Virtual Seminar by Lingyang Song

Title: Holographic Radio: A New Paradigm for Ultra-Massive MIMO

Time and Date: July 22, 2022, at 9:00AM ET

Presenter:  Professor  Lingyang Song, Peking University, China

Venue: (Password: 4Zn7xZ)

Abstract: To enable a ubiquitous intelligent information network, the forthcoming sixth generation (6G) wireless communications are expected to provide revolutionary mobile connectivity and high-throughput data services through ultra-massive multiple input multiple output (MIMO). Widely-utilized phased arrays relying on costly components make the implementation of ultra-massive MIMO in practice become prohibitive from both cost and power consumption perspectives. The recent developed reconfigurable holographic surfaces (RHSs) composing of densely packing sub-wavelength metamaterial elements can achieve holographic beamforming without costly hardware components. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight surface antenna integrated with the transceiver, thereby providing a promising alternative to phased arrays. In this talk, we will first provide a basic introduction of RHSs. We then introduce the unique features of RHSs which enables ultra-massive MIMO for both communication and sensing, in a comprehensive way. Related design, analysis, optimization, and signal processing techniques will be presented along with typical RHS-based applications for wireless communications. The implementation issues along with our developed prototypes and experiments will also be discussed.

Bio: Lingyang Song (S’03-M’06-SM’12-F’19) received his PhD from the University of York, UK, in 2007, where he received the K. M. Stott Prize for excellent research. He worked as a research fellow at the University of Oslo, Norway until rejoining Philips Research UK in March 2008. In May 2009, he joined the School of Electronics Engineering and Computer Science, Peking University, and is now a Boya Distinguished Professor. His main research interests include wireless communications, mobile computing, and machine learning. Dr. Song is the co-author of many awards, including IEEE Leonard G. Abraham Prize in 2016, IEEE ICC 2014, IEEE ICC 2015, IEEE Globecom 2014, and the best demo award in the ACM Mobihoc 2015. He received National Science Fund for Distinguished Young Scholars in 2017, First Prize in Nature Science Award of Ministry of Education of China in 2017. Dr. Song has served as a IEEE ComSoc Distinguished Lecturer (2015-2018), an Area Editor of IEEE Transactions on Vehicular Technology (2019-), Co-chair of IEEE Communications Society Asia Pacific Board Technical Affairs Committee (2020-). He is a Clarivate Analytics Highly Cited Researcher.

Virtual Seminar by John M. Shea

Title: AI and Privacy in Collaborative Spectrum Sharing: Perspectives from the Spectrum Collaboration Challenge and Beyond

Date and Time: April 27, 2022 at 10AM ET

Registration Process: Please register at

Abstract: Dynamic spectrum access has the potential to greatly improve the utilization of the radio spectrum over existing static allocation techniques. Systems like Citizens Broadband Radio Service (CBRS) are first steps in realizing this vision. However, CBRS is still very limited in its need for centralized coordination (via spectrum access systems) and the potentially long delays (up to 24 hours) in issuing new spectrum grants. In the DARPA Spectrum Collaboration Challenge (SC2), teams performed distributed and collaborative spectrum sharing with spectrum usage patterns changing on time-scales of seconds. This was facilitated by the exchange of information via a collaboration channel, including teams’ usage of spectrum across time and space, as well as information on teams’ performance in delivering traffic. Although this data greatly helps in developing strategies for spectrum use, it also presents risks from both the disclosure of private information and from the potential for manipulating systems’ actions through misreporting. In this talk, I will discuss our experiences in collaborative spectrum sharing during the SC2, our work on applying ML to these problems, and our work on providing privacy in distributed spectrum sensing. I will conclude with a discussion of open problems in these areas.

