificial intelligence.
More MOFs, less problems
CMU researchers introduce MOFormer, a machine learning model that can achieve higher accuracy on prediction tasks than leading models without explicitly relying on 3D atomic structures.
Barati Farimani to work on self-charging power sources
MechE’s Amir Barati Farimani was mentioned in Tech Explorist as a contributor for new research on self-charging power sources for space applications.
Machine learning fights global warming
Researchers at Carnegie Mellon University have introduced a machine learning model to identify ionic liquid molecules to make identifying candidates for CO2 storage easier.
Machine learning gets smarter to speed up drug discovery
This self-supervised learning framework is better at predicting molecular properties than other machine learning models because it can use large amounts of unlabeled data that other models can’t.
Using AI to provide the world with drinking water
Amir Barati Farimani is seeking new possibilities in water purification through using AI agents in the desalination process.
The Academic Minute
CMU Engineering week on The Academic Minute
August 16 begins Carnegie Mellon Engineering week on National Public Radio’s (NPR) The Academic Minute. Each day, a different professor will discuss interesting facets of their research. The faculty lineup includes: Daniel Armanios, Amir Barati Farimani, Bin He, Destenie Nock, and Larry Pillegi.
Using deep learning to research material transport in the brain
Understanding the causes of degenerative diseases like Alzheimer's, Huntington's, and Parkinson's will require the meticulous investigation into the complex, branch-like neurite networks of the brain. Machine learning can be an efficient, highly accurate part of this process.