Charles L. and Ann Lee Brown Distinguished Seminar Series

We are proud to welcome leaders in electrical and computer engineering and related disciplines as participants in the Fall 2023 Charles L. and Ann Lee Brown Distinguished Seminar Series.


Our speakers are eager to share their insight and expertise relevant to our research strengths, including image processing with a focus on health and medicine; machine learning, signal processing and communications; hardware for artificial intelligence and the internet of things; devices and circuits; robotics and control systems; and the design and integration of materials in novel electronic, thermal and optical devices.

We will conduct the Distinguished Seminar Series in a hybrid learning environment, combining in-person engagement with a Zoom webinar on Friday afternoons from Date to Date.

Fall 2023 Program

  • Yangying Zhu

    Microscale Thermal-Fluids Engineering for Energy and Water Applications


    Effective management of thermal-fluids transport has become a critical challenge in many energy, water, and electronic applications due to the increasing power density and shrinking length scales.  In this talk, I will first describe our effort to manipulate multi-phase fluid motion using lightresponsive surfactants. 

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  • Yinzhi Cao

    Towards Collaborative, Intelligent, Fair Skin Disease Diagnostics with Differentially Private Federated Learning


    Early detection of skin lesions based on medical images can aid in identifying a range of infectious diseases with cutaneous manifestations. Lyme disease is an example of an infection with a potentially diagnostic skin lesion—which is caused by the bacterium Borrelia burgdorferi and causes nearly 476,000 cases per annum during 2010–2018.  The talk on skin disease diagnostics has two parts. 

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  • Yingying (Jennifer) Chen

    AI-Driven Efficiency and Security in Edge Sensing and Computing: Advancements, Vulnerabilities, and Opportunities


    The pervasive usage of edge devices such as IoT devices, smartphones, AR/VR headsets, delivery drones, and autonomous vehicles, has experienced a notable upward trend. This trend offers unprecedented opportunities for on-device intelligence and a wide range of edge sensing and computing applications. Artificial Intelligence (AI) has emerged as a key enabler enhancing the efficiency of these emerging applications, including AR/VR applications, intelligent audio assistant systems, and in-baggage dangerous object detection, while also providing enhanced security.

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  • Amanda Watson

    Wearable Technology for Healthcare and Athletic Performance


    In the future, wearable technology will provide a continuous, autonomous, and comprehensive assessment of a person’s health. This future presents a tremendous opportunity for the development of new wearable devices that will support predictive analytics and personalized medicine. On the hardware side, we are liberating sensors from the bench top in the clinical lab and introducing them into the wild. Most commonly, smartwatches provide sensors that enable vital sign monitoring, such as heart rate and pulse oximetry.

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  • Jing Sun

    Bridging the Gap in Prediction, Optimization, and Control of Integrated Dynamic Systems


    Integrated systems are ubiquitous as more heterogeneous physical entities are combined to form functional platforms. With increased connectivity, new and “invisible” feedback loops and physical couplings are introduced, leading to emerging dynamics and making the integrated systems more control-intensive. The multi-physics, multi-time scale, and distributed-actuation natures of integrated systems present new challenges for modeling and control. Understanding their operating environments, achieving sustained high performance, and incorporating rich but incomplete data also motivate the development of novel design tools and frameworks.

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  • Sung-Kyu Lim

    Micro-bumping vs. Hybrid Bonding: 3D IC Performance and Reliability Comparisons


    Two of the most popular technologies being actively adopted by the semiconductor industry for 3D heterogenous integration are micro-bumping and hybrid bonding. In this talk, we quantify and compare the power, performance, and area (PPA) of 3D ICs built with these two bonding technologies. We use the pitch values that represent the current and the future of these options.

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  • Christopher Brinton

    Fog Learning under Heterogeneity: The Role of Ad-Hoc Wireless Topologies


    Fog learning is an emerging paradigm for optimizing the orchestration of artificial intelligence services over contemporary network systems. Different from existing distributed techniques such as federated learning, fog learning emphasizes intrinsically in its design the unique node, network, and data properties encountered in today’s fog networks that span computing elements from the edge to the cloud.

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  • Brian Hoskins

    Developing CMOS+X Platforms for Artificial Intelligence and Beyond


    Crossing the gap from “lab-to-fab” requires more than just demonstrating a new concept or device – it requires building prototype systems at intermediate scales which can achieve both a far higher volume of statistics as well as greater degree of measurement precision. Unfortunately, developing such prototypes is largely out of the reach of most academic researchers due to the high non-recurring engineering costs for the test vehicles to demonstrate these prototypes.

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  • Yoshiaki Nakamura

    Nanostructure design and fabrication for high performance thermoelectric material


    High performance thermoelectric materials have been intensively studied because thermoelectric conversion has been expected to generate an ideal energy source. The thermoelectric conversion efficiency is monotonically increasing with dimensionless figure of merit ZT.

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  • Ambuj Singh

    Analyzing deep learning representations for robustness and explainability


    Artificial intelligence and machine learning have been extremely successful in predicting, optimizing, and controlling the behavior of complex interacting systems. Robustness and explainability of existing methods, however, remain big challenges. I will introduce some ideas on how to analyze the space of representations produced by deep learning and explain how representation patterns are intricately connected to the kind of machine learning tasks. These patterns also drive the robustness of the models and their explainability.

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  • Christopher Nordquist

    Heterogeneous Integration for Next-Generation RF Microsystems


    Heterogeneous Integration (HI) reduces size weight and power (SWaP) and provides high-density electrically short interconnect by eliminating the overhead of traditional packaging, thus enabling electronic microsystems to perform functions beyond that achievable with any single technology.  A practical HI strategy must allow for a broad range of technologies and materials:  state-of-the art electronics such as advanced node CMOS integrated circuits, specialty electronics such as compound semiconductor RF or power electronics, optoelectronic components in either silicon or compound semiconductors, microelectromechanical components, RF passives and antennas, high-density connectors, and advanced early-stage R&D prototypes in emerging technologies.  In all cases, supply chain and economic realities force tradeoffs between realizing the smallest integrated microsystem with the tightest integration dimensions versus lower cost manufacturable approaches with somewhat relaxed interconnect densities.

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The Charles L. Brown Department of Electrical and Computer Engineering provides thought leadership via award-winning research, out-of-the-box solutions and turning-point ideas.

We aspire to be an inclusive and welcoming place for all, and a hub of innovation where research and teaching go hand-in-hand.

Our mission is to prepare the next generation of electrical and computer engineering leaders to solve society’s grand technological challenges and improve quality of life.