The Massachusetts Institute of Technology has appointed Justin Solomon, an associate professor in the Department of Electrical Engineering and Computer Science, as associate dean of engineering education in the School of Engineering, a move that signals a strengthening commitment to reshaping how engineering is taught in an era increasingly defined by artificial intelligence. Effective July 1, Solomon steps into a role that positions him at the center of MIT’s effort to rethink pedagogical models, integrate AI across curricula, and build new bridges between academia and industry. The appointment reflects not just a personnel change but a strategic recalibration of engineering education at one of the world’s most influential institutions.
A Mandate for Transformation: What the New Role Entails for Justin Solomon as Associate Dean of Engineering Education
The responsibilities assigned to Solomon are broad and deliberately forward-looking. He will focus on advancing innovation in engineering education across the school, with a particular emphasis on developing new pedagogical approaches suited to an AI-enabled world. This includes exploring experiential, hands-on, and other modes of learning that move beyond traditional lecture-based instruction. Working closely with academic departments, Solomon will serve as a thought partner in integrating AI into curricula and will help facilitate interdisciplinary and shared teaching opportunities across departments and other schools. He will also play a key role in implementing relevant recommendations from the Committee on AI Use in Teaching, Learning, and Research Training, a body whose work has become increasingly central to MIT’s academic strategy.
The role extends beyond curriculum design. Solomon will explore opportunities to build industry collaborations, including new models for internships and industry-engaged learning on campus. Collaborating with department heads and the School of Engineering leadership team, he will also support faculty in designing new courses and evolving existing programs to meet emerging opportunities in engineering. This combination of responsibilities — curriculum innovation, AI integration, interdisciplinary coordination, and industry engagement — makes the associate dean position a pivotal one for the school’s future direction.
Why This Appointment Matters: Context and Strategic Significance
Justin Solomon’s appointment comes at a moment when engineering education faces pressures from multiple directions. The rapid advancement of AI is reshaping what engineers need to know and how they should learn it. Traditional curricula, often structured around fixed disciplinary boundaries, are struggling to keep pace with the cross-cutting nature of modern engineering challenges. At the same time, employers increasingly demand graduates who can work across domains, apply computational thinking, and collaborate with colleagues from diverse backgrounds.
MIT’s School of Engineering, led by Dean Paula T. Hammond, has been proactive in addressing these shifts. Hammond, an Institute Professor and a leading figure in chemical engineering, has championed the integration of AI across engineering disciplines and has called for new models of education that prepare students for a rapidly changing technological landscape. Solomon’s appointment is a concrete step in that direction, placing a faculty member with deep expertise in both AI and interdisciplinary collaboration into a role specifically designed to drive curricular innovation.
The AI Imperative: Integrating Machine Learning into Engineering Curricula
A central question that Justin Solomon will help answer is how to integrate AI into engineering education in a way that is meaningful and sustainable. Many universities have rushed to add courses on machine learning or data science, but the challenge is deeper than simply offering new electives. It involves rethinking core engineering courses to incorporate computational thinking, embedding AI tools into design and laboratory experiences, and ensuring that students understand both the power and the limitations of these methods.
Solomon is well-positioned to lead this effort. His own teaching includes 6.C01 (Modeling with Machine Learning: From Algorithms to Applications), a core class in MIT’s Common Ground for Computing program that he co-teaches with Regina Barzilay, the Delta Electronics Professor in EECS and an affiliate faculty member at the Institute for Medical Engineering and Science. The Common Ground for Computing initiative is itself a model for interdisciplinary computing education, designed to ensure that every MIT student, regardless of major, gains foundational competence in computing and data science. Solomon’s work in this program gives him firsthand experience with the practical challenges of integrating AI into a broad-based curriculum.
What does effective AI integration look like in practice? It means helping students understand not just how to use AI tools but how to evaluate them critically, how to recognize when AI is the right solution and when it is not, and how to design systems that are robust, fair, and transparent. It also means preparing students for a world where AI is embedded in virtually every engineering discipline, from mechanical design to biomedical engineering to environmental systems. Solomon’s role will involve working with departments across the school to identify opportunities for integration and to develop shared resources and best practices.
Justin Solomon’s Background: A Profile in Interdisciplinary Excellence
To understand why Solomon is the right person for this role, it helps to look at his trajectory. He joined the MIT faculty in 2016 after holding an NSF Mathematical Sciences Postdoctoral Research Fellowship in Princeton University’s Program in Applied and Computational Mathematics. He earned his bachelor’s, master’s, and doctoral degrees from Stanford University, and while at Stanford, he also worked as a research assistant at Pixar Animation Studios. This combination of academic rigor and industry experience — particularly at a company known for pushing the boundaries of computer graphics and computation — gives him a practical perspective on how engineering education connects to real-world applications.
