Mohammed Qasem, PhD, MSc, BSc
ATS Assistant Lecturer, Augustana - Sciences
- (780) 679-1116
1-15 Heather Brae Hall
4901-46 AveCamrose ABT4V 2R3
Mohammed Qasem is an assistant lecturer at the department of computing science. He recently started working at Augustana in July of 2019. Prior to arriving at Augustana, he has taught introductory programming courses, visual programming, logic design, computer architecture, and operating systems. He also worked at Manitoba Hydro International as a Software Developer. His research centers mostly on three areas: (i) ant-inspired clustering models and their applications to dynamic real-world clustering problems; (ii) semi-supervised classification and clustering; (iii) aspect-based sentiment analysis.
A swarm of organisms, following simple rules, can achieve incredible intelligent tasks. This natural behaviour is called Swarm Intelligence (SI) and is pervasive through diverse ecological systems. An individual ant, for instance, can perform minimal functions, but an ant colony can perform complicated, intelligent tasks such as building bridges, creating superhighways, collecting and sorting piles of food and waging war. More interestingly, such tasks are achieved without central control and beyond the comprehension of any single ant. A colony of ants is self-organized by a process called stigmergy, a small action by individual ant stimulates others to behave differently, leading to a new pattern of behaviour — likewise, flocks of birds, herds of animals, schools of fish and beehives. Very generally, Dr. Qasem primary field of research interest is SI. Within SI, he focused on ant-inspired algorithms with their applications to data analytics. His doctoral research centers on developing two clustering algorithms, inspired by how real ants sort their nests, and their application to classifying large-scale dynamic data. Dr. Qasem current research work focuses on swarm intelligence for social network mining.
Teaching philosophy in a nutshell
“All students can learn and succeed, but not in the same way and not in the same day”.
– William G. Spady
My role as a facilitator (rather than a traditional instructor) is central to my teaching philosophy. I perceive myself as a facilitator in the sense that I take responsibility for the learning environment: it is my responsibility to find out the best approach for teaching computing science in different classes.
AUCSC 450 Parallel and Distributed Systems
AUCSC 310 Algorithm Design and Analysis
Digital Logic Design
Computer Architecture and Organization
Operating System Concepts
Computer Science Fundamentals
AUCSC 218 - Web Design, Development and Scripting
AUCSC 310 - Algorithm Design and Analysis
Algorithm design techniques (divide-and-conquer, dynamic programming, the greedy method). Merge-sort and the analysis of divide-and-conquer algorithms with recurrence relations; bucket-sort, radix-sort, and the lower bound on sorting; comparison of sorting algorithms. Trees, binary trees, search trees, their implementation, traversal, and search and update operations. Introduction to graph theory; data structures for the representation of graphs, digraphs, and networks, and their associated algorithms (traversal, connected components, topological sorting, minimum- spanning trees, shortest paths, transitive closure). Dynamic equivalence relations and union-find sets; amortized analysis. String matching. Prerequisites: AUCSC 112, or AUCSC 211 and AUSCI 235; and AUMAT 250.
AUCSC 330 - Database Management Systems I
Introduction to current database management systems in theory and practice. Topics include relational database design (including entity-relationship modelling, relational schema, and normal forms); relational algebra, use of a query language (typically SQL) and other components of a current database management system; overview of database system architecture, file structures (including B-tree indices), query processing, and transaction management; new directions. Prerequisites: AUCSC 112, or AUCSC 211 or AUSCI 235. Corequisite: AUMAT 250.
AUCSC 395 - Directed Study I
Intensive study of a specific area of Computing Science as defined by the student and a supervising instructor, including completion of a software project in the selected area. Prerequisite: *9 of senior-level Computing Science. Notes: Admission to AUCSC 395 normally requires a minimum GPA of 3.0 in Computing Science. An Application for Individual Study must be completed and approved before registration in the course.
AUSCI 385B - Mentoring in Computing and Mathematics
This course involves tutoring students learning first-year material in mathematics and computing science for 3 hours per week in the Math & Computing Support Centre (MCSC). Tutors will also receive coaching from the MCSC Director in how best to perform their duties. Prerequisites: *15 in MAT or CSC and third-year standing.
AUSCI 430 - Ethical Issues in Computing and Mathematics
This course explores a variety of ethical issues related to computing and mathematics. Students will study ethical theory, professional codes of ethics, and apply them to make moral decisions. Topics involve information privacy and security, surveillance, cryptography, data mining, intellectual property and copyrights, computer crime and abuse, etc. The course includes extensive writing assignments and oral presentations. Prerequisite: At least *15 in Computing Science or Mathematics or at least third-year standing. Note: Credit may be obtained for only one of AUCSC 490 (2021) and AUSCI 430.