PADHRAIC SMYTH
`Department of Computer Science, Bren Hall 4216
`School of Information and Computer Sciences
`University of California, Irvine
`CA 92697-3435
`telephone: (949) 824 2558
`fax: (949) 824 4056
`email: smyth@ics.uci.edu
`
`Professional Positions
`
`April 1996–present: Professor, Department of Computer Science, University of California, Irvine
`• Chancellor’s Professor: 2018 to present
`• Full Professor: July 2003 to 2018
`• Associate Professor: July 1998 to June 2003
`• Assistant Professor: April 1996 to June 1998
`
`October 1988–March 1996: Member of Technical Staff and Technical Group Leader (from 1992), Jet
`Propulsion Laboratory, California Institute of Technology, Pasadena.
`
`Education
`
`PhD, 1988: California Institute of Technology, Department of Electrical Engineering.
`
`MSEE, 1985: California Institute of Technology, Department of Electrical Engineering.
`
`BE, 1984: National University of Ireland, University College Galway. Bachelor of Engineering (Electronic)
`with First-Class Honors.
`
`Additional Professional Roles and Affiliations
`
`Joint Faculty Appointments:
`• Department of Statistics, UC Irvine, July 2008–present.
`• Department of Education, UC Irvine, July 2017–present.
`
`Founding Director, UCI Data Science Initiative, University of California, Irvine, July 2014–June 2018.
`
`Founding Director, Center for Machine Learning and Intelligent Systems, University of California, Irvine,
`January 2007–June 2014.
`
`Faculty Member, Institute for Genomics and Bioinformatics (IGB), UC Irvine, Member 2001–present.
`
`Faculty Member, Institute for Mathematical Behavioral Sciences (IMBS), UC Irvine, 1999-present.
`
`Faculty Member, Center for Digital Transformation, UC Irvine, 2012–present.
`
`Faculty Member, Program for Mathematical, Computational, and Systems Biology (MCB), UC Irvine,
`2007–present.
`
`Faculty Member, Center for Research on Information Technology and Organizations (CRITO), UC Irvine,
`2008–2012.
`
`Founding Director and Executive Committee Member of the ACM Special Interest Group on Knowledge
`Discovery and Data Mining (SIGKDD), 1998.
`
`ELASTIC - EXHIBIT 1009
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`Visiting Principal Researcher, Jet Propulsion Laboratory, California Institute of Technology, Pasadena,
`1996–2001.
`
`Member of IEEE (1988–present), American Statistical Association (1997–present), and the Association for
`Computing Machinery (ACM) (1999–present).
`
`Honors and Awards
`
`Fellow, Association for Computing Machinery (ACM), 2013
`
`Fellow, Association for the Advancement of Artificial Intelligence (AAAI), 2010
`
`ACM SIGKDD Innovation Award, 2009
`
`Best paper awards: ACM SIGKDD Conference (best paper(1997, 2002), runner-up best paper (1998, 2000)),
`ACM/IEEE Joint Conference on Digital Libraries (JCDL) (shortlist for best paper, 2007), Educational
`Data Mining Conference (best paper, 2018)
`
`Qualcomm Faculty Award, 2019
`
`Google Faculty Research Awards, 2008 and 2014
`
`IBM Faculty Partnership Award, 2001.
`
`National Science Foundation CAREER award, 1997
`
`ACM Teaching Award, UC Irvine, 1997
`
`NASA Group Achievement award, Jet Propulsion Labaratory, 1997
`
`Lew Allen Award for Excellence in Research, Jet Propulsion Laboratory, 1993
`
`17 NASA Certificates for Technical Innovation (1991–1996)
`
`Publications List
`
`Books and Conference Proceedings
`
`B5 A. Nicholson and P. Smyth (eds.), Uncertainty in Artificial Intelligence: Proceedings of the 29th Con-
`ference, ISBN 978-0-9749039-9-6, AUAI Press, Corvallis, OR, 2013.
`
`B4 C. Apte, J. Ghosh, P. Smyth (eds.), Proceedings of the 17th ACM SIGKDD International Conference
`on Knowledge Discovery and Data Mining, ISBN 978-1-4503-0813-7, ACM Press, New York, NY, 2011.
`
`B3 Modeling the Internet and the Web: Probabilistic Methods and Algorithms, P. Baldi, P. Frasconi, and
`P. Smyth, John Wiley, June 2003.
`
`B2 Principles of Data Mining, D. Hand, H. Mannila, and P. Smyth, Cambridge, MA: MIT Press, 2001.
`
`B1 Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and
`R. Uthurasamy (eds.), Palo Alto, CA: AAAI/MIT Press, 1996.
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`Journal Papers
`
`J82 A. Mamalakis, J. T. Randerson, J-Y Yu, M. Pritchard, G. Magnusdottir, P. Smyth, P. A. Levine, S.
