Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection
Elham Abolhasani, Maryam Ramezani, Hamid R. Rabiee
arXiv|2026|Preprint
Data-centric AI systems, built to survive real data.
We build AI systems that treat data as a first-class citizen — learning to optimize databases and infrastructure, making data accessible through natural language, and understanding the networks that data lives in.
Department of Computer Engineering
Sharif University of Technology, Tehran
Selected publications
11 papers across 6 research directions.
DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property
Maryam Ramezani, Aryan Ahadinia, Erfan Farhadi, Hamid R. Rabiee
Scientific Reports|2024
Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization
Maryam Ramezani, Aryan Ahadinia, Amirmohammad Ziaei Bideh, Hamid R. Rabiee
ACM Transactions on Knowledge Discovery from Data|2023
FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media
Faeze Ghorbanpour, Maryam Ramezani, Mohammad Amin Fazli, Hamid R. Rabiee
Social Network Analysis and Mining|2023
The lab
A small group of researchers in the Department of Computer Engineering.
Latest news
Announcement
Sample: applications open for MSc and undergraduate researchers
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Publication
New preprint on audio-visual deepfake detection across domains
EAV-DFD uses a teacher-student structure to adapt an ensemble audio-visual deepfake detector to unseen domains with only a small sample of target data.
Publication
Uncertainty-aware recommendation with Bayesian deep ensembles
BDECF combines Bayesian neural networks with deep ensembles so a recommender can report how certain it is, not only what it predicts.
We are looking for students
3 open positions for students who want to work between machine learning and data systems at Sharif University of Technology.