6G communication system and the United Nations Sustainable Development Goals (UN SDGs) all target 2030. Although the relationship between 6G and SDG is not currently well defined yet, both SDG and 6G cover a wide range of the same topics that can share the mutually reinforcing forces, such as energy saving and smart cities. In this talk, we introduce the vision of 6G and its relations to UN SDGs through a set of indicators. This measuring tool for data collection can help build a 6G ecosystem in line with the UN SDGs. We will show some 6G technologies, such as novel cellular architecture and radio resource management, that can be incorporated towards reaching the UN SDGs

Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph.D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was a Senior Technical Staff Member at AT&T Laboratories. Since August 2000, he has joined National Yang Ming Chiao Tung University (NYCU) in Taiwan. He is now the Chair Professor and serves the Dean of College of Electrical and Computer Engineering of NYCU. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architecture and radio resource management in wireless networks. He won two Distinguished Research Awards from National Science and Technology Council (2012, 2017), IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He was recognized as Top 2% Scientists Worldwide in a study from Stanford University. His recent research interests are in the areas of cross-layer optimization for wireless systems, AI-enabled radio resource management for heterogeneous mobile networks, and big data analysis for industrial Internet of things. He holds 26 US patents, and has published over 300 journal and conference papers, and co-edited the book, ”Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).

Today connected devices, which are continuously increasing in number due to the emergence of the Internet-of-Things paradigm, as well as various smart sectors generate a significant amount of data. Tailoring machine learning algorithms to exploit this massive amount of data can lead to many new applications and open-up new markets in medical care, finance, and enabling ambient intelligence. The question is how to use this decentralized data to enhance the system intelligence beneficial for everyone while protecting the sensitive information.

It is not desirable to offload such massive amounts of data available at the edge devices to a cloud server for centralized processing due to latency, bandwidth, and power constraints, as well as privacy concerns of the users. Furthermore, due to the growing storage and computational capabilities of the edge devices, it is increasingly attractive to store and process the data locally by shifting network computations to the edge. This enables decentralized intelligence where local computations on the data converts decentralized data to a global intelligence; hence, enhancing data privacy while learning from the collection of data available across the network.

In this talk, I highlight some of the challenges and advances in enabling decentralized intelligence by jointly designing communications, computations, and collaboration, the three essential components of enabling collective intelligence. Communications help connecting the clients in this distributed environment; computations help converting data into intelligence; and collaboration is a method of aggregating local intelligence into a global intelligence. I discuss about the advances in integrating these components and how this can help with efficient system development.

Dr. Mohammadi Amiri received the B.Sc. degree in Electrical Engineering from the Iran University of Science and Technology in 2011 and the M.Sc. degree in Electrical and Computer Engineering from the University of Tehran in 2014, both with the highest rank in classes. He also obtained the Ph.D. degree in Electrical and Electronic Engineering at Imperial College London in 2019. He then spent two years as a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering at Princeton University. He is currently a Postdoctoral Associate at MIT where he joined in early 2022. He received the Best Ph.D. Thesis Award from the Department of Electrical and Electronic Engineering at Imperial College London, as well as the IEEE Information Theory Chapter of UK and Ireland in the year 2019. He is also the recipient of the IEEE Communications Society Young Author Best Paper Award (2022) for the paper titled “Federated learning over wireless fading channels.” His research interests include machine learning, wireless communications, information theory, edge computing, privacy, and data science.

Quantum materials host exotic states of matter with unique macroscopic phenomena, ranging from various correlated electron states to topological orders. The ability to create and manipulate their emergent properties with nanoscale precision is the key in driving the future progress of new electronic, photonic and quantum technologies. In particular, 2D van der Waals (vdW) materials combined with complex transition-metal oxides exhibiting strong electron correlations open up exciting opportunities for designing new functional properties at their interface. In this talk, I will discuss a robust strategy to design novel photonic device platforms by integrating oxides into 2D materials using the notion of oxidation-activated charge transfer. Taking graphene as a model 2D system, I will describe applications of this strategy in controlling the propagation of polaritons—hybrid light-matter excitations with extreme light confinement—and in implementing low-loss nanostructured optical elements. I will further discuss future prospects of 2D/; oxide heterostructures in next-generation device applications.

