Computational Sciences
The School of Computer Science (SoCS) offers an Interdisciplinary PhD degree in Computational Sciences that encompasses multiple Departments/Schools across the University of Guelph. The program provides a unique opportunity for students to study computing within the context of another discipline commensurate with their interests and career goals. Students entering this PhD program perform research that bridges Computer Science with at least one other discipline such as Economics and Finance, Engineering, English and Theatre Studies, Geography, History, Integrative Biology, Mathematics and Statistics, Pathobiology, Population Medicine and Psychology.
Administrative Staff
Director
Minglun Gong (1117 Reynolds, Ext. 52824)
socsdirector@uoguelph.ca
Graduate Program Coordinator
Stacey Scott (3308 Reynolds, Ext. 54153)
graddir@socs.uoguelph.ca
Graduate Program Assistant
Jennifer Hughes (1116 Reynolds, Ext. 56402)
csgradassist@uoguelph.ca
Graduate Faculty
This list may include Regular Graduate Faculty, Associated Graduate Faculty and/or Graduate Faculty from other universities.
Hussein A. Abdullah
B.Sc. Univ. of Technology, M.Sc., PhD Glasgow, P.Eng. - Professor
Graduate Faculty
Naseem Al-Aidroos
B.Sc. Waterloo, MA, PhD Toronto - Associate Professor
Graduate Faculty
Genevieve Ali
B.Sc., M.Sc., PhD Université du Montréal - Associate Professor
Associated Graduate Faculty
Luiza Antoine
B.Sc. Politehnica Bucharest (Romania), M.Sc., PhD Alberta - Associate Professor
Graduate Faculty
Shawki Areibi
B.A.Sc. Al-Fateh, M.A.Sc. Waterloo, PhD Waterloo, P.Eng. - Professor
Graduate Faculty
Felix Arndt
Diplom-Ingenieur Berlin, PhD Ilmenau (Germany) - Associate Professor
Graduate Faculty
Susan Brown
BA King's College and Dalhousie, MA Dalhousie, PhD Alberta - Professor
Graduate Faculty
Neil Bruce
B.Sc. Guelph, M.A.Sc., Waterloo, PhD York - Associate Professor
Graduate Faculty
David A. Calvert
BA, M.Sc. Guelph, PhD Waterloo - Associate Professor
Graduate Faculty
Ataharul Chowdhury
B.Sc., M.Sc. Bangladesh, M.Sc. Wageningen, PhD Vienna - Associate Professor
Graduate Faculty
Rozita Dara
B.Sc. Shahid Teheshti, M.Sc. Guelph, PhD Waterloo - Associate Professor
Graduate Faculty
Gerarda Darlington
B.Sc., M.Sc. Guelph, PhD Waterloo - Professor
Graduate Faculty
Fantahun Defersha
B.Sc. Ethiopia, M.Eng. India, PhD Concordia, P.Eng. - Associate Professor
Graduate Faculty
Ali Dehghantanha
BSE Azad, M.Sc., PhD Putra Malaysia - Associate Professor
Graduate Faculty
Trevor DeVries
B.Sc., PhD British Columbia - Professor
Graduate Faculty
Bob Dony
B.A.Sc., M.A.Sc. Waterloo, PhD McMaster, P.Eng., FIET, FEC - Associate Professor
Graduate Faculty
Zeny Feng
B.Sc. York, MMath., PhD Waterloo - Professor
Graduate Faculty
Mark J. Fenske
B.Sc. Lethbridge, MA, PhD Waterloo - Professor
Graduate Faculty
David Flata
B.Sc., M.Sc., PhD Saskatchewan - Associate Professor
Graduate Faculty
Evan Fraser
BA, M.Sc. Toronto, PhD British Columbia - Professor
Graduate Faculty
Dan Gillis
B.Sc., M.Sc., PhD Guelph - Associate Professor
Graduate Faculty
Minglun Gong
B.Eng. Harbin Engineering, M.Sc. Tsinghua, PhD Alberta - Professor and Director
Graduate Faculty
Amy L. Greer
B.Sc., Mount Allison, M.Sc., Trent, PhD Arizona State - Associate Professor
Graduate Faculty
Stefano Gregori
Laurea, Doctorate Pavia, P.Eng - Professor
Graduate Faculty
Gary Gréwal
B.Sc. Brock, M.Sc., PhD Guelph - Associate Professor
Graduate Faculty
Getu Hailu
B.Sc., M.Sc. Alemaya, PhD Alberta - Professor
