Yuanjun Gao

I got my Ph.D. degree from Department of Statistics at Columbia University in May 2017 (yay!). I was lucky to work with Prof. John P. Cunningham and Prof. Liam Paninski in the exciting field of machine learning and neuroscience. Before coming to Columbia, I was a student at Tsinghua University in China, where I received my Bachelors degree in Pure and Applied Mathematics.

I worked as a research summer intern at Jane Street Capital in 2016, and a quantitative research summer intern Susquehanna International Group (or SIG) in 2015.

I have always been fascinated by the power of human brain, and the idea of understanding this power via math and statistics sounds really cool to me!

I am also interested in music, psychology, history and finance (although I am not an expert in any of these).

My email address is yg2312 AT columbia DOT edu.

Publication

Zhou D, Gao Y, Paninski L (2020) Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model. ECML 2020.
- Download: PDF.

Gao Y, Goetz J, Mazumder R, Connelly M (2020) Mining Events with Declassified Diplomatic Documents. Annals of Applied Statistics 14 (4), 1699-1723
- Press: BuzzFeed.
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Loaiza G*, Gao Y*, Cunningham JP (2017) Maximum entropy flow networks. ICLR 2017. (*=equal contribution)
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Gao Y*, Archer E*, Paninski L, Cunningham JP (2016) Linear dynamical neural population models through nonlinear embeddings. NIPS 2016. (acceptance rate:22%, *=equal contribution)
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Pnevmatikakis EA, Soudry D, Gao Y, Machado TA, Merel J, Pfau D, Reardon T, Mu Y, Lacefield C, Yang W, Ahrens M, Bruno R, Jessell TM, Peterka DS, Yuste R, Paninski L (2016) Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron, 89(2), 285-299.
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Gao Y, Buesing L, Shenoy KV, Cunningham JP (2015) High-dimensional neural spike train analysis with generalized count linear dynamical systems. NIPS 2015. (selected for spotlight presentation, top 5% of submitted)
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Pnevmatikakis EA, Gao Y, Soudry D, Pfau D, Lacefield C, Poskanzer K, Bruno R, Yuste R, Paninski L (2014) A structured matrix factorization framework for large scale calcium imaging data analysis. arXiv preprint arXiv:1409.2903.
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Professional Service

Reviewer of Journal of Computational Neuroscience, AISTATS(2016), NIPS(2015, 2016), ICML(2015)

Other Projects/Activities

During my spare time (which I don't have a lot right now...) I enjoy competing on Kaggle to hone my machine learning skill.

I had the pleasure of working with Prof. Andrew Gelman on stan, "a probabilistic programming language implementing full Bayesian statistical inference". I have been involved in an extension of the HMC algorithm and a data visualization tool.

I have been a co-organizer of student seminar (2014-2015) in Department of Statistics, Columbia University.