Since the late 1990s, metamaterials have sparked significant theoretical research, fundamentally transforming our understanding of electromagnetic (EM) theory. The promise of metamaterials has fueled anticipation of breakthroughs, especially in antenna technology. However, translating these novel physical concepts into practical technologies and real-world applications has proven challenging, primarily due to the stringent and unique requirements of antennas in different applications. Since 2010, our group has focused on the translational research of metamaterials, combining deep conceptual understanding with expertise in antenna engineering. We have successfully addressed key engineering challenges—including bandwidth, efficiency, and fabrication—that have historically constrained the practical adoption of metamaterial-based antennas. Our efforts have led to the development and commercialization of advanced antenna technologies based on metamaterials, including metasurfaces and metalines, across a range of applications such as RFID, WLAN, radar, cellular base stations, small cells, and satellite communications. What was once the “hype” of metamaterials has now become a reality—metantennas. In our research and development, we have employed various mathematical tools such as Characteristic Modes Analysis (CMA), Transformation Optics (TO), Time Reverse (TR), and other optimization algorithms. Notably, we have recently achieved promising results in prior-knowledge-guided, deep learning-enabled generative synthesis for metasurfaces and metamaterials, paving the way for next-generation metantenna design. In this talk, I will share recent advancements in metantenna technology and offer insights into the future directions of metamaterial research. Co-sponsored by: IEEE North Jersey Section Speaker(s): Dr. Zhi Ning Chen Agenda: 5:30 PM - Meet and Greet and complimentary refreshment/dinner 5: 45 PM- Introducing the speaker 6:00 PM -7:30 PM-DL Talk, followed by Q/A and discussion Room: 202, Bldg: ECE Building, 141 Warren St, New Jersey Institute of Technology, The Lewis and Julia P. Kleman Conference Room, Newark, New Jersey, United States, 07102

Special Presentation by Larry Arnold (LoreTokens, USA) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AI/ML) Working Group Date/Time: Thursday, 2 April 2026 @ 6 PM Eastern Time (3 PM Pacific Time) Topic: LoreTokens: Cognition, not just Compression Abstract: Tokens are units of data processed by AI models during training and inference to enable prediction, generation, and reasoning. LoreTokens, an AI-native serialization format, are reframed not as a compression scheme, but as semantic pointers — symbolic anchors that reference structured meaning rather than merely reducing textual size. This presentation explores how LoreTokens function as high-density semantic indices that preserve relational structure while enabling efficient traversal by language models. Instead of collapsing information, LoreTokens encode conceptual scaffolding, allowing models to reconstruct modular systems, infer implied architecture, and maintain coherence across large codebases or documents. We examine their bidirectional transformation pipeline, structural implications, and potential role as a reasoning substrate for AI-mediated development workflows. Speaker: [] Larry Arnold is an independent researcher and systems thinker focused on AI-mediated symbolic architecture and semantic abstraction. His current work centers on LoreTokens, reframed as semantic pointers designed to interface structured human intent with large language models. With a background spanning technical systems, philosophical inquiry, and long-form speculative storytelling, he approaches AI not as a tool for automation, but as a partner in structured reasoning. His work explores how symbolic indirection, modular design, and conceptual scaffolding can enable more coherent AI-assisted development workflows. He is particularly interested in the intersection of cognition, computation, and the long-term implications of AI-native knowledge systems. Brochure (PDF): (https://drive.google.com/file/d/1eQwP5gKAFOziqX6ZAB1bZ8XSfW3cm_M3/view) Co-sponsored by: Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/539769

Join Anil Malakar, Cloud & AI Solutions Architect with 22+ years of experience and author of practical guides on generative AI and agentic systems, for a fast-paced 60-minute session. This IEEE workshop directly addresses the IEEE's interest in "making" and "engineering design" by empowering students to build their own personal AI engineering workstation. We move AI from an abstract, cloud-based service to a tangible, configurable system on their own hardware. Learning Objectives: Participants will be able to: · Understand the benefits and possibilities of running modern AI models locally versus relying solely on cloud APIs. · Install and configure a complete, open-source GenAI stack (Ollama, Llama 3.1, or any other LLM available) on their personal computers. · Integrate the Strands Agents SDK to create basic agentic workflows. · Build and interact with a functional local AI Agent to demonstrate core Agentic AI concepts. · Identify minimum hardware requirements and performance trade-offs for local AI development. Co-sponsored by: IEEE North Jersey Section Speaker(s): Mr. Anil Malakar Room: 205, Bldg: Becton Hall, 960 River Road, TEANECK, New Jersey, United States, 07666, Virtual: https://events.vtools.ieee.org/m/546175