Graduate Faculty
Andrew Hamilton-Wright
B.Sc., M.Sc. Guelph, PhD Waterloo - Associate Professor
Graduate Faculty
Robert Hanner
B.Sc. Eastern Michigan, PhD Oregon - Associate Professor
Graduate Faculty
Kris Inwood
BA Trent, MA, PhD Toronto - Professor
Graduate Faculty
Hassan Khan
B.Sc. NUST, M.Sc. Southern California, PhD Waterloo - Assistant Professor
Graduate Faculty
Stefan C. Kremer
B.Sc. Guelph, PhD Alberta - Professor
Graduate Faculty
Xiaodong Lin
B.A.Sc. Nanjing, M.Sc. East China Normal, PhD Beijing, PhD Waterloo - Professor
Graduate Faculty
Pascal Matsakis
B.Sc., M.Sc., PhD Paul Sabatier (France) - Professor
Graduate Faculty
Judi R. McCuaig
B.Ed., B.Sc., MS, PhD Saskatchewan - Associate Professor
Graduate Faculty
Mary Ruth McDonald
B.Sc., M.Sc., PhD Guelph - Professor
Graduate Faculty
Robert L. McLaughlin
B.Sc. Windsor, M.Sc. Queen's, PhD McGill - Associate Professor
Graduate Faculty
Medhat A. Moussa
B.Sc. American, M.A.Sc. Moncton, PhD Waterloo, P.Eng. - Professor
Graduate Faculty
Radu Muresan
Dipl. Eng. Technical Cluj-Napoca (Romania); M.A.Sc., PhD Waterloo, P.Eng. - Associate Professor
Graduate Faculty
Mihai Nica
B.Math., Waterloo, PhD Courant Institute NYU - Assistant Professor
Graduate Faculty
Denis Nikitenko
B.Sc. Ryerson, M.Sc., PhD Guelph - Assistant Professor
Graduate Faculty
Charlie F. Obimbo
M.Sc. Kiev, PhD New Brunswick - Professor
Graduate Faculty
Beth Parker
BS Pennsylvania, MS North Carolina, PhD Waterloo - Professor
Graduate Faculty
David L. Pearl
B.Sc. McGill, M.Sc. York, DVM, PhD Guelph - Associate Professor
Graduate Faculty
Miana Plesca
B.Sc. Technical Cluj (Romania); MA Georgetown (Washington, D.C.); PhD Western - Professor
Graduate Faculty
Zvonimir Poljak
DVM Croatia, M.Sc., PhD Guelph - Associate Professor
Graduate Faculty
Joseph Sawada
B.Sc., PhD Victoria (British Columbia) - Professor
Graduate Faculty
Stacey Scott
B.Sc. Dalhousie, PhD Calgary - Professor
Graduate Faculty
Shayan Sharif
DVM Tehran, PhD Guelph - Professor and Interim Dean, Ontario Veterinary College
Graduate Faculty
Fei Song
B.Sc. Jilin (China), M.Sc. Academia Sinica (China), PhD Waterloo - Associate Professor
Graduate Faculty
Petros Spachos
Diplom Crete, M.A.Sc., PhD Toronto, P.Eng. - Associate Professor
Graduate Faculty
Deborah A. Stacey
B.Sc. Guelph, M.A.Sc., PhD Waterloo - Associate Professor
Graduate Faculty
Graham Taylor
B.A.Sc., M.A.Sc. Waterloo, PhD Toronto, P.Eng. - Professor
Graduate Faculty
Lana M. Trick
B.Sc. Calgary, MA, PhD Western Ontario - Professor
Graduate Faculty
Fangju Wang
BE Changsha, M.Sc. Peking, PhD Waterloo - Professor
Graduate Faculty
Mark Wineberg
B.Sc. Toronto, M.Sc., PhD Carleton - Associate Professor
Graduate Faculty
Michael A. Wirth
B.Sc. New England (Australia), M.Sc. Manitoba, PhD RMIT Melbourne - Associate Professor
Graduate Faculty
Yan Yan
B.Sc. Northwestern Polytech, PhD Saskatchewan - Assistant Professor
Graduate Faculty
Simon X. Yang
B.Sc. Peking, M.Sc. Sinica, M.Sc. Houston, PhD Alberta, P.Eng. - Professor
Graduate Faculty
Wanhong Yang
B.Sc., Hubei, M.Sc. Chinese Academy of Sciences, PhD Illinois - Professor
Graduate Faculty
Fattane Zarrinkalam
B.Sc., M.Sc., PhD Ferdowsi University of Mashhad (Iran) - Assistant Professor
Graduate Faculty
PhD Program
Admission Requirements
In addition to the Office of Graduate Studies admission requirements, applicants must submit:
- a current CV including research publications; and
- a statement of research (maximum of 1500 words).
