A sum­mary of the lab's re­search pub­li­ca­tions is at Google Scholar.

Lab mem­bers have also pub­lished sev­eral text­books and re­search mono­graphs:

• Geo­met­ric span­ner net­works ▼

  Aimed at an au­di­ence of re­searchers and grad­u­ate stu­dents in com­pu­ta­tional geom­e­try and al­go­rithm de­sign, this book uses the Geo­met­ric Span­ner Net­work Prob­lem to show­case a num­ber of use­ful al­go­rith­mic tech­niques, data struc­ture strate­gies, and geo­met­ric analy­sis tech­niques with many ap­pli­ca­tions, prac­ti­cal and the­o­ret­i­cal. The au­thors pre­sent rig­or­ous de­scrip­tions of the main al­go­rithms and their analy­ses for dif­fer­ent vari­a­tions of the Geo­met­ric Span­ner Net­work Prob­lem. Though the basic ideas be­hind most of these al­go­rithms are in­tu­itive, very few are easy to de­scribe and an­a­lyze. For most of the al­go­rithms, non­triv­ial data struc­tures need to be de­signed, and non­triv­ial tech­niques need to be de­vel­oped in order for analy­sis to take place. Still, there are sev­eral basic prin­ci­ples and re­sults that are used through­out the book. One of the most im­por­tant is the pow­er­ful well-sep­a­rated pair de­com­po­si­tion. This de­com­po­si­tion is used as a start­ing point for sev­eral of the span­ner con­struc­tions.

  Pub­lisher's web­site

• Open data struc­tures: An in­tro­duc­tion ▼

  Open Data Struc­tures cov­ers the im­ple­men­ta­tion and analy­sis of data struc­tures for se­quences (lists), queues, pri­or­ity queues, un­ordered dic­tio­nar­ies, or­dered dic­tio­nar­ies, and graphs.

  Data struc­tures pre­sented in the book in­clude stacks, queues, de­ques, and lists im­ple­mented as ar­rays and linked-lists; space-ef­fi­cient im­ple­men­ta­tions of lists; skip lists; hash ta­bles and hash codes; bi­nary search trees in­clud­ing treaps, scape­goat trees, and red-black trees; in­te­ger search­ing struc­tures in­clud­ing bi­nary tries, x-fast tries, and y-fast tries; heaps, in­clud­ing im­plicit bi­nary heaps and ran­dom­ized meld­able heaps; graphs, in­clud­ing ad­ja­cency ma­trix and aja­cency list rep­re­sen­ta­tions; and B-trees.

  The data struc­tures in this book are all fast, prac­ti­cal, and have prov­ably good run­ning times. All data struc­tures are rig­or­ously an­a­lyzed and im­ple­mented in Java and C++. The Java im­ple­men­ta­tions im­ple­ment the cor­re­spond­ing in­ter­faces in the Java Col­lec­tions Frame­work.

  The book and ac­com­pa­ny­ing source code are free (libre and gratis) and are re­leased under a Cre­ative Com­mons At­tri­bu­tion Li­cense. Users are free to copy, dis­trib­ute, use, and adapt the text and source code, even com­mer­cially. The book's LaTeX sources, Java/C++ sources, and build scripts are avail­able through github. A pa­per­back ver­sion of the book is pub­lished by Athabasca Uni­ver­sity Press.

  Book's web­site
  Pub­lisher's we­biste

• In­tro­duc­tion to the­ory of com­pu­ta­tion ▼

  This is a free text­book for an un­der­grad­u­ate course on the The­ory of Com­pu­ta­tion, which has been taught at Car­leton Uni­ver­sity since 2002.

  Book's web­site

• Dis­crete struc­tures for com­puter sci­ence ▼

  This is a free text­book for an un­der­grad­u­ate course on Dis­crete Struc­tures for Com­puter Sci­ence, which has been taught at Car­leton Uni­ver­sity since 2013.

  Book's web­site

Lab mem­bers also list their pub­li­ca­tions on their per­sonal web pages.