`Yu, E. Foufoula-Georgiou, ‘Zonally contrasting shifts of the tropical rainbelt in response to climate
`change,’ Nature Climate Change, https://doi.org/10.1038/s41558-020-00963-x, 11: 143151, January
`2021
`
`J81 Park, J., Jindal, A., Kuo, P., Tanana, M., Elston Lafata, J., Tai-Seale, M., Atkins, D. C., Imel, Z.
`E., Smyth, P, ‘Automated rating of patient and physician emotion in primary care visits,’ Patient
`Education and Counseling, https://doi.org/10.1016/j.pec.2021.01.004, in press, 2021.
`
`J80 A, Stevens, R. Willett, A. Mamalakis, E. Foufoula-Georgiou, A. Tejedor, J. Randerson; P. Smyth,
`S. Wright.,
`‘Graph-guided regularized regression of Pacific Ocean climate variables to increase
`predictive skill of southwestern US winter precipitation,’ Journal of Climate, 34(2), 737–754,
`https://doi.org/10.1175/JCLI-D-20-0079.1, 2021.
`
`J79 Y. Chen, J. T. Randerson, S. R. Coffield, E. Foufoula-Georgiou, P. Smyth, C. A. Graff, D. C. Morton,
`N. Andela, G. R. van der Werf, L. Giglio, L. E. Ott, ‘Forecasting global fire emissions on sub-seasonal
`to seasonal (S2S) timescales,’ Journal of Advances in Modeling Earth Systems, 12(9), e2019MS001955,
`doi:/10.1029/2019MS001955, 2020.
`
`J78 C. Galbraith, P. Smyth, H. S. Stern, ‘Statistical methods for the forensic analysis of geolocated event
`data,’ Forensic Science International, https://doi.org/10.1016/j.fsidi.2020.301009, 33:1–12, July 2020.
`
`J77 C. Galbraith, P. Smyth, H. Stern, ’Quantifying the association between discrete event time series with
`applications to digital forensics,’ Journal of the Royal Statistical Society A, 183(3):1005–1027, 2020.
`
`J76 C. A. Graff, S. R. Coffield, Y. Chen, E. Foufoula-Georgiou, J. T. Randerson, P. Smyth, ‘Forecasting
`daily wildfire activity using Poisson regression,’ IEEE Transactions on Geoscience and Remote Sensing,
`58(7):4837–4851, 2020.
`
`J75 R. Baker, D. Xu, J. Park, R. Yu, Q. Li, B. Cung, C. Fischer, F. Rodriguez, M. Warschauer, P.
`Smyth, ‘The benefits and caveats of clickstream data to understand student self-regulatory behaviors:
`opening the black box of learning processes,’ International Journal of Educational Technology in Higher
`Education, 17(13):1–24, 2020.
`
`J74 D. Ji, P. Putzel, Y. Qian, I. Chang, A. Mandava, R. H. Scheuermann, J. D. Bui, H-Y Wang, P. Smyth,
`‘Machine learning of discriminative gate locations for clinical diagnosis,’ Cytometry A: Special Issue:
`Machine Learning for Single Cell Data, 97(3):296–307, 2020.
`
`J73 C. Fischer, Z. Pardos, R. Baker, J. J. Williams, P. Smyth, R. Yu, S. Slater, R. Baker, M. Warschauer,
`Mining big data in education: Affordances and challenges, Review of Research in Education, 44(1):130-
`160, 2020.
`
`J72 S. Coffield, C. Graff, Y. Chen, P. Smyth, E. Foufoula-Georgiou, J. Randerson, ‘Machine learning to
`predict final fire size at the time of ignition,’ International Journal of Wildland Fire, 28(11):861–873,
`2019.
`
`J71 J. Park, D. Kotzias, P. Kuo, R. L. Logan, K. Merced, S. Singh, M. Tanana, E. Karra-Taniskidou, J.
`Elston Lafata, D. C. Atkins, M. Tai-Seale, Z. E. Imel, and P. Smyth, ‘Detecting conversation topics
`in primary care office visits from transcripts of patient-provider interactions,’ Journal of the American
`Medical Informatics Association (JAMIA), 26(12):1493–1504, 2019.
`
`J70 D. Kotzias, M. Lichman, and P. Smyth, ‘Predicting consumption patterns with repeated and novel
`events,’ IEEE Transactions on Knowledge and Data Engineering, 31(2), 371-384, 2018.
`
`J69 J. R. Hipp, C. Bates, M. Lichman, and P. Smyth, ‘Using social media to measure temporal ambient
`population: does it help explain local crime rates?’ Justice Quarterly, 36(4), 714-748, March 2018.
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`J68 C. Galbraith and P. Smyth, ‘Analyzing user-event data using score-based likelihood ratios with marked
`point processes,’ Journal of Digital Investigation, 22, 106-114, 2017.
`
`J67 T. Holsclaw, A. M. Greene, A. W. Robertson, P. Smyth, ‘Bayesian non-homogeneous Markov mod-
`els via Polya-Gamma data augmentation with applications to rainfall modeling’, Annals of Applied
`Statistics, 11(1):393–426, 2017.
