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Education
- B.Sc. (Mathematics and Science College), Concordia University,1991
- Master of Computer Science, Concordia University, 1993
- Ph.D. (Computer Science), Concordia University, 1999
Teaching
- Winter 2022:
- COSC 4P03 (Advanced Algorithms)
Research
My research interests include combinatorial optimization, algorithms (in the general sense), and computational intelligence. My research is directed at various application areas, including bioinformatics, coding theory and more. My profile on ResearchGate is here.
In line with my research interests, I am a member (and past chair) of the IEEE Computational Intelligence Society Bioinformatics and Bioengineering Technical Committee. I am also a member of the IEEE Computational Intelligence Society Task Force on Ethical and Social Implications of Computational Intelligence. Finally, I serve on the IEEE Computational Intelligence Society Conference Committee, chairing the Conference Competitions Subcommittee.
Prospective Students: If you are a student considering completing a PhD, Master's or an undergraduate research project and you are interested in any of the above topics, then please contact me. I welcome and encourage applicants from diverse backgrounds. Students should be motivated and comfortable with mathematics (primarily discrete mathematics). Since it can be very difficult to evaluate applications, you should provide as much information as possible about your interests - for example, you might describe projects which you have already undertaken or in which you are interested. Please also see my FAQ.
I work closely with my students and ensure that all research students have the opportunity to publish their work in journals or conference proceedings. You may also wish to look at the list of projects completed by some of my past and present students.
Online tables and data:
Some of my papers consider the problem of establishing bounds on the number of codewords in optimal edit-metric codes and insertion-deletion correcting codes. Tables on these bounds are maintained at the following locations: Edit Metric Codes and Insertion-Deletion Correcting Codes.
Some of my papers consider the problem of graph compression. One of the application areas involves compression of graphs for games. The data can be found at the following location: Game Graphs
Recent Papers
The following is a list of recent publications (2017-present only). Click here to see a full list of papers, technical reports and theses, including those recently submitted.
M. Dube and S. Houghten, Evaluation of Frameworks for Epidemic Variants and Infectivity using an Evolutionary Algorithm, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 9 pages, 2022.
J. Sargant, M. Dube and S. Houghten, Evolving Lockdown Strategies to Minimize Infections in an Epidemic, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2022.
O.M. Beigi, L.R. Nobrega, S. Houghten, A. de Oliveira Andrade and A.A. Pereira, Classification of Parkinson's Disease Patients and Effectiveness of Medication for Freezing of Gait, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2022.
T. Navikevicius, T.K. Collins and S. Houghten, GADGIT: A Toolbox for Disease Gene Identification, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, short paper (2 pages), 2022.
J. Sargant, S. Houghten and M. Dube, Evolving Weighted Contact Networks for Epidemic Modeling: the Ring and the Power, 2022 IEEE Congress on Evolutionary Computation, 9 pages, 2022.
M. Dube and S. Houghten, Now I Know my Alpha, Beta, Gammas: Variants in an Epidemic Scheme, 2022 IEEE Congress on Evolutionary Computation, 8 pages, 2022.
S. Houghten and S. Banik, Effective Decoders for DNA Codes, Biosystems 211: 104583, 2022.
G. Chen, J. Sargant, S. Houghten and T.K. Collins, Identification of Genes Associated with Alzheimer's Disease using Evolutionary Computation, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 9 pages, 2021.
D. Ashlock, J.A. Brown, S. Houghten and M. Makhmutov, One Moose, Two Moose, Three Fields, More?, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 7 pages, 2021.
S. Amin, S. Houghten and J. Hughes, Vaccinating a Population is a Changing Programming Problem, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 10 pages, 2021.
O.M. Beigi, M. Dube and S. Houghten, Simulating Partial Immunity in an Epidemic, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, short paper (2 pages), 2021.
V. Suaste Morales and S. Houghten, Lossy Compression of Quality Values in Sequencing Data, ACM/IEEE Transactions on Computational Biology and Bioinformatics 18, p. 1958-1969, 2021.
E. Rutkowski, J. Sargant, S. Houghten and J.A. Brown, Evaluation of Communities from Exploratory Evolutionary Compression of Weighted Graphs, 2021 IEEE Congress on Evolutionary Computation, p. 434-441, 2021.
R. Vega Jimenez, M. Dube, S. Houghten and J. Hughes, Weighting on the World to Change... an Epidemic, 2021 IEEE Congress on Evolutionary Computation, p. 450-457, 2021.
J. Hughes, W. Hannah, P. Kikkert, B. MacKenzie, W. Ashlock, S. Houghten, D. Ashlock, M. Stoodley, M. Dube, R. Brown and A. Saunders, We Are Not Pontius Pilate: Acknowledging Ethics and Policy, IEEE Symposium Series on Computational Intelligence, p. 2975-2984, 2020.
J.A. Hughes, S. Houghten and J.A. Brown, Models of Parkinson's Disease Patient Gait, IEEE Journal of Biomedical and Health Informatics 24, p. 3103-3110, 2020.
M. Dube, S. Houghten, D. Ashlock and J. Hughes, Evolving the Curve, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2020.