The minimum academic requirement for admission to the PhD program is normally a recognized Master's degree that included a thesis or major independent project. We do not require students entering the program to have a credential in Computer Science. Such students are required to identify their experience using computerized techniques and demonstrate that they have the necessary background to complete the tasks outlined in a research proposal.
In exceptional circumstances, a student who has completed an honours undergraduate Computer Science degree (or an equivalent 4-year undergraduate degree) may apply for direct admission to the PhD program. The successful applicant must have an outstanding academic record, breadth of knowledge in Computer Science, demonstrated research accomplishments and strong letters of recommendation.
Prospective students should check the School of Computer Science (SoCs) website for further details, procedures and deadlines.
Program Requirements
The objective of the PhD program is to produce interdisciplinary scholars who are capable of tackling emerging problems in a variety of disciplines through investigation and application of current computer technologies. Students require two co-advisors: one from the School of Computer Science; and the second from another discipline (see Graduate Faculty).
The PhD program requires completion of CIS*6890 Technical Communication and Research Methodology, coupled with any additional courses and/or Computational Learning Modules assigned by the Advisory Committee on entry to the program. To achieve candidacy, students are expected to present a research proposal in a two-part seminar and successfully complete the Qualifying Examination (QE). Finally, students must present and defend a thesis.
Collaborative Specializations
One Health
Computational Sciences participates in the collaborative specialization in One Health. Master’s and Doctoral students wishing to undertake thesis research or their major research paper/project with an emphasis on one health are eligible to apply to register concurrently in Computational Sciences and the collaborative specialization. Students should consult the One Health listing for more information.
Courses
An examination of Artificial Intelligence principles and techniques such as: logic and rule based systems; forward and backward chaining; frames, scripts, semantic nets and the object-oriented approach; the evaluation of intelligent systems and knowledge acquisition. A sizeable project is required and applications in other areas are encouraged.
Relational and other database systems, web information concurrency protocols, data integrity, transaction management, distributed databases, remote access, data warehousing, data mining.
Artificial neural networks, dynamical recurrent networks, dynamic input/output sequences, communications signal identification, syntactic pattern recognition.
Data mining and bioinformatics, molecular biology databases, taxonomic groupings, sequences, feature extraction, Bayesian inference, cluster analysis, information theory, machine learning, feature selection.
This course will discuss problems where optimization is required and describes the most common techniques for discrete optimization such as the use of linear programming, constraint satisfaction methods, and genetic algorithms.
This course introduces the student to basic genetic algorithms, which are based on the process of natural evolution. It is explored in terms of its mathematical foundation and applications to optimization in various domains.
Representation of uncertainty, Dempster-Schafer theory, fuzzy logic, Bayesian belief networks, decision networks, dynamic networks, probabilistic models, utility theory.
Objects, modeling, program design, object-oriented methodology, UML, CORBA, database.
Intelligent systems consisting of multiple autonomous and interacting subsystems with emphasis on distributed reasoning and decision making. Deductive reasoning agents, practical reasoning agents, probabilistic reasoning agents, reactive and hybrid agents, negotiation and agreement, cooperation and coordination, multiagent search, distributed MDP, game theory, and modal logics.
