Mahdi Ahmadi
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About Me

I am a Ph.D. student in Computer Science and a Graduate Research Associate at Arizona State University. My research sits at the intersection of machine learning, generative AI, computer vision, NLP, and probabilistic modeling, with an emphasis on methods that remain practical outside the lab.

Before ASU, I worked as a Data Scientist at Divar, where I developed and deployed machine learning systems for content moderation, chat safety, anomaly detection, and recommendation. That industry experience still shapes how I frame research questions and evaluate real-world impact.

My earlier training is in Electrical Engineering from Isfahan University of Technology; that background and the associated hardware and signal-processing work are collected on the Electrical Engineering page. A separate Projects page covers my research and production work in computer science and AI.

Education

Arizona State University
Arizona State University
Ph.D. in Computer Science, Aug 2023 - Present (GPA: 4.00/4.00)
Isfahan University of Technology
Isfahan University of Technology
M.Sc. in Electrical and Electronics Engineering, Sep 2016 - Apr 2019
Isfahan University of Technology
Isfahan University of Technology
B.Sc. in Electrical and Electronics Engineering, Sep 2012 - Apr 2016

Teaching

Teaching has been a major part of my academic path in both computer science and electrical engineering.

  • Arizona State University: Graduate Teaching Assistant for CSE 571 (Artificial Intelligence) and CSE 471 (Introduction to Artificial Intelligence).
  • Isfahan University of Technology: Teaching Assistant for M.Sc. Digital Image Processing, B.Sc. Multimedia Systems, B.Sc. C Programming, and multiple EE laboratories including Digital Systems, Analog and Digital Electronics, and Electrical Circuits.

Publications

Fair Image Generation from Pre-trained Models by Probabilistic Modeling

SafeGenAI Workshop at NeurIPS 2024, OpenReview
Paper Workshop Page
ReDMark: Framework for Residual Diffusion Watermarking by Deep Networks

Expert Systems with Applications, 2020, DOI
Paper Code
A Learning-Inspired Strategy to Design Binary Sequences With Good Correlation Properties: SISO and MIMO Radar Systems

IEEE Transactions on Aerospace and Electronic Systems, 2023
Paper
Context-aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks

Multimedia Tools and Applications, 2021
Paper