Bio: John M. Shea is a Professor and the Associate Chair for Academics in the Department of Electrical and Computer Engineering at the University of Florida, where he has been on the faculty since 1999. His research is in the areas of wireless communications and networking, with emphasis on military communications, software-defined radio, networked autonomous systems, and security and privacy in communications. He was co-leader of Team GatorWings, the overall winner of the DARPA Spectrum Collaboration Challenge (DARPA’s fifth grand challenge), in which the teams used software-defined radios to implement intelligent radio networks for collaborative spectrum sharing. He received the Lifetime Achievement Award for Technical Achievement from the IEEE Military Communications Conference (MILCOM) and is a two-time winner of the Ellersick Award from the IEEE Communications Society for the Best Paper in the Unclassified Program of MILCOM. He has been an editor for IEEE Transactions on Wireless Communications, IEEE Wireless Communications magazine, and IEEE Transactions on Vehicular Technology. He is the author of more than 130 refereed journal and conference papers, as well as six book chapters.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

Virtual Seminar by Jun Luo

Title: Algorithmic Sensing in the Age of Artificial Intelligence

Time and Date: Apr. 8, 2022, at 9:00 am ET

Presenter:  Professor  Jun Luo, Nanyang Technological University, Singapore

Venue: (Password: 4Zn7xZ)

Abstract: Promoted by the increasing compactness of embedded sensors and tremendous progress in artificial intelligence, algorithmic sensing is gaining momentum as it has the potential to push the limit of physical sensors. However, realizing the promises of algorithmic sensing can be highly non-trivial, as specific machine/deep learning techniques have to be fine-tuned to suit diversified sensing modalities, as opposed to direct feature extraction and classification largely ignoring the distinctive physics of respective sensors. In this talk, we take Radio Frequency (RF) sensing as a representative example. We first explain the basic idea of RF-based algorithmic sensing, then we present a unified framework to prepare RF sensing data for deep analytics. Building upon this framework, we further explain the peculiarity of RF sensing data (against, e.g., conventional image data) and present our approaches in designing deep interpreted RF sensing, leveraging our latest developments on a (software-defined sensing) platform, along with corresponding applications and products as instances.

Bio: Dr. LUO Jun is a tenured professor in NTU. He has been working on wireless sensing, deep learning, and computing system integration for more than two decades and has established a solid foundation on all these topics. Building upon this foundation, his research team has made contributions on mobile/ pervasive computing and smart sensing technologies, by leading several national research projects and corporate labs (including MoE Tier2, BMW, SAP, and CSIJRI, with more than S$10 million funding), aiming to transfer the research outcomes to practical applications.
In the meantime, Dr. Luo and his team have kept publishing at relevant venues such as IEEE and ACM top conferences and journals, including MobiCom, CVPR, SenSys, INFOCOM, UbiComp, ToN, and TMC. With only 130+ publications, he has earned over 8000 Google Scholar citations, with the top-cited paper bearing more than 1,200 of them. In the past few years, Dr. Luo has put most his efforts on algorithmic sensing aiming to, on one hand, push sensing capabilities far beyond conventional sensor design using novel machine learning techniques, and on the other hand, repurpose existing sensors to achieve functionalities beyond their original intentions. More detailed information can be found at:

Virtual Seminar by Aylin Yener

Title: 6G for Information Security and Information Security for 6G

Date and Time: March 30, 2022 at 11AM ET

Registration Process: Please register at

Abstract: 6G is envisioned as the next wireless revolution, introducing novel materials and devices, metrics and requirements, designs and applications of wireless communications, as well as better integration of communications, computing, sensing and learning/AI. Accordingly, new physical layer and network design paradigms are coming into the picture, with the potential of accommodating foundational design advances that can innately secure information. This talk will provide an overview of the directions for information security and privacy relevant to the 6G connected world vision. We will be covering approaches providing information theoretic guarantees and argue their renewed role in the 6G vision. In addition to utilizing the wireless medium for improving communication security, the information theoretic security and privacy lens extends to securing in-network/edge storage. In the former, applications range from networks of devices with heterogeneous capabilities to large sensor or IoT networks. For the latter, applications include content delivery, edge computing and edge learning. We will summarize efforts in these directions and outline future research opportunities.

