John MacCormick | Dickinson College

Associate Professor of Computer Science

B.A., University of Cambridge, 1993
M.S., University of Auckland, 1996
Ph.D., University of Oxford, 2000

John MacCormick has degrees in mathematics from the University of Cambridge and the University of Auckland, and a doctorate in computer vision from the University of Oxford. He was a research fellow at Linacre College, Oxford from 1999-2000, a research scientist at HP Labs from 2000-2003, and a computer scientist with Microsoft Research from 2003-2007. Since 2007, Dr. MacCormick has been a professor of computer science at Dickinson College. He is the author of three books, including Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers and What Can Be Computed?: A Practical Guide to the Theory of Computation. Dr. MacCormick has filed over a dozen US patents on novel computer technologies and is the author of numerous peer-reviewed articles; his Nine Algorithms book has been translated into eight languages. Dr. MacCormick's work spans several sub-fields of computer science, including computer vision, large-scale distributed systems, computer science education, and the public understanding of computer science.

Books

This is an undergraduate textbook for a course on Theory of Computation. The key characteristic of the book is that it is designed to be uniquely accessible to undergraduates, focussing on real computational problems using real computer programs as the computational model. Please see the official What Can Be Computed? website.

Here are direct links to ancillary materials for the book, which are released under the Creative Commons Attribution 4.0 International License:

Python progams, wcbc-programs-v1.1.zip
Java programs, wcbc-java-v1.0.zip
the online appendix, wcbc-appendixA-v1.0.pdf
lecture slides, wcbc-lecture-slides-v1.0.zip

The book also has a page at Princeton University Press, and is available from the usual sources such as Barnes & Noble and Amazon.

cover of "What Can Be Computed?" by John MacCormick

Nine Algorithms That Changed the Future:
The Ingenious Ideas That Drive Today's Computers

9Algs cover

What is this book about? It explains how your cell phone, laptop, tablet, and other computers do the amazing things they do. How they can search the entire web in a fraction of a second. How they can transmit vast amounts of data over unreliable wireless connections, without making a single error. How they can recognize your speech and handwriting. And many others.

The book assumes no knowledge of computer science. It's designed to be read and enjoyed by anyone who's curious about how our computing devices manage to be so smart. Interested? Please, peek inside, or check out the table of contents. There's more good stuff at the 9 Algorithms Facebook page, and the book's official page at Princeton University Press. If you want to buy it, 9 Algorithms is available as an eBook or hardcover from all the usual suspects, including Barnes & Noble and Amazon. And of course there's a YouTube video:

Some people were kind enough to say nice things about the book; here are two of them:

Chuck Thacker, winner of the 2010 Turing Award—the highest award in computer science—said: "This book is for those who have wondered, 'What actually goes on in my computer?' MacCormick clearly explains some of the algorithms used by hundreds of millions of people daily. Not the simple algorithms like arithmetic and sorting, but more complex things such as how to determine the importance of web pages, if and when we are justified in trusting a computer-mediated conversation with another person, and the puzzling issue of what cannot be computed. I recommend it highly."
Andrew Fitzgibbon, who created the Emmy-award-winning camera software boujou, said: "It's been a long time since any book has given me the excitement I remember from reading Hawking and Feynman in my teens. This book does exactly that. It reminds me why I love computer science. MacCormick's explanations are easy to understand yet they tell the real story of how these algorithms actually work. This is a book that deserves not just to be admired, but celebrated."

Award from the Association of American Publishers

9 Algorithms received an Honorable Mention for the 2012 Award for Best Professional/Scholarly Book in Computing & Information Sciences, awarded by the Association of American Publishers.

Translations

9 Algorithms has been translated (or in some cases, is currently being translated) into the following eight languages: traditional Chinese, simplified Chinese, Japanese, Korean, Russian, Italian, Greek, and Turkish.

