Events
Calendar of Events
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The Stanford IEEE and its internal sub-chapter the Power and Energy Society will be hosting an informative presentation on Palo Alto Utility's Grid Modernization efforts. Specific location on campus TBA. Details and RSVP: https://luma.com/v5gz9zz9 Bldg: Stanford University (room TBA), 450 Jane Stanford Way, Stanford, California, United States, 94305 |
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Abstract: A recent report by the US Department of Energy defines the area of scientific machine learning as “a core component of artificial intelligence (AI) and a computational technology that can be trained, with scientific data, to augment or automate human skills”, which has “the potential to transform science and energy research”. In this presentation, we discuss the potential of scientific machine learning methods to problems in computational electromagnetics starting from standard electromagnetic structure analysis and multi-physics modeling in the time-domain, employing unsupervised, supervised and reinforcement learning. An unsupervised learning strategy is based on a modified Physics-Informed Neural Network (PINN). PINNs directly integrate physical laws into their loss function, so that the training process does not rely on the generation of training ground truth data from simulations. We introduce a physics-informed deep- operator-network (PI-DON) for Maxwell’s equations. We show applications of the PI-DON to the analysis, design and uncertainty quantification study of finite and defect periodic structures, including frequency selective surfaces and metasurfaces. Moreover, we demonstrate the impact of machine learning on the computational modeling of radiowave propagation scenarios and reconfigurable intelligent surfaces, using supervised learning. We build convolutional neural network models that can process the geometry of indoor environments, along with physics-inspired parameters, to rapidly estimate received signal strength (RSS) maps. Emphasis is placed on the generalizability of these models, which is their ability to "learn" the physics of radiowave propagation and produce accurate modeling predictions in new geometries well beyond those included in their training set. Finally, the use of reinforcement learning for the optimization of reconfigurable intelligent surfaces will be discussed. Speaker(s): Costas Room: 204, Bldg: David Packard Electrical Engineering Building, 350 JANE STANFORD WAY, STANFORD, California, United States, 94305
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Stanford IEEE hosts a fireside chat with Shang-Yi Chiang, former co-COO of TSMC and one of the most influential figures in modern semiconductor process technology. The conversation will cover the evolution of process technology, the future of the foundry industry, and advanced packaging, followed by open audience Q&A. Thursday, October 29, 5-6 PM. Free and open to Stanford students, staff, and faculty. Details and RSVP: https://luma.com/gft0fwna Room: 101, Bldg: Packard Electrical Engineering Building, 350 Jane Stanford Way, Stanford, California, United States, 94305 |
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Abstract: A recent report by the US Department of Energy defines the area of scientific machine learning as “a core component of artificial intelligence (AI) and a computational technology that can be trained, with scientific data, to augment or automate human skills”, which has “the potential to transform science and energy research”. In this presentation, we discuss the potential of scientific machine learning methods to problems in computational electromagnetics starting from standard electromagnetic structure analysis and multi-physics modeling in the time-domain, employing an unsupervised learning strategy based on Physics-Informed Neural Networks (PINN). PINNs directly integrate physical laws into their loss function, so that the training process does not rely on the generation of training ground truth data from simulations. We demonstrate the unconditionally stable solution of coupled electromagnetic-thermal problems based on PINNs and introduce a physics-informed deep-operator-network (PI-DON) for the unconditionally stable solution of Maxwell’s equations. We show applications of the PI-DON to the analysis, design and uncertainty quantification study of finite and defect periodic structures, including frequency selective surfaces and metasurfaces, demonstrating significant computational efficiency gains with respect to the conventional finite-difference time-domain technique (FDTD), including training time of the neural network. Moreover, we demonstrate the impact of machine learning on the computational modeling of radiowave propagation scenarios. We build convolutional neural network models that can process the geometry of indoor environments, along with physics-inspired parameters, to rapidly estimate received signal strength (RSS) maps. Emphasis is placed on the generalizability of these models, which is their ability to "learn" the physics of radiowave propagation and produce accurate modeling predictions in new geometries well beyond those included in their training set. Speaker(s): Costas Sarris Virtual: https://events.vtools.ieee.org/m/583371 |
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Magnetic components' cost, size and loss inhibit progress in power electronics. The required size is largely driven by the energy storage required, and co-design of circuits and magnetics to minimize energy storage can result in small and efficient converters. Examples presented will include magnetics for high-current-density low voltage power delivery at >4 A/mm2, and new circuits to replace 60 Hz transformers, including in V2H bidirectional EV charging. Speaker(s): Charles R Sullivan Murata Electronics North America, Inc., 1732 North First Street #500, San Jose, California, United States, 95112 |
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Stanford IEEE is hosting "The Power of Service," an evening on the many different paths of service through IEEE. It is open to all Stanford students and geared toward undergraduate and graduate students in electrical engineering, computer science, and related fields. The evening opens with a short talk on what service through IEEE can look like over a career, from student member to local volunteer to leadership. That is followed by a small panel of IEEE volunteers and Stanford IEEE industry advisors, who will share their own paths through service in industry, their local IEEE section, and IEEE societies, and take audience questions. Officers of the IEEE Santa Clara Valley Section will then give a brief introduction to the local section and how students can get involved. The event ends with informal networking. Admission is free and no ticketing is required. Room TBD, check back closer to the date. Details: https://luma.com/1yqa8y4f Room: 101X, Bldg: Paul G. Allen Building, 420 Via Palou, Stanford, California, United States, 94305 |
