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Michael I. Miller

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Michael I. Miller
Born1955 (age 68–69)
Brooklyn, New York, United States
NationalityAmerican
Alma materThe State University of New York at Stony Brook
The Johns Hopkins University
Known forComputational anatomy[6]

LDDMM[7]

Diffeomorphometry and BrainGPS[8]
SpouseElizabeth Patton Miller[9]
ChildrenEliza Ariel Miller
AwardsPresidential Young Investigator Award
Johns Hopkins University Gilman Scholar[1]
Scientific career
FieldsBiomedical Engineering
Neuroscience
Pattern Theory
InstitutionsWashington University in St. Louis
The Johns Hopkins University Center for Imaging Science
Kavli Neuroscience Discovery Institute at Johns Hopkins University[2]
The Johns Hopkins University
The Division of Applied Mathematics[3]
Brown University
ThesisStatistical Coding of Complex Speech Stimuli in the Auditory Nerve (1983)
Doctoral advisorMurray B. Sachs[4]
Other academic advisorsEric. D. Young[5]
Website[1]

Michael Ira Miller (born 1955) is an American born biomedical engineer and neuroscientist, and the Massey Professor and Director of the Johns Hopkins University Department of Biomedical Engineering. He worked with Ulf Grenander and Computational Anatomy on brain mapping using medical imaging. Miller is the Director of Johns Hopkins Center for Imaging Science, Whiting School of Engineering and codirector of Johns Hopkins Kavli Neuroscience Discovery Institute with Richard L. Huganir. Miller is also a Johns Hopkins University Gilman Scholar.[10]

Biography

Miller received his Bachelor of Engineering degree from The State University of New York at Stony Brook in 1976. He then joined the Department of Biomedical Engineering at the Johns Hopkins University, where he received his Master of Science degree in Electrical Engineering in 1978 and a Ph.D. degree in Biomedical Engineering in 1983.[11]

After completing his graduate studies, Miller joined the Biomedical Computer Laboratory[12] at Washington University in St. Louis to work on medical imaging with Donald L. Snyder, then chair of Electrical Engineering at Washington University School of Engineering and Applied Science. He then joined the faculty of Electrical Engineering in 1985 and remained on the faculty at Washington University through 1998 as the Newton R. and Sarah Louisa Glasgow Wilson Professor in Engineering.[13] During the period 1994 through 2001, Miller was a visiting professor at Brown University's Division of Applied Mathematics where he worked with Ulf Grenander on image analysis.

In 1998 Miller joined the Department of Biomedical Engineering [2] at Johns Hopkins University, where he is currently the Massey Professor and Director of Biomedical Engineering, as well as the Director of the Center for Imaging Science. In March 2011, Miller was appointed by President Ronald J. Daniels as one of 17 inaugural University Gilman Scholars selected from all divisions of the University in recognition of their efforts to uphold the highest ideals of the University, demonstrated through distinguished research, teaching, service, and creativity.[10]

In 2015, Miller became the co-director of the newly awarded Kavli Institute for Discovery Neuroscience.[14]

In July, 2017 Miller became the Director of the Department of Biomedical Engineering at the Johns Hopkins University.[15]

Academic career

Neural Coding at Johns Hopkins University

Miller did his doctoral work on neural codes in the Auditory system under the direction of Murray B. Sachs and Eric D. Young in the Neural Encoding Laboratory[16] at Johns Hopkins University. Fellow graduate students included Patrick Barta,[17] Pete Bernardin, Dan Gibson, Lisa Hellstrom,[18] Thomas Schalk, Herbert Voigt,[19] Raimond L. Winslow.[20]

With Sachs and Young, Miller focussed on rate-timing population codes of complex, speech features including voice-pitch[21] and consonant-vowel syllables [22] encoded in the discharge patterns across the primary auditory-nerve. These neural codes formed the basis for the discussions at the 1982 New York Academy of Science[23] meeting on efficacy and timeliness of Cochlear implants.

Medical Imaging at Washington University

Miller's impact in the field of brain mapping in Medical imaging, specifically statistical methods for iterative image reconstruction began in the mid 80's when he joined Donald L. Snyder at Washington University to work on time-of-flight positron emission tomography (PET) systems being instrumented in Michel Ter-Pogossian's group. With Snyder, Miller worked to stabilize likelihood-estimators of radioactive tracer intensities via the method-of-sieves[24] .[25] This became one of the main approaches for controlling noise artifacts in the Shepp-Vardi algorithm[26] in the context of low count, time-of-flight emission tomography. It was during this period that Miller met Lawrence (Larry) Shepp, and subsequently visited Shepp several times at Bell Labs to speak as part of the Henry Landau seminar series. Shepp remained a mentor and friend throughout Miller's career.