Media coverage

Media coverage of the book includes:

Review in EE Times, by Clive Maxfield, April 3, 2012.
Featured on the Australian Broadcasting Corporation's national radio program, Future Tense, in an episode called "The Algorithm": web page, mp3 audio.
Review in Financial World, by Diana Hunter, p45, April 2012: Html; PDF.
Review in SIAM News, March 2012 issue, by Ernest Davis. Free excerpt; full text available in print only at present; appears online at SIAM News after 6-8 weeks.
Review in the journal Science, Volume 335, p1305, 16 March 2012: Stunningly Successful Solutions, by Paul Curzon. Free excerpt; full text in HTML (requires subscription); full text in PDF (requires subscription).
Scientific American Book Club's dual-featured selection for March 2012.
Interview with Sol Lederman, part of the Inspired by Math podcast series on the Wild about Math blog, March 12, 2012.
Review in Frankfurter Allgemeine Zeitung, by Detlef Borchers, March 4, 2012.
Review in the Barnes & Noble Review, February 23, 2012, by Paul Di Filippo.
Review in Nature Physics, Vol 8(2), February 2012, p105, by Andreas Trabesinger. Free excerpt; Full text (subscription required).
A review by Brent Yorgey on his blog, The Math Less Traveled, February 4, 2012.
Review in BBC Focus magazine, p103, February 2012, by Robert Matthews. Free excerpt; full text (requires subscription).
Times Higher Education’s Book of the Week in the January 26, 2012 edition (issue 2034), pages 52-53. Reviewed by John Gilbey.
Interview with Michael "TechTalk" Kastler and Dave "Sparky" Saganaki on the WRLR radio show and podcast, TechTalk. January 21, 2012.
An article about algorithms in the Italian daily newspaper Corriere Della Sera, written by me and translated by Maria Sepa: the article, entitled Gli Algoritmi del Successo, appeared on page 7 of the La Lettura section, on January 15, 2012.
Review in New Scientist magazine (issue 2847, 14 January 2012, page 47), by Kevin Slavin. Also published as a New Scientist CultureLab blog post.
An interview with Sean Moncrieff on the Irish radio station Newstalk. The interview took place on January 18, 2012, on the show Moncrieff.
The Enlightened Economist blog post, by Diane Coyle, January 8, 2012.
New York Journal of Books review, by Richard Isaacman, January 3, 2012.
Review for the MAA (Mathematical Association of America) Digital Library, by Art Gittleman, December 26, 2011.
CTK Insights blog post, November 23, 2011.

Stochastic Algorithms for Visual Tracking: Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking

Springer, 2002. 174 pages.

This technical monograph describes a mathematical and computational framework for implementing algorithms that track moving objects in video. The approach emphasizes the use of probability distributions to model knowledge of the object state. A key consideration is how to approximate, store, and efficiently update such distributions when the dimensionality of the object state is large.

Teaching

Spring 2019:

SCIE300: Science and Society [global study course for the Dickinson Sciences Program, based at the University of East Anglia in Norwich, England]

Fall 2018:

SCIE258 (Topics in History of Science): The history of science, technology and culture through the lens of London and the United Kingdom [global study course for the Dickinson Sciences Program, based at the University of East Anglia in Norwich, England]

Spring 2018:

SCIE300: Science and Society [global study course for the Dickinson Sciences Program, based at the University of East Anglia in Norwich, England]

Fall 2017:

SCIE258 (Topics in History of Science): The history of science, technology and culture through the lens of London and the United Kingdom [global study course for the Dickinson Sciences Program, based at the University of East Anglia in Norwich, England]

Spring 2017:

Fall 2016:

Spring 2016:

Fall 2015:

Spring 2015:

Fall 2014:

Spring 2014:

Fall 2013:

Spring 2013:

COMP204/INST290: Information Security and Cyber Warfare
COMP492: Senior Seminar in Computer Science
Independent study: C++ for Financial Engineering

Fall 2012:

Spring 2011:

Fall 2010:

Spring 2010:

Fall 2009:

Spring 2009:

Fall 2008:

Spring 2008:

Fall 2007:

Research

My main area of research is computer vision—that is, getting computers to understand images and video—but I enjoy working on a wide variety of problems in computer science. For example, I've also worked on large-scale distributed data storage systems, and pair programming techniques for undergraduate computer science education. Here are a few of the questions my research has attempted to address over the years:

How can we track a person's finger accurately enough with a video camera to allow fluent human-computer interaction without using any type of touchscreen, mouse, or pointing device? (This is work from my PhD thesis, later published as chapter 7 of the book Stochastic Algorithms for Visual Tracking, and in the conference paper Partitioned sampling, articulated objects, and interface-quality hand tracking.)
When undergraduate students work in pairs on programming assignments in an introductory computer science course, how should pairs be assigned? Randomly? Or is it better to pair strong students with other strong students and less able students with other less able students? Or do pairs of mixed ability lead to better outcomes? (This question is tackled in a SIGCSE conference paper, The Benefits of Pairing by Ability.)
Suppose you are trying to store, in a data center, millions of files on behalf of thousands of customers. To guard against data loss, each file should have, say, three copies stored on different computers in the data center. How do you decide where to store the copies of each file, in such a way that the file can be efficiently retrieved later, while keeping the load on each computer balanced? (A possible solution is provided by an article in the ACM Transactions on Storage, Kinesis: A New Approach to Replica Placement in Distributed Storage Systems.)
These days it's not uncommon to use video chat software such as Skype with two cameras, such as the front and back cameras on a tablet or cell phone. But can we enhance the experience using more cameras? If so, is this technically possible on commodity hardware? Is the conversation further enhanced by giving the remote viewer the ability to control the viewpoint? These questions are addressed by the MultiCam research project and MultiCam software. The results appear in a conference paper and a technical report.

To get a more complete picture of my research, please take a look at my publications and patents. You may also be interested in the list of talks I have given, and a separate section describing research with undergraduate students below.

Advisor for honors thesis: An exploration of the incremental learning of neural networks. Jiahao Han, 2017.
Advisor for honors thesis: Whiteboard Scanning using Super-resolution. Wode Ni, 2016.
Advisor for student senior research project: A Hidden Markov Model Approach to Distinguishing between Non-Prototypical Displays of Boredom and Interest. Justine Heritage, 2014.
Advisor for student senior research project: Multicam Video Stitching. Daniel Appello and Danielle Erickson, 2014.
Advisor for student senior research project: Dimension Reduction of the SIFT descriptor and its Object Recognition Application. Danfei Xu, 2013.
Advisor for NSF-funded student research project: Developing a Technique for Classifying Pain-Related SEP Waveforms. Danfei Xu, 2012. NSF grant IIP-0917466, subaward 4501-DC-NSF-7466.
Co-advisor for student senior research project: Recapitulation of Leukemia Cell Gene Clusters using Transcription Factor Binding Sites as Indicators of Gene Expression. Philip Hubert. Dickinson College senior project thesis, 2012.
Advisor for honors thesis: PathFinder in CUDA. James Doyle. Dickinson College computer science honors thesis, 2010. Presented at Dickinson Science Research Symposium, 2010 (poster).
Advisor of student-faculty summer research project: The PathFinder image segmentation algorithm. Fabio Drucker, 2009. Also appeared as: Fast Superpixels for Video Analysis, Fabio Drucker and John MacCormick, in Proc. IEEE Workshop on Motion and Video Computing (WMVC), 2009.
Advisor for honors thesis: Improving the two color prior Bayesian demosaicing algorithm. Ke Zhou. Dickinson College computer science honors thesis, 2009. Presented at Sigma Xi Research Symposium, St. Joseph's University, 2009 (poster).
Advisor for honors thesis: Dynamical Effects of Non-Linearities and Time-Varying Gain Modulation in Neurally Plausible Network Models of Perceptual Decision-Making. Ritwik Niyogi. Dickinson College mathematics honors thesis, 2009. Presented at Sigma Xi Research Symposium, St. Joseph's University, 2009 (poster). It also appeared as: Time-varying gain modulation on neural circuit dynamics and performance in perceptual decisions, R. Niyogi and K.F. Wong-Lin, Abstract #246, Computational and Systems Neuroscience (CoSyNe) 2010, Salt Lake City, UT, USA (Feb, 2010), and was later published in a peer-reviewed journal as: Niyogi RK, Wong-Lin K (2013) Dynamic Excitatory and Inhibitory Gain Modulation Can Produce Flexible, Robust and Optimal Decision-making. PLoS Comput Biol 9(6).

