Murray Patterson

Assistant Professor

Department of Computer Science
Georgia State University
25 Park Place
Atlanta, Georgia 30303 (USA)

office: Room 1807
tel: (+1) 475 330 0978
email: mpatterson[at]cs[dot]gsu[dot]edu

Since the fall of 2020, I am a tenure track Assistant Professor of the Bioinformatics Cluster in the Computer Science Department at Georgia State University, Atlanta, Georgia.

In the 2019—2020 academic year, I was a Visiting Assistant Professor of Computer Science in the School of Engineering at Fairfield University.

From the summer of 2016 until the summer of 2019, I was a postdoctoral fellow in the Experimental Algorithmics Lab at the University of Milano-Bicocca with Gianluca Della Vedova and Paola Bonizzoni. My project was on haplotyping and cancer phylogenies.

From the spring of 2014 I did a 2-year postdoctoral fellowship at the LBBE as part of the Ancestrome Project with Daniel Kahn and Vincent Daubin. My project was on the evolution of metabolic networks from the perspective of the enzymes that catalyse reactions, but also of the structure of the network itself.

From the beginning of 2012 I was an ERCIM fellow with a 2-year Marie Curie ABCDE fellowship. I held my first year of the fellowship with the INRIA at the Laboratoire de Biométrie et Biologie Évolutive (LBBE) of the Université de Lyon in France with Eric Tannier. I held my second year in the Life Sciences Group at the CWI with Alexander Schoenhuth and Gunnar Klau.

Teaching

Spring 2023

a senior year / masters-level course on artificial intellgence.

Fall 2022

a senior year / masters-level programming language concepts course.

Spring 2022

a senior year / masters-level programming language concepts course.

Fall 2021

a senior year / masters-level course on database management systems.

Spring 2021

a senior year / masters-level programming language concepts course.

Fall 2020

a senior year / masters-level programming language concepts course.

Spring 2020

an introductory programming course in Java, and
a junior year theory of programming languages course using Haskell and Prolog.

Fall 2019

an introductory programming course in Java,
an intermediate programming course, focusing on the object-oriented features of Java, and
a senior year / masters-level course on database management systems.

Publications

View also at Google Scholar, DBLP or see my full CV, among a list of supporting documents

