Dr Cristina Ribeiro
Faculty of Applied Science & Technology
Currently, Cristina shares her knowledge and passion for computer science as a Professor in the Honours Bachelor of Computer Science (Mobile Computing) and Honours Bachelor of Information Science (Cyber Security) degree programs. She teaches a range of courses in the School of Applied Computing, including programming, algorithms, and operating systems.
Cristina's expertise spans various domains within computer science, and her contributions have made an impact on software engineering, natural language pragmatics, information retrieval, and computational public safety.
In her doctoral research, Cristina focused on determining the severity and prevalence of ambiguity in software engineering requirements. She devised an innovative inspection process based on a computational linguistics ambiguity model. This process effectively identifies and eliminates ambiguity types that often persist through multiple inspections due to a lack of awareness. By addressing these issues, Dr. Cristina's work minimizes the need for code rewrites, reduces defects, and prevents delays, ultimately leading to substantial cost savings in software development.
Cristina has also made strides in the realm of persuasive language technologies. She developed a virtual human application aimed at coaching users to optimize their sleep patterns for improved health. By incorporating a computational representation of natural language pragmatics, her application significantly influences reasoning and meaning in text, fostering behavioral change in users.
Cristina engineered a niche search engine designed for sentence-level fact-based information retrieval. This project involved the creation of a scalable classifier for quality-based sentence indexing, categorization, and ranking, achieving an impressive accuracy rate of over 80% in providing relevant fact-based results.
Cristina's master's research delved into computational public safety, focusing on enhancing situational awareness in search and rescue operations. She developed a unique capability to determine canine pose as a method of communication. Her contributions included the engineering of a multi-sensor device, a real-time pose prediction algorithm, and algorithms to assess wireless mesh network viability in disaster environments. This device proved instrumental in decreasing search times, earning recognition from the Ontario Provincial Police (OPP) Emergency Response Team and winning two Ontario Government excellence awards. The device's success garnered media coverage in outlets such as the Toronto Star, Toronto Sun, Discovery Channel, and more.