`
`J66 G. Gaut, M. Steyvers, Z. E. Imel, D. C. Atkins, P. Smyth, ‘Content coding of psychotherapy transcripts
`using labeled topic models,’ IEEE Journal of Biomedical and Health Informatics, 21(2):476–487, 2017.
`
`J65 C. Haffke, G. Magnusdottir, D. Henke, P. Smyth, Y. Peings, ‘Daily states of the March-April east
`Pacific ITCZ in three decades of high-resolution satellite data,’ Journal of Climate, doi:10.1175/JCLI-
`D-15-0224.1, 29(8):2981-2995, 2016.
`
`J64 P. Arnesen, T. Holsclaw, P. Smyth, ‘Bayesian detection of changepoints in finite-state Markov chains
`for multiple sequences,’ Technometrics, doi:10.1080/00401706.2015.1044118, 58(2), 205-213, 2016.
`
`J63 T. Hoslclaw, A. Greene, A. R. Robertson, P. Smyth, ‘A Bayesian hidden Markov model of daily
`precipitation over South and East Asia,’ Journal of Hydrometeorology, doi:10.1175/JHM-D-14-0142.1,
`17(1):3–25, 2016.
`
`J62 T. Hoslclaw, K. A. Hallgren, M. Steyvers, P. Smyth, D. C. Atkins, ‘Measurement error and outcome
`distributions: Methodological issues in regression analyses of behavioral coding data,’ Psychology of
`Addictive Behaviors, doi:10.1037/adb0000091, 29(4):1031-1040, 2015
`
`J61 M. L. Salmans, Z. Yu, K. Watanabe, E. Cam, P. Sun, P. Smyth, X. Dai, B. Andersen, ‘The co-factor of
`LIM domains (CLIM/LDB/NLI) maintains basal mammary epithelial stem cells and promotes breast
`tumorigenesis,’ PLOS Genetics, July 2014, doi: 10.1371/journal.pgen.100452.
`
`J60 A. J. Frank, P. Smyth, A. T. Ihler, ‘Beyond MAP estimation with the track-oriented multiple hypothesis
`tracker,’ IEEE Transactions on Signal Processing, 62(9):2413–2423, 2014.
`
`J59 D. C. Atkins, M. Steyvers, Z. E. Imel, P. Smyth, ‘Scaling up the evaluation of psychotherapy: evaluating
`motivational interviewing fidelity via statistical text classification,’ Implementation Science, 9:49:1–11,
`2014.
`
`J58 C. DuBois, C. T. Butts, D. McFarland, P. Smyth, ‘Hierarchical models for relational event sequences,’
`Journal of Mathematical Psychology, 57(6):297–309, 2013.
`
`J57 N. Navaroli, C. DuBois, P. Smyth, ‘Modeling individual email patterns over time with latent variable
`models,’ Machine Learning, 92(2–3):431-455, May 2013.
`
`J56 M. Geyfman, V. Kumar, Q. Liu, R. Ruiz, W. Gordon, F. Espitia, E. Cam, S. E. Millar, P. Smyth,
`A. Ihler, J. Takahashi, B. Andersen, ‘Bmal1 controls circadian cell proliferation and susceptibility
`to UVB-induced DNA damage in the epidermis,’ Proceedings of the National Academies of Science,
`109(29):11758-63, doi:10.1073/pnas.1209592109, July 2012.
`
`J55 D. Henke, P. Smyth, C. Haffke, G. Magnusdottir, ‘Automated analysis of the temporal behavior of
`the double Intertropical Convergence Zone over the east Pacific,’ Remote Sensing of Environment,
`123:418–433, August 2012.
`
`J54 T. Rubin, A. Chambers, P. Smyth, and M. Steyvers, ‘Statistical topic models for multi-label document
`classification,’ Machine Learning, doi: 10.1007/s10994-011-5272-5, 88(1-2):157–208, July 2012.
`
`J53 B. Gretarsson, J. O’ Donovan, S. Bostandjiev, T. Hollerer, A. Asuncion, D. Newman, and P. Smyth,
`‘TopicNets: Visual analysis of large text corpora with topic modeling,’ ACM Transactions on Intelligent
`Systems and Technology, 3(2):1–26, February 2012.
`
`J52 M. Steyvers, P. Smyth, and C. Chemudugunta, ‘Combining background knowledge and learned topics,’
`Topics in Cognitive Science, 3(1):18–47, January 2011.
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`J51 A. M. Greene, A. W. Robertson, P. Smyth, and S. Triglia, ’Downscaling projections of Indian monsoon
`rainfall using a nonhomogeneous hidden Markov model,’ Quarterly Journal of the Royal Meteorological
`Society, 137(655):347–359, January 2011.
`
`J50 T. T. Van Leeuwen, A. J. Frank, Y. Jin, P. Smyth, M. L. Goulden, G. R. van der Werf, J. T. Randerson,
`’Optimal use of land surface temperature data to detect changes in tropical forest cover,’ Journal of
`Geophysical Research—Biogeosciences, 116, G02002, doi:10.1029/2010JG00148, 2011.