This course concentrates on the theoretical and practical issues related to the design and study of interactive technologies for human use. Topics include: general principles of design, qualitative and quantitative research methods, prototyping techniques, theoretical issues underlying designing to individuals and groups, and ethical issues related to conducting research involving humans.
This course introduces software tools and data science techniques for analyzing big data. It covers big data principles, state-of-the-art methodologies for large data management and analysis, and their applications to real-world problems. Modern and traditional machine learning techniques and data mining methods are discussed and ethical implications of big data analysis are examined. May be offered in conjunction with DATA*6300.
This course emphasizes machine learning for sequential data processing. It covers common challenges and pre-processing techniques for sequential data such as text, biological sequences, and time series data. Students are exposed to machine learning techniques, including classical methods and more recent deep learning models, so that they obtain the background and skills needed to confront real-world applications of sequential data processing. May be offered in conjunction with DATA*6400.
Brightness transformation, image smoothing, image enhancement, thresholding, segmentation, morphology, texture analysis, shape analysis, applications in medicine and biology.
Neural networks, artificial intelligence, connectionist model, back propagation, resonance theory, sequence processing, software engineering concepts.
This course provides an overview of concepts and technical measures that are employed to enforce security policies and protect networks and systems from malicious activities. Students will learn how to engineer a secure system and how to secure networks in an ethical manner.
This course provides an in-depth understanding of theoretical concepts and practical issues in the field of digital forensics and incident response. Students will develop necessary skills, methodologies, and processes to detect cyber incidents and conduct in-depth computer and network investigation.
This course provides an in-depth understanding of techniques for detecting, responding to, and defeating Advanced Persistent Threats (APT) and malware campaigns using artificial intelligence and data mining techniques. Students will identify, extract, and leverage intelligence from different types of cyber threat actors.
This course provides an in-depth understanding of techniques for detecting, responding to, and defeating Advanced Persistent Threats (APT) and malware campaigns using artificial intelligence and data mining techniques. Students will identify, extract, and leverage intelligence from different types of cyber threat actors.
This course provides an in-depth view of the privacy, regulatory, and ethical issues surrounding cybersecurity. It covers methods of mitigating/treating privacy risks associated with emerging technologies that collect, manage, and analyse data. This course also examines data protection regulations and compliance strategies.
Students plan, develop, and write an industry- or faculty-led report and produce required tools, services, and software. Projects should advance knowledge or practice, and address an emerging challenge in cybersecurity, cyber threat intelligence, digital forensics and incident response, cyber threat hunting, or a closely related field.
This course provides an in-depth understanding of modern cryptography, with emphasis on practical applications. Topics covered include classical systems, information theory, symmetrical cryptosystems, block ciphers, stream ciphers, DES, AES, asymmetric cryptosystems, ECC, provable security, keyexchange and management, and authentication and digital signatures, among others.
This course provides a comprehensive review of tools, techniques, and procedures for monitoring network events and assets to build a secure network architecture. It trains students in methods for hunting attackers that could bypass designed network defense mechanisms in an enterprise.
This two-semester course offers a multidisciplinary forum for discussion of topics related to cybersecurity. The seminar fosters professional skills development (academic and industry), promotes collaboration between industry experts and graduate students, facilitates mentoring and project development, and contributes to the transfer of knowledge between industry and academia.
This special topics course examines selected, advanced topics in computer science that are not covered by existing courses. The topic(s) will vary depending on the need and the instructor.
This is a reading course. Its aim is to provide background knowledge to students who need to get a head-start in their thesis research fields early during their program while no suitable regular graduate courses are offered. Admission is under the discretion of the instructor.
This course provides an in-depth view of a variety of advanced topics within cybersecurity. Subject areas discussed in any particular semester will depend upon the interests of both the students and the instructor. Students should check with the School of Computer Science to determine what topics will be offered during specific semesters.
This course aims to develop students' ability in technical communication and general research methodology. Each student is expected to present a short talk, give a mini lecture, review a conference paper, write a literature survey and critique fellow students' talks and lectures.