Geoffrey Schiebinger Postdoctoral Fellow Broad Institute of MIT and Harvard MIT Statistics + Data Science geoff@broadinstitute.org gschiebinger@gmail.com CV My research is supported by a Career Award at the Scientific Interface from the Burroughs Welcome Fund. |

I am interested in the interplay between theory and experiment in the natural and mathematical sciences. My current research focuses on ontogenetic stochastic processes in developmental biology and cellular reprogramming. I am working with Eric Lander, Aviv Regev, and Philippe Rigollet to develop a mathematical model of development based on optimal transport, study its theoretical properties, and use the model to study the dynamics of gene regulation.

I am a postdoc at the Broad Institute of MIT and Harvard and the MIT Center for Statistics + Data Science.

Before coming to the Broad Institute, I studied Statistics at the University of California, Berkeley, where I earned my Ph.D. in May 2016. Here is a link to my thesis on the mathematics of precision measurement. While at Berkeley, I was fortunate to be advised by Benjamin Recht, and also work with Martin Wainwright, Bin Yu, and Adityanand Guntuboyina.

I did my undergrad at Stanford University, where I earned a B.S. in Mathematics and an M.S. in Electrical Engineering in 2011.

In my spare time, I like to go kite boarding!

A. Forrow, J.C. Hutter, M. Nitzan, P. Rigollet, G. Schiebinger, and J. Weed. Statistical Optimal Transport via Factored Couplings. arXiv. (2018).

G. Schiebinger*, J. Shu*, M. Tabaka*, B. Cleary*, V. Subramanian, J. Gould, A. Solomon, S. Liu, S. Lin, P. Berube, L. Lee, J. Chen, J. Brumbaugh, P. Rigollet, K. Hochedlinger, R. Jaenisch, A. Regev and E. Lander (2018). Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming. Cell.

N. Boyd, G. Schiebinger and B. Recht (2017). The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems. SIAM Journal on Optimization. Our code is available here.

G. Schiebinger, E. Robeva and B. Recht (2017). Superresolution without Separation. Information and Inference, Oxford University Press.

G. Schiebinger, M. J. Wainwright and B. Yu (2015). The Geometry of Kernelized Spectral Clustering. Annals of Statistics. vol. 43, no. 2, pages 819-846.

A. Guntuboyina, S. Saha and G. Schiebinger (2014). Sharp Inequalities for f-divergences. IEEE Transactions on Information Theory. vol. 60, pages 104-121.

L. A. Warren, D. J. Rossi, G. Schiebinger, I. L. Weissman, S. K. Kim and S. R. Quake (2007). Transcriptional instability is not a universal attribute of aging. Aging Cell. vol. 6, pages 775-782.

Career Award at the Scientific Interface from the Burroughs Welcome Fund, 2018.

Chan Zuckerberg Initiative grantee (co-PI with Philippe Rigollet), 2018

Honorable mention for best paper at CAMSAP, 2015

NSF Graduate Fellowship, 2011 - 2016

VIGRE Fellowship, 2011 - 2012

Hertz finalist, 2011

Schiebinger, G. The Maximum Entropy Distribution of Orbiting Asteroids Forms a Belt, (2010). Supervised by Professor Thomas Cover.

I was the graduate student instructor for Statistics 153: Introduction to Time Series Analysis in the spring semester, 2014 at UC Berkeley.