The Pattern Theory Era and Computational Anatomy

During the mid 90's, Miller joined the Pattern Theory group at Brown University and worked with Ulf Grenander on problems in image analysis within the Bayesian framework of Markov random fields. Their first noteworthy project was to establish the ergodic properties of jump-diffusion processes for inference in hybrid parameter spaces, which was presented by Miller at the Journal of the Royal Statistical Society as a discussed paper. [27] Significantly, these were the first results on a class of random sampling algorithms with ergodic properties proven to sample from distributions supported across discrete sample spaces and simultaneously over the continuum, likening it to the extremely popular Gibb's sampler of Geman and Geman[28] as well as more classical diffusion based samplers associated to Langevin dynamics.

Grenander and Miller continued their collaborations for approximately 15 years, starting their work together on human shape and form during this period in which Miller remained a visiting Professor within the Pattern Theory group of the Division of Applied Mathematics at Brown University. Grenander had already published influential papers on deformable templates for hands;[29] Miller, Christensen, and Rabbitt, had published on the use of flows for dense template or image matching.[30][31] Together, Grenander and Miller introduced Computational anatomy as a formal theory of human shape and form at a joint lecture in May 1997 at the 50th Anniversary of the Division of Applied Mathematics at Brown University,[32] and subsequent publication.[33] In the same year with Paul Dupuis,[34] they published the foundational paper establishing the necessary Sobolev smoothness conditions requiring vector fields to have strictly greater than 2.5, square-integrable, generalized derivatives (in space of 3-dimensions) to ensure that smooth submanifold shapes are carried smoothly by the flows.[35]

By 2005, the Computational anatomy framework establishing high-dimensional brain mapping via diffeomorphisms at the morphological scale of MRI had become the de facto standard for cross-section analyses of populations studied via 1mm MRI. Codes now exist for diffeomorphic template or atlas mapping, including ANTS,[36] DARTEL,[37] DEMONS,[38] LDDMM,[39] StationaryLDDMM,[40] all actively used codes for constructing correspondences between coordinate systems based on sparse features and dense images.

Collaborating with École normale supérieure de Cachan on Shape and Form

David Mumford appreciated the smoothness results on existence of flows, and encouraged collaboration between Miller and the École normale supérieure de Cachan group which had been working independently. In 1998, Mumford organized a Trimestre on "Questions Mathématiques en Traitement du Signal et de l'Image" at the Institute Henri Poincaré; from this emerged the collaboration on shape between Miller, Alain Trouve[41] and Laurent Younes[42] which continues to date as continued exchanges between The Johns Hopkins University Center for Imaging Science and the CMLA Center for Mathematical studies.[43] They published three significant papers together over the subsequent 15 years; the equations for geodesics generalizing the Euler equation on fluids supporting localized scale or compressibility appearing in 2002,[44] the conservation of momentum law for shape momentum appearing in 2006,[45] and the Hamiltonian formalism summarized in 2015.[46]

Contributions to neurodegeneration in brain mapping

During these years, Miller and Csernansky [47] had developed a long-term research effort on neuroanatomical phenotyping of Alzheimer's disease, Schizophrenia 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.[48] This was one of the papers that contributed to a deeper understanding of the disorder in it earlier stages and the recommendations of the working group for the first time in 27 years to revise the diagnostic criteria for Alzheimer’s disease dementia.[49]

In 2009, the Johns Hopkins University BIOCARD[50] project was initiated, led by Marilyn Albert,[51] to study preclinical Alzheimer's disease. In 2014, Miller and 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,[52] and subsequently that this could be measured via MRI in clinical populations upwards of 10 years before clinical symptom.[53] This has the potential to impact clinical treatment of the disease.

Selected works

Miller has published two books, the first with Donald L. Snyder, the second with Ulf Grenander.

  • Snyder, Donald L.; Miller, Michael I. (1991). Random Point Processes in Time and Space. Springer. ISBN 978-0199297061.
  • Grenander, Ulf; Miller, Michael (2007). Pattern Theory: From Representation to Inference. Oxford University Press. ISBN 978-0199297061.