`
`J49 A. Asuncion, P. Smyth, and M. Welling, ’Asynchronous distributed estimation of topic models for
`document analysis,’ Statistical Methodology, 8(1):3–17, January 2011.
`
`J48 C. Bain, G. Magnusdottir, P. Smyth, H. Stern, ‘The diurnal cycle of the intertropical convergence zone
`in the east Pacific,’ Journal of Geophysical Research, 115, D23116, doi:10.1029/2010JD014835, 2010.
`
`J47 C. Bain, J. DePaz, J. Kramer, G. Magnusdottir, P. Smyth, H. Stern, C-C. Wang, ’Detecting the ITCZ
`in instantaneous satellite data using spatial-temporal statistical modeling: ITCZ climatology in the
`east Pacific,’ Journal of Climate, 138(6):2132-2148, 2010.
`
`J46 S. Kim, P. Smyth, and H. Stern, ’A Bayesian mixture approach to modeling spatial activation patterns
`in multi-site fMRI data,’ IEEE Transactions on Medical Imaging, 29(6):1260–1274, June 2010.
`
`J45 L. Scharenbroich, G. Magnusdottir, P. Smyth, H. Stern and C. Wang, ‘A Bayesian framework for storm
`tracking using a hidden-state representation,’ Monthly Weather Review, 138(6):2132–2148, June 2010.
`
`J44 Q. Liu, K. K. Lin, B. Andersen, P. Smyth, and A. Ihler, ’Estimating replicate time-shifts using Gaussian
`process regression,’ Bioinformatics, 26(6):770–776, 2010.
`
`J43 M. Rosen-Zvi, C. Chemudugunta, T. Griffiths, P. Smyth, and M. Steyvers, ‘Learning author-topic
`models from text corpora,’ ACM Transactions on Information Systems, 28(1):1–38, 2010.
`
`J42 D. Chudova, A. T. Ihler, K. K. Lin, B. Andersen, P. Smyth, ’Bayesian detection of non-sinusoidal
`periodic patterns in circadian expression data,’ Bioinformatics, 25(23):3114–3120, 2009.
`
`J41 D. Newman, A. Asuncion, P. Smyth, and M. Welling, ‘Distributed algorithms for topic models,’ Journal
`of Machine Learning Research, 10:1801–1828, 2009.
`
`J40 K. K. Lin, V. Kumar, M. Geyfman, D. Chudova, A. T. Ihler, P. Smyth, R. Paus, J. S. Takahashi, B.
`Andersen, ‘Circadian clock genes contribute to the regulation of hair follicle cycling,’ PLOS Genetics,
`5(7): e1000573. doi:10.1371/journal.pgen.1000573, 2009.
`
`J39 A. Ihler, J. Hutchins, and P. Smyth, ‘Learning to detect events with Markov-modulated Poisson pro-
`cesses,’ ACM Transactions on Knowledge Discovery from Data, 1(3):1–23, 2007.
`
`J38 S. J. Gaffney, A. W. Robertson, P. Smyth, S. J. Camargo and M. Ghil, ‘Probabilistic clustering of
`extratropical cyclones using regression mixture models,’ Climate Dynamics, 29(4):423–440, 2007
`
`J37 S. J. Camargo, A. W. Robertson, S. J. Gaffney, P. Smyth, and M. Ghil, ‘Cluster analysis of typhoon
`tracks. Part I: general properties,’ Journal of Climate, 20:3635-3653, 2007.
`
`J36 S. J. Camargo, A. W. Robertson, S. J. Gaffney, P. Smyth, and M. Ghil, ‘Cluster analysis of typhoon
`tracks. Part II: large-scale circulation and ENSO,’ Journal of Climate, 20:3654-3676, 2007.
`
`J35 L. Friedman, Stern, Brown, Mathalon, Turner, Glover, Gollub, Lauriello, Lim, Cannon, Greve, Bock-
`holt, Belger, Mueller, Doty, He, Wells, Smyth, Pieper, Kim, Kubicki, Vangel, and Potkin, Test-retest
`and between-site reliability in a multicenter fMRI study, Human Brain Mapping, 29(8):958–972, 2008.
`
`J34 A. Ihler, S. Kirshner, M. Ghil, A. Robertson, P. Smyth, ‘Graphical models for statistical inference and
`data assimilation,’ Physica D, 230(1–2):72–87, 2007.
`
`J33 S. Kim and P. Smyth, ‘Segmental hidden Markov models with random effects for waveform modeling,’
`Journal of Machine Learning Research, 7(Jun):945–969, 2006.
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`J32 A. W. Robertson, S. Kirshner, P. Smyth, S. P. Charles, B. Bates, ‘Subseasonal-to-interdecadal variabil-
`ity of the Australian monsoon over North Queensland,’ Quarterly Journal of the Royal Meteorological
`Society, 132:519–542, 2006.
`
`J31 Turner, J.A., Smyth, P., Macciardi, F., Fallon, J.H., Kennedy, J.L., Potkin, S.G., ‘Imaging phenotypes
`and genotypes in schizophrenia,’ Neuroinformatics, 4(1):21–50, March 2006.
