{"id":577753,"date":"2026-03-10T20:35:06","date_gmt":"2026-03-11T00:35:06","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ece\/?post_type=news&p=577753"},"modified":"2026-03-11T06:15:16","modified_gmt":"2026-03-11T10:15:16","slug":"mohsenin-discusses-efficient-ai-in-edge-ai-foundation-talk","status":"publish","type":"news","link":"https:\/\/engineering.jhu.edu\/ece\/news\/mohsenin-discusses-efficient-ai-in-edge-ai-foundation-talk\/","title":{"rendered":"Mohsenin Discusses Efficient AI in Edge AI Foundation Talk"},"content":{"rendered":"

Johns Hopkins Electrical and Computer Engineering Associate Professor\u00a0<\/span>Tinoosh Mohsenin<\/a>\u00a0recently sat down with the\u00a0<\/span>Edge AI Foundation<\/strong>\u00a0<\/span>to discuss one of the biggest challenges in artificial intelligence today: how to make powerful generative AI models run on small, energy-efficient devices.<\/p>\n

Generative AI is often associated with massive cloud computers and large data centers. But many real-world technologies, such as medical imaging systems, robotics platforms, and smart sensors, need AI to run directly on compact hardware close to where data is collected.<\/p>\n

In the conversation, Mohsenin shared how her research group is redesigning AI models so they can operate efficiently on these resource-limited \u201cedge\u201d devices. Her work focuses on shrinking large models and reducing how much memory and energy they require while keeping their performance nearly the same. Using techniques such as model pruning and quantization, Mohsenin and her team have achieved\u00a0<\/span>up to 43\u00d7 smaller models and latency improvements of up to 22\u00d7, making advanced AI far more practical outside the cloud.<\/p>\n

Watch the full talk on YouTube.<\/a><\/p>\n","protected":false},"template":"","class_list":["post-577753","news","type-news","status-publish","hentry","news_categories-department-news"],"acf":[],"yoast_head":"\nMohsenin Discusses Efficient AI in Edge AI Foundation Talk - Department of Electrical and Computer Engineering<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/ece\/news\/mohsenin-discusses-efficient-ai-in-edge-ai-foundation-talk\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mohsenin Discusses Efficient AI in Edge AI Foundation Talk - Department of Electrical and Computer Engineering\" \/>\n<meta property=\"og:description\" content=\"Johns Hopkins Electrical and Computer Engineering Associate Professor\u00a0Tinoosh Mohsenin\u00a0recently sat down with the\u00a0Edge AI Foundation\u00a0to discuss one of the biggest challenges in artificial intelligence today: how to make powerful generative…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/engineering.jhu.edu\/ece\/news\/mohsenin-discusses-efficient-ai-in-edge-ai-foundation-talk\/\" \/>\n<meta property=\"og:site_name\" content=\"Department of Electrical and Computer Engineering\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-11T10:15:16+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Mohsenin Discusses Efficient AI in Edge AI Foundation Talk - 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