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This will be RESCHEDULED in 2027! IEEE Global and SCV-OEB SSIT Chapter: Diffie-Hellman Key Exchange 50th Anniversary Global Celebration Drs. Diffie and Hellman, recipients of the 2015 ACM A.M. Turing Award, will be joining us in person. The day will honor what their work made possible and look ahead to the next 50 years of digital public infrastructure. IEEE members and guests are welcome at 10:00 AM on Saturday, November 7, at Santa Clara University. Venue details will be confirmed. Speaker(s): Martin Hellman, Whitfield Diffie ***CANCELED*** Agenda: Agenda TBD Room: Classroom, Bldg: Discovery and Innovation (SCDI) building, Santa Clara University, 500 El Camino Real, Santa Clara, California, United States, 95053, Virtual: https://events.vtools.ieee.org/m/581226 |
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This is a hybrid in-person and online event. Pre-registration is required for either. Doug Engelbart was an engineer who worked at Stanford Research Institute (later SRI International) from 1957 to 1986. On December 9, 1968, in San Francisco’s Civic Auditorium, he demonstrated a vision of computing that reached beyond calculation wherein computers could augment a human’s ability to understand and solve complex problems. Later called The Mother of All Demos, his vision included many aspects of computing that have become truly ubiquitous. Amongst these was the mouse, his invention which had its first public exposure on that day 58 years ago. He also demonstrated collaborative online editing, hypertext, video conferencing, word processing, spell checking, and revision control. This talk will trace the development of Engelbart’s vision, which was first inspired by (https://en.wikipedia.org/wiki/Memex) of 1945, and which led to Engelbart’s 1962 paper (https://www.dougengelbart.org/pubs/papers/scanned/Doug_Engelbart-AugmentingHumanIntellect.pdf) and his 1968 Demo. It will also explore the paradigm shift that he championed, and which we all use on a daily basis. Importantly, this talk will also discuss the unfinished revolution that Engelbart left us. The timing of this talk coincides with other events happening later that week: SRI’s 80th anniversary celebration on Nov. 12, and the dedication of an IEEE Milestone for the Mouse on Nov. 13. Speaker(s): Jeff Rulifson, Bldg: Recreation Center, 550 E Remington Drive, Sunnyvale, California, United States, 94087, Virtual: https://events.vtools.ieee.org/m/584786 |
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Stanford IEEE and the Global Semiconductor Alliance (GSA) Talent Initiative present an industry panel featuring Stanford alumni from GSA partner companies across the semiconductor industry, followed by informal networking with the panelists. Wednesday, November 11, 5-6 PM. Free and open to all Stanford students (undergrad and grad) in EE, CS, and related engineering programs. Room TBD. Panelists are still being finalized. Details and RSVP: https://luma.com/tocxory8 Co-sponsored by: Global Semiconductor Alliance (GSA) Talent Initiative Bldg: Stanford Electrical Engineering (room TBD), 350 Jane Stanford Way, Stanford, California, United States, 94305
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Key Takeaways from this talk: - The 4 Types of People You Need In Your Corner for a Successful Career - What is Mentorship? And why do you need it? - How to be a good Mentor - How to be a good Mentee - How you can get started - Build your own personal Board of Directors for your Career today Co-sponsored by: Santa Clara University Student Chapter Speaker(s): Avery Lu, Room: 2302, Bldg: SCDI, Santa Clara University, Santa Clara, California, United States |
1 event,[]This 2-day symposium focuses on quantified reliability, accelerated testing and probabilistic assessments of the useful lifetime of electronic, photonic, MEMS and MOEMS materials, assemblies, packages and systems in electronics and photonics packaging. This includes failure modes, stress levels, mechanisms, testing schemes, accelerated testing, and environmental stresses. The intent is to bring together electrical, reliability, materials, mechanical, and computer engineers and applied scientists to address the state-of-the-art in all the interconnected fields of electronic and photonic packaging, with an emphasis on various reliabilit-yrelated aspects: design-for-reliability, manufacturing, reliability modeling and accelerated testing. Location: In person at UCLA, and over IEEE WebEx Dates: November 12-13, 2026 Cost: $90 ($75 for IEEE members, $35 for WebEx) through Oct. 16 UCLA, Los Angeles, California, United States |
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Stanford IEEE, co-hosted with the IEEE Circuits and Systems Society (CASS) and Samsung Semiconductor, is holding a half-day technical workshop on how AI compute is designed and orchestrated, from the transistor up to the datacenter. It is open to Stanford students, staff, and faculty along with guests from the co-hosting organizations, and is aimed especially at undergraduate and graduate students in electrical engineering and computer science. The program features talks and a panel from industry and Stanford faculty speakers, covering AI software, circuit design, advanced packaging, and hardware co-design, with time for audience Q&A. Speakers will be announced soon. Admission is free and no registration is needed. Just show up! Light refreshments and lunch will be provided. Details: https://luma.com/kv8u97ga Co-sponsored by: IEEE Circuits and Systems Society (CASS); Samsung Semiconductor Room: 101X, Bldg: Paul G. Allen Building, 420 Via Palou, Stanford, California, United States, 94305 |
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An evening with Pixar co-founder Ed Catmull, hosted by Stanford IEEE: a live interview and Q&A with Ed, followed by a screening of the original Toy Story. Thursday, December 3, 6-9 PM at CEMEX Auditorium. Free. Students, faculty, staff, and families are all welcome (kids with a parent or guardian). Details are tentative. Details and RSVP: https://luma.com/zfh6spdm Bldg: CEMEX Auditorium (Knight Management Center), 655 Knight Way, Stanford, California, United States, 94305 |
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