7 events found.
Week of Events
Using Whisker Lab’s Ting Sensor to Improve the Grid / Prevent Fires
Greetings IEEE Members of East TN, Come and join us on Oct 16th at 6 PM to hear our ownTheo Laughner share a talk entitled “Using the Whisker Lab’s Ting Sensor to Improve Grid Health While Protecting Homes from Electrical Fire Hazards” (abstract below). The talk will be held at the Zeanah Engineering Complex, University of Tennessee Knoxville Room: 270-278, Parking in G10 Lot. Come, join us and bring a friend! We should have a short tour of the building as well. Abstract: Electrical fires impact nearly 50,000 homes each year. These fires cause approximately $1.3B in property losses each year and result in thousands of injuries and deaths annually. A low-cost sensor network has been deployed in over 1M homes with the primary goal of preventing electrical fires.While the prevention of electrical fires was the primary focus of the sensor network, the data collected is also useful in describing the power quality performance of utilities where the sensors are deployed. By aggregating data from individual homes, the performance of areas within a utility can be described and more broadly the overall performance of each utility. The system records voltage sags, voltage swells, voltage interruptions and harmonic distortion.Previous studies (EPRI, 2022) namely DPQI, DPQII, and DPQ/TPQIII have been previously conducted by EPRI. While those studies were limited by the few utilities involved. Interestingly, the data has good correlation to the distributed sensor network described herein. In the report, the estimated impacts due to power quality disturbances was estimated between $145B and $240B USD. Naturally, knowing where these events occur, and fixing them in a timely fashion is important. Room: 270-278, Bldg: Zeanah Engineering Complex, 863 Neyland Dr, Knoxville, Tennessee, United States, 37916
October 17, 2025
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Fortifying AI: Tackling Adversarial Threats and Building Defenses
Fortifying AI: Tackling Adversarial Threats and Building Defenses
IEEE WIE East Tennessee is hosting a webinar Fortifying AI: Tackling Adversarial Threats and Building Defenses by Dr. Pravi Devineni is a Lead AI Scientist at Duke Energy. Abstract: AI systems now influence high-stakes decisions across sectors. Securing them requires a clear view of where vulnerabilities arise across the ML lifecycle. This session introduces common weaknesses from data collection and training through deployment and operations, and explains major adversarial threats—poisoning, evasion, inversion, and extraction, with real-world context. We’ll focus on practical detection and mitigation techniques and how these controls align with broader enterprise security practices. Attendees will leave with a simple framework and checklist to assess risk and prioritize safeguards. Learning objectives: - Map security risks across the AI lifecycle (data → training → validation → deployment → operations) - Explain key attack types—poisoning, evasion, inversion, extraction—and their impacts. - Apply defenses: data integrity and provenance checks, secure/robust training, evaluation and monitoring, incident response, red teaming, and governance controls. - Connect AI security work to enterprise security (threat modeling, identity, logging, SDLC). Bio: Dr. Pravallika (“Pravi”) Devineni is a Lead AI Scientist at Duke Energy. She designs secure-by-design, auditable AI systems for critical infrastructure, with a focus on grid reliability and nuclear operations. Her work spans adversarial ML Threat Modeling, evaluation and monitoring, and the practical application of NIST AI RMF and ISO/IEC 42001 to enterprise controls and evidence. Previously, she was a research scientist at Oak Ridge National Laboratory. She writes and speaks on AI risk and governance and mentors young scientists. She holds a Ph.D. in Computer Science from UC Riverside. Virtual: https://events.vtools.ieee.org/m/501800