Books

What Can Be Computed?: A Practical Guide to the Theory of Computation.
J. MacCormick.
Princeton, 2018. 408pp.
Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers.
J. MacCormick.
Princeton, 2012. 248pp.
Stochastic Algorithms for Visual Tracking.
J. MacCormick.
Springer, 2002. 174pp.

Peer-reviewed conference papers

Strategies for basing the CS theory course on non-decision problems.
John MacCormick.
In Proc. ACM SIGCSE, pp521-526, 2018.
An extended version is also available as a technical report (and also on the arXiv).
Curvature regularization for resolution-independent images.
John MacCormick and Andrew Fitzgibbon.
In Proc. Intl. Conf. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'13).
Springer, Lecture Notes in Computer Science Volume 8081, pp 165-179, 2013.
The link above is for the version typeset by the authors. The official published version is available from Springer.
An extended version is also available as a technical report.
The Benefits of Pairing by Ability.
Grant Braught, John MacCormick, and Tim Wahls.
In Proc. ACM SIGCSE, pp249-253, 2010.
Fast Superpixels for Video Analysis.
Fabio Drucker and John MacCormick.
In Proc. IEEE Workshop on Motion and Video Computing (WMVC), 2009.
Java code: PathFinder.zip.
Continuously-adaptive discretization for message-passing algorithms.
Kannan Achan, Michael Isard, John MacCormick.
In Proc. Neural Information Processing Systems (NIPS), 2008.
CT-NOR: Representing and Reasoning About Events in Continuous Time.
Aleksandr Simma, Moises Goldszmidt, John MacCormick, Paul Barham, Richard Black, Rebecca Isaacs, Richard Mortier.
In Proc. Uncertainty in Artificial Intelligence (UAI), 2008.
Experiences with Formal Specification of Fault-Tolerant File Systems.
Roxana Geambasu, Andrew Birrell, and John MacCormick.
In Proc. Dependable Computing and Communications Symposium (DCCS), 2008.
Discovering Dependencies for Network Management.
Paramvir Bahl, Paul Barham, Richard Black, Ranveer Chandra, Moises Goldszmidt, Rebecca Isaacs, Srikanth Kandula, Lun Li, John MacCormick, David A. Maltz, Richard Mortier, Mike Wawrzoniak, Ming Zhang.
In HotNets'06.
Dense Motion and Disparity Estimation via Loopy Belief Propagation.
Michael Isard and John MacCormick.
In ACCV 2006, Hyderabad, India, January 2006.
Estimating Disparity and Occlusions in Stereo Video Sequences.
Oliver Williams, Michael Isard and John MacCormick.
In CVPR 2005, San Diego, USA, June 2005.
Boxwood: Abstractions as the Foundation for Storage Infrastructure.
John MacCormick, Nick Murphy, Marc Najork, Chandramohan A. Thekkath, Lidong Zhou
In OSDI 2004, pages 105-120.
Computational and performance issues in Coliseum, an immersive videoconferencing system.
H. Harlyn Baker, Nina Bhatti, Donald Tanguay, Irwin Sobel, Dan Gelb, Michael E. Goss, John MacCormick, W. Bruce Culbertson, and Thomas Malzbender.
In Proc. 11th ACM Int. Conf. on Multimedia, 2003.
Block-based security for network-attached disks.
M. Ji, M. Lillibridge, J. MacCormick, E. Oertli, M. Aguilera, D. Andersen, M. Burrows, T. Mann, and C. Thekkath.
In Proc. Usenix Conf. on File and Storage Technology (FAST'03), pages 159-174, 2003.
Automatic camera calibration from a single Manhattan image.
M. Isard and J. MacCormick.
In European Conf. Computer Vision, 2002.
Myriad: Cost-effective disaster tolerance.
F. Chang, M. Ji, S. Leung, J. MacCormick, S. Perl, and L. Zhang.
In Usenix Conf. on File and Storage Technology (FAST'02), pages 103-116, 2002.
BraMBLe: A Bayesian Multiple-Blob Tracker.
M. Isard and J. MacCormick.
In Proc. 8th Int. Conf. Computer Vision, 2001.
Learning switching linear models of human motion.
V. Pavlovic, J. Rehg, and J. MacCormick.
In Neural Information Processing Systems, 2000.
Partitioned sampling, articulated objects, and interface-quality hand tracking.
J. MacCormick and M. Isard.
In Proc European Conf. Computer Vision, 2000.
Demo (clunky by modern standards, but it was 1999, after all).
A probabilistic exclusion principle for tracking multiple objects.
J. MacCormick and A. Blake.
In Proc. 7th Intl. Conf. Computer Vision, pages 572-587, 1999.
Object localisation by Bayesian correlation.
J. Sullivan, A. Blake, M. Isard, and J. MacCormick.
In Proc. 7th Intl. Conf. Computer Vision, pages 1068-1075, 1999.
Spatial dependence in the observation of visual contours.
J. MacCormick and A. Blake.
In Proc. 5th European Conf. Computer Vision, pages 765-81, 1998.
A probabilistic contour discriminant for object localisation.
J. MacCormick and A. Blake.
In Proc. 6th Int. Conf. Computer Vision, pages 390-95, 1998