`
`J30 A. Robertson, S. Kirshner, and P. Smyth, ‘Hidden Markov models for modeling daily rainfall occurrence
`over Brazil,’ Journal of Climate, 17(22):4407-4424, November 2004.
`
`J29 K. K. Lin, D. Chudova, G. W. Hatfield, P. Smyth, and B. Andersen, ‘Identification of hair cycle-
`associated genes from time-course gene expression profile data by using replicate variance,’Proceedings
`of the National Academy of Sciences, 101:15955–15960, November 2004.
`
`J28 D. Pavlov, H. Mannila, and P. Smyth, ‘Beyond independence: probabilistic models for query ap-
`proximation on binary transaction data,’ IEEE Transactions on Knowledge and Data Engineering,
`15(6):1409–1421, September 2003.
`
`J27 I. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White, ‘Model-based clustering and visualization
`of navigation patterns on a Web site’, Journal of Data Mining and Knowledge Discovery, 7(4):399–424,
`2003.
`
`J26 D. Chudova and P. Smyth, ‘Analysis of pattern discovery in sequences using a Bayes error rate frame-
`work,’ Journal of Data Mining and Knowledge Discovery, 7(3):273–299, 2003.
`
`J25 I. V. Cadez, P. Smyth, G. J. McLachlan, and C. E. McLaren, ‘Maximum likelihood estimation of
`mixture densities for binned and truncated multivariate data,’ Machine Learning, 47:7–34, 2002.
`
`J24 X. Ge, D. Eppstein, and P. Smyth, ‘The distribution of cycle lengths in graphical models for iterative
`decoding’ IEEE Transactions on Information Theory, 47(6):2549–2552, September 2001.
`
`J23 P. Smyth, ‘Data mining: data analysis on a grand scale?”, Statistical Methods in Medical Research,
`9:309–327, 2000.
`
`J22 P. Smyth ‘Model selection for probabilistic clustering using cross-validated likelihood,’ Statistics and
`Computing, 9:63–72, 2000.
`
`J21 U. Fayyad and P. Smyth, ‘Cataloging and mining massive databases for science data analysis,’ Journal
`of Computational Graphics and Statistics, 8(3):589–610, 1999.
`
`J20 P. Smyth, K. Ide, and M. Ghil, ‘Multiple regimes in Northern hemisphere height fields via mixture
`model clustering,’ Journal of the Atmospheric Sciences, 56(21):3704–3723, 1999.
`
`J19 P. Smyth and D. Wolpert, ‘Linearly combining density estimators via stacking,’ Machine Learning,
`36(1-2):59–83, July 1999.
`
`J18 M. C. Burl, L. Asker, P. Smyth, U. M. Fayyad, P. Perona, L. Crumpler, and J. Aubele, ‘Learning to
`recognize volcanoes on Venus,’ Machine Learning, 30(2-3):165–194, 1998.
`
`J17 P. Smyth, ‘Belief networks, hidden Markov models, and Markov random fields: a unifying view,’
`Pattern Recognition Letters, 18:1261–1268, 1997.
`
`J16 C. Glymour, D. Madigan, D. Pregibon, and P. Smyth, ‘Statistical themes and lessons for data mining’
`Journal of Knowledge Discovery and Data Mining, 1(1):11–28, 1997.
`
`J15 C. Brodley and P. Smyth, ‘Applying classification algorithms in practice,’ Statistics and Computing,
`7(1):45–56, March 1997.
`
`J14 P. Smyth, D. Heckerman, M. Jordan, ‘Probabilistic independence networks for hidden Markov proba-
`bility models,’ Neural Computation, 9(2):227–269, 1997.
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`J13 P. Smyth, ‘Bounds on the mean classification error rate of multiple experts,’ Pattern Recognition
`Letters, 17:1253–1257, 1996.
`
`J12 U. M. Fayyad, P. Smyth, N. Weir, and S. Djorgovski, ‘Automated analysis and exploration of large
`image databases: results, progress, and challenges,’ Journal of Intelligent Information Systems, 4:7–25,
`1995.
`
`J11 P. Smyth, ‘Markov monitoring with unknown states,’ IEEE Journal on Selected Areas in Communica-
`tions, special issue on Intelligent Signal Processing for Communications, 12(9):1600–1612, December
`1994.
`
`J10 A. Y. Lee and P. Smyth, ‘Synthesis of minumum-time nonlinear feedback laws for dynamic systems
`using neural networks,’ Journal of Guidance and Control, 17(4):868–870, 1994.
`
`J9 Z. Zheng, R. Goodman, and P. Smyth, ‘Discrete recurrent networks for grammatical inference,’ IEEE
`Transaction on Neural Networks—Special Issue on Dynamic Recurrent Neural Networks: Theory and
`Applications, 5(2):320–330, March 1994.
`
`‘Hidden Markov models for fault detection in dynamic systems,’ Pattern Recognition,
`J8 P. Smyth,
`27(1):149–164, 1994.