Bibliography

1.: Sarwan Ali, Tamkanat E. Ali, Taslim Murad, Haris Mansoor, and Murray Patterson. Molecular sequence classification using efficient kernel based embedding. Information Sciences, 2024. doi:10.1016/j.ins.2024.121100.
2.: Sarwan Ali, Prakash Chourasia, and Murray Patterson. From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets. Medical & Biological Engineering & Computing, 2024. doi:0.1007/s11517-024-03074-3.
3.: Zahra Tayebi, Sarwan Ali, Taslim Murad, Imdadullah Khan, and Murray Patterson. PseAAC2Vec protein encoding for TCR protein sequence classification. Computers in Biology and Medicine, 170:107956, 2024. doi:10.1016/j.compbiomed.2024.107956.
4.: Sarwan Ali, Prakash Chourasia, and Murray Patterson. When protein structure embedding meets large language models. MDPI Genes, 15(1):25, 2023. doi:10.3390/genes15010025.
5.: Sarwan Ali and Murray Patterson. Improving IOSOMAP efficiency with RKS: a comparative study with t-distributed neighbor embedding on protein sequences. MDPI J — Multidisciplinary Scientific Journal, 6(4):579–591, 2023. doi:10.3390/j6040038.
6.: Taslim Murad*, Sarwan Ali*, Imdad Ullah Khan, and Murray Patterson. Spike2CGR: an efficient method for spike sequence classification using chaos game representation. Machine Learning, 112:3633–3658, 2023. doi:10.1007/s10994-023-06371-4.
7.: Sarwan Ali, Prakash Chourasia, Zahra Tayebi, Babatunde Bello, and Murray Patterson. ViralVectors: compact and scalable alignment-free virome feature generation. Medical & Biological Engineering & Computing, 2023. doi:10.1007/s11517-023-02837-8.
8.: Taslim Murad, Sarwan Ali, and Murray Patterson. Exploring the potential of GANs in biological sequence analysis. MDPI Biology, 12(6):854, 2023. doi:10.3390/biology12060854.
9.: Bikram Sahoo*, Sarwan Ali, Pin-Yu Chen, Murray Patterson, and Alexander Zelikovsky*. Assessing the resilience of machine learning classification algorithms on SARS-CoV-2 genome sequences generated with long-read specific errors. Biomolecules, 13(6):934, 2023. doi:10.3390/biom13060934.
10.: Danushka Bandara, Karen Exantus, Cristian Navarro-Martinez, Murray Patterson, and Ashley Byun. Identifying distinguishing acoustic features in felid vocalizations based on call type and species classification. Acoustics Australia, 2023. doi:10.1007/s40857-023-00298-5.
11.: Sarwan Ali, Babatunde Bello, and Murray Patterson. Solvent accessibility of coronaviridae spike proteins through the lens of information gain. MDPI J — Multidisciplinary Scientific Journal, 6(2):236–247, 2023. doi:10.3390/j6020018.
12.: Sarwan Ali, Bikram Sahoo, Alexander Zelikovsky, Pin-Yu Chen, and Murray Patterson. Benchmarking machine learning robustness in covid-19 genome sequence classification. Scientific Reports, 13(1):4154, 2023. doi:10.1038/s41598-023-31368-3.
13.: Prakash Chourasia, Sarwan Ali, Simone Ciccolella, Gianluca Della Vedova, and Murray Patterson. Reads2Vec: Efficient embedding of raw high-throughput sequencing reads data. Journal of Computational Biology, 2022. doi:10.1089/cmb.2022.0424.
14.: Sarwan Ali, Babatunde Bello, Zahra Tayebi, and Murray Patterson. Characterizing SARS-CoV-2 spike sequences based on geographical location. Journal of Computational Biology, 2022. doi:10.1089/cmb.2022.0391.
15.: Sarwan Ali, Bikram Sahoo, Muhammad Asad Khan, Alexander Zelikovsky, Imdad Ullah Khan*, and Murray Patterson*. Efficient approximate kernel based spike sequence classification. Transactions on Computational Biology and Bioinformatics, 2022. doi:10.1109/TCBB.2022.3206284.
16.: Kaustubh Khandai, Cristian Navarro-Martinez, Brendan Smith, Rebecca Buonopane, S. Ashley Byun*, and Murray Patterson*. Determining significant correlation between pairs of extant characters in a small parsimony framework. Journal of Computational Biology, 29(10):1132–1154, 2022. doi:10.1089/cmb.2022.0141.
