Hi! I am currently working as a machine learning engineer at Moveworks.
I used to be a Senior Applied Scientist at Amazon from 2018-2022.
I defended my thesis and graduated from University of Southern California in 2018.
My PhD research focuses on Information Theory and Its Applications. I developed practical information-theoretic methods and apply them to real world problems.
I used to be a Senior Applied Scientist at Amazon from 2018-2022.
I defended my thesis and graduated from University of Southern California in 2018.
My PhD research focuses on Information Theory and Its Applications. I developed practical information-theoretic methods and apply them to real world problems.
News
- May 2022, I left Amazon and joined Moveworks as a machine learning engineer.
Selected Publications
- Dialog State Tracking: A Neural Reading Comprehension Approach.
- Auto-Encoding Total Correlation Explanation.
- Efficient Representation for Natural Language Processing via Kernelized Hashcodes
- Invariant Representations without Adversarial Training.
- Sifting Common Information from Many Variables.
- Variational Information Maximization for Feature Selection.
- Understanding Confounding Effects in Linguistic Coordination: An Information-Theoretic Approach.
- Estimating Mutual Information by Local Gaussian Approximation.
- Efficient Estimation of Mutual Information for Strongly Dependent Variables. (oral)
- Role of fractal dimension in random walks on scale-free networks.
- Scaling of mean first-passage time as efficiency measure of nodes sending information on scale-free Koch networks.
- Impact of degree heterogeneity on the behavior of trapping in Koch networks.
- Mapping Koch curves into scale-free small-work networks.
- Explicit determination of mean first-passage time for random walks on deterministic uniform recursive trees.
- Trapping in scale-free networks with hierarchical organization of modularity.
Contact
- Email: shuyangg AT gmail DOT com