Summary
Mostafa Sadeghi is a researcher at Inria with nine years of experience at the intersection of statistical machine learning, variational inference, deep learning, and generative models, with a particular focus on audio-visual speech enhancement. He holds a PhD in Electrical Engineering from Sharif University of Technology and has a strong track record of academic and applied research, including a formative stint at KTH where he pioneered global optimization approaches for system identification. Mostafa blends theoretical depth—especially in probabilistic modeling and variational methods—with practical skills in deep learning frameworks such as PyTorch. His work bridges sparse representations and modern neural architectures, reflecting an ability to translate classical signal-processing insights into contemporary ML systems. Based in France at Inria, he combines rigorous methodological development with hands-on implementation, often tackling noisy, multimodal data problems. Notably, his background in convex relaxations and branch-and-bound optimization gives him an unusual edge when designing provably sound solutions for complex estimation tasks.
9 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Electrical, Electronics and Communications Engineering, Bachelor's degree, Electrical, Electronics and Communications Engineering at Ferdowsi University of Mashhad
Doctor of Philosophy - PhD, Electrical, Electronics and Communications Engineering, Doctor of Philosophy - PhD, Electrical, Electronics and Communications Engineering at Sharif University of Technology
English, French, Persian