I am a Ph.D. student at the Rochester Institute of Technology. I work as a research assistant at the Machine Learning and Data Intensive Computing Laboratory (MINING Lab) advised by Dr. Qi Yu. I obtained my Bachelor’s degree in Software Engineering from the Computer Engineering Department at Sharif University of Technology, Tehran, Iran, and my Master’s degree from the Electrical Engineering Department University of South Florida.

Google scholar

Research Overview

My research broadly focuses on machine learning and multimodal learning, with an emphasis on addressing the reliability of deep neural networks. The core of my work involves accurate uncertainty estimation in machine learning algorithms, thereby enhancing the ability of AI models to make dependable decisions. Concurrently, my research also seeks to enhance the efficiency of DNNs, targeting to improve the performance and resource efficiency of DNNs. Particularly, I focus on problems such as Question Answering, Visual Question Answering, Large-Language Models, Generative Models, and Cross-Modal Generation.

Research Interests

Machine Learning, Deep Learning, Computer Vision, Generative AI, Multimodal Machine Learning, Tensor Learning, Uncertainty Quantification, Reliable AI.


  • (Mar 2024) I am invited to deliver an advanced PhD student talk in CHAI seminar series, at RIT.
  • (Feb 2024) I am visiting the Computer Science Department at Wellesley College to present my doctoral research, in Boston.
  • (Dec 2023) Our paper “Enhancing GAN Performance Through Neural Architecture Search And Tensor Decomposition”, is accepted to ICASSP 2024.
  • (June 2023) Our paper “Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams”, is published in Electronics.
  • (May 2023) I am accepted into the AWARE-AI NSF Research Traineeship program, funded by the National Science Foundation (NSF), where I will be working in the area of multimodal AI.
  • (Oct 2022) Our paper “Online multivariate anomaly detection and localization for high-dimensional settings”, is published in Sensors.
  • (Feb 2022) I passed my doctoral qualifying exam.
  • (Jan 2022) I am selected to participate in Grad Cohort for Women (CRA-WP), in April 2022.
  • (July 2021) I am selected as a student scholar to participate at vGHC 2021, in September 2021.
  • (July 2021) Our paper “Robust Barron-Loss Tucker Tensor Decomposition” is accepted to ASILOMAR 2021.
  • (June 2021) I’m selected as a speaker in vGHC-21, to present a poster “Robust Multilinear Subspace Estimation”.
  • (May 2021) Our paper “Improved L1-Tucker via L1-Fitting” is accepted to EUSIPCO 2021.
  • (Aug 2020) I joined Machine Learning Optimization and Signal Processing Laboratory, at Rochester Institute of Technology.
  • (July 2019) Our paper “Online Anomaly Detection in Multivariate Settings” is accepted to MLSP 2019.


  • Enhancing GAN Performance Through Neural Architecture Search and Tensor Decomposition
    P. P. Pulakurthi, M. Mozaffari, S. A. Dianat, M. Rabbani, J. Heard, R. Rao
    IEEE ICASSP 2024 [PDF]
  • Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams
    M. Mozaffari, K. Doshi, and Y. Yilmaz
    Electronics 2023 [PDF]
  • Online multivariate anomaly detection and localization for high-dimensional settings
    M. Mozaffari, K. Doshi, and Y. Yilmaz
    Sensors 2022 [PDF]
  • Real-Time Detection and Classification of Power Quality Disturbances
    M. Mozaffari, K. Doshi, and Y. Yilmaz
    Sensors 2022 [PDF]
  • Improved L1-Tucker via L1-Fitting
    M. Mozaffari and P. P. Markopoulos and A. Prater-Bennette
    EUSIPCO 2021 [PDF]
  • Robust Barron-Loss Tucker Tensor Decomposition
    M. Mozaffari, P. P. Markopoulos
    IEEE ACSSC 2021. [PDF]
  • Online Anomaly Detection in Multivariate Settings
    M. Mozaffari and Y. Yilmaz
    IEEE MLSP 2019 [PDF]
  • RAPID: Real-time Anomaly-based Preventive Intrusion Detection
    K. Doshi, M. Mozaffari and Y. Yilmaz
    ACM WiseML 2019 [PDF]