See US-ATMC site for details.
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Updates will be posted on this page, as well as emailed to the EE student mail list.
As always, use your best judgement and consider your own and others' well-being at all times.
See US-ATMC site for details.
The series is scheduled to begin Thursday, September 17, 2020 and continues weekly until November 19, 2020. All seminars will be held online via Zoom, 4:30 PM -5:50 PM, with informal networking following until about 6:30 PM (PST).
The series is available to Stanford students for credit; register for EASTASN 402A "Topics in International Technology Management," also cross-listed as EE-402A and EALC-402A, 1 unit S/NC
This talk introduces a generalized framework for valuating batteries based on their degradation mechanism and market signals. The proposed framework follows a dynamic programming approach, and novel solution algorithms are designed to accurately model years of battery degradation accumulation, while simulating high granularity optimal control for batteries over time resolutions of minutes or even seconds, including the capability to model stochastic operations. Results from this research will provide pricing and operation references for new and used batteries in grid-interactive applications, and demonstrate how batteries of different degradation mechanisms should be priced differently.
Transforming the grid to a Smart Grid requires real time sensing, of energy producers, consumers, stored capacity in batteries, weather conditions, and more, essential to develop usage models, and anticipate need. Along with the ability to take 'action' such as bringing online cleaner sources of power and curbing demand we can help smooth load peaks. In this talk we introduce the open source project EdgeX Foundry and its viability to deliver a secure, extensible, endpoint to collect real time data, develop models, and control devices. Data processing at the edge while reducing network bandwidth needs, reduces response latency, and helps preserve data privacy. We illustrate this in the context of VMware's on-campus electric vehicle charging facility. We conclude by sharing our longer term goals of developing and controlling hierarchical composite device models and introduce open source project Kinney.
Seminars are open to all Stanford students, faculty, staff and community. Register via the RSVP link
Current wholesale electricity market designs of day-ahead and real-time markets have inherent inefficiencies that are amplified with greater shares from intermittent renewable sources. Building in flexibility through time and geographically substitutable bids and stochastic unit commitments can yield significant gains. This talk will discuss how to model and assess these advantages and their implications for renewable generation.
Welcome back to a new academic year!
We would like to invite everyone to our Fall Virtual Welcome Event on Friday Sep. 25 at 4pm. We will talk more in depth about the events we have planned this year, and how you can get involved! Some highlights: our new Advocacy chair and committee, big/lil sibling pair ups and much more. There will also be a round of trivia (winning team gets a prize) so come by to meet new and returning students in our community and have a great time!
In the meantime and throughout this virtual quarter, feel free to connect with each other and returning students by filling out an entry in our WEE Study/Hobby Buddy doc.
Semiconductor quantum dots embedded in photonic nanostructures offer a highly efficient and coherent deterministic photon-emitter interface . It constitutes an on-demand single-photon source for quantum-information applications, enables single-photon nonlinear, optics and the constructing of deterministic quantum gates for photons . We review recent experimental progress, and demonstrate that the current technology can be scaled up to reach quantum advantage  with the demonstration of near-transform-limited emitters in high-cooperativity planar nanophotonic waveguides . The coherent control of a single spin in the quantum dot [5, 6] offers additional opportunities of generating advanced multi-photon entangled states . We finally discuss how this emergent hardware may be applied in a resource-efficient manner, e.g., for constructing a one-way quantum repeater .
 Lodahl et al., Rev. Mod. Phys. 87, 347 (2015).
 Lodahl, Quantum Science and Technology 3, 013001 (2018).
 Uppu et al., Arxiv: 2003.08919.
 Pedersen et al., ACS Photonics (2020).
 Javadi et al., Nature Nanotechnology 13, 398 (2018).
 Appel et al., Arxiv: 2006.15422.
 Tiurev et al., Arxiv: 2007.09295.
 Borregaard et al., Phys. Rev. X 10, 021071 (2020).
Hear the voice of the new generation of engineers, engineering students and aspiring engineering students – the Gen Z engineers! You're invited to the online panel, The Engineer of 2020, on September 17 at 5 PM ET, where we will discuss the enabling power of engineering as we address today’s challenges and the post-COVID future.
The panelists are engineering students or engineering professionals, beginning their careers during a time of change. In this first-of-its-kind event, intended for Gen Z, we'll discuss attributes and mindsets for engineering graduates needed to engineer a better world for all humanity.
A keynote address by USC Viterbi Professor Andrea Armani will precede the panel discussion.
The event is organized under the aegis of the National Academy of Engineering's COVID-19 Call To Action.
As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a range of sources. We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead. Using data from January 22 to June 20, 2020, we develop and combine multiple forecasts using ensembling techniques, resulting in an ensemble we refer to as Combined Linear and Exponential Predictors (CLEP). Our individual predictors include county-specific exponential and linear predictors, a shared exponential predictor that pools data together across counties, an expanded shared exponential predictor that uses data from neighboring counties, and a demographics-based shared exponential predictor. We use prediction errors from the past five days to assess the uncertainty of our death predictions, resulting in generally-applicable prediction intervals, Maximum (absolute) Error Prediction Intervals (MEPI). MEPI achieves a coverage rate of more than 94% when averaged across counties for predicting cumulative recorded death counts two weeks in the future. Our forecasts are currently being used by the non-profit organization, Response4Life, to determine the medical supply need for individual hospitals and have directly contributed to the distribution of medical supplies across the country. We hope that our forecasts and data repository at https://covidseverity.com can help guide necessary county-specific decision-making and help counties prepare for their continued fight against
● "Curating a COVID-19 data repository and forecasting county-level death counts in the Unite States"
● "Perceptual Audio Coding Using Adaptive Pre and Post-Filters and Lossless Compression"
● "A tutorial on conformal prediction"
Modern deep neural networks for image classification have achieved superhuman performance. Yet, the complex details of trained networks have forced most practitioners and researchers to regard them as blackboxes with little that could be understood. This talk considers in detail a now-standard training methodology: driving the cross-entropy loss to zero, continuing long after the classification error is already zero. Applying this methodology to an authoritative collection of standard deepnets and datasets, we observe the emergence of a simple and highly symmetric geometry of the deepnet features and of the deepnet classifier; and we document important benefits that the geometry conveys, thereby helping us understand an important component of the modern deep learning training paradigm.
This is joint work with Vardan Papyan, University of Toronto, and XY Han, Cornell.