people

I am a machine learning researcher specializing in building innovative and practical AI solutions at the intersection of Deep Learning, Generative Models, and Computer Vision. As the top-ranked graduate from the University of Tehran’s Master of Science program in Computer Engineering, my work is focused on addressing complex, real-world challenges, from enhancing video compression and speech processing to advancing the field of trustworthy AI.

My Master’s research, conducted under the expert guidance of Dr. Mehdi Modarressi and Dr. Mohammad Amin Sadeghi, culminated in my thesis on designing generative models for video compression. The cornerstone of this work was the development of “NU-Class Net,” a novel deep neural network architected to enhance the quality of low-bitrate video significantly. This research not only demonstrated a substantial reduction in video size but also led to a journal paper submitted to Elsevier’s Engineering Applications of Artificial Intelligence. In addition to my thesis, my research experience includes pioneering work on generative networks for voice adaptation and optimizing the efficiency of GANs and LSTMs.

Currently, I am applying my expertise as a researcher at the Qatar Computing Research Institute (QCRI). I am contributing to Fanar, a large-scale generative AI initiative, where I am specifically responsible for benchmarking the image generation capabilities of this multi-modal project. My passion for bridging the gap between research and real-world application is also reflected in my experience as a Computer Vision Developer and as the co-founder of a startup. I am committed to advancing the field and actively share my knowledge through technical writing and content creation. I am always eager to connect with fellow researchers and industry professionals to explore new collaborations and tackle the next generation of AI challenges.

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I am a machine learning researcher specializing in building innovative and practical AI solutions at the intersection of Deep Learning, Generative Models, and Computer Vision. As the top-ranked graduate from the University of Tehran’s Master of Science program in Computer Engineering, my work is focused on addressing complex, real-world challenges, from enhancing video compression and speech processing to advancing the field of trustworthy AI.

My Master’s research, conducted under the expert guidance of Dr. Mehdi Modarressi and Dr. Mohammad Amin Sadeghi, culminated in my thesis on designing generative models for video compression. The cornerstone of this work was the development of “NU-Class Net,” a novel deep neural network architected to enhance the quality of low-bitrate video significantly. This research not only demonstrated a substantial reduction in video size but also led to a journal paper submitted to Elsevier’s Engineering Applications of Artificial Intelligence. In addition to my thesis, my research experience includes pioneering work on generative networks for voice adaptation and optimizing the efficiency of GANs and LSTMs.

Currently, I am applying my expertise as a researcher at the Qatar Computing Research Institute (QCRI). I am contributing to Fanar, a large-scale generative AI initiative, where I am specifically responsible for benchmarking the image generation capabilities of this multi-modal project. My passion for bridging the gap between research and real-world application is also reflected in my experience as a Computer Vision Developer and as the co-founder of a startup. I am committed to advancing the field and actively share my knowledge through technical writing and content creation. I am always eager to connect with fellow researchers and industry professionals to explore new collaborations and tackle the next generation of AI challenges.

Write your biography here. Tell the world about yourself. Link to your favorite subreddit. You can put a picture in, too. The code is already in, just name your picture prof_pic.jpg and put it in the img/ folder.

Put your address / P.O. box / other info right below your picture. You can also disable any these elements by editing profile property of the YAML header of your _pages/about.md. Edit _bibliography/papers.bib and Jekyll will render your publications page automatically.

Link to your social media connections, too. This theme is set up to use Font Awesome icons and Academicons, like the ones below. Add your Facebook, Twitter, LinkedIn, Google Scholar, or just disable all of them.