Machine learning theory, algorithms and applications in related areas such as robotics, computer vision, data mining, speech, and visualization.
2014 - present
Ph.D. student in Electrical Engineering and Computer Science
Advisors: Prof. Stefanie Jegelka, Prof. Leslie Kaelbling, and Prof. Tomás Lozano-Pérez
2014 - 2016
S.M. in Electrical Engineering and Computer Science
Master thesis: Optimization as Estimation with Gaussian Process Bandits
Thesis Supervisors: Prof. Stefanie Jegelka and Prof. Leslie Kaelbling
2010 - 2014
B.Eng. in Computer Science and Technology
Undergraduate thesis: Fast Dropout Training for Deep Neural Networks (in Chinese)
Thesis advisors: Prof. Fei Sha and Prof. Jun Zhu
- Studied Gaussian process optimization, contextual and continuous bandit problems, etc.
- Proposed and implemented a MAP algorithm for GP optimization with applications to robotics/vision.
- Derived the theoretical bounds for the new method, and analyzed relations to other methods.
- Researched auto-encoders, deep neural networks and dropout training.
- Derived and implemented fast training algorithm with regularizer via noise marginalization.
- Analyzed and compared performance of training with regularizer and training with dropout noise for deep neural networks both on CPU and GPU.
- Studied topic modeling, variational inference, Gibbs sampling and data augmentation.
- Implemented the prototype for scalable inference algorithm for correlated and dynamic topic models.
- Researched different evaluation methods, and compared perplexity results for variational LDA, Gibbs LDA, variational CTM, and our partially collapsed Gibbs sampling algorithm for CTM.
- Created hierarchical visualizations for 1000 topics learned from New York Times with graphviz, C#, D3.js.
- Researched non-negative matrix factorization (NMF) with sparse coding for speech separation.
- Derived the algorithm and implemented the prototype for Discriminative NMF.
- Our method yields a 11.5% improvement on signal-to-noise ratio over traditional sparse NMF.
- Competed in Baidu's Movie Recommendation Algorithm Contest. Ranked top 10.
To family and friends who think computer science is about making websites and fixing your computer: yes, I am physically able to do these, but they are not intellectually challenging enough for me, as an AI major. Please consult the people who study networks and architecture on the 7th floor of Stata (JK!).