Flora Salim
Professor
Research Interests
Explore related searches
Contact this professor
Get a free first email built on one of their papers, ready for you to edit and send yourself.
Positions (1)
Flora Salim
University of New South Wales
2 months ago
UNSW School of Computer Science and Engineering is advertising multiple academic openings across computing and AI-related areas, including Cybersecurity , Cryptography , Machine Learning , Natural Language Processing , Neurosymbolic AI , Software Engineering , and Systems . The post highlights 12 positions in total, spanning Associate Professor, Lecturer, and Senior Lecturer roles, with both education-focused and teaching-and-research opportunities. The linked job advert shown here is for a Lecturer/Senior Lecturer (Education Focussed) in Cybersecurity at the University of New South Wales in Sydney, Australia. The role involves teaching and curriculum development in cybersecurity topics such as penetration testing, reverse engineering, wargaming, offensive and defensive security, and related undergraduate/postgraduate teaching. UNSW emphasizes its strong environment in computer science, cybersecurity, systems, AI, and software engineering. Eligibility highlights: Lecturer (Level B) applicants should have a PhD in a related discipline and/or relevant work experience, plus teaching experience and evidence of teaching effectiveness. Senior Lecturer (Level C) applicants should also have a PhD and/or relevant experience, with stronger evidence of educational leadership, curriculum innovation, mentoring, and contribution to teaching excellence. Applicants should be able to support diverse student cohorts and demonstrate commitment to equity, diversity, inclusion, and health and safety. Funding / employment terms: This is a paid academic appointment, not a scholarship. The advertised salary for Lecturer (Level B) is AUD $127,947–$150,830 per annum plus 17% superannuation, and for Senior Lecturer (Level C) AUD $155,403–$178,290 per annum plus 17% superannuation. The role is described as a full-time 2-year convertible tenure track or continuing appointment. Application window: Applications close on 31 July 2026 (Sydney time). Applicants should apply through the UNSW careers portal, upload a CV and cover letter, and address the Skills and Experience criteria. The post also notes that a ROPE statement may be included if relevant.
2 months ago
Articles (14)
The spread of infectious diseases is a highly complex spatiotemporal process, difficult to understand, predict, and effectively respond to. Machine learning and artificial intelligence (AI) have achieved impressive results in other learning and prediction tasks; however, while many AI solutions are developed for disease prediction, only a few of them are adopted by decision-makers to support policy interventions. Among several issues preventing their uptake, AI methods are known to amplify the bias in the data they are trained on. This is especially problematic for infectious disease models that typically leverage large, open, and inherently biased spatiotemporal data. These biases may propagate through the modeling pipeline to decision-making, resulting in inequitable policy interventions. Therefore, there is a need to gain an understanding of how the AI disease modeling pipeline can mitigate biased input data, in-processing models, and biased outputs. Specifically, our vision is to develop a large-scale micro-simulation of individuals from which human mobility, population, and disease ground truth data can be obtained. From this complete dataset – which may not reflect the real world – we can sample and inject different types of bias. By using the sampled data in which bias is known (as it is given as the simulation parameter), we can explore how existing solutions for fairness in AI can mitigate and correct these biases and investigate novel AI fairness solutions. Achieving this vision would result in improved trust in such models for informing fair and equitable policy interventions.
Year:
2024
Year:
2024
Year:
2024
Year:
2023
Year:
2023
Year:
2023
Year:
2023
Year:
2023
Year:
2022
Year:
2022
Year:
2022
Year:
2021
Year:
2021
Year:
2020
Collaborators (18)
Luca Bedogni
Associate Professor
University of Modena and Reggio Emilia
Azadeh Ghari Neiat
Deakin University
Klaus David
University of Kassel
Aaqib Saeed
Eindhoven University of Technology
Allison Kealy
Professor
RMIT University
Athman Bouguettaya
University of Sydney
Li Xiong
Professor
Emory University
C. Raina MacIntyre
University of New South Wales
Matthew Scotch
Professor
Arizona State University
Sandra Ohly
Professor and group leader
University of Kassel
Matt Duckham
RMIT University
Andreas Züfle
Emory University
Jelena Gabela
TU Wien
Jeffrey Chan
Associate Professor
RMIT University
Du Yong Kim
Curtin University
Yaguang Tao
RMIT University
JIANZHONG QI
University of Melbourne
Mohammad Saiedur Rahaman
RMIT University
Make your next move.
Prepare your application
Review drafts before you send them.