Hugh Chipman - Talks

Only talks since 2001 are listed. Papers for many of these talks are available online.
 

(pdf) Let's practice what we preach: Planning and interpreting simulation studies with design and analysis of experiments SSC Annual Meeting, May 30, 2022
(pdf) Simulation Studies for Statistical Procedures: Why Can't We Practice What We Preach? WLU & McMaster, April 2021
(pdf) Why Most Published Research Findings Are False AARMS Kitchen Party, August 6, 2020
(pdf) A hierarchical state-space approach for modeling population indices data SSC annual meeting, Calgary, May 29, 2019
(pdf) Panel discussion on tenure and promotion SSC annual meeting, Calgary, May 28, 2019
(pdf)
(pdf)
An overview of statistical learning (version including deep learning; 2nd pdf longer) Rendez-Vous 3, SSO, Sep 22, 2017
AARMS workshop, Oct 15, 2017
(pdf) An overview of statistical learning Fields Lecture, Science Atlantic MSCS Conference, CBU, October 15, 2016
(pdf) Dispersion Modelling with an Ensemble of Trees SSC Annual Conference, St. Catherines, May 30. 2016.
JSM, Chicago, Aug 1, 2016.
Spring Research Conference, May 17,2017.
(pdf) Doing more with less: Statistical planning of experimental studies Acadia University Math Society Seminar (November 13, 2015)
(pdf) Panel discussion on education for big data analytics Closing Conference of Big Data Thematic Program, Dalhousie (June 13, 2015)
(pdf) Statistical and Computational Challenges in Networks and Cybersecurity Closing Conference of Big Data Thematic Program, Dalhousie (June 12, 2015)
(pdf) Simulation Studies for Statistical Procedures: Why Can't We Practice What We Preach? SSC Annual Conference, Dalhousie (June 15, 2015)
(pdf) A Statistical Pocket Knife: Generalizing and Extending Ensemble Models Harvard Statistics Seminar (April 13, 2015)
(pdf) | (video) An overview of statistical learning Opening conference and boot camp, Thematic Program on Statistical Inference, Learning, and Models for Big Data, Fields Institute (Jan 12, 2015), and ICES (Jan 27, 2015)
(website) Statistical Learning with Big Data AARMS Summer School, co-taught with Sunny Wang, July 21-Aug 15, 2014
(pdf) Bayes for model search and representing uncertainty Building Statistical Methodology and Theory 2014" a conference in honor of Jeff Wu's 65th Birthday
Mile, China, July 8, 2014.
(pdf) Statistical Learning for Networks AARMS CRG meeting,
MSVU, June 16 2014
(pdf) Google search and other network problems - the role of randomness Acadia Mathematics and Statistics Undergraduate Seminar Series, January 17, 2014
Acadia University
(pdf) Ensemble Learning for Big Data Joint Statistical Meetings, August 4, 2013, Montreal, Conference on Data Analysis, Santa Fe NM March 6, 2014, and Statistical Society of Canada, May 26, 2014.
(pdf) Sequential Optimization of a Computer Model and other �Active Learning� problems CRM-SIM-GERAD colloquium, UQAM, March 9, 2012
ISyE, Georgia Tech, March 29, 2012
UQ 2012 Conference, Raleigh, NC, April 2, 2012
(pdf) Better Statistical Learning via Sequential Design or ``Active Learning'' Mary Thompson Conference, October 29, 2011
(pdf) Sequential optimization of a computer model: what's so special about Gaussian processes anyway? Spring Research Conference, June 22, 2011
