burkh rt.github.io | michael_burkhart@alumni.brown.edu

EducationBrown University

Ph.D. Applied Mathematics–Rutgers University

M.Sc. Mathematics–Purdue University

B.Sc.’s Mathematics, Statistics, & Economics–University ofChicago

Sr. Data Scientist–____

University ofCambridge

Research Associate (Visiting Researcher in

Adobe, Inc.

Machine Learning Scientist (Sr. inBrown University

Graduate Research Assistant

Providence, RI

New Brunswick, NJ

West Lafayette, INExperience• joined the Beaulieu-Jones lab to develop machinelearning solutions for healthcare applications

Chicago, IL)

San Jose, CA)–• developed a novel nonlinear lter for online neural
decoding (Matlab / Python)• collaborated with the BrainGate Clinical Trail to test thislter in a brain–computer interface• experimented with Bayesian solutions to provide
robustness against common non-stationarities

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Summer research internships at Spotify, U.S.A. (Data Research Intern in New York, NY,Argonne National Laboratory (Graduate Research Aide in Lemont, IL,)0–

• built and validated predictive models to personalize user
experience (PySpark / LightGBM / Air ow)• designed and tested personalized pricing interventions
within the cancellation ow (causal forests)• supervised intern projects in representation learning forcausal inference and semi-supervised learning (Keras /Tensor ow)

Providence, RI0–• developed multimodal trajectory models for the early
diagnosis of neurodegenerative disease• worked with research engineers at the Alan Turing
Institute to automate the detection of covariate shift

Cambridge, UK

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Michael C. Burkhart)&Publications
M. Burkhart, L. Lee, D. Vaghari, A. Toh, E. Chong, C. Chen, P. Tiňo, & Z. Kourtzi, Unsupervised multimodal
modeling of cognitive and brain health trajectories for early dementia prediction, Scienti c Reports()
M. Burkhart & G. Ruiz, Neuroevolutionary representations for learning heterogeneous treatment e ects,
Journal of Computational Science ()M. Burkhart, Discriminative Bayesian ltering lends momentum to the stochastic Newton method forminimizing log-convex functions, Optimization Letters ()R. Li, E. Harsh eld, S. Bell, M. Burkhart, A. Tuladhar, …, C. Chen, Z. Kourtzi, & H. Markus, Predicting
incident dementia in cerebral small vessel disease: comparison of machine learning and traditional
statistical models, Cerebral Circulation – Cognition and Behavior ()R. Borchert, T. Azevedo, A. Badhwar, J. Bernal, M. Betts, R. Bru aerts, M. Burkhart, I. Dewachter, …, D.
Llewellyn, M. Veldsman, & T. Rittman, Arti cial intelligence for diagnostic and prognostic neuroimaging
in dementia: a systematic review, Alzheimer's & Dementia()
M. Burkhart, Conjugacy conditions for supersoluble complements of an abelian base and a xed point
result for non-coprime actions, Proceedings of the Edinburgh Mathematical Society()
M. Burkhart & G. Ruiz, Neuroevolutionary feature representations for causal inference, Computational
Science – ICCS
M. Burkhart, Discriminative Bayesian ltering for the semi-supervised augmentation of sequential
observation data, Computational Science – ICCS
M. Burkhart & K. Shan, Deep low-density separation for semi-supervised classi cation, Computational
Science – ICCS
M. Burkhart, D. Brandman, B. Franco, L. Hochberg, & M. Harrison, The discriminative Kalman lter for
Bayesian ltering with nonlinear and nongaussian observation models, Neural Computation()
M. Burkhart & K. Modarresi, Determining adaptive loss functions and algorithms for predictive models,Computational Science – ICCS
M. Burkhart & K. Modarresi, Adaptive objective functions and distance metrics for recommendation
systems, Computational Science – ICCS
D. Brandman, M. Burkhart, J. Kelemen, B. Franco, M. Harrison, & L. Hochberg, Robust closed-loop control
of a cursor in a person with tetraplegia using Gaussian process regression, Neural Computation()
D. Brandman, T. Hosman, J. Saab, M. Burkhart, B. Shanahan, J. Ciancibello, …, M. Harrison, J. Simeral, & L.
Hochberg, Rapid calibration of an intracortical brain computer interface for people with tetraplegia,
Journal of Neural Engineering()
M. Burkhart, Y. Heo, & V. Zavala, Measurement and veri cation of building systems under uncertain data: aGaussian process modeling approach, Energy and Buildings()

