AI and Machine Learning

In a world of increasingly complex challenges, our experts are using artificial&#160; technologies and machine learning as integral tools in nearly every area of mechanical engineering.

Machine learning and AI

Safe AI

The idea of self-driving cars or robot aids to help around the house was once a dream of the future. Today, these things are a reality and our faculty are using machine learning to make human-robot interaction safer, and smarter.

Faculty involved: Jonathan Cagan, Amir Barati Farimani, Eni Halilaj, Philip LeDuc, Conrad Tucker, Ding Zhao

Human health

Machine learning can help us to improve human health in many ways, like predicting and preventing musculoskeletal injuries, personalizing rehabilitation, and developing antibodies to thwart quickly-mutating pathogens.

It can also help us to enhance the analysis of medical imaging, model the complex geometry of neurons, design synthetic biological systems, and provide a patient's vital signs to a medical team before arriving at the hospital. Our mechanical engineers bring their unique expertise to these biomedical challenges.

Faculty involved: Jonathan Cagan, Amir Barati Farimani, Eni Halilaj, Philip LeDuc, Conrad Tucker, Jessica Zhang, Inseung Kang

Material discovery for energy applications

Safer, more robust batteries... new ceramic polymer hybrid materials that require less energy to produce... heat transport prediction and improved energy conversion... These are some of the areas our faculty are investigating with the assistance of machine learning tools.

The computational power of machine learning can screen and reject millions of possible combinations that allow researchers to hone in on the best solutions. This process would otherwise take decades of trial and error in a laboratory setting.

Faculty involved: Amir Barati Farimani, B. Reeja Jayan, Shawn Litster

Design and manufacturing

With artificial intelligence and machine learning, our experts are transforming and optimizing design and manufacturing. Here are a few examples: creating new concepts for cars and aircraft with design DNA; using computer vision to detect flaws during 3D printing; turning static drawings into active simulations with smart design tools; and developing virtual reality engineering simulations to place students in an interactive, immersive manufacturing environment.

Faculty involved: Amir Barati Farimani, Jack Beuth, Jonathan Cagan, Burak Kara, Christopher McComb, Sneha Prabha Narra, Kenji Shimada, Conrad Tucker, Jessica Zhang, Inseung Kang

Infrastructure, smart cities, and society

Inspecting canals and power plants with drones, mapping air pollution and calibrating low-cost air quality sensors, desalinating water with new, energy-efficient materials, and using social media data to predict threats... these are a few examples of how machine learning can play a role in maintaining and improving the infrastructure in our communities.

Faculty involved: Amir Barati Farimani, Albert Presto, Kenji Shimada, Conrad Tucker

People

Learn who in Mechanical Engineering is working in maching learning and artifical intelligence.

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The trend is your friend … and so is machine learning Opens in new window

Grasping both general market trends and finer details, a new, hybrid machine learning model from researchers in the Department of Mechanical Engineering predicts financial market volatility with increased accuracy.

Multimodal machine learning model increases accuracy Opens in new window

Researchers have developed a novel ML model combining graph neural networks with transformer-based language models to predict adsorption energy of catalyst systems.

Putting the ghost in the machine Opens in new window

To build AI teammates that better reflect human designers, Chris McComb is emulating cognitive styles in large language models.

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Safe AI for safe streets for all Opens in new window

Ding Zhao is teaming up with the City of Pittsburgh to improve road safety and eliminate traffic fatalities using AI.

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Trustworthy artificial intelligence for the greater good Opens in new window

MechE’s Ding Zhao trains students to enhance the affordability, reliability, and inclusivity of AI through the Trustworthy AI Autonomy course.

Introducing the incoming CBI fellows Opens in new window

CBI is proud to announce a bright new cohort of CBI Fellows! Meet Jinfeng Lou, Lisa Masserova, Evan Walter Clark Spotte-Smith, Qiaosi Wang, and Xiang Yue!