Book chapters

Statistical models of shape and motion.
A. Blake, M. Isard, and J. MacCormick.
Sequential Monte Carlo methods in practice, ed. A. Doucet, J.F.G. de Freitas and N.J. Gordon, pages 339-357. Springer, 2000.
Earlier version available as a journal article.
A geometrical approach to calculating determinants of Wiener-Hopf operators.
J. MacCormick and B. Pavlov.
In A. Bohm, H. Doebner, and P. Kielanowski, editors, Irreversibility and causality: semigroups and rigged Hilbert spaces, pages 333-42. Springer, 1998.
Full version available as a technical report.

Journal articles

Differences in Judgments of Creativity: How Do Academic Domain, Personality, and Self-Reported Creativity Influence Novice Judges’ Evaluations of Creative Productions?.
Mei Tan, Catalina Mourgues, Sascha Hein, John MacCormick, Baptiste Barbot, and Elena Grigorenko.
J. Intell., 3(3), 2015, pages 73-90. Abstract, pdf.
Kinesis: A New Approach to Replica Placement in Distributed Storage Systems.
John MacCormick, Nicholas Murphy, Venugopalan Ramasubramanian, Udi Wieder, Junfeng Yang, Lidong Zhou
ACM Trans. Storage, 4(4), 2009, pages 1-28.
Niobe: A Practical Replication Protocol.
John MacCormick, Chandu Thekkath, Marcus Jager, Kristof Roomp, Lidong Zhou, Ryan Peterson.
ACM Trans. Storage, 3(4), 2008, pages 1-43.
Ad hoc Extensibility and Access Control.
U. Erlingsson and J. MacCormick.
ACM Operating Systems Review, July 2006.
Bayesian object localisation in images.
J. Sullivan, A. Blake, M. Isard, and J. MacCormick.
Intl J. Computer Vision, 44(2):111-135, 2001.
Probabilistic exclusion and partitioned sampling for multiple object tracking.
J. MacCormick and A. Blake.
Intl J. Computer Vision, 39(1):57-71, 2000.
Statistical models of shape and motion.
A. Blake, B. Bascle, M. Isard, and J. MacCormick.
Phil. Trans. R. Soc. Lond. A, 356:1283-1302, 1998.
Spectral theory of Wiener-Hopf operators and functional model.
J. MacCormick and B. Pavlov.
Operator Theory: Advances and Applications, 123:434-452, 2001. >
Rearranging the Davenport formula.
J. MacCormick.
N.Z. J. Math. 26(2), pages 101-11, 1997.

