Dr. Nasimuddin
Research and Development (Antenna System), Principal Scientist at Institute for Infocomm Research, A*STAR
Dr. Nasimuddin (M’2003-SM’2009) earned his M.Tech. and Ph.D. from the University of Delhi in 1998 and 2004, respectively. He served as a Senior Research Fellow at the University of Delhi from 1999 to 2003 and as an Australian Postdoctoral Research Fellow at Macquarie University from 2004 to 2006. Currently, Dr. Nasimuddin is a Principal Scientist at the Institute for Infocomm Research (I2R), A*STAR, Singapore. With over 255 published papers, three granted patents, and four additional patents filed, Dr. Nasimuddin is a prolific contributor to the field. He has also edited three books, including a notable volume on microstrip antennas. Recognized as the top 2% of world scientists in 2023 and 2024, he is esteemed for his work as a distinguished speaker, reviewer, and organizer of special sessions and conferences.
As Senior Member of IEEE and IEEE APS, Dr. Nasimuddin has received several accolades, including the URSI Young Scientist Award (2005) and IEEE AP-T/APPL Exceptional Performance Reviewer certificates. He serves as an Associate Editor for the IEEE Open Journal of Antennas and Propagation (OJAP) and is actively involved with editorial boards of leading journals in Antennas and Propagation. Additionally, he was the Chair of the IEEE Singapore MTT/AP Joint Chapter from 2021 to 2022.
Next-Generation Circularly Polarized Antennas for Future Communications: Innovations, Applications, and Emerging Trends
This talk delves into the latest advancements in compact, wideband circularly polarized (CP) microstrip antennas, with a particular emphasis on metasurface-enabled designs. We will explore cutting-edge methodologies that drive innovation in CP antenna technology, highlighting how metasurfaces are revolutionizing antenna performance by enhancing key metrics such as gain, axial ratio bandwidth, and size reduction—without compromising polarization purity. The session will cover the transformative role of these antennas in modern and emerging wireless systems, including satellite communications, IoT networks, and 5G/6G infrastructures. By unpacking both theoretical frameworks and practical implementations, attendees will gain a comprehensive understanding of how these novel antenna architectures are paving the way for high-performance, future-ready communication systems. The talk will conclude with insights into current challenges, open research directions, and the broader impact of CP antennas on next-generation wireless technologies.
Prof. Dr. Lei Guo
Professor of Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS)，and Director of the National Center for Mathematics and Interdisciplinary Sciences.
Lei GUO, Professor of Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS)，and Director of the National Center for Mathematics and Interdisciplinary Sciences. He is a Fellow of IEEE, Member of CAS, Foreign Member of the Royal Swedish Academy of Engineering Sciences, and Fellow of the International Federation of Automatic Control (IFAC). In 2014, he was awarded an honorary doctorate by the Royal Institute of Technology (KTH), Sweden. In 2019, he was awarded the Hendrik W. Bode Lecture Prize by the IEEE Control Systems Society “for fundamental and practical contributions to the field of adaptive control, system identification, adaptive signal processing, stochastic systems, and applied mathematics”. His current research interests include adaptive (learning, filtering, control and games) theory of stochastic systems, control of uncertain nonlinear systems, game-based control systems, multi-agent complex systems, and man-machine integration systems, etc. For more information, please visit http://lsc.amss.cas.cn/guolei/english/grjj/
Adaptive Learning-based Feedback Control of Uncertain Dynamical Systems
Learning and feedback are two fundamental mechanisms for addressing uncertainties in dynamical systems. Learning underpins the design of adaptive systems, while feedback makes it possible for these systems to keep their desired performance in open environments plagued by various uncertainties. This keynote will present foundational results on adaptive systems that integrate online learning with feedback control. First, we revisit the celebrated self-tuning regulator (STR) in adaptive control of uncertain linear stochastic systems, wherein the STR is designed by combining a recursive least-squares estimator with a minimum tracking variance controller. Despite its intuitive structure, the global convergence of this adaptive system remained a longstanding open problem in control theory. Next, we examine the theoretical rationale underpinning the widespread industrial success of proportional-integral-derivative (PID) control for nonlinear uncertain systems and introduce a novel, robust online learning-based design framework. Finally, we explore fundamental questions concerning the inherent capabilities and limitations of feedback mechanisms in nonlinear systems, defined here as the ensemble of all feasible feedback strategies. This talk synthesizes classical and contemporary perspectives to elucidate the synergistic potential of learning and feedback in tackling complex dynamical uncertainties.