(pdf) "Discovering Regression Structure with a Bayesian Ensemble" Dalhousie University, March 10, 2011
(pdf) "Mining transactional network data" Los Alamos, New Mexico, January 26, 2011
(pdf) "Knowledge Discovery in Network Data" Carleton University, October 30, 2010
(pdf) Discussion of session on Dynamic Networks SAMSI, August 31, 2010
(pdf) Discussion of "Nonparametric Profile Monitoring by Mixed Effects Modeling" Joint Statistical Meetings, August 3, 2010
(pdf) Sequential optimization of a computer model: what's so special about Gaussian processes anyway? Statistical Society of Canada, May 26, 2010
INFORMS, Austin Tx, Nov 9
(pdf) Math and networks 15 min talk at Acadia Huggins Science Seminar, May 6, 2010
(pdf) | (pdf, with annotations) Data Mining 1-day workshop at Statistical Society of Ottawa, April 19, 2010
(pdf) Mining Transactional Network Data Statistical Society of Ottawa, April 20, 2010
Workshop on "Challenging problems in Statistical Learning", Paris, January 28, 2010
Simon Fraser Dept. of Statistics & ActSci, February 12, 2010 Dalhousie School of Computer Science, February 18, 2010
(pdf) Feature Selection with a Bayesian Ensemble Model Fall Technical Conference, Indiannapolis, IN, October 8, 2009
(pdf) Discussion of presentations by Jeremy Oakley and Brian Reich Joint Statistical Meetings, Washington DC, August 4, 2009
(pdf) Variable Selection via a Bayesian Ensemble Joint Statistical Meetings, Washington DC, August 4, 2009
(pdf) Statistical learning for networks Spring Research Conference, Burnaby, BC, May 29, 2009
(pdf) Statistical learning with trees (CRM-SSC lecture) SSC Annual Meeting, Vancouver, BC, June 3, 2009
(pdf) Modelling Networks: Google, Social Networks, and Beyond Acadia Math and Stat Seminar, Feb 6, 2009
(pdf) Variable selection via a Bayesian ensemble Joint Statistical meetings, August 7, 2008
(pdf) Supervised learning for graph-structured data UBC Statistics department, June 27, 2008.
(pdf) Supervised classification for graph-structured data, using latent social networks MITACS/MASCOS Joint Workshop on Fusion, Mining and Security for Networks, June 19, 2008.
(pdf) Statistical Learning via Bayesian Computation and Other Methods Bluenose Numerical Analysis Day, June 13, 2008, Dalhousie University.
(pdf) (ppt) Tips for preparing an NSERC Discovery Grant SSC annual meeting, Ottawa, May 26, 2008
(pdf) Mining Network Data Poster at SSC annual meeting, Ottawa, May 28/2008
(pdf) Statistical learning from complex data University of Waterloo, March 19, 2008.
(pdf) Supervised learning via Bayesian Computation Coast to Coast Mathematics Seminar, November 6, 2007
(pdf) Statistical learning and virtual screening in drug discovery McGill University, October 24, 2007
(pdf) Bayesian Ensemble Active Learning Joint Statistical Meetings, Salt Lake City, August 29, 2007
(pdf) Supervised Learning via Bayesian Computation Statistical Science: Present position and future prospects, University of Waterloo, May 30, 2007.
(pdf) BART; Bayesian Additive Regression Trees University of Washington, April 16, 2007
(pdf) Learning from data: statistical ideas and algorithms Acadia Science Cafe, March 5, 2007
(pdf) Monitoring functional data: mixed effects and high-dimensional clustering CRM-ISM-Gerad Colloquium de Statistique
February 23, 2007
Talk: (pdf)

