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
Architecture of historical and contemporary computer systems, including CPU chips and buses, memory, secondary memory devices, and I/O interfaces. Performance enhancement techniques, including prefetching, pipelining, caching, branch prediction, out-of-order and speculative execution, explicit parallelism, and predication are discussed. The course also includes the data path and control logic at the microarchitecture level; error detection and correction; floating-point number representation and calculation; fast arithmetic circuits; instruction sets and formats; and an overview of alternative and parallel architectures, including RISC/CISC, SIMD/MIMD, shared memory and message passing architectures. Prerequisite: AUCSC 250.Winter Term 2021
Operating system functions, concurrent process coordination, scheduling and deadlocks, memory management and virtual memory, secondary storage management and file systems, protection. Prerequisites: AUCSC 250.Winter Term 2021
Historical and social context of computing; the social and ethical responsibilities of the computing professional; the risks and liabilities that can accompany a computing application; intellectual property. The course includes extensive writing assignments and oral presentations. Prerequisite: At least *15 in Computing Science; at least third-year standing.Fall Term 2020