As a postdoctoral researcher in the Security and Networks Group within the Database and Information Techniques division, I specialise in developing and utilising statistical modeling and machine learning techniques to analyse complex patterns across diverse data types—including social networks, textual data, images, and more. With a strong background in data science, I am passionate about transforming data into actionable insights for the research community and industry beneficiaries. My research —encompassing both methodological advancements and practical applications—has been published in top venues such as AAAI ICWSM, ACM WebSci, IEEE/ACM ASONAM, and PAM. This commitment extends to lecturing, mentoring students, and supervising multiple master's theses on related topics, showcasing my dedication to practical applications in networks, security, and data science.
Alireza Mohammadinodooshan
Postdoc
Conducting research in data science, leveraging statistical modeling and machine learning techniques to uncover complex patterns across social networks, text, images, and other data modalities.
Presentation
Professional Contributions
Explore below to learn more about my scholarly activities, including my contributions to research, teaching, and service.
Research
- For a selected list of my publications, please see below and for a comprehensive list, please visit my Google Scholar or DBLP page.
- I have been selected as one of the best Program Committee members of AAAI ICWSM.
- Our paper, "Understanding Engagement Dynamics with (Un)Reliable News Publishers on Twitter," has been accepted for ASONAM 2024.
- Our paper "Successful Rhetorics: How Do Linguistic Dimensions Affect User Engagement with Different News Categories on Twitter?" has been accepted to AAAI ICWSM 2025.
Service
- TPC member
- AAAI ICWSM 2024, 2025
- ASONAM 2024
- BESC 2023, 2024 - International Conference on Behavioural and Social Computing
- Reviewer
- Master's Thesis Supervisor
- Viewership forecast on a Twitch broadcast
- Predicting television advertisement reach with machine learning models
- Access management in organizations: a comprehensive study and scenario-based analysis
- Studying the effectiveness of dynamic analysis for fingerprinting Android malware behavior
- Temporal Analysis of User Engagement on Instagram