Talks and presentations

TCA-TWAS: Identification of Cell-Type-Specific Genetic Regulation of Gene Expression for Transcriptome-Wide Association Studies
Qiurui Ma*, Duo Zhang*, Brandon Jew, Sriram Sankararaman,
July 2019 - Sep 2019 | supported by CSST scholarship, UCLA
[code] [poster] [presentation] [report on data simulation]

In this study, we deconvolute builk-level gene expressions into cell-type-specific gene expressions with cell-type weights using bayesian models, circumventing the centrifusion that traditional methods require to acqure cell-type specific gene expressions. We then associate specific gene expressions with phenotypes on UKBiobank blood tissue data.

Uncertainty-Aware Model-Based Reinforcement Learning in Autonomous Driving using PILCO
Qiurui Ma*, Sirui Xie*,
Feb 2019 - June 2019 | work done at Sensetime HK
[Contact me for detailed design and implementation for IP reasons]

In this study, we bring uncertainty estimation to model based RL for autonomous driving. The model is parenthesized by a bayesian neural network to approximate PILCO and dropouts are used to estimate the uncertainty. We further train a multilayer perceptron as a controller, whose gradient could flow through the model network. We demonstrate that our model could output uncertainty towards its projections, and could navigate safely in complex environments.

Double Q Learning for Long-Short Derivatives Trading
Qiurui Ma, | Advised by James Tin-Yau Kwok
Oct 2018 - Dec 2018 | Undergraduate Research Opportunity Project at HKUST

In this project, we apply double q-learning for long and short trading on twenty years of oil derivatives. My work envolved first scraped 20 years of oil derivative data from Bloomberg and Yahoo Finance; then implemented a support-resistance line visualization tool to better analyze and feature engineer; finally implemented a double dqn module to long or short the derivative, with its performance beating the benchmark buy-and-hold strategy