Other publications

Seeking Evidence for Basing the CS Theory Course on Non-decision Problems (Abstract Only).
John MacCormick.
Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE '17), page 700 (2017).
Review of The Thrilling Adventures of Lovelace and Babbage: The (Mostly) True Story of the First Computer. (Plain text.)
John MacCormick.
Math Horizons, p16, February 2017.
Alan Turing and the Turing Test. (Plain text; e-magazine.)
John MacCormick.
Dickinson Science Magazine, volume 1, issue 2, p31 (2014).
Curvature regularization for resolution-independent images.
John MacCormick and Andrew Fitzgibbon.
Dickinson College technical report. May, 2013.
Video Chat with Multiple Cameras.
John MacCormick.
Extended abstract in Proc. ACM CSCW Companion, 2013.
Also available as a poster; a 10-page version (this is probably the most useful version for a first reading); and a technical report.
Video Chat with Multiple Cameras.
J. MacCormick.
Technical report, Dickinson College, 2012.
Also available as on arXiv as arXiv:1209.1399 [cs.MM].
Gli algoritmi del successo.
J. MacCormick (translated by Maria Sepa).
Corriere Della Sera, La Lettura, p7, 15 Jan 2012.
The efficiency of methods for storing capabilities and revocations in a secure SAN.
J. MacCormick.
Technical note 2002-005, HP Systems Research Center, Palo Alto, California, 2002.
Constellation: automated discovery of service and host dependencies in networked systems.
Barham, P., Black, R., Goldszmidt, M., Isaacs, R., MacCormick, J., Mortier, R., and Simma, A.
Microsoft Research Technical Report, MSR-TR-2008-67, 2008.
Hand tracking for vision-based drawing.
M. Isard and J. MacCormick.
Technical report, Visual Dynamics Group, Dept. Eng. Science, University of Oxford, 2000.
Probabilistic modelling and stochastic algorithms for visual localisation and tracking.
J. MacCormick.
PhD thesis, University of Oxford, 2000.
Learning switching linear models of human motion.
V. Pavlovich, J. Rehg, J. MacCormick.
Technical report 99-15, HP Cambridge Research Laboratory, 1999.
A Bayesian theory of multi-scale cross-correlation in images.
A. Blake, J. Sullivan, M. Isard and J. MacCormick.
In K. Mardia, R. Aykroyd, and I. Dryden, editors, Spatial-temporal modelling and its applications, 18th LASR Workshop, Leeds University Press, 1999.
Towards tracker initialisation by random sampling.
J. MacCormick and A. Blake.
In K. Mardia, C. Gill, and R. Aykroyd, editors, The Art and Science of Bayesian Image Analysis, pages 184-188, 17th LASR Workshop, Leeds University Press, 1997.

