AI Engineering: Digital Twins & Analytics
Online Graduate Certificate
When AI Meets Digital Twins, Systems Get Smarter.
Complex systems can no longer design, operate, or improve using static models alone. Mastering AI-driven digital twins is essential to predicting issues instead of reacting to them, continuously optimize performance, reduce risk, and cost.
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| "The coursework is challenging in all the right ways. You're constantly applying what you learn to real-world systems, and that rigor is what makes the experience so rewarding.. " Shane Michael |
2026 U.S. News & World Report Rankings
# 1
For CMU's AI graduate programs
# 7
For CMU's graduate engineering programs
# 10
For CMU's graduate civil engineering programs
Digital twin technology is rapidly evolving, and engineering leaders need the most cutting-edge, widely respected approaches to stay ahead. At CMU, you will learn the principles of digital twins as defined by expert researchers in the National Academies of Sciences, Engineering and Medicine. With comprehensive training and hands-on, industry-focused coursework, you will learn how to lead your organization with confidence through the next phase of digital transformation.
Each course will appear on your Carnegie Mellon transcript with the grade earned. To earn the certificate, you must successfully complete both courses in the program. If you are only interested in one course, however, you may complete that course only and it will show on your transcript with the grade earned.
The online Graduate Certificate in AI Engineering - Digital Twins & Analytics includes two graduate-level, credit-bearing courses taught by expert CMU faculty and features the following course progression:
| Spring 2026 Start | |
|---|---|
| Semester | Course |
| Spring 2026 | Principles of Digital Twins |
| Fall 2026 | Digital Twins and AI for Predictive Analytics |
Course List
Course Number: 12-830
Number of Units: 12 units
This course will introduce you to the concept of digital twins and digital twin modeling. Not only will you learn how to generate and use digital twin models, but you will also learn how to select an appropriate digital twin environment given specific project requirements.
In addition, you will learn how to build a business case for digital twin adoption, study the role of sensing and information flow within digital twins, and review the role of machine learning in the creation or use of digital twin technology. Finally, you will review the importance of visualization when creating impactful digital twins with different stakeholders and use cases in mind.
By the end of this course, you should be able to:
- Discuss digital twins and digital twin requirements with diverse stakeholders.
- Justify the design of a specific digital twin environment that fulfills project and application requirements.
- Represent and model physical systems within digital environments.
- Understand how information flows between the physical and digital environments.
- Identify challenges and opportunities of integrating digital twins and relevant automated data collection, processing and interpretation techniques in a professional setting.
- Build a case for digital twin adoption.
Course Number: 12-831
Number of Units: 12 units
This course explores the transformative power of digital twins to harness data-driven insights and improve decision making with predictive analytics. You will study topics like data analysis, statistical inference, and applied machine learning to understand the process of collecting, cleaning, interpreting, transforming, exploring and analyzing data generated by digital twin models.
Using this process, you will learn how to extract pertinent information, communicate insights, and support decision making based on the predictions of how engineered systems might perform under various conditions. The advantages of using visualization techniques to explore data and communicate outcomes will also be highlighted throughout the course.
By the end of this course, you should be able to:
- Plan, design, and implement projects using statistical, computational, and quantitative applied machine learning techniques
- Predict system response to support data-driven decision making using digital twins
- Discuss the ethical implications of AI-driven decision making
Meet Our World-Class Faculty
Dr. Pingbo Tang
Associate Professor of Civil and Environmental Engineering
Education: Ph.D., Carnegie Mellon University
Research Focus: Remote sensing, human systems engineering, and information modeling technology in support of the spatiotemporal analyses needed to effectively manage workspaces, constructed facilities, and civil infrastructure systems. Examining sensing and modeling methods for understanding the Human-Cyber-Physical-Systems (H-CPS) in accelerated construction and infrastructure operations (e.g., airport and power plant operations, water treatment plant control).
Dr. Mario Bergés
Professor of Civil and Environmental Engineering
Education: Ph.D., Carnegie Mellon University
Research Focus: Making our built environment more operationally efficient and robust through the use of information and communication technologies, so that it can better deal with future resource constraints and a changing environment. Bergés’ current work focuses on developing approximate inference techniques to extract useful information from sensor data coming from civil infrastructure systems, with a particular focus on buildings and energy efficiency.
Damon Weiss
Professor of Practice in Civil and Environmental Engineering
Education: M.S. Carnegie Mellon University
Research Interests: Advanced infrastructure systems; climate-resilient environmental systems and technologies; smart infrastructure; construction and building technology; and more. Weiss is also the co-founder of Ethos Collaborative, a Pittsburgh-based civil and ecological engineering firm, where he bridges the tactile, human-scaled aspects of infrastructure with digital twin systems, AI models, and geospatial analytics to elevate decision-making, performance, and public value.
