Kevin Shuai Xu
Kevin S. Xu

Assistant Professor, EECS Department
University of Toledo
2801 W. Bancroft St. MS 308
Toledo, OH  43606-3390, USA



Recent news

  • Oct. 3: Our recent research using physiological data collected from wearables to predict autonomic nervous system states is featured in the IEEE Xplore Innovation Spotlight.
  • Sept. 17: Presented our paper "Unsupervised motion artifact detection in wrist-measured electrodermal activity data" (co-authored with Yuning Zhang and Maysam Haghdan) at the International Symposium on Wearable Computers (ISWC) 2017: Preprint Slides
  • Aug. 18: One of the listed prerequisites for my course EECS 4750/5750 Machine Learning is out of date. Students interested in enrolling for Fall 2017 but unable to due to the prerequisite error can email me to obtain an override.
  • Jan. 31: Our paper "A Compressed Sensing Based Decomposition of Electrodermal Activity Signals" has been accepted to the IEEE Transactions on Biomedical Engineering. A preprint is available at https://arxiv.org/abs/1602.07754

Upcoming events

Research

I direct the Interdisciplinary Data Engineering and Science (IDEAS) Laboratory at the University of Toledo. Our research is primarily in the areas of machine learning and statistical signal processing with applications to network science, human dynamics, and health. Current projects:
  • Statistical models and efficient inference procedures for discrete-time and continuous-time dynamic network data, particularly social network data
  • Development of robust algorithms for analysis of physiological data (including electrodermal activity and heart rate) collected using wearable sensors
  • Prediction of human performance and cognitive load levels for human-machine collaboration
  • Interactive methods for scalable and interpretable visualization of large dynamic networks

Education

  • PhD, Electrical Engineering: Systems, University of Michigan, 2012, Advisor: Alfred O. Hero III
  • MSE, Electrical Engineering: Systems, University of Michigan, 2009
  • BASc, Honors Electrical Engineering, University of Waterloo, 2007

Honors and awards

  • Exceptional Service Award, International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), 2016
  • Winner, International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction Challenge (SBP), 2013
  • Postgraduate scholarship: Doctorate, Natural Sciences and Engineering Research Council of Canada (NSERC), 2010-2011
  • Postgraduate scholarship: Master’s, Natural Sciences and Engineering Research Council of Canada (NSERC), 2008

Selected publications

For a full publication list, please see my CV or my Google Scholar Citations profile.
  • Zhang, Y., Haghdan, M., & Xu, K. S. (2017). Unsupervised motion artifact detection in wrist-measured electrodermal activity data. In Proceedings of the 21st ACM International Symposium on Wearable Computers (pp. 54–57). arXiv:1707.08287
  • Jain, S., Oswal, U., Xu, K. S., Eriksson, B., & Haupt, J. (2017). A compressed sensing based decomposition of electrodermal activity signals. IEEE Transactions on Biomedical Engineering, 64(9), 2142–2151. arXiv:1602.07754
  • Han, Q., Xu, K. S., & Airoldi, E. M. (2015). Consistent estimation of dynamic and multi-layer block models. In Proceedings of the 32nd International Conference on Machine Learning (pp. 1511–1520). arXiv:1410.8597
  • Xu, K. S. (2015). Stochastic block transition models for dynamic networks. In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (pp. 1079–1087). arXiv:1411.5404
  • Xu, K. S., & Hero III, A. O. (2014), “Dynamic stochastic blockmodels for time-evolving social networks,” IEEE Journal of Selected Topics in Signal Processing, 8(4), 552–562. arXiv:1403.0921.
  • Xu, K. S., Kliger, M., & Hero III, A. O. (2014). Adaptive evolutionary clustering. Data Mining and Knowledge Discovery, 28(2), 304–336. arXiv:1104.1990.
  • Hsiao, K.-J., Xu, K. S., Calder, J., & Hero III, A. O. (2012). Multi-criteria anomaly detection using Pareto depth analysis. In Advances in Neural Information Processing Systems 25 (pp. 854–862). arXiv:1110.3741

Teaching

  • EECS 1100 Digital Logic Design (Fall 2017)
  • EECS 1510 Introduction to Object-Oriented Programming (Fall 2015)
  • EECS 4750/5750 Machine Learning (Fall 2017, Fall 2016)
  • EECS 4980/5980 Special Topics – Social and Information Networks (Spring 2017)
  • EECS 6980/8980 Special Topics – Social Network Analysis (Spring 2016)
  • EECS 6980/8980 Special Topics – Probabilistic Methods in Data Science (Spring 2017)

Selected service to research community

Software

Software packages resulting from my research can be found at the IDEAS Lab Github page. Older software packages:

Recent presentations

  • Unsupervised motion artifact detection in wrist-measured electrodermal activity data: ISWC 2017 Slides
  • The block point process model for continuous-time event-based dynamic networks: JSM 2017 Slides
  • Consistent estimation of dynamic and multi-layer block models: ICML 2015 Slides Video lecture
  • Stochastic block transition models for dynamic networks: AISTATS 2015 Slides

Curriculum vitae