MIT grants tenure to 10 School of Engineering faculty members
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MIT grants tenure to 10 School of Engineering faculty members

Robotics Reporter
4 min read

MIT’s 2026 tenure cohort in engineering brings together faculty working on AI, aerospace materials, cloud systems, combustion, plant genetics, plasma physics, brain imaging, microbiomes, quantum measurement, and fluid simulation.

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MIT granted tenure to 10 faculty members in the School of Engineering across five units, the institute said June 15.

The cohort includes faculty from Aeronautics and Astronautics, Civil and Environmental Engineering, Electrical Engineering and Computer Science, Mechanical Engineering, and the Institute for Medical Engineering and Sciences. Their work connects core engineering research with applications in AI systems, aviation, energy, medicine, quantum computing, and climate resilience.

Paula T. Hammond, MIT’s dean of engineering, said the promotions recognize research impact, teaching, and mentoring.

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Jacob Andreas, an associate professor in Electrical Engineering and Computer Science and a member of CSAIL, studies natural language processing and AI. Andreas examines language learning as a computational problem and builds systems that learn from human guidance. That work matters as engineers push AI systems toward tools that can follow instruction, handle ambiguity, and explain their choices to users.

Zachary Cordero, the Esther and Harold E. Edgerton Associate Professor in Aeronautics and Astronautics, serves as associate director of the MIT Gas Turbine Laboratory. Cordero develops advanced materials, manufacturing methods, and structures for aviation and space systems. His research targets high-temperature platforms, where engineers need materials that retain strength under thermal loads that limit turbine performance and spacecraft design.

Christina Delimitrou, the KDD Career Development Professor in Communications and Technology and an associate professor in EECS, works at the intersection of computer architecture and computer systems. She applies machine learning to cloud design and management, including resource allocation and performance control. Cloud operators face a hard constraint: modern workloads share infrastructure, compete for compute, and change during operation. Delimitrou’s research gives operators methods to predict those demands and reduce waste without breaking service targets.

Sili Deng, the Doherty Career Development Professor in Ocean Utilization and an associate professor in Mechanical Engineering, combines experiments with scientific machine learning to study reacting systems. Her group works on sustainable energy, advanced manufacturing, and climate-resilient technologies. Chemical reactions drive combustion, materials synthesis, and carbon conversion, but engineers still struggle to model them across scales. Deng’s approach links measured behavior with computation so teams can design systems with fewer trial runs.

David Des Marais, the Amgen Career Development Professor in Civil and Environmental Engineering, studies plant-environment interaction. His lab uses molecular, quantitative, and population genetics to identify how plants respond to environmental signals. Crop and ecosystem models need better biological detail as heat, drought, and shifting seasons alter growing conditions. Des Marais’ work gives researchers a clearer view of plant traits that could support adaptation.

Carmen Guerra-Garcia, the Esther and Harold E. Edgerton Associate Professor in Aeronautics and Astronautics, directs the Aerospace Plasma Group. She studies aerospace engineering, low-temperature plasma technologies, and gas discharge physics. Her group works on electrical discharges in flowing gases, plasma-assisted combustion and chemical conversion, and lightning protection for aircraft. Those areas connect laboratory plasma physics with aviation problems that affect emissions and aircraft safety.

Laura Lewis, the Athinoula A. Martinos Associate Professor in EECS and the Institute for Medical Engineering and Sciences, develops methods to analyze multimodal neuroimaging data. She focuses on fast fMRI, EEG, and PET, with sleep as a major application. Brain imaging tools often trade spatial detail, temporal resolution, and physiological specificity. Lewis’ work helps researchers measure brain activity that older methods missed or blurred.

Tami Lieberman, the Hermann L.F. von Helmholtz Career Development Professor in Civil and Environmental Engineering and IMES, studies the human microbiome. Her lab examines how ecology and evolution shape personalized microbial communities and affect health. Microbiome research faces large variation across patients, diets, drugs, and environments. Lieberman’s work treats that variation as a biological signal that can help explain infection risk, treatment response, and long-term community change.

Kevin O’Brien, an associate professor in EECS and a member of the Research Laboratory of Electronics, leads the Quantum Coherent Electronics Group. He develops tools, techniques, and devices that improve measurement of quantum systems, including superconducting quantum computers. Quantum hardware needs precise control and readout because small disturbances can corrupt fragile states. O’Brien’s research addresses that measurement problem at the device level.

Wim van Rees, an associate professor in Mechanical Engineering and the Leonardo Career Development Professor in Engineering, develops high-order numerical methods for fluid flows interacting with moving or deforming bodies. His work applies to wake vortex dynamics, bio-inspired propulsion, and morphing structures. These problems challenge simulation because fluids and structures affect each other during motion. Van Rees builds computational tools that help engineers study those coupled effects before they commit to prototypes.

MIT’s 2026 engineering tenure group shows how much modern engineering now crosses departmental lines. Faculty in the cohort use machine learning for language, combustion, and cloud systems; physics for aviation, plasma, and quantum devices; and biological measurement for plants, brains, and microbiomes. The common thread comes from engineering practice: each researcher builds methods that must work outside a narrow lab setting, where noise, cost, scale, and safety shape the result.

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