Jacob Andreas and Brett McGuire named Edgerton Award winners

2026-04-17 · Source: MIT News - Artificial intelligence · Field: Science & Research — Mathematics & Computational Sciences, Space Science & Astronomy, Research Methodology & Innovation · Depth: Intermediate, medium

Summary

MIT Associate Professors Jacob Andreas from the Department of Electrical Engineering and Computer Science (EECS) and Brett McGuire from the Department of Chemistry have been awarded the 2026 Harold E. Edgerton Faculty Achievement Award. This annual award, established in 1982, recognizes exceptional distinction in teaching, research, and service. Andreas, who joined MIT in 2019, specializes in natural language processing (NLP) and AI, focusing on compositional generalization and developing advanced NLP courses. McGuire, who joined in 2020 and was promoted in 2025, conducts research at the intersection of physical chemistry, molecular spectroscopy, and observational astrophysics, notably discovering polycyclic aromatic hydrocarbons in the interstellar medium and excelling in teaching large General Institute Requirement courses.

Key takeaway

For AI Scientists and Research Scientists focused on language learning or astrochemistry, this recognition highlights the value of foundational research that bridges theory with real-world impact. Consider how your work can address persistent challenges like compositional generalization in NLP or contribute to understanding the chemical building blocks of life, while also prioritizing effective teaching and community service.

Key insights

Exceptional faculty are recognized for significant contributions across teaching, research, and service.

Principles

• Interdisciplinary research drives novel discoveries.
• Strong teaching fosters deep student engagement.

Method

Andreas combines computational and linguistically informed approaches to build foundations of language learning, while McGuire uses laboratory spectroscopy, radio astronomy, and signal analysis to identify molecular fingerprints in space.

In practice

• Integrate social and ethical considerations into ML courses.
• Develop new course materials from scratch for clarity.

Topics

• Harold E. Edgerton Award
• Natural Language Processing
• Astrochemistry
• Machine Learning
• Molecular Spectroscopy

Best for: AI Scientist, Research Scientist, AI Student

Related on AIssential

• 🔬Why There Is No "AlphaFold for Materials" — AI for Materials Discovery with Heather Kulik — Deep integration of domain expertise with AI and critical assessment of model outputs are crucial for success in AI for science.
• Congratulations to the #AAAI2026 award winners — AAAI 2026 recognized significant contributions across AI research, education, service, and societal impact.
• How to win a best paper award — Impactful research stems from good taste, collaboration, rigorous execution, and clear communication, with luck playing a significant role.
• David W. Hogg on why we do astrophysics (in the face of LLMs and the lack of clinical value) — Hogg's paper challenges conventional views on LLM intelligence, academic publication, and the core motivations behind scientific research.
• Co-Scientist: A multi-agent AI partner to accelerate research — Co-Scientist is a multi-agent AI system that iteratively generates, debates, and refines scientific hypotheses to accelerate discovery.
• 142 - Science Of Science, with Kyle Lo — SciSci uses computational methods to study scientific processes, guiding tool development for community-wide benefit.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by MIT News - Artificial intelligence.