Bio: Aylin Yener is the Roy and Lois Chope Chair Professor at The Ohio State University and a Professor in the Departments of Electrical and Computer Engineering, Computer Science and Engineering, and Integrated Systems Engineering. Previously, Dr. Yener was a Distinguished Professor and a Dean’s Fellow at Penn State and during which she also held visiting professor appointments at Stanford, and Telecom Paris Tech. Her core expertise areas are in wireless communications, information theory and learning, with interests ranging from physical layer optimization, resource allocation and algorithmic design for wireless, wireless AI, to information security, energy conscious communications, content delivery and edge computing. She received the 2020 IEEE Communications Society Communication Theory Technical Achievement Award, 2019 IEEE Communications Society Best Tutorial Paper Award, 2018 IEEE Communications Society Women in Communications Engineering (WICE) Outstanding Achievement Award, 2014 IEEE Communication Society Marconi Paper Award, 2010 IEEE International Conference on Communications Best Paper award, and several other research and technical awards. She is a fellow of the IEEE.

An active volunteer of the IEEE, Dr. Yener served as the President of the IEEE Information Theory Society in 2020. She has served on the board of governors of the IEEE Information Theory Society since 2012, as the society treasurer, elected member, vice president, and presently as past president. In 2008, she co-founded the North American School of Information Theory which grew to be the largest outreach activity of the society that runs annually in university campuses in North America. Her previous service record includes editorship in ComSoc flagship journals and organization at the track and symposium chair level at ComSoc conferences. She is presently on the senior editorial boards of the IEEE Journal on Selected Areas in Communications, and IEEE Journal on Selected Areas in Information Theory. She serves as an area editor for Security and Privacy for the IEEE Transactions on Information Theory, and is the technical program committee chair for the 2022 IEEE International Symposium on Information Theory.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

Virtual Seminar by Marwan Krunz

Title: Machine Learning Classification of RF Signals over Congested and Contested Spectrum: Algorithms and Experimentation

Date and Time: February 23, 2022 at 11AM ET

Registration Process: Please register at

Abstract: Machine learning (ML) has recently been applied for the classification of radio frequency (RF) signals. One use case of interest relates to the discernment between different wireless protocols that operate over a shared and potentially contested spectrum. Although highly accurate classifiers have been developed for various wireless scenarios, research points to the vulnerability of such classifiers to adversarial machine learning (AML) attacks. In one such attack, a surrogate deep neural network (DNN) model is trained by the attacker to produce intelligently crafted low power “perturbations” that degrade the classification accuracy of the legitimate classifier. In this talk, I will first present several novel DNN protocol classifiers that our team designed for a shared spectrum environment. These classifiers performed quite well in both simulations and OTA experimentation, considering benign (non-adversarial) noise. I will then present several AML techniques that an attacker may use to generate low power perturbations. When combined with a legitimate signal, these perturbations are shown to uniformly degrade the classification accuracy, even in the very high SNR regime. Different attack models are studied, depending on how much information the attacker has about the defender’s classifier. These models range from a “white-box’” attack (attacker has full knowledge of the defender’s DNN, including its hyperparameters, its training dataset, and even the seeds used to train the network), to a “black-box” attack. Time permitting, I will discuss possible defense mechanisms as well as other research efforts related to detection of adversarial transmissions.