Cyber-attacks come in many different shapes and forms. In order to combat modern cyber-attacks, cyber-security researchers have to play the game of “cat and mouse” in analyzing and discovering the vulnerabilities of the system to come out on top against the malicious attackers. However, the traditional static detection systems are not able to differentiate between benign and malicious behaviors effectively. In this talk, I will share our thoughts on the detection of different attacks through modeling the execution behavior of an application using low-level hardware information. Our approaches can provide more flexibility for detection schemes by performing dynamic behavioral analysis at run-time. With anomaly detection methods, the abnormal behaviors that deviate from benign behaviors at run-time can be flagged and captured. Co-sponsored by: Fairleigh Dickinson University Speaker(s): Chen Liu, Agenda: IEEE North Jersey Section Computer Chapter and Signal Processing Chapter Seminar Title: Low-Level Hardware Information Assisted Approach Towards System Security Speaker: Prof. Chen Liu, Department of Electrical and Computer Engineering, Clarkson University Time: 1:30pm-2:30pm Fairleigh Dickinson University 1000 River Road, Building: Muscarelle Center, Room Number: 105 Teaneck, New Jersey, United States 07666 For additional information about the venue and parking, please contact Dr. Hong Zhao [email protected] Bldg: Muscarelle M105, 1000 River Road, Teaneck, New Jersey, United States, 07666

Large Language Models (LLMs) are powerful but suffer from two primary limitations: knowledge cutoff (they only know what they were trained on) and hallucinations (they confidently invent facts). Retrieval-Augmented Generation (RAG) solves this by grounding the model in external, verifiable data. Retrieval-Augmented Generation (RAG) is emerging as a core architectural pattern for building production-ready AI assistants because it overcomes the closed-world and staleness limitations of stand-alone large language models (LLMs) by grounding generation in external knowledge sources. Instead of relying solely on pre-training, a RAG system ingests heterogeneous documents, indexes them in a vector database, retrieves the most relevant snippets at query time, and injects them into the prompt to ensure responses are accurate, up-to-date, and aligned with private or domain-specific data. This talk presents a practical, end-to-end blueprint for RAG pipelines, emphasizing that most failures stem from the retrieval layer rather than from the LLM itself. Speaker(s): Dr. Deepak Garg Room: 205, Bldg: Becton Hall, 960 River Road, TEANECK, New Jersey, United States, 07666, Virtual: https://events.vtools.ieee.org/m/546172

Special Presentation by Mohamed Amine Ferrag (UAE University, UAE) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, 16 April 2026 @ 12:00 UTC (12 PM GMT) Topic: 6G-Bench: Evaluating Semantic Communication and Network-Level Reasoning in AI-Native 6G Systems Abstract: Emerging sixth-generation (6G) networks are envisioned as AI-native systems in which foundation models act as high-level reasoning and coordination layers above standardized network functions. While large language models (LLMs) have demonstrated strong capabilities in isolated wireless and networking tasks, their ability to perform network-level semantic reasoning over intents, policies, trust constraints, and multi-agent coordination remains insufficiently evaluated. In this talk, I will present 6G-Bench, an open benchmark designed to rigorously assess semantic communication and network-level reasoning in AI-native 6G environments. The benchmark defines a taxonomy of 30 decision-making tasks aligned with ongoing standardization efforts in 3GPP, IETF, ETSI, ITU-T, and the O-RAN Alliance. These tasks are grouped into five capability categories: intent and policy reasoning, network slicing and resource management, trust and security awareness, AI-native networking and agentic control, and distributed intelligence for emerging 6G use cases. Starting from over 113,000 realistic 6G operational scenarios, we construct 10,000 very-hard, task-conditioned multiple-choice questions that require multi-step quantitative reasoning under uncertainty and worst-case regret minimization. After automated filtering and expert validation, 3,722 high-confidence questions form the final evaluation set. I will also present a comprehensive evaluation of 22 contemporary foundation models and discuss key insights for deploying AI reasoning layers in future AI-native 6G networks. Speaker: [] Mohamed Amine Ferrag earned his Bachelor’s, Master’s, Ph.D., and Habilitation degrees in Computer Science from Badji Mokhtar—Annaba University, Algeria, in 2008, 2010, 2014, and 2019, respectively. He served as an Associate Professor at Guelma University, Algeria (2014–2022), and as a Senior Researcher at the NAU-Lincoln Joint Research Center for Intelligent Engineering, Nanjing Agricultural University, China (2019–2022). From 2022 to 2024, he was Lead Researcher at the Technology Innovation Institute (TII), Abu Dhabi, where he led AI-driven cybersecurity research initiatives. In 2025, he joined the United Arab Emirates University (UAEU) as an Associate Professor in the Department of Computer and Network Engineering. His research focuses on cybersecurity and AI-native systems, including wireless network security, network coding security, applied cryptography, blockchain, Generative AI, large language models (LLMs), software security, and AI applications in cybersecurity. He has authored over 200 peer-reviewed publications with more than 16,700 citations and an h-index of 61. He has led international collaborative research projects with institutions in the UK, Australia, USA, Canada, and China, and has created four widely used cybersecurity datasets — Edge-IIoT, FormAI, CyberMetric, and DIA — now extensively adopted by the AI research community. His work has received multiple prestigious awards, including the 2021 IEEE TEM Best Paper Award, the 2022 Scopus Algeria Award, the 2024 ICT Express Best Paper Award, and the 2024 IEEE ComSoc CSIM TC Best Journal Paper Award. He has been listed among Stanford University’s Top 2% Scientists six consecutive times (2020–2025) and was named in the 2025 Clarivate Highly Cited Researchers list. He currently serves as Associate Editor for the IEEE Internet of Things Journal and ICT Express (Elsevier) and is a Senior Member of IEEE. Brochure (PDF): (https://drive.google.com/file/d/190jBcLtqAj4zH02fSihwyZhuruU0fuu4/view) Co-sponsored by: Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/544689

