Amir Barati Farimani
Russell V. Trader Associate Professor, Mechanical Engineering
Courtesy Appointments, Biomedical Engineering, Chemical Engineering, Machine Learning
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
Education
2015 Ph.D., Mechanical Science and Engineering, University of Illinois at Urbana-Champaign
Research Interests
- additive manufacturing
- advanced manufacturing
- autonomous vehicles
- bioengineering
- computational engineering
- computational fluid dynamics
- computational modeling
- digital twins
- energy
- generative manufacturing
- human health
- infrastructure
- machine learning
- manufacturing workforce
- materials for energy efficiency
- micro/nanoengineering
- renewable energy
- robotics
- smart cities
- transport phenomena
Media mentions
CMU Engineering
Mission accomplished
CMU engineers and scientists undertook more than 45 research projects to develop artificial intelligence approaches to enable the use of metal additive manufacturing for the U.S. Army.
CMU Engineering
Transformer-based models for identifying alloy properties
Researchers develop AlloyBert, a modeling tool designed to predict the properties of alloys.
CMU Engineering
Multimodal machine learning model increases accuracy
Researchers have developed a novel ML model combining graph neural networks with transformer-based language models to predict adsorption energy of catalyst systems.
CMU Engineering
Engineering faculty awarded professorships
Carnegie Mellon University has awarded professorships to five exceptional faculty members in the College of Engineering.