New course harnesses AI to kindle creativity Opens in new window

New course, AI for Humanities, offers a unique perspective on how AI can revolutionize our perception and interaction with creative expressions.

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AI accelerates process design for 3D printing metal alloys Opens in new window

Researchers use AI and high-speed in-situ imaging to optimize process parameters for 3D printing metal alloys.

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RoboTool enables creative tool use in—you guessed it—robots Opens in new window

Large language models enable robots to “brainstorm” creative tool use and perform seemingly impossible tasks.

Head-to-head: Human vs. AI-human teams Opens in new window

New research from Carnegie Mellon University’s Department of Mechanical Engineering and the Human+AI Design Initiative underlines the adage “teamwork makes the dream work,” especially when it comes to human-AI collaboration.

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Chop, chop: Improving food prep with the power of AI Opens in new window

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.

Predicting portable power Opens in new window

Researchers at CMU and the University of Texas at Austin have designed a model that accurately predicts a battery’s charge curve, which could impact the safety and reliability of electric vehicles.

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Cagan presents future of AI-human teaming at congressional briefing Opens in new window

Jon Cagan participated in a congressional briefing on AI in the workforce, focusing on the future of AI-human teaming.

Generating better mutli-lattice transitions for manufacturing

How can engineers produce smooth transitions between lattice cells in complex structures? Combining additive manufacturing and machine learning to look at latent space endpoints may help.

Move over metals, plastics can be thermally conductive too Opens in new window

An interdisciplinary team led by Jon Malen has received funding from the Department of Defense to discover switchable high thermal conductivity polymers.

Virtual reality partners become real world neighbors Opens in new window

In July, the YKK AP Technologies Lab opened at Mill 19. Their focus on the development of a virtual factory is well aligned with the digital twin work underway at the Manufacturing Futures Institute.

Training robotic arms with a hands-off approach Opens in new window

Researchers at Carnegie Mellon University recently trained a robotic arm with human movements generated by artificial intelligence.

2023 Dean’s Early Career Fellows announced Opens in new window

Xi (Charlie) Ren, Victoria Webster-Wood, and Ding Zhao have received the honor for their contributions to their respective fields.

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.

Machine learning fights global warming Opens in new window

Researchers at Carnegie Mellon University have introduced a machine learning model to identify ionic liquid molecules to make identifying candidates for CO2 storage easier.

U.S. Chamber AI Commission releases final report Opens in new window

Conrad Tucker served as a commissioner on the US Chamber AI Commission aimed to position the US as a leader in responsible AI development and deployment. The commission’s final report has been released.

AI Engineering will change the world around us Opens in new window

Carnegie Mellon University’s College of Engineering is equipping next generation engineers with the ability to integrate AI into the constraints of the engineering problem by way of seven new master’s degrees in AI Engineering.

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DfAI: The missing piece of Artificial Intelligence Engineering Opens in new window

Breakthrough improvements in how industries develop new technology using AI in engineering design has a starting point thanks to a framework developed by researchers at Carnegie Mellon and Penn State University.

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Jon Cagan named head of Mechanical Engineering Opens in new window

Jon Cagan will be the new head of the Department of Mechanical Engineering at Carnegie Mellon University, effective November 1, 2022.

Robots that can learn to safely navigate warehouses Opens in new window

Advances in chips, sensors, and AI algorithms are enabling robots to continuously learn how to plan routes, avoid obstructions, and operate safely in large dynamic warehouse environments.

Students’ ingenuity automates scientific research Opens in new window

In an effort to modernize scientific research, CMU students used only commercial parts and the cardboard boxes they came in to build a modular robot able to autonomously conduct experiments.

Tucker appointed to AI commission Opens in new window

Conrad Tucker will serve on the U.S. Chamber of Commerce’s newly-launched Commission on Artificial Intelligence Competition, Inclusion, and Innovation.

Automating engineering’s ideal manager Opens in new window

A recent paper by a collaboration of CMU mechanical engineering and psychology researchers explored the use of artificial intelligence as a process manager for human design teams.