References

  1. ^ "University taps 17 as inaugural Gilman Scholars". The JHU Gazette. Johns Hopkins. 2011.
  2. ^ http://www.KavliJHU.org. {{cite web}}: Missing or empty |title= (help)
  3. ^ Brown University. "Division of Applied Mathematics".
  4. ^ Sachs, M.B. (February 2002). "Member of National Academy of Engineering".
  5. ^ Young, E.D. "Google Scholar Citations".
  6. ^ Grenander, Ulf; Miller, Michael I. (December 1998). "Computational Anatomy: An Emerging Discipline". Quarterly of Applied Mathematics. 56 (4): 617–694. JSTOR 43638257.
  7. ^ Beg, M.F.; Miller, M.I.; Trouve, A.; Younes, L. (2005). "Computing large deformation metric mappings via geodesic flows of diffeomorphisms" (PDF). International Journal of Computer Vision. 61 (2): 139–157. doi:10.1023/b:visi.0000043755.93987.aa.
  8. ^ Miller, M.I.; Trouve, A.; Younes, L. (2014). "Diffeomorphometry and Geodesic Positioning Systems for Human Natomy". Technology (Singapore World Science). 2 (1): 36–43. doi:10.1142/s2339547814500010. PMC 4041578. PMID 24904924.
  9. ^ Patton Miller, Elizabeth. "Johns Hopkins Humanities Center".
  10. ^ a b "University taps 17 as inaugural Gilman Scholars". The JHU Gazette. Johns Hopkins. 14 March 2011.
  11. ^ Miller, Michael I. (1983). Statistical coding of complex stimuli in the auditory nerve (PhD thesis). The Johns Hopkins University.
  12. ^ "Institute for Biomedical Computing". Digital Commons@Becker.
  13. ^ "Newton R. and Sarah Louisa Glasgow Wilson Professorship in Engineering" (PDF).
  14. ^ "RESEARCH: New Kavli Neuroscience Discovery Institute at The Johns Hopkins University | Whiting School of Engineering". Whiting School of Engineering. 2015-10-02. Retrieved 2017-11-12.
  15. ^ "Michael Miller Named Director of Biomedical Engineering - 06/30/2017". Retrieved 2017-11-12.
  16. ^ "Neural Encoding Laboratory".
  17. ^ Barta, Patrick. "Associate Professor of BME in the Center for Imaging Science".
  18. ^ Hellstrom, Lisa. http://www.behs.com/apps/pages/index.jsp?uREC_ID=304153&type=u. {{cite web}}: Missing or empty |title= (help)
  19. ^ Voigt, Herbert F. "Professor of Biomedical Engineering".
  20. ^ Winslow, Raimond L. "Biomedical Engineering Faculty".
  21. ^ Miller, M.I.; Sachs, M.B. (June 1984). "Representation of voice pitch in discharge patterns of auditory-nerve fibers". Hearing Research. 14 (3): 257–279. doi:10.1016/0378-5955(84)90054-6. PMID 6480513.
  22. ^ Miller, M.I.; Sachs, M.B. (1983). "Representation of stop consonants in the discharge patterns of auditory-nerve fibers". JASA. 74 (2): 502–517. doi:10.1121/1.389816.
  23. ^ Sachs, M.B.; Young, E.D.; Miller, M.I. (June 1983). "Speech Encoding in the Auditory Nerve: Implications for Cochlear Implants". Annals of the New York Academy of Sciences. 405: 94–114. doi:10.1111/j.1749-6632.1983.tb31622.x.
  24. ^ Snyder, Donald L.; Miller, Michael I. (1985). "The Use of Sieves to Stabilize Images Produced with the EM Algorithm for Emission Tomography". IEEE Transactions on Nuclear Science. NS-32(5): 3864–3872. doi:10.1109/TNS.1985.4334521.
  25. ^ Snyder, D.L.; Miller, M.I.; Thomas, L.J.; Politte, D.G. (1987). "Noise and edge artifacts in maximum-likelihood reconstructions for emission tomography". IEEE Trans. on Medical Imaging. 6 (3): 228–238. doi:10.1109/tmi.1987.4307831.
  26. ^ Shepp, L.; Vardi, Y. (1982). "Maximum likelihood reconstruction for emission tomography". IEEE Transactions Medical Imaging. 1 (2): 113–122. doi:10.1109/TMI.1982.4307558. PMID 18238264.
  27. ^ Grenander, U.; Miller, M.I. (1994). "Representations of Knowledge in Complex Systems". Journal of the Royal Statistical Society, Series B (Methodological). 56 (4): 549–603. JSTOR 2346184.
  28. ^ S. Geman; D. Geman (1984). "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images". IEEE Transactions on Pattern Analysis and Machine Intelligence. 6 (6): 721–741. doi:10.1109/TPAMI.1984.4767596.
  29. ^ Grenander, U.; Chow, Y-S; Keenan, D. (1991). Hands: a pattern theoretic study of biological shapes. New York: Springer-Verlag. ISBN 0387973869.