Bio: Marwan Krunz is a Regents Professor at the University of Arizona. He holds the Kenneth VonBehren Endowed Professorship in ECE and is also a professor of computer science. He directs the Broadband Wireless Access and Applications Center (BWAC), a multi-university NSF/industry center that focuses on next-generation wireless technologies. He also holds a courtesy appointment as a professor at University Technology Sydney. Previously, he served as the site director for  the Connection One center. Dr. Krunz’s research is on resource management, network protocols, and security for wireless systems. He has published more than 300 journal articles and peer-reviewed conference papers, and is a named inventor on 12 patents. His latest h-index is 60. He is an IEEE Fellow, an Arizona Engineering Faculty Fellow, and an IEEE Communications Society Distinguished Lecturer (2013-2015). He received the NSF CAREER award. He served as the Editor-in-Chief for the IEEE Transactions on Mobile Computing. He also served as editor for numerous IEEE journals. He was the TPC chair for INFOCOM’04, SECON’05, WoWMoM’06, and Hot Interconnects 9. He was the general vice-chair for WiOpt 2016 and general co-chair for WiSec’12. Dr. Krunz served as chief scientist for two startup companies that focus on 5G and beyond systems and machine learning for wireless communications.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

Virtual Seminar by Zhu Han

Title: Distributionally Robust Optimization and Its Applications in Communications and Networking

Time and Date: January 13, 2022, at 9:00 am ET

Presenter:  Professor  Zhu Han, University of Houston, USA

Venue: (Password: 4Zn7xZ)

Abstract: Recently, distributionally robust optimization theory is introduced to overcome the shortcomings of these two approaches, which assumes that the distribution of the random variable is within an ambiguity set. This talk will give a detailed introduction to distributionally robust optimization techniques including the mathematic foundations and their applications in the wireless communication area. First, this talk will briefly explain the decision under uncertainty and the background of the distributionally robust optimization. Second, this talk will explain the concept of uncertainty set and how to choose and build up an uncertainty set based on the statistic learning techniques and historical data samples. Third, this talk will discuss the discrepancy-based distributionally robust optimization approach with Wasserstein distance. Fourth, this talk will discuss the distributionally robust reinforcement learning method which can make the agent more robust when it makes the decision in a high noise environment. In addition, this talk will introduce various communication applications by distributionally robust optimization and distributionally robust machine learning techniques including ultra-reliable communication, age of information minimization in healthcare IoT, computation offloading in space-air-ground integrated networks, etc. Finally, this talk will discuss the conclusions and future work.

Bio: Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor in Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in Electrical and Computer Engineering Department as well as Computer Science Department at University of Houston, Texas. His research interests include security, wireless resource allocation and management, wireless communication and networking, game theory, and wireless multimedia. Dr. Han is an NSF CAREER award recipient of 2010. Dr. Han has several IEEE conference best paper awards, and winner of 2011 IEEE Fred W. Ellersick Prize, 2015 EURASIP Best Paper Award for the Journal on Advances in Signal Processing and 2016 IEEE Leonard G. Abraham Prize in the field of Communication Systems (Best Paper Award for IEEE Journal on Selected Areas on Communications). Dr. Han is the winner 2021 IEEE Kiyo Tomiyasu Award. He has been an IEEE fellow since 2014, AAAS fellow since 2020 and IEEE Distinguished Lecturer from 2015 to 2018. Dr. Han is a 1% highly cited researcher according to Web of Science since 2017.

Virtual Seminar by Xinyu Zhang

Title: Printable Smart Surfaces for IoT Communication and Sensing

Time and Date: November 17, 2021, at 9:00 am ET

Presenter:  Professor  Xinyu Zhang, University of California San Diego, USA

Venue: (Password: 4Zn7xZ)

Abstract: Embedding sensing and communication capabilities seamlessly into ambient environment is a long-term aspiration of the Internet of Things (IoT). Smart surfaces, with conformable shape, thin form factors, and ease of fabrication, can potentially materialize the vision of intelligent and connected things. In this talk, I will present the design of passive, batteryless, chipless smart surfaces that facilitate IoT communication and sensing.  These surfaces can be fabricated through ordinary inkjet printing, PCB printing, or 3D printing.  They can help sensing the interaction between human users and everyday objects, thus enabling challenging use cases such as experience sampling and mobile VR interaction.  In addition, they can communicate with ordinary radio/radar devices, and boost the quality of existing wireless links. Realizing such capabilities involves non-trivial challenges, especially since the surfaces are fully passive and do not possess the computing/communication components in typical IoT devices.  This talk will introduce a set of solutions that span the areas of electromagnetics, wireless communications, and application-specific signal processing.  

