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Events for March 18, 2022
Fri, Mar 18, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Najme Ebrahimi, University of Florida
Talk Title: Next Generation Intelligent and Secured Wireless World: From IoT and Sensors to Wideband and Multi-band Scalable Circuit and System
Abstract: The future intelligent and secured wireless world needs connectivity at any time anywhere and under extreme conditions with over one trillion sensors and Internet-of-Things (IoT) devices connected to the network. To this end, the autonomous, and yet connected, wireless world is envisioned to provide sensing and high-data-rate communications, accurate localization and ranging, and resiliency. The major challenges to attain these goals are latency and energy efficiency requirements, that are largely affected by interference, multi-path, and channel fading. To tackle these challenges, wideband high frequency scalable arrays are desired to provide high data-rate communications and directional beams for interference cancellation. Furthermore, wideband/multiband circuits and systems are needed for accurate localization in the presence of severe multipath and fading in ultra-dense environments in IoT networks.
In this talk, firstly, I will present novel techniques to overcome the challenges for future wideband/multiband scalable transceiver arrays, including power-efficient local oscillator distribution and phase shifting, image selection architecture, and novel compact antenna-IC integration. I will then discuss our ongoing work towards the wideband/multiband signal generation and modulation for 6G and beyond as well as heterogonous integration of different technologies and modules for extending the Moore\'s law. Secondly, I will present multi-band circuit generation for IoT and sensor nodes to be employed in dense wireless networks. More specifically, I will present the first bidirectional circuitry for IoT transponder that reciprocally generates harmonics and subharmonics, covering two communication frequency bands interchangeably, which makes it a premier tool for localization and sensing protocols. I will also discuss future directions on advanced multi-band reconfigurable architecture for wireless sensors and IoTs compatible with network and physical layer protocols for security, communications, and localization.
Biography: Najme Ebrahimi is an Assistant Professor of Electrical and Computer Engineering at the University of Florida. Her research focuses on Mm-Wave/THz Scalable Array for high data rate communications and sensing as well as the security and connectivity of the next generation of distributed Internet-of-Things (IoT) networks. She was a post-doctoral research fellow at the University of Michigan- Ann Arbor from 2017 to 2020 under the departmental fellowship and earned her Ph.D. from the University of California, San Diego in June 2017. She was selected as a Rising Star by MIT EECS Rising Star program in 2019 and by ISSCC Rising Star program of the IEEE Solid-State Circuits Society in 2020. She is a member of the Microwave and Mm-Wave Integrated Circuits committee (MTT-14) and serves in the IMS2022 Technical Paper Review Committee (TPRC). She is the recipient of the 2021 DARPA Young Faculty Award (YFA).
More Information: Najme Ebrahimi Flyer.pdf
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
CS Colloquium: Jieyu Zhao (UMD) - Building Accountable NLP Models: on Social Bias Detection and Mitigation
Fri, Mar 18, 2022 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jieyu Zhao , UMD
Talk Title: Building Accountable NLP Models: on Social Bias Detection and Mitigation
Series: CS Colloquium
Abstract: Natural Language Processing (NLP) plays an important role in many applications, including resume filtering, text analysis, and information retrieval. Despite the remarkable accuracy enabled by the advances in machine learning used in many applications, the technique may discover and generalize the societal biases implicit in the data. For example, an automatic resume filtering system may unconsciously select candidates based on their gender and race due to implicit associations between applicant names and job titles, causing the societal disparity discovered by researchers. Various laws and policies have been designed to ensure social equality and diversity. However, there is no such mechanism for a machine learning model for sensitive applications. My research analyzes the potential stereotypes in various machine learning models and develops computational approaches to enhance fairness in a wide range of NLP applications. The broader impact of my research aligns with one the concerns of machine learning community: how can we do AI for social good.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Jieyu Zhao is a postdoctoral research at UMD, working together with Prof. Hal DaumÃ© III. She obtained her PhD from the department of Computer Science at UCLA where she was advised by Prof. Kai-Wei Chang. Her research interest lies in fairness of ML/NLP models. Her paper got the EMNLP Best Long Paper Award (2017). She was one of the recipients of 2020 Microsoft PhD Fellowship and has been selected to participate in 2021 Rising Stars in EECS workshop. Her research has been covered by news media such as Wires, The Daily Mail and South China Morning Post. She was invited by UN-WOMEN Beijing on a panel discussion about gender equality and social responsibility. More detail can be found at https://jyzhao.net/.
Host: Xiang Ren
Audiences: By invitation only.
Contact: Assistant to CS chair