IEEE North Jersey Section

Christen Smith will discuss the impact of artificial intelligence on media literacy and how news organizations can use the technology more responsibly to reach their audiences. Speaker(s): Christen, Virtual: https://events.vtools.ieee.org/m/469049

Abstract In today's rapidly evolving software engineering landscape, building scalable AI-native software engineering intelligence analytics platforms requires both technical expertise and strategic vision. With AI-generated code becoming increasingly prevalent in enterprise environments, measuring its effectiveness has become critical for organizations seeking to optimize their development investments. Success depends on 3 fundamental foundations. First, prioritize engineering data fidelity and contextual intelligence over raw metrics volume. Our AI analytics are only as valuable as the engineering signals they interpret. Implement sophisticated data collection mechanisms that capture traditional metrics alongside specialized tracking for AI-generated code contributions, including origin identification, quality assessments, and downstream impact analysis. This foundation determines our platform's capabilities and limitations. Second, embrace architectural flexibility for workflow integration and adaptive scaling—design an analytics platform that seamlessly integrates with existing development toolchains while handling unpredictable data patterns. Cloud-native microservices allow us to iterate on analytics components independently while maintaining system resilience. Include dedicated modules for measuring AI code effectiveness, tracking acceptance rates, modification frequencies, and security patterns. Finally, center development around engineering team productivity and developer experience. Implement tight feedback loops with development teams to understand how they consume intelligence insights. The most impactful AI-native platforms strike a balance between cutting-edge AI capabilities and pragmatic engineering principles. We focus on solving today's delivery challenges and architecting tomorrow's autonomous workflows. Co-sponsored by: Avimanyou Vatsa Speaker(s): Naveen Kumar, Room: 205, Bldg: Becton Hall, 960 River Road, Teaneck, New Jersey, United States, 07666, Virtual: https://events.vtools.ieee.org/m/503289

1-Dry Air GIS technology : Industry trend Features and benefits Challenges 2-Solutions for space saving ultra-compact feeders in urban area : Industry trend Features and benefits Challenges 3-Arc Quenching vs Arc Resistant solutions Industry trend Features and benefits Challenges SF6 free dead tank circuit breakers (Dry Air) Industry trend Features and benefits Challenges Grid intelligence solution : smart sensors for real time monitoring of the distribution grid Industry trend Features and benefits Challenges Speaker(s): Christian, Alex, Roger 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: Transformer & Reactor Conference Rooms, Bldg: PSE&G - Cragwood Road Facility, 40 Cragwood Road , South Plainfield, New Jersey, United States, 07080

The 2025 IEEE Buildathon brings together students and early-career professionals for a full day of hands-on learning, networking, and innovation at NJIT. At the IEEE Buildathon, you’ll: - Gain practical skills in AI and the latest technologies - Learn directly from industry experts and IEEE leaders - Build connections with peers and professionals across disciplines - Explore how IEEE can support your career growth and technical journey Through interactive workshops, live demonstrations, and networking opportunities, you’ll walk away with new tools, fresh insights, and stronger connections to shape your future in technology. 150 Bleeker St #1982, Newark, New Jersey, United States, 07102

You are cordially invited to attend the November executive committee (ExCom) meeting in person or on Zoom. The ExCom meeting of the IEEE North Jersey Section will be held at Nokia Bell Labs, in Murray Hill, NJ. The meeting will take place in Room 6A-106, which is located at the right side of the main entrance behind the Bell Labs Showcase exhibition area. It is necessary to sign in to access this area. The meeting is also held on Zoom at the following link: https://us06web.zoom.us/j/87356164618 The meeting starts at 6:30 pm EST and typically ends at 8:30 pm. The meeting is meant to discuss and coordinate the section's activities and new initiatives. Everyone is welcome to attend this meeting. Please register in advance for this meeting using vTools, by Oct 31st, to provide the meeting organizers an accurate head count. You can change/cancel the registration if your plans change. Looking forward to seeing you there Room: 6A-106, 600 Mountain Ave, Murray Hill, New Jersey, United States, 07974, Virtual: https://events.vtools.ieee.org/m/506596

