In Brief: I am a postdoc at the The McCourt School's Massive Data Institute at Georgetown University, where I am supervised by Lisa Singh and Ali Arab. We study forced migration using penalized inference and Variational Bayes[1].

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].

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.

Google Scholar


- 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")



- 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>.

Under Review:

- Katharine Donato, Elizabeth Jacobs, Lisa Singh, Ali Arab, and Nathan Wycoff. Using Organic Data in Migration Research

- Nathan Wycoff, Ali Arab, Katharine Donato, Lisa Singh, Elizabeth Jacobs, Kornraphop Kawintiranon, and Yaguang Liu. Forecasting Ukrainian Refugee Flows with Organic Data Sources


- 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> (To appear in Neurips 2022).

-Nathan Wycoff, Mickl 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:

  • Postdoc at Georgetown's Massive Data Institute in the McCourt School of Public Policy (Summer 2021-Present).

  • Givens Scholar at Argonne National Laboratory / Math and Computer Science Division (Summer 2018).

  • Web Dev Intern at General Dynamics Mission Systems (Summer 2016).

  • Business Intelligence Intern at Comprehensive Health Services (Summer 2015).

  • Intern for Congressman Don Young (Spring 2013).

Teaching Experience (at Virginia Tech):

  • Statistics for Social Scientists (Spring 2020), Instructor of Record

  • Probability and Statistics for Electrical Engineers (Summer 2019), Instructor of Record

  • Statistics for Biologists (Fall 2018), Lab Instructor

  • Various Grading Appointments

  • I founded an undergrad academic club


  • Virginia Tech Statistics (B.S. 2016, M.S. 2018, Ph.D 2021)