Mohammed Qasem, PhD, MSc, BSc
Contact
ATS Assistant Lecturer, Augustana - Sciences
- mqasem@ualberta.ca
- Phone
- (780) 679-1116
- Address
-
1-15 Heather Brae Hall
4901-46 AveCamrose ABT4V 2R3
Overview
About
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.
Research
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
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.
Fall 2019
AUCSC 450 Parallel and Distributed Systems
AUCSC 310 Algorithm Design and Analysis
Past Courses
Digital Logic Design
Computer Architecture and Organization
Operating System Concepts
Visual Programming
C/C++ Programming
Data Structures
Computer Science Fundamentals
Courses
AUCSC 111 - Introduction to Computational Thinking and Problem Solving
An introduction to computational thinking, problem solving, and the fundamental ideas of computing. Topics include algorithms, abstraction, and modelling; the syntax and semantics of a high-level language (e.g. Python); fundamental programming concepts and data structures, including simple containers (variables, arrays, lists, strings, dictionaries); sequencing, conditionals and repetition; documentation and style; object-oriented programming; exceptions and error handling; graphical user interfaces and event-driven programming; recursion; simple algorithm analysis and run- time efficiency. Prerequisite: Mathematics 30-1. Note: Credit may be obtained for only one of AUCSC 111, AUCSC 113 (2023), and AUCSC 120 (2019).
AUCSC 218 - Web Design, Development and Scripting
Introduction to modern web architectures and technologies. Web platforms and standards. Client-side/server-side programming and web languages (e.g. HTML, JavaScript, PHP, CSS, Node.js). Introduction to internet security. Design and implementation of a simple web application. Prerequisite: one of AUCSC 113 (2023) or AUSCI 135, or AUCSC 111.
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.
AUCSC 450 - Parallel and Distributed Computing
Parallel architectures, programming language constructs for parallel computing, parallel algorithms and complexity. Message-passing, remote procedure call, and shared-memory models. Synchronization and data coherence. Load balancing and scheduling. Appropriate applications. Prerequisites: AUCSC 250 and AUCSC 370.
AUCSC 480 - Operating Systems Concepts
Operating system functions, concurrent process coordination, scheduling and deadlocks, memory management and virtual memory, secondary storage management and file systems, protection. Prerequisites: AUCSC 250 and AUCSC 370. Note: Credit may be obtained for only one of AUCSC 480 and AUCSC 380 (2022).
AUIDS 201 - Collaborative Learning
The course will introduce students to ways of working collaboratively to complete a group project. Students will examine a topic from a single disciplinary perspective. Prerequisite: AUIDS 101.
AUMAT 120 - Linear Algebra I
Vector and matrix algebra, determinants, linear systems of equations, vector spaces, eigenvalues and eigenvectors, applications. Prerequisite: Mathematics 30-1.
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.