Computer science research and education have become increasingly data-intensive as a result of the proliferation of digital technologies. Novel computational tools of mining and analyzing large-scale data are capable of revealing new knowledge and fundamental insights in many science and engineering domains. Our main goal is to conduct both theoretical and practical research in the areas of pattern recognition, data mining, and multimedia analysis, with a special focus on healthcare, automotive and biomedical imaging applications. More specifically, our recent theoretical work mainly lies in the areas of
Big Data Analytics
Advanced Machine Learning including deep learning, transfer learning, few-shot learning, embedding learning, and learning beyond supervision
Multimedia analysis and Computer Vision
We have successfully applied the theoretical results to many real-world applications, including behavior risk factor analysis, vechile interior monitoring, image classfication and brain imaging. A major characteristic of our research at WSU is its multidisciplinary nature. By cross-fertilizing ideas and rapid, seamless transition of basic research findings to practical applications, multidisciplinary projects build the foundation for highly innovative research. We have established strong, well-integrated collaborations not only within WSU, but also with local govements, community service providers such as major hospitals in the Metro Detroit area, and many other higher education institutions and non-profit organizations. The long-term collaboration has already produced joint publications, research grants, and co-advising of graduate students, and our high quality research results will bring benefits to a large population.
Our research results have been published in premium computer science journals including IEEE Transactions on Multimedia (2017, 2014), IEEE Transactions on Knowledge and Engineering(2012, 2010), IEEE Transactions on Pattern Analysis and Machine Intelligence (2009), IEEE Transactions on Computers (2006), IEEE Transactions on Visualization and Computer Graphics (2009, 2008), IEEE Transactions on Neural Networks (2005), IEEE Transactions on Fuzzy Systems (2001), Pattern Recognition Journal (2010), Journal of Knowledge and Information Systems (2008), and Journal of Data Mining and Knowledge Discovery(2015, 2008). Each year, the Journal Citation Reports (JCR) from Thomson ISI examine the impact of academic journals by determining how often journals' articles are cited by later research. It is well accepted that these journals are usually flagship publications in their field and deliver the research results of the highest quality. I have an h-index of 26 based on Google Scholar.In the area of Computer Science, the role of journal publications is different than in other disciplines. Due to the rapid pace of advances in knowledge, conferences are equally important as journals. We have published over 70 conference papers, some of which appeared in top conferences with high visibility, such as IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, 2006, IEEE International Conference on Computer Vision (ICCV) 2017, IEEE Conference on Data Mining (ICDM) 2015, 2006, 2007, ACM Multimedia (ACM MM) 2016, 2014, 2009, 2007, 2005, ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) 2016, Annual Symposium of American Medical Informatics Association (AMIA) 2015, ACM/IEEE WWW Conference 2008.
External Research Funding
Our research is multidisciplinary in nature and is continuously supported by Federal and State Governments, National and State Foundations and Automotive Industries. Our currrent active grants are listed below:
Epilepsy Foundation, The Sound of Seizures: Audio-triggered Seizure Detection, 2017 - 2018, $75,000, PI: Maysaa Basha, Co-PI: Ming Dong, News Release
Michigan Health Endorsement Fund, BusMe: An ehealth Platform to Reduce Pediatric Health Disparities by Improving Public Transportation Access in Detroit, 2017 -2018, $100,000, PIs: Ming Dong, Rayman Mohamed and Elizabeth Towner
Ford Motor Company, Facial Image-based Obese Driver Estimation Using Deep Learning Convolutional Neural Network, 2016 -2017, $25,000, PI: Ming Dong
NSF, Promoting a Healthier Urban Community: Prioritization of Risk Factors for the Prevention and Treatment of Pediatric Obesity, 2016 to 2018, $200,000, PI: Ming Dong, Co-PIs: D. Zhu and E.Towner, News Release
NIH, Automated Coding of eCoaching Exchanges to Promote Healthier Eating, 2016 to 2018, $425,000, PI: A. Carcone, Co-investigator
Ford Motor Company, University Research Program, Video-based driver monitoring system, 2015-2019, $190,000, PI: Ming Dong
In addition, we are also working on several projects on brain imaging and behavior mointoring that are supported by NIH.
We are actively seeking well-motivated graduate students/Post-docs to work on different projects. The prefered requirements are 1) An MS degree in computer science or related fields, 2) Strong background on Pattern Recognition & Deep Learning, and 3) Efficient programming skills in Python and/or Java. Interested person should contact me directly through email.