Solomon’s research sits at the intersection of geometry and computation, with applications spanning computer graphics, autonomous navigation, political redistricting, physical simulation, 3D modeling, and medical imaging. He is a principal investigator at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), where he leads the Geometric Data Processing Group. He is also a core faculty member of the MIT-IBM Watson AI Lab, contributing to research that advances the foundations and applications of artificial intelligence. This breadth of research interests — from the theoretical foundations of geometric computing to applied problems in navigation and medical imaging — gives him a broad view of the engineering landscape.
Recognitions and Awards: A Track Record of Excellence
Solomon’s contributions have been recognized with numerous distinctions. He received the 2023 Harold E. Edgerton Faculty Achievement Award for exceptional contributions in teaching, research, and service. The Edgerton Award is one of MIT’s most prestigious faculty honors, and it speaks to Solomon’s impact across multiple dimensions of academic life. In 2025, he was named a Schmidt Polymath, a program supported by Schmidt Sciences that fosters interdisciplinary research across fields such as acoustics and climate that rely on large-scale simulation of physical systems. The Polymath designation is particularly relevant to his new role, as it underscores his ability to work across disciplinary boundaries and to apply computational methods to diverse problems.
His teaching has also been recognized with the EECS Outstanding Educator Award and the Burgess (1952) and Elizabeth Jamieson Prize for Excellence in Teaching. He is the author of “Numerical Algorithms,” a textbook that presents a modern approach to numerical analysis for computer science students. The textbook reflects his commitment to making rigorous computational methods accessible to a new generation of engineers and computer scientists.
The Summer Geometry Initiative: A Model for Experiential Learning
One of Justin Solomon’s most notable educational initiatives is the Summer Geometry Initiative (SGI), a six-week program he founded that introduces students to geometry processing through intensive training, collaboration, and research experiences. The program is designed to address a gap in the educational landscape: while geometry processing is a fundamental area of computer science with applications in graphics, vision, simulation, and manufacturing, it is not always covered in depth in standard curricula. SGI provides an immersive experience that combines lectures, hands-on projects, and mentored research, giving participants the skills and confidence to pursue work in the field.
The Summer Geometry Initiative serves as a model for the kind of experiential learning that Solomon will now work to promote across the School of Engineering. It demonstrates the value of intensive, project-based programs that bring together students from different backgrounds and give them the opportunity to tackle real problems. It also shows how a focused initiative can build community and create pathways into specialized fields. As associate dean, Solomon will be in a position to support similar initiatives in other areas of engineering, helping to scale the model of intensive, hands-on learning across the school.
Industry Engagement and the Future of Engineering Education
A key component of Justin Solomon’s new role is exploring opportunities to build industry collaborations, including new models for internships and industry-engaged learning on campus. This is an area where Solomon’s own experience is directly relevant. His time at Pixar Animation Studios gave him insight into how cutting-edge research translates into commercial products and how academic training prepares students for careers in technology-intensive industries. The challenge he now faces is to create systematic mechanisms for industry engagement that benefit students across the school, not just in a few fields.
Traditional internship models, where students spend a summer working at a company, remain valuable, but there is growing interest in more flexible and integrated forms of industry engagement. These include project-based courses sponsored by companies, co-op programs that alternate academic terms with work terms, and on-campus research collaborations that involve industry partners. Solomon will work with department heads and the School of Engineering leadership team to design and implement new models that align with MIT’s educational mission and that prepare students for the realities of modern engineering practice.
The question of how to effectively integrate industry experience into engineering education is not new, but it has taken on new urgency as the pace of technological change accelerates. Companies increasingly need engineers who can work with AI tools, who understand data-driven decision-making, and who can collaborate across disciplines. At the same time, the skills that students need are evolving so rapidly that traditional curricula struggle to keep up. Industry engagement offers a way to bridge this gap, giving students exposure to current practices and challenges while also providing feedback that can inform curriculum development.
Teaching Philosophy and Practical Impact
Justin Solomon’s approach to teaching is characterized by a commitment to rigor, accessibility, and relevance. In his courses at MIT — which include 6.7350 (Numerical Algorithms for Computing and Machine Learning) and 6.8410 (Shape Analysis) — he emphasizes not just theoretical foundations but also practical implementation. Students are expected to understand the mathematics behind algorithms and also to be able to implement them and apply them to real problems. This combination of theory and practice is essential for engineers who will work in a world where computation is ubiquitous.
His textbook “Numerical Algorithms” exemplifies this philosophy. The book takes a modern approach to numerical analysis, one that is designed for computer science students rather than for mathematics majors. It emphasizes algorithms that are actually used in practice, it provides clear explanations of why they work, and it includes code examples that students can run and modify. The book has been adopted at institutions around the world, and it reflects Solomon’s belief that good teaching means meeting students where they are and giving them the tools they need to succeed.
