Kevin S. Xu

Current projects

• Creation of statistical models and efficient inference procedures for discrete-time and continuous-time dynamic network data, particularly social network data (supported by NSF CAREER award IIS-2318751 and previously by NSF grants IIS-1755824 and DMS-1830412)

• Prediction of survival times for kidney transplants by integrating high-dimensional survival analysis and network models with biological models of immunogenicity (supported by the by NIH grant R01LM013311)

• Prediction of chronic PTSD from structural and functional MRI brain imaging data (supported by NIH grants R21MH125277 and R01MH110483)

• Prediction of students' performance and mental workload levels using clickstream and wearable data analytics (supported by NSF grant EEC-2025088)

• Development of robust algorithms for analysis of physiological data (including electrodermal activity and heart rate) collected using wearable sensors

Past projects

• Prediction of human performance and cognitive load levels for human-machine collaboration (supported by the Ohio Federal Research Network)

• PhD, University of Michigan, Electrical Engineering: Systems, 2012, Advisor: Alfred O. Hero III

• MSE, University of Michigan, Electrical Engineering: Systems, 2009

• BASc, University of Waterloo, Honors Electrical Engineering, 2007

• University of Toledo College of Engineering Award for Excellence in Supervision of Undergraduate Research, 2022

• NSF CAREER Award, 2021

• NSF Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII) Award, 2018

• 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

For a full publication list, please see my CV or my Google Scholar Citations profile.

• Soliman, H., Zhao, L., Huang, Z., Paul, S., & Xu, K. S. (2022). The Multivariate Community Hawkes model for dependent relational events in continuous-time networks. In Proceedings of the 39th International Conference on Machine Learning (pp. 20329-20346). arXiv:2205.00639

• Arastuie, M., Paul, S., & Xu, K. S. (2020). CHIP: A Hawkes process model for continuous-time networks with scalable and consistent estimation. In Advances in Neural Information Processing Systems 33 (pp. 16983-16996). arXiv:1908.06940

• Junuthula, R. R., Haghdan, M., Xu, K. S., & Devabhaktuni, V. K. (2019). The Block Point Process Model for continuous-time event-based dynamic networks. In Proceedings of the World Wide Web Conference (pp. 829-839). arXiv: 1711.10967

• 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.

Case Western Reserve University (2022-present)

• CSDS 340 Introduction to Machine Learning: Fall 2022-2023

• CSDS 600 Special Topics – Machine Learning on Graphs: Spring 2023

University of Toledo (2015-2022)

• EECS 1100 Digital Logic Design: Fall 2016-2021

• EECS 1510 Introduction to Object-Oriented Programming: Fall 2015, Spring 2020

• EECS 2510 Non-linear Data Structures: Spring 2021-2022

• EECS 4750/5750 Machine Learning: Fall 2021, Fall 2016-2019

• EECS 4980/5980 Special Topics – Social and Information Networks: Fall 2020, Spring 2020, Spring 2018

• EECS 6980/8980 Special Topics – Probabilistic Methods in Data Science: Spring 2020-2021, Spring 2018

• EECS 6980/8980 Special Topics – Social Network Analysis: Spring 2016

• Associate editor for IEEE Transactions on Computational Social Systems (2016-present) and PLOS ONE

• Program co-chair of SBP-BRiMS (2016-2017) and SBP 2015

• Area chair/senior program committee member of UAI (2024), CIKM (2023-2024), and SBP (2014)

• Program committee member/reviewer of AAAI (2021-2025), NeurIPS (2020-2024), ICML (2021-2024), ICLR (2022-2024), TheWebConf (2017-2024), AISTATS (2023), UAI (2023), WSDM (2021-2023), KDD (2021)

• Top reviewer award, UAI 2023

Software packages resulting from my research can be found at the GitHub page for the IDEAS Lab (my research lab at my former institution, the University of Toledo). Older software packages:

• Dynamic stochastic block models MATLAB toolbox (now hosted on GitHub)

• AFFECT MATLAB toolbox for clustering dynamic data (660 KB, last updated Feb. 28, 2014)

• Regularized graph layout MATLAB toolbox for dynamic network visualization (24 KB, last updated Feb. 26, 2012)

• Latent variable models for continuous-time dynamic networks: Joint Research Conference on Statistics in Quality, Industry, and Technology 2024 Slides

• Continuous-time probabilistic generative models for dynamic networks: IMA Data Science Seminar (University of Minnesota) 2023 Slides Video

• Statistical models for analyzing dynamic social network data: MIDAS Seminar (University of Michigan) 2018 Slides Video

• 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

2020

• Oct. 3: Our paper "CHIP: A Hawkes process model for continuous-time networks with scalable and consistent estimation" has been accepted to NeurIPS 2020. A preprint is available at https://arxiv.org/abs/1908.06940

• Aug. 28: Congratulations to MS students Makan Arastuie and Mohammadreza Nemati for successfully defending their MS theses!

• Aug. 28: Our recent grant proposal on measuring mental demand of web-based interactive textbooks using wearable and clickstream data analytics has been funded. Thanks to the NSF for supporting my research! Link to award abstract

2019

• Dec. 2: I will be teaching two special topics courses in Spring 2020: EECS 4980/5980 (Social and Information Networks) and EECS 6980/8980 (Probabilistic Methods in Data Science). Tentative list of topics

• Sept. 16: Our recent grant proposal on predicting kidney transplant survival has been funded through the Joint DMS/NLM Initiative on Generalizable Data Science Methods for Biomedical Research. Thanks to the NIH/NLM and NSF for supporting our research! Link to award abstract

• Apr. 1: Fixed dead links for AFFECT and regularized graph layout MATLAB toolboxes.

• Feb. 25: Our paper "The Block Point Process Model for Continuous-Time Event-Based Dynamic Networks" has been accepted to WWW 2019. A preprint is available at https://arxiv.org/abs/1711.10967

2018

• Apr. 23: Our paper "Leveraging Friendship Networks for Dynamic Link Prediction in Social Interaction Networks" has been accepted to ICWSM 2018. A preprint is available at https://arxiv.org/abs/1804.08584

• Mar. 27: I have received the NSF CISE Research Initiation Initiative (CRII) award. Thank you to the NSF for supporting my research! Link to award abstract

• Jan. 16: Updated dynamic stochastic block models MATLAB toolbox with implementations of the stochastic block transition model. The toolbox is now hosted on Github: Link to toolbox

• Jan. 12: I will be teaching two special topics courses this semester: EECS 4980/5980 (Social and Information Networks) and EECS 6980/8980 (Probabilistic Methods in Data Science). Tentative list of topics

2017

• 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