17.: Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Yijing Zhou, and Murray Patterson. PWM2Vec: An efficient embedding approach for viral host specification from coronavirus spike sequences. MDPI Biology, 11(3):418, 2022. doi:10.3390/biology11030418.
18.: Sarwan Ali, Yijing Zhou, and Murray Patterson. Efficient analysis of COVID-19 clinical data using machine learning models. Medical & Biological Engineering & Computing, 60(7):1881–1896, 2021. doi:10.1007/s11517-022-02570-8.
19.: Zahra Tayebi, Sarwan Ali, and Murray Patterson. Robust representation and efficient feature selection allows for effective clustering of SARS-CoV-2 variants. Algorithms, 14(12):348, 2021. doi:10.3390/a14120348.
20.: Sarwan Ali, Simone Ciccolella, Lorenzo Lucarella, Gianluca Della Vedova, and Murray Patterson. Simpler and faster development of tumor phylogeny pipelines. Journal of Computational Biology, 28(11):1142–1155, 2021. doi:10.1089/cmb.2021.0271.
21.: Andrew Melnyk, Fatemeh Mohebbi, Sergey Knyazev, Bikram Sahoo, Roya Hosseini, Pavel Skums, Alex Zelikovsky*, and Murray Patterson*. From alpha to zeta: Identifying variants and subtypes of SARS-CoV-2 via clustering. Journal of Computational Biology, 28(11):1113–1129, 2021. doi:10.1089/cmb.2021.0302.
22.: Simone Ciccolella*, Murray Patterson*, Paola Bonizzoni, and Gianluca Della Vedova. Effective clustering for single cell sequencing cancer data. IEEE Journal of Biomedical and Health Informatics, 25(11):4068–4078, 2021. doi:10.1109/JBHI.2021.3081380.
23.: Simone Ciccolella, Mauricio Soto, Murray D. Patterson, Gianluca Della Vedova, Iman Hajirasouliha, and Paola Bonizzoni. Gpps: An ILP-based approach for inferring cancer progression with mutation losses from single cell data. BMC Bioinformatics, 21(1):413, 2020. doi:10.1186/s12859-020-03736-7.
24.: Simone Ciccolella, Camir Ricketts, Mauricio Soto Gomez, Murray Patterson, Dana Silverbush, Paola Bonizzoni, Iman Hajirasouliha, and Gianluca Della Vedova. Inferring cancer progression from single-cell sequencing while allowing mutation losses. Bioinformatics, 37(3):326–333, 2020. doi:10.1093/bioinformatics/btaa722.
25.: Raffaella Rizzi, Stefano Beretta, Murray Patterson, Yuri Pirola, Marco Previtali, Gianluca Della Vedova, and Paola Bonizzoni. Overlap graphs and de Bruijn graphs: data structures for de novo genome assembly in the big data era. Quantitative Biology, 7(4):278–292, 2019. doi:10.1007/s40484-019-0181-x.
26.: Stefano Beretta*, Murray Patterson*, Simone Zaccaria, Gianluca Della Vedova, and Paola Bonizzoni. HapCHAT: Adaptive haplotype assembly for efficiently leveraging high coverage in long reads. BMC Bioinformatics, 19(1):252, 2018. doi:10.1186/s12859-018-2253-8.
27.: Wandrille Duchemin, Yoann Anselmetti, Murray Patterson, Yann Ponty, Sèverine Bérard, Cedric Chauve, Celine Scornavacca, Vincent Daubin, and Eric Tannier. DeCoSTAR: Reconstructing the ancestral organization of genes or genomes using reconciled phylogenies. Genome Biology and Evolution, 9(5):1312–1319, 2017. doi:10.1093/gbe/evx069.
28.: Andrea Bracciali, Marco Aldinucci, Murray Patterson, Tobias Marschall, Nadia Pisanti, Ivan Merelli, and Massimo Torquati. PWhatsHap: Efficient haplotyping for future generation sequencing. BMC Bioinformatics, 17(11):342, 2016. doi:10.1186/s12859-016-1170-y.
29.: Murray Patterson*, Tobias Marschall*, Nadia Pisanti, Leo van Iersel, Leen Stougie, Gunnar W. Klau†, and Alexander Schoenhuth†. WhatsHap: Weighted haplotype assembly for future-generation sequencing reads. Journal of Computational Biology, 22(6):498–509, 2015. doi:10.1089/cmb.2014.0157.
