Research/Projects

• To rarefy or not to rarefy: robustness and efficiency trade-offs of rarefying microbiome data. Johnny Hong, Ulas Karaoz, Perry de Valpine, and William Fithian. 2022. Bioinformatics 38 (9), 2389-2396.

• Relaxed Wasserstein with Applications to GANs, Xin Guo, Johnny Hong, Tianyi Lin, and Nan Yang, 2021. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3325-3329.

• Ambiguity set and learning via Bregman and Wasserstein, Xin Guo, Johnny Hong, and Nan Yang, 2017. Preprint.

• Machine learning techniques for price change forecast using the limit order book data (with J. Han, N. Sutardja, S.F. Wong, 2015)

Abstract: In the setting of high-frequency trading, stock price movements are fairly predictable. We use support vector machines (SVMs) with various kernels and random forests to predict the midprice movements of SPY (SPDR S&P 500 Trust ETF). We find that the machine learning approach provides good prediction accuracy. This work is based on the paper “Modeling high-frequency limit order book dynamics with support vector machines” by Kercheval and Zhang.

• An introduction to the use of hidden Markov models for stock return analysis (with Y. Pitcan, 2015)

Abstract: We construct two HMMs to model the stock returns for every 10-day period. Our first model uses the Baum-Welch algorithm for inference about volatility, which regards volatility as hidden states and uses a mean zero Gaussian distribution as the emission probability for the stock returns. Our second model uses a spectral algorithm to perform stock returns forecasting. We analyze the tradeoffs of these two implementations as well. Code

• Sage program for computation and experimentation with the 1-row Gomory–Johnson infinite group problem (with M. Köppe, Y. Zhou, 2014-)