I am an Applied ML Research Engineer with 5+ years of experience delivering large-scale Computer Vision and Deep Learning solutions for end-to-end ML systems in production.
I have a proven track record of translating research into scalable ML products, including distributed inference pipelines processing 187M+ image operations. I am also experienced in building and optimizing models across diverse data modalities for performance, robustness, and deployment.
End-to-end ML for portrait imaging: cropping, color correction, face landmarks — deployed at scale via ONNX + Triton.
Show detailsProduction ML pipelines for portrait hair matting, face super-resolution, and large-scale synthetic data generation.
Show detailsML model validation tooling and ensemble time-series forecasting for supply chain demand prediction.
Show detailsResearch Assistant, Network & Multimedia Systems Lab (Prof. Mohamed Hefeeda). Thesis published in IEEE TMM 2022.
Show detailsThesis on Deep RL for NFV service provisioning — published at IEEE GLOBECOM 2019.
Show detailsDQN framework for reliability-aware NFV resource allocation.
Show detailsMember of Iran's National Elites Foundation.
S.H. RahmaniKhezri, S. Kim, M. Hefeeda. Proposed the first unsupervised framework using cross-coupled Perceptual Deep Image Priors. Achieved 37% improvement over prior methods, 24% PSNR and 12% SSIM gains in benchmarks.
S.H. RahmaniKhezri et al. DQN-based framework for reliability-aware NFV service placement optimizing admission ratio and cost under bandwidth and reliability constraints. IEEE Xplore →
Recent presentations with the posters and reports
In this work, we aim to train a model for the task of visual question answering, using only a small number of labeled data.
Read more Read lessIn this work, we considered a dynamic reliability-aware NFV placement using DQN.
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