30.: Murray Patterson, Gergely Szöllősi, Vincent Daubin, and Eric Tannier. Lateral gene transfer, rearrangement, reconciliation. BMC Bioinformatics, 14(15):S4, 2013. doi:10.1186/1471-2105-14-S15-S4.
31.: Mohammed El-Kebir*, Tobias Marschall*, Inken Wohlers*, Murray Patterson, Jaap Heringa, Alexander Schoenhuth, and Gunnar W. Klau. Mapping proteins in the presence of paralogs using units of coevolution. BMC Bioinformatics, 14(15):S18, 2013. doi:10.1186/1471-2105-14-S15-S18.
32.: Ján Maňuch*, Murray Patterson*, Roland Wittler*, Cedric Chauve, and Eric Tannier. Linearization of ancestral multichromosomal genomes. BMC Bioinformatics, 13(19):S11, 2012. doi:10.1186/1471-2105-13-S19-S11.
33.: Ján Maňuch*, Murray Patterson*, and Cedric Chauve. Hardness results on the gapped consecutive-ones property problem. Discrete Applied Mathematics, 160(18):2760–2768, 2012. doi:10.1016/j.dam.2012.03.019.
34.: Ján Maňuch* and Murray Patterson*. The complexity of the gapped consecutive-ones property problem for matrices of bounded maximum degree. Journal of Computational Biology, 18(9):1243–1253, 2011. doi:10.1089/cmb.2011.0128.
35.: Roland Wittler, Ján Maňuch*, Murray Patterson*, and Jens Stoye. Consistency of sequence-based gene clusters. Journal of Computational Biology, 18(9):1023–1039, 2011. doi:10.1089/cmb.2011.0083.
36.: Taslim Murad, Sarwan Ali, Prakash Chourasia, Haris Mansoor, and Murray Patterson. Circular arc length-based kernel matrix for protein sequence classification. In the 2023 IEEE International Conference on Big Data (IEEE BigData, Sorrento, Italy, 2023). 2023. To appear.
37.: Sarwan Ali, Prakash Chourasia, and Murray Patterson. Expanding chemical representation with k-mers and fragment-based fingerprints for molecular fingerprinting. In the 10th International Conference on Information Management and Big Data (SIMBig, Mexico City, Mexico, 2023). 2023. To appear.
38.: Prakash Chourasia, Taslim Murad, Zahra Tayebi, Sarwan Ali, Imdad Ullah Khan, and Murray Patterson. Efficient classification of SARS-CoV-2 spike sequences using federated learning. In the 10th International Conference on Information Management and Big Data (SIMBig, Mexico City, Mexico, 2023). 2023. To appear.
39.: Zahra Tayebi, Akshay Juyal, Alex Zelikovsky, and Murray Patterson. Simulating tumor evolution from scDNA-seq as an accumulation of both SNVs and CNAs. In the 19th International Symposium on Bioinformatics Research and Applications (ISBRA, Wrocław, Poland, 2023), volume 14248 of LNCS, 530–540. 2023. doi:10.1007/978-981-99-7074-2_43.
40.: Sarwan Ali*, Haris Mansoor*, Prakash Chourasia*, and Murray Patterson. Hist2Vec: Kernel-based embeddings for biological sequence classification. In the 19th International Symposium on Bioinformatics Research and Applications (ISBRA, Wrocław, Poland, 2023), volume 14248 of LNCS, 387–397. 2023. doi:10.1007/978-981-99-7074-2_30.
41.: Sarwan Ali*, Prakash Chourasia*, and Murray Patterson. PDB2Vec: Using 3D structural information for improved protein analysis. In the 19th International Symposium on Bioinformatics Research and Applications (ISBRA, Wrocław, Poland, 2023), volume 14248 of LNCS, 376–386. 2023. doi:10.1007/978-981-99-7074-2_29.
42.: Prakash Chourasia*, Taslim Murad*, Sarwan Ali*, and Murray Patterson. Enhancing t-SNE performance for biological sequencing data through kernel selection. In the 19th International Symposium on Bioinformatics Research and Applications (ISBRA, Wrocław, Poland, 2023), volume 14248 of LNCS, 442–452. 2023. doi:10.1007/978-981-99-7074-2_35.
43.: Usama Sardar*, Sarwan Ali*, Muhammad Sohaib Ayub, Muhammad Shoaib, Khurram Bashir, Imdad Ullah Khan, and Murray Patterson. Sequence-based nanobody-antigen binding prediction. In the 19th International Symposium on Bioinformatics Research and Applications (ISBRA, Wrocław, Poland, 2023), volume 14248 of LNCS, 227–240. 2023. doi:10.1007/978-981-99-7074-2_18.
