About
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.

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.
News
- (Dec 2024) US patent issued: (Patent number: 12174689).
- (Dec 2024) I received the student scholarship award for AAAI 2025.
- (Dec 2024) One paper is accepted at AAAI 2025.
- (Oct 2024) One paper is published in IEEE Access.
- (May 2024) I have started my internship at Amazon.
- (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) One paper is accepted at ICASSP 2024.
- (June 2023) One paper 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) Two papers are 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) One paper is accepted at ASILOMAR 2021.
- (June 2021) I’m selected as a speaker in vGHC-21, to present a poster “Robust Multilinear Subspace Estimation”.
- (May 2021) One paper is accepted at EUSIPCO 2021.
- (Aug 2020) I joined Machine Learning Optimization and Signal Processing Laboratory, at Rochester Institute of Technology.
- (July 2019) One paper is accepted at MLSP 2019.
Publications
- GLEN: Generalized Focal Loss Ensemble of Low-Rank Networks for Calibrated Visual Question Answering
M. Mozaffari, H. Sapkota, Q. Yu
AAAI 2025 - Enhancing GANs With MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition
P. P. Pulakurthi, M. Mozaffari, S. A. Dianat, J. Heard, R. Rao, M. Rabbani
IEEE Access 2024 [PDF] - 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]
Patents
- System and method for online multivariate anomaly detection and localization
Inventors: M. Mozaffari, K. Doshi, Y. Yilmaz
US Patent: 12174689 [LINK]