`
`J7 Z. Zheng, R. Goodman, and P. Smyth, ‘Learning finite-state machines with self-clustering recurrent
`networks,’ Neural Computation, 5(6):976–990, November 1993.
`
`J6 J. Miller, R. M. Goodman, and P. Smyth, ‘On loss functions which minimize to conditional expected
`values and posterior probabilities,’ IEEE Transactions on Information Theory, 39(4):1404–1408, July
`1993.
`
`J5 P. Smyth, ‘Admissible stochastic complexity models for classification problems,’ Statistics and Com-
`puting, 2:97–104, 1992.
`
`J4 R. M. Goodman, P. Smyth, C. Higgins and J. Miller, ‘Rule-based networks for classification and
`probability estimation,’ Neural Computation, 4:781-804, 1992.
`
`J3 P. Smyth and R. M. Goodman, ‘An information theoretic approach to rule induction from databases,’
`IEEE Transactions on Knowledge and Data Engineering, 4(4):301–316, August 1992.
`
`J2 R. M. Goodman and P. Smyth, ‘Decision tree design using information theory,’ Knowledge Acquisition,
`4(1):1–26, 1990.
`
`J1 R. M. Goodman and P. Smyth, ‘Decision tree design from a communication theory standpoint,’ IEEE
`Transactions on Information Theory,34(5):979-994, September 1988.
`
`Conference Papers
`
`C119 D. Ji, R. Logan, P. Smyth, and M. Steyvers, ‘Active Bayesian assessment for black-box classifiers,’
`Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), Vancouver, Canada,
`2021.
`
`C118 D. Ji, P. Smyth, and M. Steyvers ‘Can I trust my fairness metric? Assessing fairness with unlabeled
`data and Bayesian inference,’ Advances in Neural Information Processing Systems 33 (NeurIPS 2020),
`Vancouver, Canada, 2020.
`
`C117 A. Boyd, R. Bamler, S. Mandt, and P. Smyth, ‘User-dependent neural sequence models for continuous-
`time event data,’ Advances in Neural Information Processing Systems 33 (NeurIPS 2020), Vancouver,
`Canada, 2020.
`
`C116 E. Nalisnick, J. M. Hernandez-Lobato, P. Smyth, ‘Dropout as a structured shrinkage prior,’ Proceedings
`of the 36th International Conference on Machine Learning (ICML), PMLR 97:4712-4722, 2019.
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`C115 D. Ji, E. Nalisnick, Y. Qian, R. Scheuermann, P. Smyth, ‘Bayesian trees for automated cytometry
`data analysis,’ Machine Learning for Healthcare (MLHC) 2018 Conference: Proceedings of Machine
`Learning Research, 85:1–18, 2018.
`
`C114 J. Park, R. Yu, F. Rodriguez, R. Baker, P. Smyth, M. Warschauer, ‘Understanding student procras-
`tination via mixture models,’ Proceedings of the 2018 Educational Data Mining Conference, Buffalo,
`NY, ACM Press, pp.187–197, July 2018 (Best Paper Award).
`
`C113 E. Nalisnick and P. Smyth, ‘Learning priors for invariance,’ Proceedings of the Twenty-First Interna-
`tional Conference on Artificial Intelligence and Statistics: Proceedings of Machine Learning Research
`(84), pp.366–375, April 2018.
`
`C112 M. Lichman and P. Smyth, ‘Prediction of sparse user-item consumption rates with zero-inflated Poisson
`regression,’ Proceedings of the WWW 2018 Conference, pp.719–728, ACM Press, April 2018
`
`C111 E. Nalisnick and P. Smyth, ‘Learning approximately objective priors,’ Proceedings of the Conference on
`Uncertainty in Artificial Intelligence (UAI 2017), Association for Uncertainty in Artificial Intelligence
`(AUAI), August 2017.
`
`C110 E. Nalisnick and P. Smyth, ‘Stick-breaking variational autoencoders,’ Proceedings of the International
`Conference on Learning Representations (ICLR 2017), April 2017.
`
`C109 J. Park, K. Denaro, F. Rodriguez, P. Smyth, M. Warschauer, ‘Detecting changes in student behavior
`from clickstream data,’ Proceedings of the Learning Analytics and Knowledge (LAK) Conference, ACM
`Press, pp. 21–30, March 2017 (Honorable Mention for Best Paper).
`
`C108 M. Lichman, D. Kotzias, and P. Smyth, ‘Personalized location models with adaptive mixtures,’ Pro-
`ceedings of the ACM SIGSPATIAL Conference, New York: ACM Press, October 2016.
`
`C107 D. Kotzias, M. Denil, N. De Freitas, P. Smyth, ‘From group to individual labels using deep features,’
`Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New
`York: ACM Press, pp. 597–606, August 2015.
`
`C106 M. Tanana, K. Hallgren, Z. Imel, D. Atkins, P. Smyth, V. Srikumar, ‘Recursive neural networks
`for coding therapist and patient behavior in motivational interviewing,’ in Proceedings of the 2nd
`Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical
`Reality, Association of Computational Linguistics, pp 71–79, 2015.