44.: Sarwan Ali, Pin-Yu Chen, and Murray Patterson. Unveiling the robustness of machine learning models in classifying COVID-19 spike sequences. In the 19th International Symposium on Bioinformatics Research and Applications (ISBRA, Wrocław, Poland, 2023), volume 14248 of LNCS, 1–15. 2023. doi:10.1007/978-981-99-7074-2_1.
45.: Sarwan Ali, Usama Sardar, Imdad Ullah Khan, and Murray Patterson. Efficient sequence embedding for SARS-CoV-2 variants classification. In the 19th International Symposium on Bioinformatics Research and Applications (ISBRA, Wrocław, Poland, 2023), volume 14248 of LNCS, 16–30. 2023. doi:10.1007/978-981-99-7074-2_2.
46.: Mansoor Ahmed, Usama Sardar, Sarwan Ali, Shafiq Alam, Murray Patterson, and Imdad Ullah Khan. Robust brain age estimation via regression models and MRI-derived features. In Advances in Computational Collective Intelligence (ICCCI, Budapest, Hungary, 2023), volume 1864 of Communications in Computer and Information Science. 2023. doi:10.1007/978-3-031-41774-0_52.
47.: Zahra Tayebi*, Sarwan Ali*, Prakash Chourasia, Taslim Murad, and Murray Patterson. T cell receptor protein sequences and sparse coding: A novel approach to cancer classification. In the 2023 International Conference on Neural Information Processing (ICONIP, Changsha, China, 2023), Communications in Computer and Information Science. 2023. doi:10.1007/978-981-99-8141-0_17.
48.: Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Pin-Yu Chen, Imdad Ullah Khan, and Murray Patterson. Virus2Vec: Viral sequence classification using machine learning. In the 2023 Conference on Health, Inference, and Learning (CHIL, Cambridge, MA, USA, 2023), volume 209 of Proceedings of Machine Learning Research, 6–18. 2023. URL: https://proceedings.mlr.press/v209/ali23a.html.
49.: Sarwan Ali, Prakash Chourasia, and Murray Patterson. When biology has chemistry: Solubility and drug subcategory prediction using SMILES strings. In the 11th International Conference on Learning Representations (ICLR, Kigali, Rwanda, 2023), Tiny Papers Track. 2023. URL: https://openreview.net/forum?id=28si4RXwDt1.
50.: Taslim Murad, Sarwan Ali, and Murray Patterson. A new direction in membranolytic anticancer peptides classification: Combining spaced k-mers with chaos game representation. In the International Neural Network Society Workshop on Deep Learning Innovations and Applications (INNS DLIA, Queensland, Australia, 2023), volume 222 of Procedia Computer Science, 666–675. 2023. doi:10.1016/j.procs.2023.08.204.
51.: Prakash Chourasia*, Zahra Tayebi*, Sarwan Ali*, and Murray Patterson. Empowering pandemic response with federated learning for protein sequence data analysis. In the 2023 International Joint Conference on Neural Networks (IJCNN, Queensland, Australia, 2023), 1–8. 2023. doi:10.1109/IJCNN54540.2023.10191721.
52.: Sarwan Ali*, Taslim Murad*, and Murray Patterson. PCD2Vec: A Poisson correction distance based approach for viral host classification. In the 2023 International Joint Conference on Neural Networks (IJCNN, Queensland, Australia, 2023), 1–8. 2023. doi:10.1109/IJCNN54540.2023.10191311.
53.: Sarwan Ali, Usama Sardar, Murray Patterson, and Imdad Ullah Khan. BioSequence2Vec: Efficient embedding generation for biological sequences. In the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD, Osaka, Japan, 2023), volume 13936 of LNCS, 173–185. 2023. doi:10.1007/978-3-031-33377-4_14.
