Markus J. Buehler

McAfee Professor of Engineering

Department
Department of Civil and Environmental Engineering
Technology Areas
Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science: Quantum Computing, Bioinformatics / Electronics & Photonics: Quantum Technology / Industrial Engineering & Automation: Logistics / Sensing & Imaging: Optical Sensing / Biotechnology: Synthetic Biology

Background and Education

Markus J. Buehler is the McAfee Professor of Engineering at MIT (an Institute-wide Endowed Chair), a member of the Center for Materials Science and Engineering, and the Center for Computational Science and Engineering at the Schwarzman College of Computing. He holds academic appointments in Mechanical Engineering and Civil and Environmental Engineering. In his research, Professor Buehler pursues new modeling, design and manufacturing approaches for advanced biomaterials that offer greater resilience and a wide range of controllable properties from the nano- to the macroscale. His interests include a variety of functional material properties including mechanical, optical and biological, linking chemical features, hierarchical and multiscale structures, to performance in the context of physiological, pathological and other extreme conditions. His methods include molecular and multiscale modeling, design, as well as experimental synthesis and characterization. His particular interest lies in the mechanics of complex hierarchical materials with features across scales (e.g. nanotubes, graphene and natural biomaterial nanostructures including protein materials such as intermediate filaments and hair, collagen, silk and elastin, and other structural biomaterials). An expert in computational materials science and AI, he has pioneered the field of materiomics, and demonstrated broad impacts in the study of mechanical properties of complex materials, including predictive materials design and manufacturing. Between 2013-2020, Buehler served as Department Head of MIT’s Civil and Environmental Engineering Department. He has held numerous other leadership roles at professional organizations, including a term as President of the Society of Engineering Science (SES).

Awards and Honors

  • TMS Hardy Award | 2013
  • JOM Best Paper Award | 2013
  • IEEE Holm Conference Mort Antler Lecture Award | 2012
  • Society of Engineering Science Young Investigator Medal | 2012
  • Materials Research Society Outstanding Young Investigator Award | 2012
  • Alfred Noble Prize | 2012 (given by the combined engineering societies of the United States)
  • Thomas J.R. Hughes Young Investigator Award (ASME) | 2011
  • Leonardo da Vinci Award (ASCE Engineering Mechanics Institute) | 2011
  • Stephen Brunauer Award | 2011 (ACS)
  • Rossiter W. Raymond Memorial Award | 2011 (AIME)
  • Sia Nemat-Nasser Award (ASME) | 2010
  • Harold E. Edgerton Faculty Achievement Award | 2010
  • Chair | Fourth International Conference on the Mechanics of Biomaterials and Tissues (2010-2011)
  • Presidential Early Career Award for Scientists and Engineers (PECASE) | 2009
  • United States Navy Young Investigator Award | 2008
  • Participant and Plenary Speaker | National Academy of Engineering Frontiers of Engineering Symposium (recognized as one of the top engineers in the country between the ages of 30-45; 2007, 2008)
  • DARPA Young Faculty Award | 2008
  • Air Force Young Investigator Award | 2008
  • Esther and Harold E. Edgerton Career Development Chair Professorship | 2007
  • National Science Foundation CAREER Award | 2007
  • Poster Award | International Conference on Mechanics of Biomaterials and Tissues | 2005
  • First Prize Gold Graduate Student Award | Materials Research Society | 2004

Technologies

A Method to Extract Physical Behavior Directly from Simple Visual Empirical Observation Via a Deep Learning Model, for a Material Agnostic Approach to Predicting Compressive Beam Buckling

Technology / Case number: #24061
Markus J. Buehler / Andrew Lew
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Industrial Engineering & Automation / Sensing & Imaging
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End-to-End Deep Learning Approach to Predict Complex Stress and Strain Fields Directly from Microstructural Images

Technology / Case number: #22477
Markus J. Buehler / Chi Hua Yu / Zhenze Yang
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science / Electronics & Photonics
Impact Areas: Advanced Materials
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