Synchronization is a ubiquitous form of distributed coordination in networked dynamical systems spanning biology, climatology, ecology, sociology, and technology. The capacity of a networked system to synchronize is largely shaped by the structure of the underlying interaction network. Yet, unlike the special undirected case, where synchronizability can be characterized by a Laplacian eigenvalue ratio, general directed networks may have complex-valued Laplacian eigenvalues and thus still lack a complete theory for quantifying synchronizability and network design principles for optimal synchronization. Nishikawa and Motter (PNAS 2010) proposed a quantity, called the normalized spread of Laplacian eigenvalues, to measure synchronizability in directed networks and conjectured, based on numerical evidence and without theoretical validation, that its optimal value over all directed graphs with fixed numbers of vertices and arcs is attained when the Laplacian eigenvalues satisfy a distinctive integer-valued pattern. This work proves this conjecture and further shows that the conjectured spectral condition is not only sufficient but also necessary. Moreover, it is established that the optimal integer-valued Laplacian spectrum is always achievable by a class of almost regular directed graphs, which can be constructed through an efficient inductive algorithm. This work is a collaboration with Susie Lu and John Urschel from the Department of Mathematics at MIT. Co-sponsored by: Fairleigh Dickinson University Speaker(s): Ji Liu, Agenda: IEEE North Jersey Section Computer Chapter and Signal Processing Chapter Seminar Title: Optimal Directed Graphs for Network Synchronization Speaker: Prof. Ji Liu, Associate Professor from Stony Brook University Time: 12:00pm-1:00pm Fairleigh Dickinson University 1000 River Road, Building: Muscarelle Center, Room Number: 105 Teaneck, New Jersey, United States 07666 For additional information about the venue and parking, please contact Dr. Hong Zhao [email protected] Bldg: M105, 1000 River Road, Teaneck, New Jersey, United States, 07666, Virtual: https://events.vtools.ieee.org/m/535752

Room: 327, Bldg: SCHM, 1 Normal Ave, Montclair, New Jersey, United States, 07043

A discussion of how AI can be used, from a practical perspective, to solve engineering design problems. A case study will be on RF antenna design. Co-sponsored by: Penn State Harrisburg School of Science, Engineering, and Technology, Electrical Engineering Department Speaker(s): Jim Breakall, Agenda: 6:00 PM - 7:00 PM Dinner and Networking 7:00 PM - 7:45 PM Technical Presentation 7:45 PM - 8:00 PM Q&A and Wrap-up Room: 120, Bldg: Madlyn L Hanes Library (Building D), Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, Pennsylvania, United States, 17507, Virtual: https://events.vtools.ieee.org/m/539552

Details to follow Agenda: The seminar fee includes lunch, refreshments and handouts. Non-members joining IEEE within 30 days of the seminar will be rebated 50% of the IEEE registration charge. Four hours of instruction will be provided. If desired, IEEE Continuing Education Units (0.4 CEUs) will be offered for this course - a small fee of $55 will be required for processing. Please pay attention to the “Registration Fee” and choose the appropriate choice either with or without CEUs. CEU Evaluation Form can be found at: (https://innovationatwork.ieee.org/ieee-pes-northjersey-certificates/) Room: Auditorium, Bldg: PSE&G - Hadley Road Facility, 4000 Hadley Road, South Plainfield, New Jersey, United States, 07080

