Journey Through the Cloud: New Developments and Emerging Challenges (2nd Offering)
Presenter: Reem Al-Saidi
Date: Tuesday, March 4th,2025
Time: 3:00 PM
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
Abstract:
The workshop "Journey Through the Cloud: New Developments and Emerging Challenges" gives in-depth treatment to the constantly changing world of cloud computing in our rapidly expanding digital age. The workshop tries to imbue participants with a thorough understanding of the fundamentals of cloud computing and recent innovations, hence clarifying the nuances of such advanced technology. Cooperation between artificial intelligence (AI) and cloud computing in different disciplines, in general, is to be given special attention, highlighting AI's crucial function in expanding capabilities in the cloud and driving technical progress.
While cloud computing has several benefits, serious questions have always surrounded privacy and security. The workshop aims to provide vital questions given the expanding domain of cloud security, privacy, and applications. The participants will also recognize strategies for minimizing dangers and safeguarding personal data, thus enabling them to deal with such challenges.
Workshop Outline:
- Cloud computing and its characteristics
- Recent trends in cloud
- Necessity to multi-cloud adoption.
- AI role in the cloud
- Privacy and security issues in the cloud
- Increasing users’ awareness about the adoption of cloud across various applications
- Open issues in the current cloud adoption
Prerequisites:
- Familiarity with cloud computing basics.
- basic knowledge of programming concepts
- Basic to intermediate knowledge of cybersecurity principles
- Interest in emerging technologies
Biography:
Reem is a Ph.D. student at the University of Windsor in the School of Computer Science. She focuses on applying different privacy and security techniques in AI tools, providing trust and reputation in various AI applications, and assessing bias and fairness in NLP models