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In Brief: I am a Data Science Fellow at the The  McCourt School's Massive Data Institute at Georgetown University, where I am work with Ali Arab, Amy O'Hara and Lisa Singh. Ali, Lisa and I study forced migration using penalized inference and Variational Bayes[1][2]. I just started working with Amy on data privacy for administrative data linkage an August 1 '23.

My thesis research in statistics [1], supervised by Bobby Gramacy at Virginia Tech, was on surrogate modeling for black-box optimization and sensitivity analysis [2][3][4; 2nd most downloaded paper of ACM TELO for 2021-2022].

I also have the privilege of being involved in a neuromorphic computing (brain-like neural nets) collaboration [1][2][3] with Fangfang Xia at Argonne National Lab.


I'm very thankful to AAAS for the opportunity to give a Webinar on the modeling (with Geraldine Henningsen of UNHCR and Ali Arab) of forced displacement, a recording of which is available on Youtube.


Are you interested in data linkage, administrative data, or privacy-preserving technologies? You can't miss the 2024 IPDLN conference in Chicago, September 15-18! Check out the website, register today!


MDI Time Series Collab

Google Scholar

Software:

- R package activegp: Design for and implementation of black-box sensitivity analysis using Gaussian Processes and the Active Subspace Method.

To get started, just do:

R> install.packages("activegp")

R>example(activegp)

Preprints:

-Nathan Wycoff, John W. Smith, Annie S. Booth, Robert B. Gramacy Voronoi Candidates for Bayesian Optimization, under review <arXiv> <GitHub>.

-Nathan Wycoff Surrogate Active Subspaces for Jump-Discontinuous Functions, to appear in AISTATS '24 <arXiv> <GitHub>.

- Nathan Wycoff, Ali Arab, Katharine M. Donato, Lisa O. Singh Sparse Bayesian Lasso via a Variable-Coefficient l1 Penalty, under review <arXiv>.

- Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia, Towards On Chip Bayesian Neuromorphic Learning, <arXiv>.

Publications:

- Nathan Wycoff, Ali Arab, Lisa O. Singh A Variable-Coefficient Nuclear Norm Penalty for Low Rank Inference, OPT workshop of Neurips '22 <5min Prez (Chrome only)> <pdf>.

- RB Gramacy, A Sauer, N Wycoff Triangulation candidates for Bayesian optimization <arXiv> Neurips 2022.

-Nathan Wycoff, Mickaël Binois & Bobby Gramacy (2022) Sensitivity Prewarping for Local Surrogate Modeling, Technometrics, DOI: 10.1080/00401706.2022.2046170, <arXiv> <GitHub>

Mickaël Binois et Nathan Wycoff A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization ACM Transactions on Evolutionary Learning and Optimization

-Nathan Wycoff, Mickaël Binois & Stefan M. Wild (2021) Sequential Learning of Active Subspaces, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2021.1874962  <arXiv>, <JCGS>. 

-Lata Kodali, John Wenskovitch, Nathan Wycoff, Leanna House, and Chris North. Uncertainty in Interactive WMDS Visualizations, in 2019 Symposium on Visualization in Data Science. VDS’19. Vancouver, BC, Canada, 2019 <link>

-Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia, Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout, International Conference on Neuromorphic Systems 2019 <link><arXiv>

-Michelle Dowling, Nathan Wycoff, Brian Mayer, John Wenskovitch, Scotland Leman, Leanna House, Nicholas Polys, Chris North, Peter Hauck, Interactive Visual Analytics for Sensemaking with Big Text, Big Data Research <link>.

-Xin Chen, Jessica Zeitz Self, Leanna House, John E. Wenskovitch, Maoyuan Sun, Nathan Wycoff, Jane Robertson Evia, Scotland Leman, Chris North, Be the Data: Embodied Visual Analytics. IEEE Transactions on Learning Technology 11(1): 81-95 (2018). <link>

-L. Bradel, N. Wycoff, L. House and C. North, Big Text Visual Analytics in Sensemaking, 2015 Big Data Visual Analytics (BDVA), Hobart, TAS, 2015, pp. 1-8. <link>

Work Experience:

Teaching Experience (at Virginia Tech):

Education: