Kevin S. Xu
Department of Computer and Data Sciences
Case Western Reserve University
10900 Euclid Ave., Cleveland, OH 44106-7071, USA
I lead the Machine Learning and Network Science (MLNS) laboratory at Case Western Reserve University. Our research is primarily in the areas of machine learning and network science with applications to human dynamics, health care, education, and wearable computing.
Mar. 30: I will be giving a talk at the Institute for Mathematics and its Applications at the University of Minnesota on Tuesday, April 4. It will be livestreamed also on Zoom. Link to seminar
Aug. 16: I have joined Case Western Reserve University as an assistant professor in the Department of Computer and Data Sciences. I have several openings for both graduate and undergraduate research assistants interested in probabilistic machine learning or network science--please email me if you are interested!
Jul. 29: Congratulations to MS student Tanner Hilsabeck for successfully defending his MS thesis "A Hybrid Adjacency and Time-Based Data Structure for Analysis of Temporal Networks" and to MS student Hadeel Soliman for successfully defending her MS thesis "Community Hawkes Models for Continuous-time Networks"!
Jul. 8: Our paper "A hybrid adjacency and time-based data structure for analysis of temporal networks" has been published in Applied Network Science: https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00489-5 -- congratulations to MS student Tanner Hilsabeck and former MS student Makan Arastuie!
May 16: 2 papers accepted: "The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks" (ICML 2022: arXiv preprint) and "A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks" (UAI 2022: arXiv preprint). Congratulations to PhD student Zhipeng Huang and MS student Hadeel Soliman on their first publications since joining the IDEAS Lab!
May 7: I am serving as a tutorial chair for CIKM 2022. General areas of interest for tutorials include general areas include artificial intelligence, data science, databases, information retrieval, and knowledge management. Please feel free to contact me if you would like to discuss a tutorial proposal! Call for tutorials
Apr. 5: I have received the College of Engineering Award for Excellence in Supervision of Undergraduate Research. Thanks to all the wonderful undergraduate student researchers I've had the pleasure of advising!
Dec. 20: Congratulations to PhD student Rehan Ahmad for successfully defending his PhD dissertation and MS student Robert Warton for successfully defending his MS thesis!
Apr. 29: I am honored to receive an NSF CAREER award. Thank you to the NSF for supporting my research! Link to award abstract
Apr. 20: I will be giving a tutorial "Mining Dynamic Networks with Generative Models" at SDM 2021 along with Jimmy Foulds on May 1. Tutorial materials and additional information
Creation of statistical models and efficient inference procedures for discrete-time and continuous-time dynamic network data, particularly social network data (supported by the National Science Foundation and National Geospatial Intelligence Agency through NSF grants IIS-1755824, DMS-1830412, and CAREER award: IIS-2047955)
Prediction of survival times for kidney transplants by integrating high-dimensional survival analysis and network models with biological models of immunogenicity (supported by the National Library of Medicine and the National Science Foundation through NIH grant R01LM013311)
Prediction of students' performance and mental workload levels using clickstream and wearable data analytics (supported by the National Science Foundation through NSF grant EEC-2025088)
Development of robust algorithms for analysis of physiological data (including electrodermal activity and heart rate) collected using wearable sensors
Prediction of chronic PTSD from structural and functional MRI brain imaging data (supported by the National Institute of Mental Health through NIH grant R01MH110483)
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
Honors and awards
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 Machine Learning for Big Data: Fall 2022
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
Selected service to research community
Associate editor for IEEE Transactions on Computational Social Systems (2016-present)
Program co-chair of SBP-BRiMS (2016-2017) and SBP 2015
Program committee member of TheWebConf (2017-2022), KDD (2021), AAAI (2017-2018, 2021-2022), WSDM (2021-2022), BigData (2020), IC2S2 (2018-2021), SocInfo (2017-2020), NetSci (2017), and IJCAI (2016)
Reviewer for NeurIPS (2016, 2020-2021), ICML (2021-2022), ICLR (2022), and ICWSM (2020-2021)
Software packages resulting from my research can be found at the IDEAS Lab GitHub page. 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)
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
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
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
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
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