54.: Prakash Chourasia, Sarwan Ali, and Murray Patterson. Informative initialization and kernel selection improves t-SNE for biological sequences. In the 2022 IEEE International Conference on Big Data (IEEE BigData, Osaka, Japan, 2022), 101–106. IEEE Computer Society, 2022. doi:10.1109/BigData55660.2022.10020217.
55.: Sarwan Ali, Taslim Murad, and Murray Patterson. PSSM2Vec: A compact alignment-free embedding approach for coronavirus spike sequence classification. In the 29th International Conference on Neural Information Processing (ICONIP, Indore, India, 2022), volume 1794 of Springer CCIS, 420–432. 2023. doi:10.1007/978-981-99-1648-1_35.
56.: Taslim Murad*, Prakash Chourasia*, Sarwan Ali*, and Murray Patterson. Hashing2Vec: Fast embedding generation for SARS-CoV-2 spike sequence classification. In the 14th Asian Conference on Machine Learning (ACML, Hyderabad, India, 2022), volume 189 of Proceedings of Machine Learning Research, 754–769. 2022. URL: https://proceedings.mlr.press/v189/taslim23a.html.
57.: Prakash Chourasia, Sarwan Ali, Simone Ciccolella, Gianluca Della Vedova, and Murray Patterson. Clustering SARS-CoV-2 variants from raw high-throughput sequencing reads data. In the 11th International Conference on Computational Advances in Bio and medical Sciences (ICCABS, Virtual Conference, 2021), volume 13254 of Springer LNBI, 133–148. 2022. doi:10.1007/978-3-031-17531-2_11.
58.: Sarwan Ali, Taslim Murad, Prakash Chourasia, and Murray Patterson. Spike2Signal: Classifying coronavirus spike sequences with deep learning. In the 8th IEEE International Conference on Big Data Service and Applications (IEEE BDS, San Francisco Bay Area, USA, 2022), 81–88. 2022. doi:10.1109/BigDataService55688.2022.00020.
59.: Sarwan Ali, Bikram Sahoo, Naimat Ullah, Alex Zelikovsky, Murray Patterson*, and Imdad Ullah Khan*. A k-mer based approach for SARS-CoV-2 variant identification. In the 17th International Symposium on Bioinformatics Research and Applications (ISBRA, Shenzhen, China, 2021), volume 13064 of LNCS, 153–164. 2021. doi:10.1007/978-3-030-91415-8_14.
60.: Brendan Smith, Cristian Navarro-Martinez, Rebecca Buonopane, S. Ashley Byun*, and Murray Patterson*. Correlated evolution in the small parsimony framework. In the 17th International Symposium on Bioinformatics Research and Applications (ISBRA, Shenzhen, China, 2021), volume 13064 of LNCS, 608–619. 2021. doi:10.1007/978-3-030-91415-8_51.
61.: Sarwan Ali and Murray Patterson. Spike2Vec: An efficient and scalable embedding approach for COVID-19 spike sequences. In the 2021 IEEE International Conference on Big Data (IEEE BigData, Virtual Conference, 2021), 1533–1530. 2021. doi:10.1109/BigData52589.2021.9671848.
62.: Sarwan Ali, Tamkanat-E-Ali, Muhammad Asad Khan, Imdad Ullah Khan, and Murray Patterson. Effective and scalable clustering of SARS-CoV-2 sequences. In the 5th International Conference on Big Data Research (ICBDR, Tokyo, Japan, 2021), 42–49. 2021. doi:10.1145/3505745.3505752.
63.: Andrew Melnyk, Fatemeh Mohebbi, Sergey Knyazev, Bikram Sahoo, Roya Hosseini, Pavel Skums, Alex Zelikovsky*, and Murray Patterson*. Clustering based identification of SARS-CoV-2 subtypes. In the 10th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS, Virtual Conference, 2020), volume 12686 of LNCS, 127–141. 2021. doi:10.1007/978-3-030-79290-9_11.
64.: Simone Ciccolella*, Murray Patterson*, Paola Bonizzoni, and Gianluca Della Vedova. Effective clustering for single cell sequencing cancer data. In the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB, Niagara Falls, NY, USA, 2019), ACM, 437–446. 2019. doi:10.1145/3307339.3342149.
65.: Giulia Bernardini*, Paola Bonizzoni*, Gianluca Della Vedova*, and Murray Patterson*. A rearrangement distance for fully-labelled trees. In the 30th Annual Symposium on Combinatorial Pattern Matching (CPM, Pisa, Italy, 2019), volume 128 of LIPIcs, 1–15. 2019. doi:10.4230/LIPIcs.CPM.2019.28.