Robotics Open House for K-12 Students and Parents in Montclair Collaborative Robotics and Smart Systems Laboratory. Room: 448, Bldg: CCIS, 1 Normal Ave, Montclair, New Jersey, United States

In nano- and mesoscale systems, defects and disorder play a fundamental role in determining material properties and device performance. Such defects and disorder often present themselves on the atomic or sub-nanometer scale, while resulting transport length scales or device sizes can be orders of magnitude larger, on the micron scale. To accurately quantify and predict material properties or device performance in such situations, one often needs modeling and simulation tools that can bridge the size gap between atomic-scale defects and micron-scale systems. In this talk, I will present our group’s linear-scaling quantum transport tool, called LSQUANT. Based on the Kubo transport formalism, this tool combines accurate tight-binding models with an efficient expansion of quantum operators to allow the simulation of transport in systems containing many millions of atoms. This enables an atomic description of defects and disorder while still resolving transport properties on the experimental scale. After an introduction to the LSQUANT methodology, I will present a few examples of its application to transport in graphene, including spin and charge transport in disordered single-layer graphene and graphene nanoribbons, as well as approaches to optimize the performance of graphene photodetectors. Time permitting, I will also discuss recent efforts to update the LSQUANT methodology to study energy absorption and emission and the time-resolved dynamics of systems driven out of equilibrium, with an eye toward applications in photodetection, sensing, and optical communications. Virtual: https://events.vtools.ieee.org/m/555927

🚀 Ignite Curiosity, Inspire Innovation! Join us for an exhilarating journey through science, technology, engineering, and math at our K-6 STEM Night! 🌟 Explore hands-on activities, interactive exhibits, and mind-boggling experiments that will captivate young minds and fuel their passion for discovery. From robotics to thrilling physics demos, there's something for every budding scientist, engineer, and innovator! Don't miss out on this opportunity to spark creativity and ignite a lifelong love for STEM learning. Save the date and be part of the excitement! #STEMFair2026 #FutureInnovators" Registration: For volunteers that would like to support a project table This event is for K-6 grade students More information about: (https://futurenetworks.ieee.org/) Emerson, New York, United States

### CALL FOR CAREER PRESENTERS ### See Her, Be Her is Girl Scout Heart of New Jersey's annual career fair for Girl Scouts in grades K-12. Guests, most of whom are women, from all walks - and careers - of life are invited to share what they do, provide hands-on activities or artifacts related to their work, and be available for questions. The event is run open house style, allowing the Girl Scouts to move organically through the space and explore what catches their attention. We are looking for local volunteers to present their career at a booth to the scouts that will be in attendance. Please register through vTools if you would like to volunteer for this event Not an IEEE member or an engineer? NO PROBLEM This career fair covers all job types, STEM and non-STEM. We have engineers as well as yoga instructors, cooks, business people, fire fighters ... you name it and you can present! Date: Saturday, April 25th Time: 9:00am-2:00pm (setup from 9-10am | event 10am-1pm | lunch 1-2pm) Location: Stevens Institute of Technology, Hoboken, NJ, USA *Note: Lunch and parking are free How to prepare: Create an interactive table display featuring information about your career. Flyers, props, visuals, and short videos are encouraged. Giveaways are welcome and always a hit with the girls. Provide a brief hands-on activity (3-5 minutes) that allows girls to experience your profession in action. There will be over 30 tables at this event, and the STEM professions are always a hit with the girls, but the professions do not have to be just STEM fields. When you register to volunteer, please add a note on what is your profession is. Parking will be in the Stevens Babbio parking lot, but if you have materials to drop off, you can drive directly to the Howe Center Have a Girl Scout that wants to attend? More details and the Scout registration is at: https://www.gshnj.org/en/sf-events-repository/2026/see-her--be-her---a-career-exploration-fair-for-girls.html Thank for your support! More information about: (https://www.gshnj.org/) (https://futurenetworks.ieee.org/) (https://stevens.edu/) Agenda: Testimonial from an IEEE volunteer: It was truly inspiring to be part of this exciting event, where the energy and curiosity of tomorrow’s leaders and innovators filled the room. Engaging with the Girl Scouts and sharing insights into STEM careers was a powerful reminder of the importance of mentorship and representation in technology. A heartfelt thank you to the Girl Scouts organization, Stevens Institute of Technology, and the dedicated IEEE volunteers who participated as career exhibitors—your presence and passion helped spark imaginations and plant the seeds for future breakthroughs. [] Room: 4th Floor, Bldg: Howe Center, Stevens Institute of Technology, Hoboken, New Jersey, United States