Dr. Brian Kim received his B.S. degree in Electrical Engineering at Northwestern University. He went onto receive M.S. and Ph.D. in Electrical Engineering at Stanford University and worked with Prof. Harold Hwang on complex oxide heterostructures and devices. He is currently a post-doctoral researcher at Columbia University working with Prof. James Hone and Prof. Dmitri Basov on 2D materials and near-field nano-optics. He is interested in creating and controlling emergent properties at the interface of 2D materials and oxides.

A central question when designing an AI system in the real world is, “how to learn representations of the data that make it easier to extract useful information when building classifiers or other predictors?” Satisfactory answers are attainable for certain data, such as images, texts, or audio. However, various types of data are gathered as graphs, such as social networks, protein interaction networks, brain connectomes, etc. Despite the emerging and powerful graph neural network techniques, researchers are yet unanimous in the answer dedicated to learning embeddings from graphs due to their high irregularity, complexity, and sparsity. I have been focusing on addressing the critical challenges in graph representation learning.

In this talk, I will first introduce my recent research results on solving two key problems in graph representation learning. i) A significant limitation of the famous graph convolutional network is over-smoothed embeddings with deeper networks. ii) The scalability to big data, though facilitated by self-supervised pretraining, loses the focus on local structure. After that, I will expand the horizons beyond the nodes and discuss how they interact with the algorithm design. i) Graph-level embeddings are desired instead of node-level embeddings in some applications. ii) Data distribution implies graph structure—even if it is not explicitly given. Besides addressing the efficacy of representation learning, I also designed fairness-aware machine learning algorithms to tackle the bias in model training and data processing. I applied my new representation learning methods to successfully solve various real-world applications, such as brain disease early diagnosis, drug repositioning, and social media network predictions.

Yanfu Zhang is a Ph.D. candidate in Computer Engineering at the University of Pittsburgh, supervised by Prof. Heng Huang. His research interests span graph neural networks, efficient and robust representation learning, and fairness-aware machine learning, with applications to medical images, multi-omics, and other relevant data mining and machine learning problems. His works have been published in top-tier conferences and prestigious journals, such as KDD, ICML, NeurIPS, ICCV, ECCV, WebConf (WWW), ICDM, IPMI, MICCAI, Nuclei Acids Research, and PNAS Nexus. He served as a program committee member of KDD, MICCAI, ICCV, etc.

Silicon is the most scalable optoelectronic material but has suffered from its inability to generate directly and efficiently classical or quantum light on chips. Silicon is also an inherently weak absorbing material in near-infrared (NIR) wavelengths, which is highly important for emerging applications in the existing CMOS foundry framework. First, I will talk about a nanophotonic engineering design and CMOS-compatible fabrication technique that help attain 20-fold prodigious light absorption enhancement in 1.0\,µ{\rm m} thin silicon ultrafast optical sensors, leading to surpassing the inherent absorption efficiency of gallium arsenide (mainstream detection material) for a broad spectrum in the NIR\null. Next, I will present the first all-silicon quantum light source based on a single atomic emissive center embedded in a silicon-based nanophotonic cavity. We observe a more than 30-fold enhancement of luminescence, a near unity atom-cavity coupling efficiency, and an 8-fold acceleration of the emission from the quantum center. This talk will explore avenues for emerging applications in classical and quantum communication, sensing, imaging, and computing.

Dr. Wayesh Qarony is a postdoctoral scholar at the University of California Berkeley in the EECS department, jointly with the Molecular Foundry of Lawrence Berkeley National Lab. He focuses on nanophotonic design, fabrication, and characterization of optoelectronic, photonics, and quantum semiconductor devices for ubiquitous energy and sensing applications. He has notable scientific contributions by publishing over 40 high-quality research articles, including Nature and Advanced Science, with more than 1500 google citations. Dr. Qarony was awarded and honored many times, including summa-cum-laude gold medal in B.Sc., German Hempel & HKPFS fellowships, UC Davis PSA grant, outstanding PolyU Ph.D. graduate shining in academia honor, UC Berkeley NSF I-Corps grant award, and 2022 NSF I-Corps stipend award. Dr. Qarony received his Ph.D. in Applied Physics and M.Sc. in Electrical Engineering from Hong Kong Polytechnic University and Jacobs University Bremen, Germany, respectively. Before joining UC Berkeley & LBNL, he was a postdoctoral scholar at ECE UC Davis.