This talk introduces how to address a fundamental challenge in applying deep learning to power systems: developing neural network models that transfer across significant system changes, including networks with entirely different topologies and dimensionalities, without requiring training data from unseen reconfigurations. Despite extensive research, most ML-based approaches remain system-specific, limiting real-world deployment. This limitation stems from a dual barrier. First, topology changes shift feature distributions and alter input dimensions due to power flow physics. Second, reconfigurations redefine output semantics and dimensionality, requiring models to handle configuration-specific outputs while maintaining transferable feature extraction. To overcome this challenge, we introduce a Universal Graph Convolutional Network (UGCN) that achieves transferability to any reconfiguration or variation of existing power systems without any prior knowledge of new grid topologies or retraining during implementation. Our approach applies to both transmission and distribution networks and demonstrates generalization capability to completely unseen system reconfigurations, such as network restructuring and major grid expansions. Experimental results across different power system applications, including false data injection detection and state forecasting, show that UGCN significantly outperforms state-of-the-art methods in cross-system zero-shot transferability of new reconfigurations. Co-sponsored by: Power Systems Engineering Center (PSEC) at New Jersey Institute of Technology Speaker(s): Dr. Tong Wu 323 Dr Martin Luther King Jr Blvd, Newark, New Jersey, United States, 07102, Virtual: https://events.vtools.ieee.org/m/509297

Beyond Coding: A Complete Career Map for Building Real-World Products” When students think of technology careers, most imagine writing code. But in the real world, building great products requires a diverse ecosystem of roles , each contributing creativity, engineering, business thinking, and user empathy. This session will demystify how real products like mobile apps, AI platforms, and enterprise systems evolve from ideas to solutions used by millions. We will explore the full lifecycle of product development and the different careers that shape it — including Product Management, UI/UX Design, Software Engineering, Quality & Automation, AI/ML Engineering, Cloud & DevOps, Cybersecurity, Data Science, Business Analysis, and Technical Program Management. Students will learn what skills each role requires, how teams collaborate, and where emerging technologies like Generative AI, sustainability, and edge computing are creating new job titles. Speaker(s): Jyoti Shah, Agenda: 2:30-2:45 pm - Meet and Greet and 2:45- 3:10- Talk 3:10-3:30 - Q & A and discussion 3:30- 4:00- Networking, food and pictures Bldg: Kupf 205, 154 Summit Street, Newark, New Jersey, United States

This workshop introduces participants to the latest concepts and applications in Artificial Intelligence. Through interactive demonstrations and guided examples, attendees will explore how AI is shaping real-world solutions across industries. The session will cover practical tools, frameworks, and workflows that students and professionals can immediately apply to academic projects, research, and workplace challenges. Whether you are just starting with AI or looking to strengthen your technical foundation, this workshop provides an accessible and hands-on learning experience led by Alok Tibrewala Room: Bissinger Room, Bldg: Howe building, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, New Jersey, United States, 07030

Counts vs Engineering Units in load profile - What is a count - What is engineering units - Km, Ke, Kh, and how they are calculated Transformer Loss/Line Loss - Losses In General - What is it - Why is it important - How do measure and report - How we do it VARs/Summation - What is a VAR - Why are they important - Debate about delivered and received - Summation and billing - How we do it DNP vs Modbus - History - How they work - Options for DNP - Scaling - Live example of setting them up and using real protocol - Showing how DNP/Modbus values appear PQ Triggering Events and Measurement Logging - What is Power Quality - Class A vs Others - Triggers, Event based - Trending, Measurement Log - Storage and software Speaker(s): Josh, Jeff 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