Is this the right program for you?
Let’s face it, pursuing any kind of advanced training is an investment of your time, energy and resources. Before you begin your application, take a moment to review the program requirements below.
Successful applicants will have:
A bachelor’s degree in civil engineering or a related field
While the courses in this certificate focus on digital twins from the civil engineering perspective, applicants with engineering degrees in closely related fields such as mechanical engineering and aerospace engineering are also encouraged to apply. Degrees in complementary STEM or other related fields (such as construction, architecture, manufacturing, math, or physics) will also be considered as long as the appropriate math and programming skills are demonstrated in the application.
A future-focused mindset
Successful applicants will be forward-thinking innovators inspired by the power of digitalization and the potential impact it could have on their industry. Individuals who are eager to lead the digital transformation in their organization will feel empowered by the coursework in this certificate and equipped for the emerging jobs in their field.
Relevant work experience
Ideally, applicants will have some relevant work experience in either civil engineering or a related field. They might also have experience collaborating on a smart city project for their organization.
Proficiency in mathematical concepts
Applicants should be comfortable with mathematical concepts including linear algebra, calculus, statistics and probability theory.
Proficiency in Python or other programming languages
Applicants are expected to have some familiarity with programming e.g. from undergraduate-level coursework or engineering projects that require coding and programming.
Application Checklist
Ready to apply? Here’s what you’ll need to complete the admissions process:
Submit your resume/CV
We’d like to learn more about your employment history, academic background, technical skills, and professional achievements. Submit a 1 to 2 page resume or CV showcasing your experience.
Submit your transcripts
Submit an unofficial copy of your transcript for each school you attended. Transcripts must include your name, the name of the college or university, the degree awarded (along with the conferral date), as well as the grade earned for each course. Email your transcripts directly to apply@online.cmu.edu.
Please note: former Carnegie Mellon students and/or alumni can request a copy of their CMU transcript from The Hub.
Upload a statement of purpose
Tell us your professional story. Where have you been, and where do you hope to go? In 500 words or less, explain why you are applying to this program and what you hope to gain from completing the coursework.
Submit your TOEFL, IELTS, or DuoLingo test scores (if applicable)
An official TOEFL, IELTS, or DuoLingo test is required for non-native English speakers. However, this requirement will be waived for applicants who either completed an in-residence bachelor’s, master’s, or doctoral degree program in the United Kingdom, United States, or Canada (excluding Quebec) or have at least three years of professional work experience using English as their primary language as outlined in your resume.
So, what is the investment per course?
Below is a tuition breakdown for the 2025/2026 academic year:
| Semester | Course | Units | Investment |
|---|---|---|---|
| Spring 2026 | Principles of Digital Twins | 12 units | $8,484 |
| Fall 2026 | Digital Twins and AI for Predictive Analytics | 12 units | $8,484 |
| Total Investment | $16,968 | ||
Additional Fees & Notes: A $245 technology fee will be assessed each semester (subject to change). The rates above are for the 2025/2026 academic year only. If the certificate is not completed within that time frame, tuition may increase slightly for the following academic year.
Scholarship Opportunities
All students who submit their application by the priority deadline will be considered for a partial scholarship to help offset the cost of tuition. If admitted to the program, the amount of your award will be communicated with your admissions letter.
Students who submit after the priority deadline may be eligible for any remaining scholarships that are still available. But we highly encourage you to apply by the priority deadline if you are interested in a scholarship award.
In addition, Carnegie Mellon alumni are eligible for a scholarship to the Graduate Certificate in AI Engineering - Digital Twins & Analytics worth up to 20% of tuition. Indicate your alumni status within the application to be eligible.
About the Department of Civil and Environmental Engineering
The Graduate Certificate in AI Engineering - Digital Twins & Analytics is offered by the Department of Civil & Environmental Engineering (CEE), which is housed within CMU’s highly-ranked College of Engineering. CEE faculty members are highly distinguished in their field and many of them are currently collaborating on high-profile projects with digital twin technology. Check out some of their work:
- Intelligent maintenance for military aviation assets (funded by the U.S. Army Research Laboratory)
- Environmental control and life support systems (ECLSS) for deep space missions (funded by NASA)
- Sustainable and equitable food delivery models
- AI for the national aviation system (part of a NASA University Led Initiative)
About the College of Engineering
Carnegie Mellon’s College of Engineering faculty work is at the forefront of research and innovation, applying deep expertise to the world’s most pressing challenges. The College of Engineering leads work in a diverse and expanding array of fields, from autonomous systems and clean energy to bioengineering and advanced manufacturing.
Engineering faculty design learning experiences with intention. Courses are grounded in real-world applications, shaped by research on how people learn best, and focused on giving students the tools to lead in fast-changing industries.
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