Free Registration (with a Zoom account; you can get one for free if you don't already have it. This requirement is to avoid Zoom bombing. Please sign in using the email address tied to your Zoom account — not necessarily the one you used to register for the event.): https://sjsu.zoom.us/webinar/register/WN_DIDfdbkgRFeEd3JbzURIUQ Synopsis: How are machine learning algorithms able to answer questions from any nook and corner of the World Wide Web? How are trending hashtags from the near infinite microblog posts, unique visitors and other distinct counts in the near infinite website traffic determined? How do blogging websites avoid recommending articles a user has previously read? In general, how can we answer complex queries about enormous data streams without storing them entirely, in real-time? The answer often lies in clever approximation algorithms and data "sketches" that capture essential properties using vastly reduced space. The relentless flow of data in modern systems indeed presents significant challenges. These data streams are often too large to store and too fast to process exhaustively with traditional methods. This talk introduces key sketching and approximation techniques that help generate real-time data insights by processing data streams. --------------------------------------------------------------- By registering for this event, you agree that IEEE and the organizers are not liable to you for any loss, damage, injury, or any incidental, indirect, special, consequential, or economic loss or damage (including loss of opportunity, exemplary or punitive damages). The event will be recorded and will be made available for public viewing. Speaker(s): Dr. Vishnu S. Pendyala Virtual: https://events.vtools.ieee.org/m/482936

The number of power electronics converters in electrical networks is increasing rapidly, as they are integral to all new grid-connected generation. While their fast control and switching enhance operational flexibility, the resulting dynamic interactions can compromise transmission system stability. To address this, it is critical to develop methodologies that identify potential stability and control risks before integrating new converter-based resources such as wind farms, STATCOMs, or HVDC links. This talk presents an impedance-domain analytical framework to quantify interactions between new plants and the existing network. It informs additional specifications for grid connection studies, including detailed technical assessments to mitigate stability risks. The framework supports MMC technology, control delay, system strength, and Fault Ride-Through (FRT) capability. Case studies from the research group of Professor Bikash Pal will illustrate the approach.Future research directions will also be discussed. Co-sponsored by: University of Utah - Grid Resilience Studnet Group Speaker(s): Dr. Bikash Pal, Virtual: https://events.vtools.ieee.org/m/496686