Special Presentation by Dr. Alex G. Lee (TechIPm, USA) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, 20 November 2025 @ 12:00 UTC (12 PM GMT) PDH Certificate: while basic attendance is free, this course also offers one (1) Professional Development Hour (PDH) for a nominal fee; please choose the appropriate "Registration Fee" when registering; actual, verified real-time attendance required for PDH; additional terms and conditions apply. Topic: AI-Native 6G IP Moats: Rethinking Global Policy for SEP/FRAND Abstract: AI-native 6G treats intelligence as a built-in network function: models live in the stack (PHY/MAC/RAN/Core), steer behavior in real time, and enable semantic communications, digital-twin control, and federated learning across device, edge, and cloud. That shift changes how we innovate and how we govern IP. Using “IP moats” in the Buffett sense — durable advantage from enforceable IP (including SEPs), data rights, and standards participation — I ask: what global policy makes those moats defensible for true contributors while keeping consumer devices affordable? I propose a practical baseline with two pillars. Pillar 1: Mandatory essentiality evaluation at ETSI declaration (and at major spec revisions), performed by independent evaluators under a common, auditable protocol with rebuttal rights — recognizing that in 6G, essentiality often requires behavioral evidence (simulations/benchmarks), not text alone. Pillar 2: Transparent, method-driven FRAND (fair, reasonable and non-discriminatory) that reflects multi-layer AI value (edge silicon, radio/PHY, RAN control, edge orchestration, cloud inference) while guarding against stacking and protecting consumer pricing. To operationalize this, I introduce two agentic, provenance-first co-pilots: - Agentic AI-Powered Essentiality Evaluation Framework — aligns claim elements to standards text and versions, ingests simulation/benchmark artifacts, produces source-pinned evidence packs with confidence scores, flags family overlap/over-declaration, and supports human-in-the-loop review. - Agentic AI-Powered FRAND Evaluation Framework — builds auditable rate models (top-down, incremental value, usage-based, or hybrid) from shared inputs: portfolio size and essentiality-confidence distribution, stack-layer contribution mapping, device/IoT usage metrics, ASP tiers, geography mix, pool comparables, and anti-stacking constraints. Outputs include rate corridors, sensitivity bands, tiered pricing and safe-harbor pool options, plus triggers for de-declaration as specs evolve. Speaker: [] Alex G. Lee is a NY State attorney, USPTO-registered patent attorney, and Certified Licensing Professional (CLP) with a Ph.D. in Physics (Johns Hopkins) and J.D. (Suffolk Law). He bridges 3GPP standards and IP strategy, having led hundreds of 3G/4G/5G essentiality evaluations for global programs. As Principal Consultant at TechIPm, he has supported SEP licensing, portfolio sales, and enforcement for global companies. He has built agentic AI-powered IP intelligence for SEP development, licensing, and litigation. Earlier, he held roles at Hsuanyeh Law Group, Liquidax Capital, Korea’s National Radio Research Agency, and Korea Telecom (ITU-R/early 3GPP representation). His work focuses on agentic AI-powered frameworks for 5G/6G innovation and SEP portfolio development and monetization. Brochure (PDF): (https://drive.google.com/file/d/1iKy0C_zNIuI3EhB0qOlGQmV82UDxAsXk/view?usp=share_link) Co-sponsored by: Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/500656

Course-based Undergraduate Research Experiences (CUREs) have proven effective in engaging all students in authentic research within regular coursework, enhancing learning outcomes and preparing students for graduate studies. This talk examines a novel approach to strengthening CUREs through the strategic integration of Generative AI tools in computer science education. Drawing from empirical research conducted in a machine learning course, an exploration of how foundation models can enhance the four-week CURE framework encompassing research overview, literature review, research design and methods, and paper construction. The presentation will detail specific applications of AI in supporting literature reviews, coding assistance, and concept clarification, while addressing the pedagogical considerations essential for responsible implementation. Will also discuss the observed positive trends in student perceptions of research effectiveness, particularly in literature review processes, alongside critical challenges including instructor training needs, potential over-reliance, and accessibility concerns. Speaker(s): Paula Virtual: https://events.vtools.ieee.org/m/473024