Michael Miller,PHD


Specialization: Brain mapping, computational anatomy, pattern theory


Johns Hopkins Whiting School of Engineering

3400 N. Charles Street

Clark Hall 301

Baltimore, MD 21218



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Michael Miller is a University Gilman Scholar and is the Herschel and Ruth Seder Professor of Biomedical Engineering. Dr. Miller directs the Center for Imaging Science and is the Co-Director of the Kavli Neuroscience Discovery Institute. He received his PhD in biomedical engineering from Johns Hopkins University.

Miller’s early doctoral work in Neuroscience on neural codes in the Auditory system in the Neural Encoding Laboratory[15] at Johns Hopkins University during the Johnson, Mountcastle, Sachs and Young era. Miller focussed on rate-timing population codes in the primary auditory-nerveof complex, speech features including voice-pitch[20] and consonant-vowel syllables.[21] Such neural codes were part of the basis for the discussions at the 1982 New York Academy of Science[22] meeting on efficacy and timeliness of neuroengineered Cochlear implants.

Miller's impact in the field of  statistical iterative image reconstruction for Medical imaging and brain mapping was at Washington University working with Donald L. Snyder  on time-of-flight positron emission tomography (PET) systems being instrumented by Michel Ter-Pogossian. Miller's contribution was to stabilize likelihood-estimators  of radioactive tracer intensities via the method-of-sieves[23] .[24] This became one of the main approaches for controlling noise artifacts in the Shepp-Vardi algorithm[25] for low count, time-of-flight emission tomography.

During the 90's, Miller joined the Pattern Theory group at Brown University to work with Ulf Grenander on problems in image analysis based on Markov random fields. Grenander and Miller collaborated for two decades working on human shape and form during which time Miller remained a visiting Professor within the Pattern Theory group of the Division of Applied Mathematics at Brown University. They published together several influential papers on Computational anatomy as a formal theory of human shape and form.[31][32][33]  By 2005, the Computational anatomy framework establishing high-dimensional brain mapping via diffeomorphisms had become the de facto standard for cross-sectionl analyses of populations studied at the morphological scale of MRI. Computational codes now exist for diffeomorphic template or atlas mapping, including ANTS,[35] DARTEL,[36] DEMONS,[37]LDDMM,[38] and StationaryLDDMM,[39] all actively used codes for constructing correspondences between coordinate systems based on sparse features and dense images.

In 1998, Mumford while in Paris encouraged the collaboration on Computational Anatomy and shape between Miller and the École normale supérieure de Cachan group of Trouve[40] and Younes[41] which continues to date. M-T-Y have published continuously, supporting numerous exchanges between the The Johns Hopkins University Center for Imaging Science and the CMLA Center for Mathematical studies.[42] Noteworthy were the publication of the geodesic equations generalizing the Euler equation of hydrodynamics supporting localized scale-compressibility,[42] the law of conservation of shape momentum,[43] and the Hamiltonian formalism for shooting coordinate systems.[44]

During these years, Miller and Csernansky [45] had developed a long-term research effort on neuroanatomical phenotyping of Alzheimer's diseaseSchizophrenia and mood disorder. In 2005, they published with John Morris an early work on predicting conversion to Alzheimer's disease based on clinically available MRI measurements using the diffeomorphometry technologies.[46]  In 2009, the Johns Hopkins University BIOCARD[48] project was initiated, led by Marilyn Albert,[49] to study preclinical Alzheimer's disease. In 2014, the BIOCARD team with Younes demonstrated that the original Braak staging of earliest change associated to the entorhinal cortex in the medial temporal lobe could be demonstrated via diffeomorphometry methods in the population of clinical MRI's,[50] and subsequently that this could be measured via MRI in clinical populations upwards of 10 years before clinical symptom.[51] This has the potential to impact clinical treatment of the disease.


To advance neuroscience discovery by uniting neuroscience, engineering and computational data science to understand the structure and function of the brain.