E. Rutkowski, S. Houghten and J.A. Brown, Extracting Information from Weighted Contact Networks via Genetic Algorithms, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2020.
J. Hughes, M. Dube, S. Houghten and D. Ashlock, Vaccinating a Population is a Programming Problem, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2020.
S. Banik and S. Houghten, Effective Side Effect Machines for Decoding, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2020.
J. Hughes, R. Reid, S. Houghten and R. Anderson, Using Genetic Programming to Investigate a Novel Model of Resting Energy Expenditure for Bariatric Surgery Patients, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2020.
E. Rutkowski and S. Houghten, Cryptanalysis of RSA: Integer Prime Factorization Using Genetic Algorithms, IEEE Congress on Evolutionary Computation, 8 pages, 2020.
J.A. Hughes, S. Houghten and J.A. Brown, Gait Model Analysis of Parkinson's Disease Patients under Cognitive Load, IEEE Congress on Evolutionary Computation, 8 pages, 2020.
M. Dube, S. Houghten and D. Ashlock, Modelling of Epidemics with Vaccination Strategies using Evolutionary Computation, IEEE Congress on Evolutionary Computation, 8 pages, 2020.
J.A. Brown, D. Ashlock, S. Houghten and A. Romualdo, Evolutionary Graph Compression and Diffusion Methods for City Discovery in Role Playing Games, IEEE Congress on Evolutionary Computation, 8 pages, 2020.
T.K. Collins and S. Houghten, A Centrality Based Multi-Objective Approach to Disease Gene Association, BioSystems 193-194: 104133, 2020.
A. Fazeli and S. Houghten. Deep Learning for the Prediction of Stock Market Trends, 2019 IEEE International Conference on Big Data - Workshop on Big Data for Financial News and Data, p. 5513-5521, 2019.
J.A. Hughes, S. Houghten and J.A. Brown, Descriptive Symbolic Models of Gaits from Parkinson's Disease Patients, 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2019.
S. Houghten, A. Romualdo, T.K. Collins and J.A. Brown, Compression of Biological Networks using a Genetic Algorithm with Localized Merge, 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2019.
M. Dube, S. Houghten and D. Ashlock, Pandemic: A Graph Evolution Story. 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2019.
M. Dube, S. Houghten and D. Ashlock, Representation for Evolution of Epidemic Models. 2019 IEEE Congress on Evolutionary Computation, 8 pages, 2019.
Z. Dahi, Y. Girilishena, A. Joshi, W. Tang, S. Golem, C. Huang, C. van Schouwen, W. Yang, S. Houghten, P. Liang, Complete sequence characterization and population distribution analysis of 4,400 polymorphic mobile element insertions in humans, Abstract, FASEB Summer Research Conference: "The mobile DNA conference: 25 years of discussions", 2019.
M. Dube, S. Houghten and D. Ashlock, Parameter Selection for Modeling of Epidemic Networks, 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2018.
S. Houghten, T.K. Collins, J.A. Hughes and J.A. Brown, Edit Metric Decoding: Return of the Side Effect Machines, 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2018. Awarded Best Paper.
Y. Kazemi and S. Houghten, A Deep Learning Pipeline to Classify Different Stages of Alzheimer's Disease from fMRI Data, 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2018.
A. Saunders, D. Ashlock and S. Houghten, Hierarchical Clustering and Tree Stability, 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 8 pages, 2018.
T.K. Collins and S. Houghten, A Future Direction for the Disease Gene Association Problem, 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, Extended Abstract (2 pages), 2018.
D. Ashlock and S. Houghten, Hybridization and Ring Optimization for Larger Sets of Embeddable Biomarkers, 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, p. 1-8, 2017.T.K. Collins, A. Zakirov, J.A. Brown and S. Houghten, Single-Objective and Multi-Objective Genetic Algorithms for Compression of Biological Networks, 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, p. 1-8, 2017.
J. Orth, S. Houghten and L. Tulloch, Evaluation of the Salmon Algorithm, 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, p. 1-8, 2017.
T. Ribaric and S. Houghten, Genetic Programming for Improved Cryptanalysis of Elliptic Curve Cryptosystems, 2017 IEEE Congress on Evolutionary Computation, p. 419-426, 2017.
P.E. Becker, M. Derka, S. Houghten and J. Ulrich, Build a Sporadic Group in Your Basement, American Mathematical Monthly 124, p. 291-305, 2017. Awarded 2018 Halmos-Ford Award by the MAA (Mathematical Association of America).
J.A. Hughes, S. Houghten and D. Ashlock, Permutation Problems, Genetic Algorithms, and Dynamic Representations, book chapter for "Nature Inspired Computing and Optimization: Theory and Applications", Springer book series on Modelling and Optimization in Science and Technology, p.123-149, 2017.
Miscellaneous:
This page last modified 14th December, 2022. All items copyright 2002-2022, Sheridan HoughtenI am the coach for Brock University's teams entered in the International Collegiate Programming Contest (ICPC), formerly the ACM Programming Contest. If you are interested in participating, or wish to know more, contact me.
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