What does this mean for his new role as associate dean? It suggests that he will approach curriculum reform with a focus on practical outcomes. He is likely to prioritize changes that have a direct impact on student learning and that prepare students for the careers they will pursue. He is also likely to emphasize the importance of assessment and feedback, using data to understand what works and what does not. His track record as an educator suggests that he will be a thoughtful and evidence-based leader, one who is willing to experiment but who is also grounded in what actually helps students learn.
The Committee on AI Use in Teaching, Learning, and Research Training
One of Solomon’s specific responsibilities will be helping the School of Engineering implement relevant recommendations from the Committee on AI Use in Teaching, Learning, and Research Training. This committee, established by MIT, has been tasked with developing guidelines and best practices for the use of AI in educational settings. Its work touches on questions that are central to the future of higher education: How should AI tools be used in the classroom? What are the ethical implications of relying on AI for assessment and feedback? How can AI be used to personalize learning without compromising academic integrity? How should students be taught to use AI responsibly?
The recommendations from this committee are likely to have a significant impact on how courses are designed and taught across MIT. Solomon’s role will be to translate those recommendations into concrete actions within the School of Engineering, working with departments and individual faculty members to adapt their teaching practices. This is a complex task that requires not just a vision for what is possible but also a practical understanding of how change happens in a large organization. Solomon’s experience co-teaching a large core course and his work with the Common Ground for Computing program give him a foundation for this kind of work.
What are the key challenges that the committee’s recommendations will need to address? One is the risk of over-reliance on AI tools, which can lead to students missing out on foundational learning. Another is the need to ensure that AI tools are used equitably, so that all students benefit regardless of their background or prior experience. A third is the need to prepare faculty to use AI effectively, which will require investment in professional development and support. Solomon will need to navigate these challenges while also keeping sight of the opportunities that AI offers for enhancing learning, such as personalized tutoring, automated feedback, and data-driven insights into student performance.
Broader Implications for Engineering Education
The appointment of Justin Solomon as associate dean of engineering education at MIT has implications that extend beyond Cambridge. MIT has long been a bellwether for engineering education globally, and changes initiated here are often watched closely by other institutions. If Solomon succeeds in developing new models for AI integration, experiential learning, and industry engagement, those models are likely to be adopted and adapted by universities around the world.
Part of what makes this appointment noteworthy is the explicit focus on the “AI-enabled world.” Many universities have added AI courses or modules, but few have undertaken a systematic rethinking of their curricula in light of AI. MIT’s approach — placing a faculty member with deep AI expertise into a leadership role focused on education — suggests a recognition that AI is not just a topic to be added to the curriculum but a force that transforms how engineering is practiced and taught. This is a distinction that other institutions would do well to note.
Another important dimension is the emphasis on interdisciplinary and shared teaching opportunities. Engineering problems increasingly cross disciplinary boundaries, and students need to learn how to collaborate with colleagues who have different expertise. Solomon’s own research, which spans computer graphics, autonomous navigation, medical imaging, and political redistricting, exemplifies the kind of cross-disciplinary thinking that he will now work to promote across the school. His experience leading the Geometric Data Processing Group at CSAIL and his involvement with the MIT-IBM Watson AI Lab give him a deep understanding of how interdisciplinary collaboration works in practice.
Looking at the Path Ahead
As Justin Solomon prepares to take on his new role, the engineering education landscape he will help shape is already in motion. The integration of AI into curricula is not a future possibility but a present reality, and the challenge is to manage this integration thoughtfully and strategically. The move toward experiential and hands-on learning reflects a broader shift in higher education, driven by evidence that active learning produces better outcomes than passive lecture-based instruction. The push for industry engagement responds to the needs of employers and to the aspirations of students who want their education to be directly relevant to their careers.
Solomon’s appointment brings together these threads under a single leadership role, giving the School of Engineering a coordinated approach to educational innovation. The success of this approach will depend on many factors: the quality of the recommendations from the Committee on AI Use in Teaching, Learning, and Research Training; the willingness of faculty to experiment with new pedagogical models; the availability of resources to support change; and the ability of the school to learn from both successes and failures. But with a leader who combines deep technical expertise, a track record of educational innovation, and a commitment to interdisciplinary collaboration, the prospects are strong.
For students, faculty, and the broader engineering community, the message is clear: engineering education is not static, and MIT intends to be at the forefront of its evolution. Justin Solomon’s role as associate dean of engineering education is a statement of intent, and the work he undertakes in the coming years will help define what it means to study engineering in an AI-enabled world. The appointment is a reminder that the most important innovations in engineering are not always about technology — sometimes they are about how we prepare the next generation of engineers to use that technology wisely and effectively.