`
`C105 N. Navaroli and P. Smyth, ‘Modeling response time in digital human communication,’ Proceedings
`of the 9th International AAAI Conference on Web and Social Media (ICWSM-2015), AAAI Press,
`pp.278–287, May 2015.
`
`C104 K. Bache, P. Smyth, and D. DeCoste, ‘Hot swapping for online adaptation of optimization hyperpa-
`rameters,’ International Conference on Learning Representations (ICLR-2015), May 2015.
`
`C103 M. Lichman and P. Smyth, ’Modeling human location data with mixtures of kernel densities,’ Pro-
`ceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York:
`ACM Press, pp. 35-44, August 2014.
`
`C102 J. Foulds and P. Smyth, ‘Annealing paths for the evaluation of topic models,’ Proceedings of the 30th
`Conference on Uncertainty in Artificial Intelligence, AUAI Press: Corvallis, Oregon, pp.220–229, 2014.
`
`C101 C. DuBois, A. Korattika, M. Welling, and P. Smyth, ‘Approximate slice sampling for Bayesian posterior
`inference,’ in Proceedings of the 17th International Conference on AI and Statistics, JMLR Workshop
`and Conference Proceedings, 33:185–193, 2014.
`
`C100 J. Foulds and P. Smyth, ‘Modeling scientific impact with topical influence regression,’ Proceedings of
`the Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), Association for
`Computational Linguistics, pp.113-123, October 2013.
`
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`

`C99 R. Krestel and P. Smyth, ‘Recommending patents based on latent topics,’ Proceedings of the 7th ACM
`Recommender Systems Conference (ACM RecSys), New York: ACM Press, pp. 395–398, October
`2013.
`
`C98 J. Foulds, L. Boyles, C. DuBois, P. Smyth, M. Welling, ‘Stochastic collapsed variational Bayesian infer-
`ence for latent Dirichlet allocation,’ Proceedings of the 19th ACM SIGKDD Conference on Knowledge
`Discovery and Data Mining, New York: ACM Press, pp.446–454, August 2013.
`
`C97 K. Bache, D. Newman, P. Smyth, ‘Text-based measures of topic diversity,’ Proceedings of the 19th ACM
`SIGKDD Conference on Knowledge Discovery and Data Mining, New York: ACM Press, pp.23–31,
`August 2013.
`
`C96 C. DuBois, C. T. Butts, P. Smyth, ‘Stochastic blockmodeling of relational event dynamics,’ in Pro-
`ceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, Carvalho,
`Carlos M. and Ravikumar, Pradeep (eds.), JMLR Workshop and Conference Proceedings, 31:238-246,
`May 2013.
`
`C95 M. J. Bannister, C. DuBois, D. Eppstein, P. Smyth, ‘Windows into relational events: data structures for
`contiguous subsequences of edges,’ Proceedings of the ACM-SIAM Symposium on Discrete Algorithms
`(SODA), SIAM, pp.856–864, January 2013.
`
`C94 N. Navaroli, C. DuBois, P. Smyth, ‘Statistical models for exploring individual email communication be-
`havior,’ Proceedings of the 4th Asian Conference on Machine Learning (ACML 2012), JMLR Workshop
`and Conference Proceedings, 25:317-332, 2012.
`
`C93 A. Frank, P. Smyth, and A. T. Ihler, ‘A graphical model representation of the track-oriented multiple
`hypothesis tracker,’ Proceedings of the IEEE Statistical Signal Processing (SSP) Workshop, pp.768–771,
`2012.
`
`C92 J. Ion Titapiccolo, M. Ferrario, C. Barbieri, D. Marcelli, F. Mari, E. Gatti, S. Ceruto, P. Smyth,
`and M. G. Signorini, ‘Predictive modeling of cardiovascular complications in incident hemodialysis
`patients,’ Proceedings of the IEEE International Conference on Engineering in Medicine and Biology,
`IEEE Press, pp.3943–3946, August 2012.
`
`C91 D. Q. Vu, A. Asuncion, D. R. Hunter, and P. Smyth, ‘Continuous-time regression models for longitu-
`dinal networks,’ Proceedings of the 25th Conference on Neural Information Processing Systems (NIPS
`2011), pp.2492–2500, Dec 2011.
`
`C90 C. DuBois, J. Foulds, and P. Smyth, ‘Latent set models for two-mode data,’ Proceedings of the 5th
`International AAAI Conference on Weblogs and Social Media, AAAI Press, pp.137-144, 2011.
`
`C89 D. Q. Vu, A. Asuncion, D. R. Hunter, and P. Smyth, ‘Dynamic egocentric models for citation networks,’
`Proceedings of the 28th International Conference on Machine Learning (ICML 2011), Omnipress Con-
`ference Publishers, pp.857-864, June 2011.
