{"id":39,"date":"2018-08-18T22:59:28","date_gmt":"2018-08-18T22:59:28","guid":{"rendered":"https:\/\/research.ece.ncsu.edu\/myece\/administration\/?page_id=39"},"modified":"2018-08-21T13:35:29","modified_gmt":"2018-08-21T13:35:29","slug":"home","status":"publish","type":"page","link":"https:\/\/my.ece.ncsu.edu\/facstaff\/","title":{"rendered":"Home"},"content":{"rendered":"\n

The department maintains a general ECE Events<\/a> calendar (below) that all Faculty and Staff are encouraged to make use of. If you would like to add this calendar to your Google calendar, click on the link<\/a>, go to the bottom, right<\/g> side of the page and click on the “+Google Calendar” button.<\/p>\n\n\n

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  • \n\t\tFrom AI Agent Demo to Production: Automated Testing and Evaluation via User Simulation<\/span>\n\t\t

    From AI Agent Demo to Production: Automated Testing and Evaluation via User Simulation<\/span><\/strong><\/p>\n

    September 4, 2026<\/span>  10:15 am<\/span> - 11:15 am<\/span>
    \n EB2 1231<\/span><\/p>\n

    90% of the enterprise agents are stuck in proof of concept (POC). We don't know when the agents are good enough to deploy without a systematic testing and evaluation process. In this seminar, we'll share best practices, our practical dev tools, and lessons from the field to help you break out of the demo loop and build AI agents that deliver real business impact.
    \n--
    \nZhou (Jo) Yu is a computer science professor at Columbia University and co-founder of Arklex.ai. She obtained her Ph.D. from Carnegie Mellon University. Yu is the co-director of the Columbia DAPlab. She has received several best paper awards at top natural language processing conferences, including ACL 2024, and was named to the Forbes 30 Under 30 list in 2018. Yu has developed various AI systems that have had a real impact, including winning the Amazon Alexa Prize. She co-founded Arklex.ai, democratizing AI agent development through an enterprise-grade automated testing tool.
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    \nhttps:\/\/ncsu.zoom.us\/j\/98389016656<\/p>\n<\/div><\/div>\n

    See more details<\/a><\/p><\/div>\n\t<\/li>\n<\/ul>\n\n\t\t • <\/b><\/span>\n\t<\/div>\n<\/td>\n

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  • \n\t\tAgentic Systems and Deep Neural Networks for Photonic Metamaterials<\/span>\n\t\t

    Agentic Systems and Deep Neural Networks for Photonic Metamaterials<\/span><\/strong><\/p>\n

    September 18, 2026<\/span>  10:15 am<\/span> - 11:15 am<\/span>
    \n EB2 1231<\/span><\/p>\n

    Artificial electromagnetic materials have transformed the field of electromagnetics, enabling exotic responses. The design of these materials is often cast as a challenging inverse problem, where a desired electromagnetic response is sought. We present an agentic framework that automates the complete end-to-end workflow of metamaterial inverse design, a scope that includes the development of the forward model itself. When prompted with a desired optical spectrum, the agent autonomously plans the research process, develops a custom deep learning forward model, accesses external simulation tools, and generates a final optimized geometry using an inverse method. This autonomous agent reasons, plans, and utilizes tools to automate complex design, paving the way for the automated industries of the future.
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    \nWillie Padilla is the Dr. Paul Wang Distinguished Professor of Electrical and Computer Engineering and director of the Metamaterials Center. A highly cited Fellow of IEEE, APS and Optica, he pioneered metamaterial perfect absorbers and negative index materials. His research advances metamaterials through machine learning, computational imaging and spectroscopy.
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    \nhttps:\/\/ncsu.zoom.us\/j\/98389016656<\/p>\n<\/div><\/div>\n

    See more details<\/a><\/p><\/div>\n\t<\/li>\n<\/ul>\n\n\t\t • <\/b><\/span>\n\t<\/div>\n<\/td>\n

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  • \n\t\tFormal Methods for Safe and Interpretable Control<\/span>\n\t\t

    Formal Methods for Safe and Interpretable Control<\/span><\/strong><\/p>\n

    September 25, 2026<\/span>  10:15 am<\/span> - 11:15 am<\/span>
    \n EB2 1231<\/span><\/p>\n

    Modern engineering applications such as autonomous robots involve large-scale dynamical systems whose models are complex, uncertain, or only partially known, placing them beyond the reach of traditional model-based control techniques. Data-driven approaches, including machine learning and reinforcement learning, offer powerful tools for learning and decision-making in these settings, but often lack interpretability and provide limited guarantees of correctness and safety, which are critical requirements. Formal methods address these shortcomings by providing rich languages for expressing complex objectives and safety requirements, algorithmic techniques for verification and synthesis, and proofs that the resulting systems behave as intended. This talk brings together control theory, formal methods, and machine learning to create control systems that scale and adapt to uncertain or unknown dynamics while remaining safe, correct, and interpretable. Examples from autonomous driving and robotic manipulation illustrate how this integration enables the synthesis of controllers for complex, safety-critical tasks.
    \n--
    \nCalin Belta is the Brendan Iribe Endowed Professor of Electrical and Computer Engineering and Computer Science at the University of Maryland, College Park, where he is also part of the Maryland Robotics Center and the Institute for Systems Research. His research focuses on making control and machine learning systems safe and interpretable, with particular emphasis on robotics and systems biology. Notable awards include the AFOSR Young Investigator Program award, the NSF CAREER award and the HSCC Test of Time award. He is a Fellow of IEEE.
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    \nhttps:\/\/ncsu.zoom.us\/j\/98389016656<\/p>\n<\/div><\/div>\n

    See more details<\/a><\/p><\/div>\n\t<\/li>\n<\/ul>\n\n\t\t • <\/b><\/span>\n\t<\/div>\n<\/td>\n

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