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.