With the proliferation of IoT (Internet of Things), now we are living in an environment surrounded by various wireless facilities which provide rich information about the human body and activities. By analyzing the wireless signals that bounce off the human body, we can perceive human activities in the environment. Such wireless sensing systems can support device-free interactions between humans and their physical surroundings and enable a new generation of applications capable of performing complex sensing and recognition tasks. In this talk, I will introduce how we extract information related to the human body and activities from wireless signals by developing and applying deep learning techniques. In particular, I will introduce how we fuse complementary information from heterogeneous sensors and adapt the system to new environments on the task of human activity recognition. In addition, I will also present our work which is the first to utilize commercially available IoT wireless devices for human skeleton/; mesh reconstruction in real-time, which can facilitate more sophisticated Human-Computer Interaction (HCI). In general, the developed wireless sensing systems in our research can improve people's working efficiency and life quality by enabling a wide range of applications, such as smart homes, virtual reality, elderly monitoring, fire rescue, gesture control, no checkout shopping, security surveillance, and many others.

Hongfei Xue is a final-year Ph.D. candidate in the Department of Computer Science and Engineering at the State University of New York at Buffalo (UB), under co-supervision of Prof. Lu Su and Prof. Aidong Zhang. Before that, he received his B.Eng. in Computer Science from University of Science and Technology of China (USTC\null). His research interests lie in the intersection of Internet of Things (IoT) and Machine Learning, with a current emphasis on building the intelligent wireless IoT sensing systems. The primary goal of his research is to develop algorithms and systems that can intelligently collect, integrate, analyze and eventually transform the IoT sensory data generated by the ubiquitous human and physical sensors into useful knowledge that can draw a better understanding of the social and physical world. His research work has been published in various top venues such as MobiCom, MobiSys, SenSys, MobiHoc, UbiComp, and IJCAI.

The penetration of technologies such as Machine Learning (ML), Artificial Intelligence (AI), wireless broadband, and the Internet of Things (IoT) is propelling the rapid adoption of ubiquitous devices across a variety of sectors. However, the enhancement of machine intelligence in ubiquitous computing in the IoT is hindered by various barriers, including: 1) inefficiency and low scalability of trained models, 2) security and privacy concerns surrounding user data, 3) data heterogeneity across devices, and imbalanced data distribution on individual devices, 4) communication bottlenecks and high computational costs.

In this talk, I will present my research in the field of Indoor Location-based Services (ILBS) in the IoT, which aims to overcome the aforementioned challenges by learning useful information from ubiquitous devices while preserving user privacy. Specifically, I will discuss how we have designed a bisection reinforcement learning approach by formulating a novel Markov Decision Process (MDP) for indoor localization, as opposed to existing classification or regression formulations, to ensure efficient and effective model training in large solution spaces, and to improve the scalability of trained models. Furthermore, I will present an on-device ILBS framework by developing a personalized federated reinforcement learning method to rigorously protect the privacy of personal data on IoT devices over wireless edge networks, while addressing model oscillation/; drifting and reducing communication costs in the presence of system heterogeneity.

Fei Dou is a final-year Ph.D. candidate in the Department of Computer Science and Engineering at the University of Connecticut, under the supervision of Prof. Jinbo Bi. Her research centers on exploring how AI/ML can improve the efficiency, privacy, and scalability of the IoT\null. Specifically, Fei is mainly working on Indoor Location-based Services (ILBS), Edge Computing, and Remote Sensing Imagery Analysis by developing new methods from the perspectives of Reinforcement Learning, Federated Learning, and Contrastive Learning. Fei's work has been published in highly-selective and high-impact journals such as IEEE IOT-J, and she has served as a reviewer at top-notch conferences and journals including ICLR, AAAI, IJCAI, Sensors, etc.

Reconfigurable active photonics harnesses the nonlinear light-matter interactions in active materials to create ultrafast modulation responses, reconfigurable mode profiles, and quantum entanglement on a scalable integrated platform. Combining recent advances in both fundamental physics and fabrication technologies, large-scale reconfigurable active photonics promises revolutionary solutions to communications, sensing, and imaging in both classical and quantum regimes. In this talk, I will first show our recent progress in controlling the collective behaviors in large-scale multimode photonic systems to generate high-brightness, dynamically tunable coherent illumination. We experimentally demonstrated record-breaking intensity enhancement and robust reconfigurability in two-dimensional microlaser arrays using design modalities guided by symmetry and topology. Beam steering and ultrafast dynamical modulation were demonstrated using mechanisms intrinsic to the nonlinearity of the system. In the second part, I will discuss active sensing systems that harness reconfigurable structured illumination generated on the chip, including ultra-miniature 3D imaging modules and quantum imaging schemes surpassing the classical resolution limit. Finally, I will conclude by presenting my vision for this integrated optical platform, outlining its usages, including fully integrated lensless imaging, scalable quantum state generation and detection, and optical computing.

