Research Summary

I conduct both theoretical and applied research in the areas of large scale inverse optimization, compressive sensing, sparse learning, image/video processing, multimedia, computer vision and medical image analysis. I am most interested in creating efficient algorithms with nice theoretical guarantees and practical values (especially in practical applications involved large scale data), as well as developing novel theoretical insights into existing algorithms and problems.

Deep Graph Learning

  • Develop efficient deep learning algorithms for processing graph data
  • Develop novel generative models for graph data generation with sematic guide
  • Novel solutions for social data analysis, molecular informatics, complex image-omics data analysis

Machine Learning for Drug Discovery

  • Develop efficient machinelearning algorithms for molecule data
  • Develop novel generative models for molecue graph generation
  • Protein folding, TCR-pMHC binding, Antibody design and optimization
  • Novel algorithms for chemical sysnthesis and retrosysthesis

Deep Learning for Survival Prediction

  • Develop novel nonlinear methods for censored regression problems
  • Deep Multistance Learning for survival prediction from big pathological Images
  • Novel learning methods for small sample problems
  • Deep learning methods for multimodal censored data

Big Image-Omics Data Analytics

  • Connections between morphology and prognosis
  • Big pathological Image analytics for clinical outcome prediction
  • High-dimensional molecular profiling data analytics
  • Integrating pathological image data with molecular profiling data
  • Generating image or omics data from each other

Human/Facial Behavior

  • Real-time face tracking with a web camera or Kinect
  • Fatigue detection by tracking slow eyelid closure and blinking
  • Dyadic Synchrony as a Measure of Trust and Veracity
  • Facial expression recognition

Structure Sparsity: Theorems, Algorithms and Applications

  • Structured sparsity theorems give the insight when known structure/sparse priors
  • Convex and greedy algorithms for strcutured sparsity recovery problem
  • Sucessful applications on Compressive sensing on graph structured sparse data
  • Sucessful applications on video forground detection and abnormal detection

Magnetic Resonance Imaging

  • Compressive Sensing techniques for accelerated MR imaging
  • Fast Image reconstruction method for compressive sensing MRI
  • Gradient sparse and wavelet sparse prior for MRI

3D/4D Modeling, Simulation and Segmentation

  • Deformal moded for cardiac/lung/liver/brain segmentation
  • 4D high resolution cardiac images acquired by the 320 multi-detector CT
  • 4D cardiac reconstruction of the surface of the left ventricle (LV) for a full cardiac cycle.
  • Enabling to investigate functional significance in health and disease

Non-Intrusive Load Monitoring (NILM)

  • How to fully exploit the inherent characteristics of each appliance in a specific functional mode?
  • How to derive efficient algorithms for low-sample-rate data of each appliance to enhance model scalability for Low-frequency Energy Disaggregation?
  • How to investigate effiecient inference algorithms to learn the latent states from measured aggregation data?

Projects on Biometrics Before 2005