Bio: Xinyu Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California San Diego. Prior to joining UC San Diego in 2017, he was an Assistant Professor at the University of Wisconsin-Madison. He received his Ph.D. degree from the University of Michigan in 2012. His research interest lies in wireless systems and ubiquitous computing, and more specifically in (i) designing next-generation wireless architectures and physical-layer informed protocols;  (ii) designing ubiquitous systems that leverage wireless signals to sense micro-locations and micro-activities at near-vision precision.  His research work has been regularly published in top conferences in these areas, especially ACM MobiCom, MobiSys, USENIX NSDI, and IEEE INFOCOM. He is the recipient of two ACM MobiCom Best Paper Awards (2011 and 2020), Communications of the ACM Research Highlight (2018), ACM SIGMOBILE Research Highlight (2018), NSF CAREER Award (2014), Google Research Award (2017, 2018, 2020), and Sony Research Award (2018, 2020).  He served as the TPC chair for ACM MobiCom 2019, IEEE SECON 2017, co-chair of NSF millimeter-wave research coordination network, and Associate Editor for IEEE Transactions on Mobile Computing from 2017 to 2020.

Virtual Seminar by Danijela Cabric

Title: Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Practical Considerations

Date and Time: November 19, 2021 at 11AM ET

Registration Process: Please register at

Abstract: As the Internet of Things (IoT) continues to grow, ensuring the security of systems that rely on wireless IoT devices has become critically important. Deep learning-based passive physical layer transmitter authorization systems, referred as RF fingerprinting, have been introduced recently for this purpose. RF fingerprinting approaches use wireless signals directly to verify identity of radio frequency transmitters based on imperfections in their radio hardware. However, most of the existing work using machine learning for RF fingerprinting has mainly focused on classification approaches assuming a closed set of transmitters. In practice, most serious security problems would arise if malicious transmitters outside this closed set are misclassified and authorized. In this talk, we formulate the problem of recognizing authorized transmitters and rejecting new transmitters as open set recognition and anomaly detection. We consider approaches based on one and several binary classifiers, multi-class classifiers, and signal reconstruction, and study how these approaches scale with the number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The authorization robustness against temporal changes in fingerprints is also evaluated as a function of the approach and the dataset structure. For this work, we have created a large Wi-Fi dataset consisting of about 10 million packets sent by 174 off-the-shelf Wi-Fi radios and simultaneously captured by 41 USRPs during 4 captures performed in ORBIT testbed along a month. We have also developed generative deep learning methods to emulate unauthorized signal samples for the augmentation of training datasets. We explored two different data augmentation techniques, one that exploits a limited number of known unauthorized transmitters and the other that does not require any unauthorized transmitters. Our results indicate that data augmentation allows for significant increases in open set classification accuracy, especially when the authorized set is small. Another practical problem in authentication systems based on deep learning is training time when the new transmitters are added. We have developed a fast authentication algorithm based on information retrieval that uses feature vectors as RF fingerprints and locality sensitive hashing(LSH) to create a database that can quickly searched by approximate nearest neighbor algorithm. The proposed algorithm matches the accuracy of deep learning models, while allowing for more than 100x faster retraining.

Bio: Danijela Cabric is Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. Her research interests include novel radio architectures, signal processing, communications, machine learning and networking techniques for spectrum sharing, 5G millimeter-wave, massive MIMO and IoT systems. Dr. Cabric received the Samueli Fellowship in 2008, the Okawa Foundation Research Grant in 2009, Hellman Fellowship in 2012, the National Science Foundation Faculty Early Career Development (CAREER) Award in 2012 and Qualcomm Faculty Award in 2020 and 2021. Dr. Cabric is an IEEE Fellow. Her research on deep learning based RF transmitter fingerprinting is supported by SRC/JUMP CONIX center (