Dr. Zihe Gao is a postdoctoral researcher at the University of Pennsylvania. Zihe received the B.S. degree in physics from Nanjing University, the M.S. degree in physics, and the Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign. Prior to joining Penn in 2020, he was a postdoctoral research scientist at the Meta Reality Labs. His research interests include reconfigurable photonic devices, high-speed dynamics in coupled nonlinear resonators, quantum illumination projectors, and their applications in communications, sensing, and computing in both classical and quantum regimes.

Over the past years, driven by an increasing number of data-intensive applications, architects have proposed a variety of memory-centric strategies, e.g., processing-in-memory (PIM), near-data processing (NDP), and memory-based accelerators, to bridge the gap between computing and storage. The potential benefits of these innovations, including improved performance and energy efficiency, have been demonstrated through numerous studies. However, the increasing complexity and hardware overhead of these memory-centric innovations are often in conflict with the cost-sensitive nature of memory, which have been optimized for cost-per-bit. In this talk, I will present several memory-centric architecture solutions that offer new functionalities at negligible or zero cost by maximizing resource utilization in memory. This approach to efficiency does not rely on expensive technology advancements. The talk will include two representative studies: a compute-capable memory architecture, which increases bandwidth by moving computation to the memory side, and a multi-dimensional accessible memory architecture, which improves bandwidth efficiency through flexible memory accesses.

Xin Xin is a PhD candidate in the Department of Electrical and Computer Engineering at the University of Pittsburgh. He received his Master's degree from Tsinghua University in 2016 and his Bachelor's degree from Lanzhou University in 2013. He interned at Alibaba Group, Sunnyvale, in Fall 2021, and worked as a digital IC engineer at Huada Electronics, Beijing, in 2017. Xin's research interests reside at computer architecture with emphasis on the memory system, e.g., main memory (DRAM) performance/power/reliability, processing-in-memory (PIM)/near-data-processing (NDP), hybrid DRAM-NVM system, and memory-based accelerators.

Advanced computing systems have been a key enabler for the resounding success of computationally intensive artificial intelligence (AI) models, and computing efficiency has become a critical measurement for computing tasks. To achieve better efficiency, one promising approach is to utilize emerging nonvolatile memory technologies to build the AI accelerators. Resistive random-access memory (ReRAM) is one of the most promising emerging technologies featuring high density, low access energy, and the feasibility of realizing multi-level cells. Prior ReRAM-based processing-in-memory (PIM) designs have demonstrated their potential in performing vector-matrix multiplications (VMM) compared with pure CMOS architectures. However, the prior designs cannot achieve high efficiency in the new and appealing attention-based models, such as Transformer. Besides, the hardware non-idealities will degrade the inferencing accuracy of the in-memory computing system, especially when the hardware is approaching the endurance limit. To address the above issues, this talk will focus on improving the efficiency and robustness of in-memory computing. I will start with a case study on efficient ReRAM-based PIM design for Transformer to elaborate on key computing step optimization and function construction with in-memory logic. Next, I will highlight a systematic framework to mitigate the impact of device stochastic noise and uncover Pareto-optimal solutions for high-performance and energy-efficient PIM. In the end, I will discuss a structured stochastic gradient pruning method, which enables the endurance-aware ReRAM-based training process.

Xiaoxuan Yang is a Ph.D. candidate in Electrical and Computer Engineering at Duke University, under the supervision of Dr. Hai Helen Li and Dr. Yiran Chen. She received the B.S. degree in Electrical Engineering from Tsinghua University and the M.S. degree in Electrical Engineering from the University of California, Los Angeles (UCLA). Her research interests include emerging nonvolatile memory technologies, robustness and reliability enhancement in processing-in-memory designs, and hardware accelerators for deep learning applications. Her research work won Best Research Award at ACM SIGDA Ph.D. Forum at DAC and Third Place of ACM Student Research Competition at ICCAD\null. She is also selected as a Rising Star in EECS by UT Austin.

