EE Student Information

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EE Student Information, Spring Quarter through Academic Year 2020-2021: FAQs and Updated EE Course List.

Updates will be posted on this page, as well as emailed to the EE student mail list.

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Graduate

Probability Seminar presents "Optimal delocalization for generalized Wigner matrices"

Topic: 
Optimal delocalization for generalized Wigner matrices
Abstract / Description: 

We consider eigenvector statistics of large random matrices. When the matrix entries are sampled from independent Gaussian random variables, eigenvectors are uniformly distributed on the sphere and numerous properties can be computed exactly. In particular, it can be shown that extremal coordinates are no larger than $C\sqrt{\log N/N}$ with high probability.

There has been an extensive amount of work on generalizing such a result, known as delocalization, to a more general entry distribution. After giving a brief overview of the previous results going in this direction, we present an optimal delocalization result for matrices with sub-exponential entries for all eigenvectors. The proof is based on the dynamical method introduced by Erdös–Yau, the analysis of high moments as well as new level repulsion estimates which will be presented during the talk.

This is based on a joint work with P. Lopatto.

Date and Time: 
Monday, June 1, 2020 - 4:00pm
Venue: 
Zoom ID 959 3090 9831 (locks at 4:10pm PST)

VR / AR / XR Panel

Topic: 
New VR / AR / XR Trends
Abstract / Description: 

Join Stanford XR for an exciting panel of XR industry leaders on Monday, June 1 from 5:00-6:00 PM Pacific Time! We have 5 different speakers from across the VR/AR/XR landscape (3 of them are Stanford alums, and 2 of those helped found Rabbit Hole VR/Stanford XR), and we have a fantastic discussion and Q&A planned for you centered around new trends in XR, connecting a socially-distanced world, and the future of immersive tech.

You don't want to miss it (but if you have to please RSVP so we can send you the recording after)!
RSVP Form: bit.ly/SXRPanel20RSVP
Zoom Link: bit.ly/SXRPanel20Zoom
Google Calendar: bit.ly/SXRPanel20Cal

Date and Time: 
Monday, June 1, 2020 - 5:00pm
Venue: 
RSVP for link

OSA/SPIE, SPRC and Ginzton Lab present "Workshop on Inverse Design using SPINS"

Topic: 
Workshop on Inverse Design using SPINS
Abstract / Description: 

The goal of this workshop is to provide attendees with a practitioner's perspective of the basic ingredients and tools of inverse design using SPINS [1], a photonic optimization framework developed at Jelena Vuckovic's Nanoscale and Quantum Photonics Lab over the past decade.
SPINS is a flexible inverse-design platform that is compatible with a variety of device parametrizations, electromagnetic solvers, and objective functions. This has enabled SPINS to be used to design and experimentally demonstrate functional devices in a wide variety of different application areas including grating couplers [2-4], optical routing for LiDAR [5], and electron accelerators [6].
In this workshop, we will work through examples of using SPINS to design simple devices, including grating couplers and silicon photonics devices. We will emphasize how attendees can adapt these examples for their own applications and discuss some practical considerations for using inverse-design to produce functional devices. All example code will be available online after the workshop.

[1] Su, L. et al. Nanophotonic inverse design with SPINS: software architecture and practical considerations. Appl. Phys. Rev. https://doi.org/10.1063/1.5131263 (2020).
[2] Su, L. et al. Fully-automated optimization of grating couplers. Optics Express, 26(4): 4023–4034, 2018.
[3] Sapra, N. V. et al. Inverse design and demonstration of broadband grating couplers. IEEE J. Sel. Top. Quantum Electron. 25, 1–7 (2019).
[4] Dory, etl al. Inverse-designed diamond photonics. Nature Communications, 10(1):3309, 2019.
[5] Yang, K. Skarda, J. et al., Inverse-designed non-reciprocal pulse router for chip-based LiDAR, Nature Photonics DOI: 10.1038/s41566-020-0606-0 (2020)
[6] Sapra, N. V. et al., On-chip integrated laser-driven particle accelerator. Science 367, 79-83 (2020).

Date and Time: 
Thursday, June 4, 2020 - 1:30pm
Venue: 
Zoom ID: 945 5728 7546 (password required)

SystemX presents "Memory-Driven Computing - A perspective of this journey"

Topic: 
Memory-Driven Computing - A perspective of this journey
Abstract / Description: 

This talk will cover the use of memory technology within computing platforms, from building large memory systems, to use in neuromorphic computing. What use cases can benefit from novel use of Memory-Driven Computing techniques. How do the latest industry moves creating open memory fabrics (including Gen-Z and Compute Express Link) impact system design? The use of high bandwidth memories and non-volatile memories - where do these technologies play relative to each other? How can they impact the way we build systems to deal with the challenge of processing and gaining knowledge/insights from all the data we are collecting at exponentially growing rates.