A call to integrate AI and STEM education Opens in new window

Artificial intelligence (AI) has penetrated the job landscape and is of great interest to educators. As we develop methods for integrating AI and STEM education to transform the U.S. workforce, we also need to broaden the public’s awareness of what AI can do now.

Artificial Intelligence you can trust Opens in new window

Professor Ding Zhao developed a new course that trains engineering students how to make artificial intelligence trustworthy.

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.

Designing for a brighter future

New MechE faculty member and alumnus Christopher McComb wants to develop successful human-machine teams, create a student-centered learning environment, and give designers computational superpowers.

Pushing through nanopores: Genetic sequencing with MXene Opens in new window

MXene—a single layer, two-dimensional nanomaterial—shows potential for solid-state, nanopore-based DNA sequencing. This could lead to efficient, rapid diagnostics and personalized medicine.

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Virtually awarded

MechE graduate students earned accolades at the department’s annual research symposium poster sessions. This year's events were virtual through Gather Town, an interactive, video-chat world.

AI-fueled batteries Opens in new window

A team of CMU mechanical engineering and materials science and engineering researchers are using AI to optimize battery electrolyte designs, and they found new electrolytes that researchers hadn’t thought of.

Outsmarting a virus Opens in new window

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?

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Transforming the toolbox

The true impact of AI and machine learning occurs when these technologies are translated into the physical world. This is the role of mechanical engineers—and we’re leading the way.

AI learns to design Opens in new window

AI agents imitate engineers to construct effective new designs using visual cues like humans do.

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Six things you should know about AI from experts in the field Opens in new window

Researchers from Carnegie Mellon University’s College of Engineering share what they have learned about artificial intelligence while working in the field.

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Bridging the physical and digital worlds with human-machine learning

New MechE faculty member Conrad Tucker sees machines as tools that can help to enhance human capabilities.

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.

Smarter electronics design through machine learning

Burak Kara is collaborating with Cadence Design Systems, Inc. and NVIDIA on applying advanced machine learning techniques to develop integrated and intelligent design system flows.

Using drones in agriculture and irrigation Opens in new window

Professor of Mechanical Engineering Kenji Shimada and his team of researchers are using drone technology to help detect and restore damaged water canals in Japan that are critical for the agricultural economy.

Lightening the load Opens in new window

Kate Whitefoot and Burak Kara are developing methods allowing manufacturers to redesign multiple parts into one continuous part using 3-D printing.

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Machine learning to develop safer batteries

Safer batteries could be made with solid electrolytes, but analyzing the properties of thousands of materials would take years. Can machine learning help researchers speed up the process?

Exploring high-entropy alloys

Collaborators De Boer, Poczos, and Webler receive a Manufacturing Futures Initiative (MFI) award to explore high-entropy alloys, a new class of metal alloy.

Zhang and Yao earn MFI award for 4D printing

Jessica Zhang and Lining Yao earn a Manufacturing Futures Initiative (MFI) award to develop fiber-reinforced composite structures through a process called 4D printing.

Using AI to solve real-world problems Opens in new window

In Levent Burak Kara’s project-based graduate course, students applied their skills in artificial intelligence and machine learning to solve real-world problems outside the classroom.

The intersection of 3-D printing and machine learning Opens in new window

Self-correcting 3-D printers may soon become a reality, as Jack Beuth and Luke Scime have combined machine learning with 3-D printing to enable real time process monitoring.

Looking through the eyes of a 3-D printer Opens in new window

Luke Scime uses machine vision to optimize the metal 3-D printing process.

Synthetic muscle gets its punch from design method Opens in new window

Carnegie Mellon researchers design building blocks for synthetic muscle using computational method.

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Advancing additive manufacturing in aerospace Opens in new window

Carnegie Mellon engineers are leading a collaborative initiative to develop computational technologies for optimal design and fabrication of complex core structures for the aerospace industry.

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