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

Virtual Seminar by Jelena Mišić

Title: Adapting PBFT for Use with Blockchain-enabled IoT Systems

Time and Date: October 15, 2021, at 9:00am EDT

Presenter:  Professor Jelena Mišić, Ryerson University, Canada


Abstract: This work proposes Practical Byzantine Fault Tolerance (PBFT) ordering service needed for block formation in permissioned blockchain environments. Contrary to current PBFT implementations that only provide a single point of entry to the ordering service, we allow each ordering node to act as an entry point that proposes and conducts the consensus process of including new record in the distributed ledger. To ensure atomicity of record insertion in distributed ledger, we have developed a bandwidth reservation protocol that uses a modification of CSMA/CA protocol to regulate access to the broadcast medium formed by the P2P network of TCP connections between orderers. We have modeled record insertion service time in a cluster where ordering nodes have random position within Cartesian coordinate system. We have also modeled total request access time to the ledger which includes waiting time in the ordererís queue and record insertion time. These models are used to evaluate system performance under variable request rate ordering service, variable number of nodes and variable physical cluster dimensions. We also address cluster interconnections which can increase coverage and capacity of PBFT system.

Bio: Jelena Mišić is a Professor in the Department of Computer Science at Ryerson University, Canada. She received her PhD in Computer Engineering from University of Belgrade, Serbia, in 1993. She is an internationally recognized expert in the area of IoT, blockchain, wireless networking and network security, where she has authored or co-authored four books, 150+ journal papers, 24 book chapters, and 210+ conference papers. She has chaired more than a dozen major international events and guest-edited more than a dozen special issues of various journals. She serves on the editorial boards of IEEE Transactions on Vehicular Technology, IEEE Internet of Things Journal, IEEE Network, and Ad Hoc Networks journal (published by Elsevier). She is IEEE Fellow, ACM member and serves as IEEE VTS distinguished lecturer.

Virtual Seminar by Walid Saad

Title: Brainstorming Generative Adversarial Networks (BGANs): Framework and Application to Wireless Networks

Date and Time: October 22, 2021 at 10AM EDT

Registration Process: Please register at

Abstract: Due to major communication, privacy, and scalability challenges stemming from the emergence of large-scale Internet of Things services, machine learning is witnessing a major departure from traditional centralized cloud architectures toward a distributed machine learning (ML) paradigm where data is dispersed and processed across multiple edge devices. A prime example of this emerging distributed ML paradigm is Google’s renowned federated learning framework. Despite the tremendous recent interest in distributed ML, remarkably, prior work in the area remains largely focused on the development of distributed ML algorithms for inference and classification tasks. In contrast, in this talk, we introduce the novel framework of brainstorming generative adversarial networks (BGANs) that constitutes one of the first implementations of distributed, multi-agent generative GAN models that does not rely on a centralized parameter server. We show how BGAN allows multiple agents to gain information from one another, in a fully distributed manner, without sharing their real datasets but by “brainstorming” their generated data samples. We then demonstrate the higher accuracy and scalability of BGAN compared to the state of the art through extensive experiments. We then illustrate how BGAN can be used to address key problems in the field of wireless communications by analyzing a millimeter wave channel modeling problem for wireless networks that rely on unmanned aerial vehicles (UAVs). We conclude this talk with an overview on the future outlook of the exciting area of distributed ML and its current and future applications in wireless systems.

Bio: Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network sciEnce, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE. He is also the recipient of the NSF CAREER award in 2013 and of the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the author/co-author of ten conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM in 2018, IFIP NTMS in 2019, IEEE ICC in 2020, and IEEE GLOBECOM in 2020. He is the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, of the 2017 IEEE ComSoc Best Young Professional in Academia award, of the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and of the 2019 IEEE ComSoc Communication Theory Technical Committee. He was also a co-author of the 2019 IEEE Communications Society Young Author Best Paper and of the 2021 IEEE Communications Society Young Author Best Paper. He currently serves as an editor for several major IEEE Transactions.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.