I am an associate professor at the Computer Science Department at the University of Iowa. My research interests center around optimization, big data, machine learning and AI. I was a researcher at NEC Laboratories America, Inc. Before that, I was a Machine Learning Researcher at GE Global Research. I received my Ph.D. degree in Computer Science from Michigan State University in 2012, and received my Bachelor degree from University of Science and Technology of China in 2007. Here is my Google Scholar Citations.

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I am interested in machine learning and optimization and its applications to big data analytics.
My current research topics include:

• Deep AUC Maximization
  To develope effective and efficient algorithms for deep learning with AUC objectives.
  LibAUC


• Non-Convex Optimization Algorithms
  To develope provable and practical optimization algorithms for solving non-convex problems in machine learning
  [Unified Momentum] , [NEON] , [Adaptive NCD], [Stagewise Optimization for DL] , [Stagewise Katyusha] , [Non-Convex Concave Min-Max] , [Non-Convex Non-Concave Min-Max]


• Faster Convergent Algorithms by Leveraging Error Bound Condition
  To develope faster convergent optimization algorithms by leveraging local and global error bound conditions
  [RSG] , [HOPS] , [ASSG], [Adaptive Admm] , [adaSVRG] , [adaAGC] , [SadaGrad] , [LogProj] , [ada-Frank-Wolfe]


• Large-scale Stochastic Optimization
  To develope efficient algorithms for stochastic optimization for learning from large-scale high-dimensional data.


• Online Optimization
  To develope new online convex optimization algorithms.


• Deep Learning
  To understand, design and optimize deep neural networks for large-scale image classification.
  See demo on flower, food and vehicle recognition, collaborating with NEC Labs America.


• Randomized Algorithms for Big Data Analytics
  Developing randomized algorithms for solving big data machine learning problems. See here.


• Distributed Optimization for Big Data
  Developed a practical distributed optimization for solving big data classification and regression problems. See Software.

Some old research topics during PhD studies include:

• Social Network Analysis
  Developed algorithms for detecting communities and their dynamic evolutions in Social Networks.


• Learning from Noisy Labels
  Developed algorithms and theories for learning from Noisy labels.


• Multiple Kernel Learning
  Developed efficient and robust algorithms for multiple kernel learning.


• Improved Bounds for the Nystrom method
  Developed several improved bounds for the Nystrom method under larger eigengap condition and power law eigenvalue distribution; and the theory of their applications to large-scale kernel learning.