  30. ^ Christensen, G.E.; Miller, M.I.; Rabbitt, R.D. (March 24–26, 1993). Prince, J.; Runolfsson (eds.). "A deformable neuroanatomy based on viscous fluid mechanics". Proceedings of the 1993 Conference on Information Science and Systems. Baltimore, Maryland: Johns Hopking University: 211–216.
  31. ^ Christensen, G.E.; Rabbitt, R.D.; Miller, M.I. (1996). "Deformable templates using large deformation kinematics". IEEE Transactions on Image Processing. 5 (10): 1435–1447. doi:10.1109/83.536892. PMID 18290061.
  32. ^ Walter Freiberger (ed.). "Current and Future Challenges in the Applications of Mathematics". Quarterly of Applied Mathematics.
  33. ^ Grenander, Ulf; Miller, M.I. (December 1998). "Computational Anatomy: An Emerging Discipline" (PDF). Quarterly of Applied Mathematics. LVI (4): 617–694.
  34. ^ Dupuis, Paul. "IBM Professor of Applied Mathematics". Applied Mathematics. Brown University.
  35. ^ Dupuis, P.; Grenander, U.; Miller, M.I. (September 1998). "Variational Problems on Flows of Diffeomorphisms for Image Matching". Quarterly of Applied Mathematics. 56 (3): 587–600. JSTOR 43638248.
  36. ^ "stnava/ANTs". GitHub. Retrieved 2015-12-11.
  37. ^ Ashburner, John (2007-10-15). "A fast diffeomorphic image registration algorithm". NeuroImage. 38 (1): 95–113. doi:10.1016/j.neuroimage.2007.07.007. PMID 17761438.
  38. ^ "Software - Tom Vercauteren". sites.google.com. Retrieved 2015-12-11.
  39. ^ "NITRC: LDDMM: Tool/Resource Info". www.nitrc.org. Retrieved 2015-12-11.
  40. ^ "Publication:Comparing algorithms for diffeomorphic registration: Stationary LDDMM and Diffeomorphic Demons". www.openaire.eu. Retrieved 2015-12-11.
  41. ^ Trouve, Alain. "Alain Trouve Professeur".
  42. ^ Younes, Laurent. "Laurent Younes Webpage".
  43. ^ DOUCET, Sandra. "CMLA - CMLA Research Center for Applied Maths". cmla.ens-paris-saclay.fr. Retrieved 2017-12-06.
  44. ^ Miller, M.I.; Trouve, A.; Younes, L. (2002). "On the Metrics and Euler-Lagrange Equations of Computational Anatomy". Annual Review of Biomed. Engineering. 4: 375–405. doi:10.1146/annurev.bioeng.4.092101.125733. PMID 12117763.
  45. ^ Miller, M.I.; Trouve, A.; Younes, L. (31 January 2006). "Geodesic shooting for computational anatomy". International Journal of Computer Vision. 24 (2): 209–228. doi:10.1007/s10851-005-3624-0. PMC 2897162. PMID 20613972.
  46. ^ Miller, M.I.; Trouve, A.; Younes, L. (December 2015). "Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson". Annual Review of Biomedical Engineering. 17: 447–509. doi:10.1146/annurev-bioeng-071114-040601. PMID 26643025.
  47. ^ Csernansky, J.G. "Department of Psychiatry and Behavioral Sciences".
  48. ^ Csernansky, J.G.; Wang, L.; Swank, J.; Miller, JP; Gado, M.; McKeel, D.; Miller, M.I.; Morris, J.C. (15 April 2005). "Preclinical detection of Alzheimer's disease: hippocampal shape and volume predict dementia onset in the elderly". NeuroImage. 25 (3): 783–792. doi:10.1016/j.neuroimage.2004.12.036. PMID 15808979.
  49. ^ "Alzheimer's Diagnostic Guidelines". Division of Neuroscience.
  50. ^ Albert, M. S. "BIOCARD: Predictors of Cognitive Decline Among Normal Individuals". Alzheimer's Disease Research Center. Johns Hopkins University School of Medicine.
  51. ^ Albert, Marilyn. "Johns Hopkins Medicine".
  52. ^ Miller, M.I.; Younes, L.; Ratnanather, J.T.; Brown, T.; Trinh, H.; Postal, E.; Lee, D.S.; Wang, M.C; Mori, S.; Obrien, R.; Albert, M.; Research Team, BIOCARD (16 September 2013). "The diffeomorphometry of temporal lobe structures in preclinical Alzheimer's disease". NeuroImage: Clinical. 3 (352–360): 352–360. doi:10.1016/j.nicl.2013.09.001.
  53. ^ Younes, L.; Albert, M.; Miller, M.I.; Research Team, BIOCARD (21 April 2014). "Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer's disease". NeuroImage: Clinical. 5: 178–187. doi:10.1016/j.nicl.2014.04.009. PMC 4110355. PMID 25101236.

External links

Wikimedia Commons has media related to Michael I. Miller.
  • [3] Miller's Curriculum Vitae
  • [4] Miller's Short Biography
  • [5] Center for Imaging Science Website
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