`
`C88 J. Foulds ,C. DuBois, A. Asuncion, C. T. Butts, and P. Smyth, ‘A dynamic relational infinite feature
`model for longitudinal social networks,’ Proceedings of the 14th International Conference on AI and
`Statistics, in volume 15 of the Journal of Machine Learning Research, 15:287-295, April 2011.
`
`C87 J. Foulds, N. Navaroli, P. Smyth, and A. T. Ihler, ’Revisiting MAP estimation, message passing, and
`perfect graphs,’ Proceedings of the 14th International Conference on AI and Statistics, in volume 15
`of the Journal of Machine Learning Research, 15:278-286, April 2011.
`
`C86 J. Foulds and P. Smyth, ‘Multi-instance mixture models and semi-supervised learning,’ Proceedings of
`the 11th SIAM International Conference on Data Mining, pp.606–617, April 2011.
`
`C85 A. Chambers, P. Smyth, and M. Steyvers, ‘Learning concept graphs with stick-breaking priors,’ Neural
`Information Processing Systems (NIPS) 23, Cambridge, MA: MIT Press, pp.334–342, December 2010.
`
`9
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`

`C84 C. DuBois and P. Smyth, ’Modeling relational events via latent classes,’ Proceedings of the Sixteenth
`ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York: ACM
`Press, pp.803–812, July 2010.
`
`C83 A. Asuncion, Q. Liu, A. Ihler, and P. Smyth, ‘Particle filtered MCMC-MLE with connections to
`contrastive divergence,’ 27th International Conference on Machine Learning (ICML 2010), Omnipress
`Conference Publishers, pp.47–54, July 2010.
`
`C82 A. Asuncion, Q. Liu, A. Ihler, and P. Smyth, ‘Learning with blocks: composite likelihood and con-
`trastive divergence,’ 13th International Conference on AI and Statistics, JMLR Conference Proceedings,
`9:33-40, May 2010.
`
`C81 A. Ihler, A. J. Frank, P. Smyth, ‘Particle-based variational inference for continuous systems,’ Neural
`Information Processing Systems (NIPS) 22, Cambridge, MA: MIT Press, pp.826–834, December 2009.
`
`C80 A. Asuncion, M. Welling, P. Smyth, and Y. Teh ‘On smoothing and inference for topic models,’
`Proceedings of the 25th Conference on Uncertainty in AI, AUAI Press, pp.27–34, June 2009.
`
`C79 A. Asuncion, P. Smyth, and M. Welling, ‘Asynchronous distributed learning of topic models,’ Neural
`Information Processing Systems (NIPS) 21, Cambridge, MA: MIT Press, pp.81–88, December 2008.
`
`C78 C. Chemudugunta, P. Smyth, and M. Steyvers, ‘Combining concept hierarchies and statistical topic
`models,’ Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM-
`08), New York: ACM Press, pp.1469-1470, October 2008.
`
`C77 C. Chemudugunta, A. Holloway, P. Smyth, and M. Steyvers, ’Modeling documents by combining
`semantic concepts with unsupervised statistical learning,’ in Proceedings of the International Semantic
`Web Conference (ISWC-08), Springer Verlag, Berlin, pp.229–244, October 2008.
`
`C76 I. Porteous, D. Newman, A. Ihler, A. Asuncion, P. Smyth, M. Welling, ’Fast collapsed Gibbs sampling
`for latent Dirichlet allocation,’ Proceedings of the Fourteenth ACM SIGKDD International Conference
`on Knowledge Discovery and Data Mining, New York: ACM Press, pp.569–577, August 2008.
`
`C75 J. Hutchins, A. Ihler, P. Smyth, ‘Modeling count data from multiple sensors: a building occupancy
`model,’
`in Computational Advances in Multisensor Adaptive Processing (CAMSAP), IEEE Press,
`pp.241–244, 2007.
`
`C74 D. Newman, A. Asuncion, P. Smyth, M. Welling, ’Distributed inference for latent Dirichlet allocation’,
`Advances in Neural Information Processing Systems 20, Cambridge MA: MIT Press, pp.1081–1088,
`December 2007.
`
`C73 S. Kirshner and P. Smyth, ‘Infinite mixtures of trees,’ in Proceedings of the 24th International Confer-
`ence on Machine Learning, New York: ACM International Conference Proceeding Series, pp.417–723,
`June 2007.
`
`C72 D. Newman, K. Hagedorn, C. Chemudugunta, and P. Smyth, ‘Subject metadata enrichment using
`statistical topic models,’ in Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, New
`York: ACM Press, pp.366–375, June 2007.
`
`C71 A. Ihler and P. Smyth, Learning time-intensity profiles of human activity using non-parametric Bayesian
`models, Advances in Neural Information Processing Systems 19, Cambridge MA: MIT Press, pp.625–
`632, December 2006.
`
`C70 C. Chemudugunta, P. Smyth, and M. Steyvers, Modeling general and specific aspects of documents
`with a probabilistic topic model, Advances in Neural Information Processing Systems 19, Cambridge
`MA: MIT Press, pp. 241-248, 2006.
`
`C69 S. Kim and