I am a 5th year PhD candidate in the ECE Department at the University of Texas at Austin. I am fortunate to be advised by Sujay Sanghavi and Alex Dimakis. I got my BS degree in 2011 and MS degree in 2014 from Shanghai Jiao Tong University, advised by Xudong Wang.

My PhD research focuses on large-scale machine learning, which can be splitted into two parts: the algorithm/theory part and the practice/implementation part. For the algorithm/theory part, I have worked on several different projects, including graphical models, dimensionality reduction, compressed sensing, gradient tree boosting, neural networks, kernel learning, collaborative ranking, and natural language processing. For the practice/implementation part, I have used different platforms/libraries, including Apache Spark, XGBoost, scikit-learn, Tensorflow, PyTorch, and Gurobi. Code of some projects can be found under my GitHub.

Here is my resume. I can be reached at shanshan@utexas.edu


June 2018 - August 2018

    Software Engineer Intern | Google Research, New York City

    Boosting Random Features

    With Petros Mol and Natalia Ponomareva

June 2017 - August 2017

    Software Engineer Intern | Google Research, New York City

    Representation Learning for High-Dimensioanl Sparse Data

    With Dmitry Storcheus, Felix X. Yu, Dan Holtmann-Rice, Afshin Rostamizadeh, and Sanjiv Kumar

Jan 2017 - April 2017

    Applied Scientist Intern | Amazon AI, East Palo Alto

    Joint Learning for Named Entity Recognition and Neural Machine Translation

    With Hyokun Yun and Anima Anandkumar


Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
Shanshan Wu, Sujay Sanghavi, Alex Dimakis
NeurIPS Workshop on Relational Representation Learning, 2018. [workshop version]

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
[Code] [Poster]
Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix X. Yu, Dan Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, and Sanjiv Kumar
International Conference on Machine Learning (ICML) 2019.

Single Pass PCA of Matrix Products
[Code] [Spotlight Video] [Poster]
Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, and Alex Dimakis
Conference on Neural Information Processing Systems (NIPS) 2016.

Leveraging Sparsity for Efficient Submodular Data Summarization
[Spotlight Video]
Erik Lindgren, Shanshan Wu, and Alex Dimakis
Conference on Neural Information Processing Systems (NIPS) 2016.

Sparse and Greedy: Sparsifying Submodular Facility Location Problems
Erik Lindgren, Shanshan Wu, and Alex Dimakis
NIPS workshop OPT 2015.

Distributed Opportunistic Scheduling with QoS Constraints for Wireless Networks with Hybrid Links
Wenguang Mao, Xudong Wang, and Shanshan Wu
IEEE Transactions on Vehicular Technology, 2015.
An earlier version appears in Proceedings of the IEEE Globecom, 2013.

Performance Study on a CSMA/CA-Based MAC Protocol for Multi-User MIMO Wireless LANs
Shanshan Wu, Wenguang Mao, and Xudong Wang
IEEE Transactions on Wireless Communications, 2014.
An earlier version appears in Proceedings of the IEEE Globecom, 2013.

Information-theoretic study on routing path selection in two-way relay networks
Shanshan Wu, Wenguang Mao, and Xudong Wang
Proceedings of the IEEE Globecom, 2013.

Professional services

Conference reviewer:
NIPS 2016/2017/2018/2019 (top 30% highest-rating reviewer for NeurIPS 2018)
ICML 2018/2019

Journal reviewer:
Journal on Machine Learning Research
IEEE Trans. on Mobile Computing
IEEE Trans. on Wireless Communications
IEEE Trans. on Vehicular Technology
Ad Hoc Networks

Graduate Courses at UT-Austin

2016 Fall
CS395T Sublinear Algorithms (Prof. Eric Price)
    Course project: Rescaled JL Embedding

2016 Spring
EE381K-6 Estimation Theory (Prof. Haris Vikalo)
    Course project: A Survey of Fast Kernel Sketching Algorithms

2015 Fall
EE381V Advanced Probability in Learning, Inference, and Networks (Prof. Sanjay Shakkottai)
    Course project: Low-Rank Approximation of Matrix Product in One Pass
CS388G Algorithms: Techniques/Theory (Prof. Vijaya Ramachandran)
    Course project: PTAS for the Euclidean Traveling Salesman Problem

2015 Summer (Online courses provided by edX)
CS100.1x Introduction to Big Data with Apache Spark (Prof. Anthony D. Joseph)     Certificate
CS190.1x Scalable Machine Learning (Prof. Ameet Talwalkar)     Certificate

2015 Spring
EE381V Advanced Algorithms (Prof. Evdokia Nikolova)
    Course project: Signal Recovery from Permuted Observations
EE381K Information Theory (Prof. Alex Dimakis)

2014 Fall
EE380L Data Mining (Prof. Joydeep Ghosh)
    Course project: Ranking by Alternating SVM and Factorization Machine
EE381V Large-Scale Optimization (Prof. Sujay Sanghavi)
EE381J Probability and Stochastic Processes (Prof. Sanjay Shakkottai)

Teaching Experiences

Teaching Assistant, EE381V (Machine Learning for Large Scale Data), UT-Austin, Spring 2016.

Teaching Assistant, EE313 (Linear Systems and Signals), UT-Austin, Fall 2014.

Teaching Assistant, VE489 (Computer Networks), UM-SJTU Joint Institute, Summer 2013.

Teaching Assistant, VP140 (Physics I), UM-SJTU Joint Institute, Summer 2009.

Last update: April 22, 2019