Date and Time: 
Thursday, May 28, 2020 - 4:30pm
Venue: 
Zoom

QFARM Quantum Seminar Series presents "Robust Quantum Information Processing with Bosonic Modes"

Topic: 
Robust Quantum Information Processing with Bosonic Modes
Abstract / Description: 

Bosonic modes are widely used for quantum communication and information processing. Recent developments in superconducting circuits enable us to control bosonic microwave cavity modes and implement arbitrary operations allowed by quantum mechanics, such as quantum error correction against excitation loss errors. We investigate various bosonic codes, error correction schemes, and potential applications.

Date and Time: 
Wednesday, May 27, 2020 - 12:00pm
Venue: 
Zoom ID: 987 676 025

Statistics Department Seminar presents "Data denoising and transfer learning in single cell transcriptomics"

Topic: 
Data denoising and transfer learning in single cell transcriptomics
Abstract / Description: 

Cells are the basic biological units of multicellular organisms. The development of single-cell RNA sequencing (scRNA-seq) technologies have enabled us to study the diversity of cell types in tissue and to elucidate the roles of individual cell types in disease. Yet, scRNA-seq data are noisy and sparse, with only a small proportion of the transcripts that are present in each cell represented in the final data matrix. We propose a transfer learning framework based on deep neural nets to borrow information across related single cell data sets for denoising and expression recovery. Our goal is to leverage the expanding resources of publicly available scRNA-seq data, for example, the Human Cell Atlas which aims to be a comprehensive map of cell types in the human body. Our method is based on a Bayesian hierarchical model coupled to a deep autoencoder, the latter trained to extract transferable gene expression features across studies coming from different labs, generated by different technologies, and/or obtained from different species. Through this framework, we explore the limits of data sharing: How much can be learned across cell types, tissues, and species? How useful are data from other technologies and labs in improving the estimates from your own study? If time allows, I will also discuss the implications of such data denoising to downstream statistical inference.

Date and Time: 
Tuesday, May 26, 2020 - 4:30pm
Venue: 
Zoom Meeting ID 910 4626 3951

SystemX BONUS LECTURE: Edge TPU program and architecture overview

Topic: 
Edge TPU program and architecture overview
Abstract / Description: 

This talk will give an overview of how publicly announced products that have the Edge TPU make use of the product. The talk will then focus on what the Edge TPU architecture philosophy is the approach it takes to building custom silicon for ML workloads.


Join mailing list: Additional questions: Jon Candelaria, SystemX Seminar Instructor (jjcandel@stanford.edu)

Date and Time: 
Tuesday, May 26, 2020 - 4:30pm
Venue: 
Zoom

Probability Seminar presents "Induced subgraphs with prescribed degrees mod q"

Topic: 
Induced subgraphs with prescribed degrees mod q
Abstract / Description: 

A classical result of Galai asserts that the vertex-set of every graph can be partitioned into two sets such that each induces a graph with all degrees even. Scott studied the (harder) problem of determining for which graphs can we find a partition into arbitrary many parts, each of which induces a graph with all odd degrees. In this talk we discuss various extensions of this problem to arbitrary residues mod $q\geq 3$. Among other results, we show that for every $q$, a typical graph $G(n,1/2)$ can be equi-partitioned (up to divisibility conditions) into $q+1$ sets, each of which spans a graph with a prescribed degree sequence.

A completely unrelated problem: Based on the same approach we obtained a non-trivial bound (but weaker than known results) on the singularity probability of a random symmetric Bernoulli matrix. The new argument avoids both decoupling and distance from random hyperplanes and it turns this problem into a simple and elegant exercise.

This is mostly based on a joint work with Liam Hardiman (UCI) and Michael Krivelevich (Tel Aviv University).

Date and Time: 
Monday, May 18, 2020 - 4:00pm
Venue: 
Zoom ID: 917 2019 2125 (meeting locked 10 min. after start)

Statistics Department Seminar presents "Advancing medical research with 3D shape analysis of bioimaging data"

Topic: 
Advancing medical research with 3D shape analysis of bioimaging data
Abstract / Description: 

Advances in bioimaging techniques have enabled us to access the 3D shapes of a variety of structures: organs, cells, proteins. Since biological shapes are related to physiological functions, medical research is poised to incorporate more shape statistics. This leads to the question: how can we build quantified descriptions of shape variability from biomedical images

We first consider two biomedical analyses that require shape learning on small imaging datasets: (1) surgical planning for orthopedic surgery, and (2) research on pre-symptomatic biomarkers of Alzheimer's disease. We introduce elements of shape statistics to assess the accuracy of these studies. Then, we address a shape reconstruction challenge in pharmacological research: protein shape reconstruction using cryo-electron microscopy.

This talk shows how shape descriptors at different scales contribute to the development of precision medicine. The elements of geometric statistics required for this work are implemented in the open-source Python library Geomstats.

Date and Time: 
Tuesday, May 19, 2020 - 4:30pm
Venue: 
Zoom ID: 998 6129 8033 (meeting locked 10 min. after start)

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