Dissertation

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10022M. Burkhart, A Discriminative Approach to Bayesian Filtering with Applications to Human NeuralDecoding, Ph.D. advised by Prof. Matthew Harrison, Division of Applied Mathematics, Brown University,Providence,

Patents & Pending
M. Burkhart & G. Ruiz, Causal inference via neuroevolutionary selection, U.S. Patent Application# / ,led, published as US/A
M. Burkhart & K. Shan, User classi cation from data via deep segmentation for semi-supervised learning,U.S. Patent Application # / ,led, granted
as US ,,B
M. Burkhart & K. Modarresi, Digital experience enhancement using an ensemble deep learning model, U.S.
Patent Application # / ,led, granted
as US , ,B

Preprints & Working Papers
M. Abroshan, M. Burkhart, O. Giles, S. Greenbury, Z. Kourtzi, J. Roberts, M. van der Schaar, J. Steyn, A.
Wilson, & M. Yong, Safe AI for health and beyond – monitoring to transform a health service,arχiv:.
[cs.LG]
L. Lee, D. Vaghari, M. Burkhart, P. Tiňo, M. Montagnese, Z. Li, K. Zühlsdor , J. Giorgio, G. Williams, E. Chong,C. Chen, B. Underwood, T. Rittman, & Z. Kourtzi, Robust and interpretable AI-guided marker for early
dementia prediction in real-world clinical settings, eClinicalMedicine (in press)
M. Burkhart, Fixed point conditions for non-coprime actions, Proceedings of the Royal Society ofEdinburgh Section A: Mathematics (in press)

Recent Talks
M. Burkhart, L. Lee, P. Tiňo, & Z. Kourtzi, Clustering trajectories of neurodegenerative disease,
Trustworthy AI for medical & health research workshop, Cavendish Laboratory, Cambridge, UK,
M. Burkhart & G. Ruiz, Neuroevolutionary feature representations for causal inference, International
Conference on Computational Science (ICCS), London, UK,
M. Burkhart, Discriminative Bayesian ltering for the semi-supervised augmentation of sequential
observation data, ICCS, Kraków, Poland,(virtual)

Selected Presentations

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M. Burkhart, L. Lee, D. Vaghari, J. Venton, S. Thomas, N. Smith, R. Everson, P. Tiňo, & Z. Kourtzi, AI-guided
patient strati cation for neurodegenerative disorders using unsupervised trajectory modelling,
Alzheimer's Association International Conference (AAIC), Amsterdam, Netherlands,
D. Vaghari, L. Lee, M. Burkhart, M. Montagnese, …, T. Rittman, P. Tiňo, & Z. Kourtzi, Validating the clinicalutility of AI-guided tools for early dementia prediction, AAIC,L. Lee, D. Vaghari, M. Burkhart, M. Montagnese, K. Zühlsdor , …, T. Rittman, P. Tiňo, & Z. Kourtzi,Translating AI-guided tools for early dementia prediction to clinical practice, Dementias Platform UK(DPUK) Translation, London, UK,
F. Marinaro, C. Morvan, R. Au, S. Bond, M. Burkhart, N. Carlebach, …, Z. Walker, R. Everson, C. Hinds, & Z.
Kourtzi, The Early Detection of Neurodegenerative diseases initiative: an international andmultidisciplinary e ort for transforming the early detection of dementia-causing diseases, AAIC, San
Diego, CA,

Cambridge Psych. Dept.

Research Sta Representative–ICCS Conference

Program Committee Member–Brown SIAM StudentChapter

Vice President, Chapter RecordsInterdepartmental Liaison O cer–Rutgers Math Dept.

Graduate Liaison Committee Member–

Purdue Student
PublishingFoundation

Member, Corporate Board of Directors
Chairperson, Finance Committee–Cambridge, UK

Providence, RI• thematic track on Applications of Computational
Methods in Arti cial Intelligence and Machine Learning• organized events within the applied math community

New Brunswick, NJ

West Layfayette, INOnline• oversaw the Exponent, Purdue’s Independent Daily (atthe time) Student Newspaper

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73122111000002222229529211100000Homepage • LinkedIn • Google Scholar • Github • OrcID022222

Community Involvement