Undecidability and more, using real computer programs. Computer science seminar, Open University, UK. 11/1/18. PDF.
Algorithms do change the world. Conference on Mathematics for the 21st Century, Fondation Helvetica Educatio, Geneva. 5/25/18. details; video; PDF.
Strategies for basing the CS theory course on non-decision problems. SIGCSE 2017 (Baltimore). 2/23/17. PDF.
Practical approaches to teaching the CS theory module: nondecision problems and real computer programs. Colloquium talk, School of Computing Sciences, University of East Anglia. 10/4/17. PDF.
Some Reasons Why Computer Scientists Should Study Math, and Mathematicians Should Study Computer Science. Math and Computer Science Majors' Dinner, Dickinson College, April 2017. (This is an updated version of the 2011 majors' dinner talk.) PDF.
The Science of Search Engines. 9 Algorithms Workshop, Defence Institute of Advanced Technology, India (delivered via video link) 1/21/17. PDF.
Why are password rules so annoying? (Dickinson college FaculTea talk, presented as part of cybersecurity month.) 10/28/15. PDF.
Some ideas about computer programming, for the Dickinson College Idea Fund, 4/13/2015. PDF.
A few connections between Dickinson College, privacy, and security. Thoughts for a "contemporary moment" discussion between Dickinson College students, 11/13/2013. PDF.
The magic of error-correcting codes. Gettysburg College CS Colloquium, 10/3/13. PDF.
9 Algorithms that Changed the Future, Microsoft author talk series, Microsoft Corp., Redmond, WA, 10/16/12.
A Brief Overview of Some of the Most Interesting Cutting-Edge Research on Computer Systems: highlights from the 2011 Symposium on Operating Systems Principles (SOSP). Informal departmental seminar, Dickinson College, 12/8/11. PDF.
How does the Kinect work? Dickinson College Math/CS Chat, 9/6/11. PDF.
Some Reasons Why Computer Scientists Should Study Math, and Mathematicians Should Study Computer Science. Math and Computer Science Majors' Dinner, Dickinson College, 4/26/11. PDF.
Did Alan Turing analyze the Halting Problem? Microsoft Research Silicon Valley Lab, July 2010. PDF.
The Benefits of Pairing By Ability. SIGCSE 2010 (Milwaukee). PDF.
The Coolest Theorem in Computer Science: Computers Can't Do Everything. Dickinson College Math/CS Chat, 4/6/10. PDF (fragmentary).
What should a scientist know about computer science? Dickinson College Rush Hour interdisciplinary seminar, 10/13/09. PDF (fragmentary).
How to keep a secret, even on the Internet. Dickinson College Math/CS Chat, 11/4/08. PDF (fragmentary). Similar talks were given at Princeton (3/3/10, as part of Princeton's Lunch and Learn series) and Messiah College (11/31/09).
Retrospective on leaving Silicon Valley Lab of Microsoft Research. July 2007. PDF (fragmentary).
The Need for Polite Software. Microsoft Research Silicon Valley Lab, July 2007. PDF.
Can Computers See? Dickinson College Math/CS Chat, 2/2/07. PDF.
The Science of Search Engines. Dickinson College Math/CS Chat, 11/14/06. PDF.
The Online Index-Merging Problem. Princeton Computer Science Colloquium, 10/14/05. PDF.

Distributed Detection With Diagnosis.
Paul Barham, Richard Black, Moises Goldszmidt, Rebecca Isaacs, John MacCormick, Richard Mortier. Filed October 2006.

Data replication in a distributed system.
William R. Hoffman, Marcus J. Jager, John P. MacCormick, Kristof Roomp, Chandramohan A. Thekkath, Lidong Zhou. Filed June 2006.

Searchable storage system.
William R. Hoffman, Marcus J. Jager, John P. MacCormick, Kristof Roomp, Chandramohan A. Thekkath. Filed June 2006.

Scheduling of index merges.
John P. MacCormick, Frank D. McSherry. Filed January 2006, issued March 2010.

Balanced prefetching exploiting structured data.
John P. MacCormick, Nicholas Charles Murphy, Chandramohan A. Thekkath, Lidong Zhou. Filed September 2005.

Method and system for estimating the three dimensional position of an object in a three dimensional physical space.
Adrian E. Broadhurst, Michael Harville, John P. MacCormick, Donald O. Tanguay. Filed in April 2005.

Systems and methods for structuring distributed fault-tolerant systems.
John MacCormick, Chandu Thekkath, Lidong Zhou. Filed January 2005.

Efficient Recovery of Replicated Data Items.
John MacCormick, Chandu Thekkath, Lidong Zhou. Filed December 2004, issued June 2010.

Method and apparatus for estimating time delays in systems of communicating nodes.
Marcos K. Aguilera, John MacCormick. Filed in October 2004, issued April 2006.

Method and system for renaming consecutive keys in a B-tree.
John MacCormick. Filed in April 2004.

System and method for preventing replay attacks.
Marcos K. Aguilera, Mark Lillibridge, John MacCormick. Filed in June 2003.

Method and system for securing block-based storage with capability data.
Marcos K. Aguilera, Minwen Ji, Mark David Lillibridge, John Philip MacCormick, Erwin Oertli, David Godbe Andersen, Michael Burrows, Timothy P. Mann, Chandramohan A. Thekkath. Filed May 2003.

Method and system for managing access control.
Marcos K. Aguilera, Minwen Ji, Mark David Lillibridge, John Philip MacCormick, Erwin Oertli, David Godbe Andersen, Michael Burrows, Timothy P. Mann, Chandramohan A. Thekkath. Filed May 2003.