Justin Solomon Takes the Helm of Engineering Education at MIT

Associate Professor Justin Solomon of the Department of Electrical Engineering and Computer Science (EECS) has been named associate dean of engineering education for the MIT School of Engineering, effective July 1, 2026. In this role he will drive the school’s transition to AI‑augmented curricula, expand experiential learning models, and forge new pathways for industry‑engaged education.

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Research Foundations That Inform the New Role

Solomon’s research sits at the intersection of geometry, computation, and machine learning. As principal investigator of the Geometric Data Processing Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL), he has produced algorithms that power autonomous navigation, medical‑image analysis, and political redistricting. His work on shape analysis and numerical algorithms is codified in the textbook Numerical Algorithms, which is now a standard reference for computer‑science students.

These research credentials give Solomon a practical perspective on how AI can be woven into engineering fundamentals. He has already acted as a thought partner for departmental curriculum redesigns, helping faculty translate cutting‑edge research into classroom material.

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Technical Approach to AI‑Enabled Pedagogy

1. Curriculum Co‑Design with AI Modules – Solomon will partner with department chairs to embed AI concepts directly into existing courses. For example, the core class 6.C01 – Modeling with Machine Learning (co‑taught with Regina Barzilay) will serve as a template for adding machine‑learning labs to traditionally “hardware‑first” courses such as circuits and control systems.

2. Hands‑On, Project‑Centric Learning – Building on the success of the Summer Geometry Initiative, a six‑week intensive that immerses students in geometry processing, Solomon plans to scale similar short‑term bootcamps across the school. These bootcamps will combine

   • real‑world data sets (e.g., LiDAR scans for autonomous vehicles),
   • cloud‑based compute resources (MIT’s Supercloud), and
   • industry mentorship from partners such as IBM Watson AI Lab and autonomous‑driving firms.

3. AI‑Assisted Assessment – Leveraging recent advances in large language models, Solomon’s office will pilot automated feedback tools that evaluate code style, numerical stability, and documentation quality. The goal is to free faculty time for higher‑order mentorship while ensuring consistent grading standards.

4. Interdisciplinary Teaching Platforms – By creating shared “learning sandboxes” that span EECS, the Institute for Medical Engineering and Science (IMES), and the Schwarzman College of Computing, students can work on cross‑domain projects—e.g., applying geometric deep learning to cardiac‑imaging pipelines.

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Real‑World Applicability and Expected Impact

Accelerated Industry Readiness

Solomon’s plan includes new internship models where students spend a semester embedded in an industry team, contributing to a production‑grade AI pipeline. Early pilots with the MIT‑IBM Watson AI Lab will let students co‑author research papers while delivering deliverables that directly benefit the partner’s product roadmap.

Scalable Experiential Learning

The expanded bootcamp format will be open to undergraduate, graduate, and continuing‑education cohorts, allowing MIT to serve a broader talent pipeline. By standardizing the curriculum and providing cloud‑based labs, the school can replicate the model at partner universities worldwide.

Informed Policy on AI Use in Teaching

Working with the Committee on AI Use in Teaching, Learning, and Research Training, Solomon will translate the committee’s recommendations into actionable guidelines—covering data privacy, bias mitigation, and the ethical deployment of AI grading tools.

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Looking Ahead

Dean Paula T. Hammond highlighted Solomon’s interdisciplinary mindset as a key asset for the school’s evolving educational mission. With his background in both theoretical algorithm development and hands‑on teaching, Solomon is positioned to balance rigorous technical depth with accessible, experiential pedagogy.

The upcoming year will see the rollout of pilot AI‑enhanced courses, the launch of the first cross‑departmental learning sandbox, and the formalization of industry‑engaged internship pathways. If these initiatives succeed, MIT’s engineering education could become a model for how top‑tier research institutions adapt to an AI‑infused future while preserving the hands‑on, problem‑solving ethos that defines the field.

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For more information on Justin Solomon’s research, visit the Geometric Data Processing Group page or explore his publications on the CSAIL website.