The mid-infrared (mid-IR) region of the electromagnetic spectrum, also known as the molecular fingerprint region, has long been a focus of scientific and technological research. Mid-IR microscopy is a non-destructive tool that can measure the molecular content of biological samples by probing fundamental vibrational modes, with potential applications in early disease detection and diagnosis. However, limitations such as long acquisition times, limited spatial detail, and a lack of understanding of light-matter interactions have impeded progress in this field. In this talk, I will present advanced mid-IR spectroscopic imaging platforms that address these challenges by using a decision theory framework to improve perceived spatial resolution and enabling label-free classification of surgical tissue sections within minutes. Additionally, I will discuss the development of technology for imaging site-specific chirality of molecules, including the specific challenges and roadblocks to creating a viable and accurate system. The focus of this talk is on using theory and modeling to guide the development of measurement technology and open new opportunities for understanding biomolecules.

Yamuna Phal is a Ph.D. candidate in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). She received her B.Tech. from Indian Institute of Technology Roorkee (IIT-R) and an M.S. from California Institute of Technology (Caltech) in Electrical Engineering. Prior to joining the University of Illinois, Yamuna worked as an analog research engineer for Finisar and the Swedish Institute of Space Physics. Currently, she is working with Professor Rohit Bhargava's research group to develop next-generation IR imaging instruments. Yamuna has been recognized for her research through several scientific awards and publications, including her invention of VCD imaging [patented technology] and the use of decision theory to provide an analytical formulation for the resolution limit for spectral imaging systems. Her work has been featured on the covers of Analytical Chemistry and Journal of Physical Chemistry C. Additionally, Yamuna has been recognized for her teaching and mentoring skills, receiving awards such as the Harold Olsen Award and E. A. Reid Fellowship for undergraduate teaching and engineering education at UIUC\null. Four of her mentored teams have also won awards, including the best engineered project award for ECE senior design capstone project.

Margaret Smith talks about her role as a Technology Manager in the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO). In this role, she provides oversight for a portfolio of projects to ensure consistency with Technology Integration (TI) Program goals including Electric Vehicle (EV) Community Partner projects. She also provides programmatic direction for the Clean Cities Coalition Network and oversees work performed by DOE national laboratories. Margaret leads energy and environmental justice initiatives at VTO, including the Justice40 pilot.

Before joining DOE in 2020, she provided VTO programmatic and technical support as a contractor for over 10 years. Margaret supported various TI activities, such as organizing Clean Cities director trainings and peer sharing discussions, developing resources for Clean Cities coalitions, and assisting with program management responsibilities. Additionally, Margaret worked with Clean Cities coalitions on developing and implementing projects addressing barriers to EV deployment including Vehicle Charging Innovations for Multi-Unit Dwellings (VCI-MUD) and Electric Vehicle Widescale Analysis for Tomorrow's Transportation Solutions (EV WATTS).

Prior to working with VTO, Margaret worked for Sustainable Energy Strategies, Inc. In this role she supported biodiesel and ethanol projects that improve infrastructure, develop feedstocks, educate stakeholders, and promote the use of biodiesel. She has two years of experience with the design, manufacturing, and sales of underground stormwater treatment systems that remove pollution from stormwater runoff. Margaret holds a Bachelor of Science in computer engineering with a concentration in cognitive science from the University of Virginia.

Security research has largely focused on employing cryptographic or information-theoretic methods to protect the content of the message from being decoded by an eavesdropper, but there are important applications where even the detection of a communication signal's presence by an adversary is undesirable. Over the last 10 years, there has been significant research on the fundamental limits of undetectable communications, which has been termed “covert communications” in the contemporary literature. Early results established the difficulty of the problem: for discrete-time additive white Gaussian noise (AWGN) channels between all parties, a transmitter Alice can reliably and covertly transmit O(\sqrt{n}) bits in n channel uses (and no more) to an intended recipient Bob without detection by an attentive and capable adversary Willie. A major line of subsequent research demonstrated that, given uncertainty at Willie about the state of the channel when Alice is not transmitting, Alice can reliably and covertly transmit O(n) bits in n channel uses to Bob without detection, hence implying that there are situations under which positive rate covert communications is possible. This uncertainty might arise because of natural limitations of Willie's receiver or from active transmissions in the environment.

Nearly all work on covert communications has been performed on standard discrete-time models, with the implicit understanding that the results should be applicable to the true continuous-time system. Whereas this is true for many of the results, particularly those that result in a throughput of O(\sqrt{n}) bits in n channel uses, this is not necessarily true for major works that have established positive rate covert communications; rather, the continuous-time channel must be considered directly in many cases. We will demonstrate in this talk how approaches developed for discrete-time models might not be covert when employed on the true continuous-time channel. We will then demonstrate an approach by which positive rate covert communications can be established in such a situation. Further, by digging even deeper into the physical layer, we will discuss challenges to even this solution. Finally, we will talk about important ongoing challenges to the deployment of covert communications and ideas for future research.