66.: Gianluca Della Vedova*, Murray Patterson*, Raffaella Rizzi*, and Mauricio Soto*. Character-based phylogeny construction and its application to tumor evolution. In the 13th Conference on Computability in Europe (CiE, Turku, Finland, 2017), volume 10307 of LNCS, 3–13. 2017. doi:10.1007/978-3-319-58741-7_1.
67.: Marco Aldinucci, Andrea Bracciali, Tobias Marschall, Murray Patterson, Nadia Pisanti, and Massimo Torquati. High-performance haplotype assembly. In the 11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB, Cambridge, UK, 2014), volume 8623 of LNCS, 245–258. 2015. doi:10.1007/978-3-319-24462-4_21.
68.: Murray Patterson*, Tobias Marschall*, Nadia Pisanti, Leo van Iersel, Leen Stougie, Gunnar W. Klau†, and Alexander Schoenhuth†. WhatsHap: Haplotype assembly for future-generation sequencing reads. In the 18th Annual International Conference on Research in Computational Molecular Biology (RECOMB, Pittsburgh PA, USA, 2014), volume 8394 of LNCS, 237–249. 2014. doi:10.1007/978-3-319-05269-4_19.
69.: Cedric Chauve*, Murray Patterson*, and Ashok Rajaraman*. Hypergraph covering problems motivated by genome assembly questions. In the International Workshop on Combinatorial Algorithms (IWOCA, Rouen, France, 2013), volume 8288 of LNCS, 428–432. 2013. doi:10.1007/978-3-642-45278-9_37.
70.: Cedric Chauve*, Ján Maňuch*, Murray Patterson*, and Roland Wittler*. Tractability results for the consecutive-ones property with multiplicity. In the 22nd Annual Symposium on Combinatorial Pattern Matching (CPM, Palermo, Italy, 2011), volume 6661 of LNCS, 90–103. 2011. doi:10.1007/978-3-642-21458-5_10.
71.: Ján Maňuch*, Murray Patterson*, and Arvind Gupta. Towards a characterization of the generalized cladistic character compatibility problem for non-branching character trees. In the 7th International Symposium on Bioinformatics Research and Applications (ISBRA, Changsha, China, 2011), volume 6674 of LNCS, 440–451. 2011.
72.: Ján Maňuch*, Murray Patterson*, Sheung-Hung Poon*, and Chris Thachuk*. Complexity of finding non-planar rectilinear drawings of graphs. In the 18th International Symposium on Graph Drawing (GD, Konstanz, Germany, 2010), volume 6502 of LNCS, 305–316. 2010. doi:10.1007/978-3-642-18469-7_28.
73.: Cedric Chauve*, Ján Maňuch*, and Murray Patterson*. On the gapped consecutive-ones property. In the European Conference on Combinatorics, Graph Theory and Applications (EuroComb, Bordeaux, France, 2009), volume 34 of ENDM, 121–125. 2009. doi:10.1016/j.endm.2009.07.020.
74.: Ján Maňuch*, Murray Patterson*, and Arvind Gupta. On the generalised character compatibility problem for non-branching character trees. In the 15th Annual International Computing and Combinatorics Conference (COCOON, Niagara Falls, USA, 2009), volume 5609 of LNCS, 268–276. 2009. doi:10.1007/978-3-642-02882-3_27.
75.: Murray Patterson, Yongmei Liu, Eugenia Ternovska, and Arvind Gupta. Grounding for model expansion in k-guarded formulas with inductive definitions. In the 20th International Joint Conferences on Artificial Intelligence (IJCAI, Hyderabad, India, 2007), 161–166. 2007.

Education

2006—2012

Doctor of Philosophy, Department of Computer Science, University of British Columbia, Vancouver, Canada, GPA — 4.0/4.
Advisors: Ján Maňuch, Cedric Chauve and Arvind Gupta

2004—2006

Master of Science, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada, GPA — 3.9/4.
Advisors: Eugenia Ternovska and Arvind Gupta

1999—2003

Bachelor of Computer Science (Honours), Jodrey School of Computer Science, Acadia University, Wolfville, Nova Scotia, Canada, GPA — 3.8/4 (top 5%).
Advisors: Raymond J. Spiteri and Jim Diamond