Poster: (pdf)

Bayesian Ensemble Learning NIPS 2006, Dec 8, 2006
(pdf) Sequential design for drug discovery DEMA workshop, Southampton, UK, Sept 9, 2006. Also presented a similar version at AARMS Multivariate analysis workshop, Cape Breton University, October 14, 2006.
(pdf) Mining transactional data on networks CAIMS/MITACS annual meeting, York University, June 19, 2006
(R lecture; html)
(R lab 1; html)
(Nnet lecture; pdf)
(R lab 2;html)
Introduction to R
R lab on statistical learning and resampling methods
A neural net example
R lab on neural nets
NPCDS/MITACS/CRM Spring School on Statistical and Machine Learning, University of Montreal, May 23-27, 2007

(pdf)

Bayes 101: An introduction to Bayesian Methods

Biology Department, Acadia University, April 25, 2006

(pdf) Research in Mathematical Modelling Acadia CRC event, January 20, 2006.

(pdf)

Statistical Modelling in Medical Compliance Problems

Statistical Consulting Centre workshop on compliance, December 9, 2005

(pdf)

Opportunities in Parallel Statistical Computing

AARMS workshop, Acadia University, October 23, 2005

Also presented at Fields Workshop on Data Mining closed research session, Sunday November 13, 2005.

(pdf)

Pattern Discovery in Massive Social Networks

SAMSI NDHS workshop, September 14, 2005

(pdf)

Bayesian Additive Regression Trees

PAMI group, University of Waterloo, April 26, 2005 (see Dalhousie link below for "movies").

(html)

Data analysis using R

Statistical Consulting Centre workshop, Acadia University.

(see links below for Dalhousie talk)

Bayesian Additive Regression Trees (2 hour seminar)

CIMAT, Mexico, November 29 and December 1.

(pdf)
(movie1)
(movie2)
(movie3)
(movie4)

Bayesian Additive Regression Trees (2 hour seminar)

Dalhousie University, November 18, 2004.

Daily discussion

Generation 5/NPCDS/Fields/SAMSI workshop on data mining methodology and applications, October 29, 2004.

(pdf)

Data Mining and Statistical Learning

2004 Acadia Symposium on Modelling and Computation, Oct 4/2004.

(pdf)

Interpretability of trees from high-throughput screening data

2004 JSM, Toronto, August 11, 2004

(pdf)

Discussion on Ensemble Methods

2004 JSM, Toronto, August 9, 2004

(pdf)
(pdf)

Statistical Learning in Drug Discovery

University of Guelph March 26, 2004. (talk is in 2 parts)

(pdf)

Some research in data mining

SAMSI mid-year workshop on data mining and machine learning. Research Triangle Park, NC, Feb 4, 2004.

(pdf)

Using Data Mining to Uncover Rare, Valuable Outcomes

Mitacs Quebec Interchange, Montreal, Nov 13, 2003.

(pdf)

Bayesian Additive Regression Trees

UW AI group, Nov 7, 2003.

(pdf)

Bayesian Additive Regression Trees

Snowbird Workshop, April 1, 2003.

(pdf)

Bayesian Additive Regression Trees

York University Department of Mathematics and Statistics, March 21, 2003.

(pdf)

Statistical Learning and Data Mining

Notes from a roundtable luncheon I gave at the Joint Statistical Meetings, New York, August 13, 2002.

(pdf)

Learning Treed Generalized Linear Models

Joint Statistical Meetings, New York, August 12, 2002. (modified version of Interface, Stanford/Chicago talks below).

(tech report only) (pdf)

Optimal Designs for Model Selection

Valencia International Meetings on Bayesian Statistics, Tenerife, Wednesday June 5, 2002.

(pdf - 0.6 Mb)

Hybrid Hierarchical Clustering With Applications To Microarray Data }

Statistical Society of Canada Annual Meeting Hamilton, ON, May 27, 2002
Institute of Mathematical Statistics Annual Meeting Banff, AB, July 29, 2002

(pdf)

Mining Functional Process Data

Spring Research Conference on Statistics in Industry and Technology, Ann Arbor, MI, May 20, 2002

(pdf)

Learning Treed Generalized Linear Models

Interface meeting, Montreal, April 20, 2002. (modified version of Stanford/Chicago talk below).

(pdf)
(abstract)

Treed Generalized Linear Models

Stanford University Department of Statistics, Jan 29, 2002
University of Chicago Graduate School of Business, Feb 6, 2002.

(HTML)

Data Mining Workshop

SSC/WNAR/IMS, 2001
IIQP 2001
2002 IISA meeting

(pdf)

Statistical methods for high throughput screening data

MITACS IT-theme meeting, 2001

(pdf)

Regression and Classification With Tree Models: Forests, Hybrids, and Other New Methods.

University of Michigan, 2001

(pdf)

Monitoring production with immense datasets

Guest speaker, UW Graduate student seminar day, 2001

(pdf)

Managing Multiple Models

Joint Statistical Meetings, 2001

(pdf)

Classification and Regression Trees

ICES, 2001