Dennis Goeckel received his BS from Purdue University in 1992, and his MS and PhD from the University of Michigan in 1993 and 1996, respectively. Since 1996, he has been with the ECE Department at the University of Massachusetts at Amherst, where he is currently a Professor. Prof. Goeckel has been a Lilly Teaching Fellow (2000-2001) and received the University of Massachusetts Distinguished Teaching Award in 2007. His work with collaborators on covert communications received Honorable Mention in the NSA Best Scientific Cybersecurity Paper Competition in 2016. He received the NSF CAREER Award in 1999 and is an IEEE Fellow.

Surgical resection is still the major treatment for solid tumors. The complete surgical resection of the cancer tissues in the surgery is essential to the prognosis of cancer patients. However, 40% of the US patients have the local recurrence in 5 years from the initial surgery, due to the failure to detect all the cancer tissues intraoperatively. To address this issue, we developed a spectroscopic device and machine-learning algorithms for rapid intraoperative tissue identification; we also designed a portable visible/near-infrared camera system to directly visualize the tissue fluorescence. Human clinical studies were conducted with various cancers, including pancreatic and breast cancers, in several major hospitals in Louisiana. In the surgeries, our method may help to identify the cancer, the normal tissue, and the positive and the negative margins, within seconds.

Similar approaches, i.e., machine-learning-aided biomedical imaging, also helps to develop novel dental imaging scheme to overcome the significant challenge of current dental X-ray/CT. More than 2\over3 of the US population has to receive dental imaging routinely. Current dental diagnosis largely relies on dental X-ray/CT, with several major drawbacks, such as failure to detect some critical dental diseases (e.g., early stage cracks and caries), ionizing radiation risks, and the necessity of holding immobile bulky imaging sensors during imaging. The objective of this project is to address these drawbacks by developing a novel dental imaging scheme with a lab-designed sensitive NIR fluorescence photonic imaging system, consisting of a high-resolution camera and a spectroscopic device, together with nanofluorophore, for real-time imaging of critical dental structures and diseases, including the diseases that are undetectable by the dental X-ray/CT.

Dr. Jian Xu earned his M.S., M. Phil, and Ph.D. degrees in Electrical Engineering from Yale University. He is an Associate Professor in the LSU Division of Electrical and Computer Engineering. Dr. Xu primarily focuses on medical instrumentation tailored for image-guided cancer surgery, as well as the design of biomimetic and environmentally sustainable energy transducers, featuring natural nanoconductors. Dr. Xu was honored with the prestigious NSF CAREER Award in 2021. His research has garnered substantial attention, with his work being featured in an OUTLOOK report titled “Optics shine a light on dental imaging” published in Nature. Dr. Xu's medical devices have been employed in three clinical studies across several hospitals in the U.S. These studies encompassed patient applications spanning pancreatic cancer, laryngeal cancer, oral cancer, and dental diseases.

The future standard of living hinges on our ability to harness energy sustainably, with a primary focus on clean sources such as wind, solar, and ocean waves. Renewable energy systems are pivotal in this transition, offering a promising path toward reduced carbon emissions and a more sustainable energy future. At the heart of these systems lie power electronic converters (PECs), which play a critical role in efficiently converting and integrating renewable energy into the grid.

PECs are far more than mere energy conversion devices. To fully realize the potential of renewable energy sources, PECs must facilitate dynamic control of both active and reactive power, allowing for precision in managing energy output. They also need to facilitate active participate of renewable energy resources in grid-balancing acts, taking on responsibilities like frequency control. This critical function of PECs is paramount to ensure the stability and reliability of the power grid as we transition to a more renewable-centric energy landscape.

The control systems governing PECs are the linchpin for efficient power flow and grid management, allowing renewable energy systems to not only generate clean electricity but also actively contribute to grid ancillary services. In this presentation, we will delve into high- performance control of PECs and explore strategies that ensure a seamless integration of renewable energy systems into the power grid.

Dr. Mehdi Farasat (Senior Member, IEEE) received the Ph.D. degree in electrical engineering from the University of Nevada, Reno, in 2014. He is currently an Associate Professor with the Division of Electrical & Computer Engineering. His research interests are modeling and control of power electronics converters in renewable energy and electrified transportation systems.