EE Student Information

The Department of Electrical Engineering supports Black Lives Matter. Read more.

• • • • •

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.

Please see Stanford University Health Alerts for course and travel updates.

As always, use your best judgement and consider your own and others' well-being at all times.

Graduate

Probability Seminar presents Emergence of communication through reinforcement learning with invention

Topic: 
Emergence of communication through reinforcement learning with invention
Abstract / Description: 

Reinforcement learning in a two-player Lewis signaling game is a simple model to study the emergence of communication in cooperative multi-agent systems. When there are a fixed number of states and signals there is a positive probability that a successful communication system does not emerge. If the learning dynamics are modified to include invention – rather than fixing the number of signals, at each step there is always a chance to introduce a new signal – then the system converges to successful signaling almost surely. The reinforcement process can be modeled as an interacting urn system, and the proof uses a combination of stochastic approximation techniques and comparison with simpler urn models.

Date and Time: 
Monday, October 12, 2020 - 4:00pm
Venue: 
Zoom

Probability Seminar presents "Random matrix statistics though pseudo-randomness"

Topic: 
Random matrix statistics though pseudo-randomness
Abstract / Description: 

We introduce the $N\times N$ random matrices $X_{j,k}=\exp(2\pi i \sum_{q=1}^d \omega_{j,q} k^q)$ with i.i.d. random variables $\omega_{j,q}$ for $1\leq j\leq N$ and $1\leq q\leq d}$, where $d$ is a fixed integer. We prove that the distribution of their singular values converges to the local Marchenko-Pastur law at scales $N^{-\theta_d}$ for an explicit, small $\theta_d>0$, as long as $d\geq 18$. To our knowledge, this is the first instance of a random matrix ensemble that is explicitly defined in terms of only $O(N)$ random variables exhibiting a universal local spectral law. Our main technical contribution is to derive concentration bounds for the Stieltjes transform that simultaneously take into account stochastic and oscillatory cancellations. Important ingredients in our proof are strong estimates on the number of solutions to Diophantine equations (in the form of Vinogradov's main conjecture recently proved by Bourgain-Demeter-Guth) and a pigeonhole argument that combines the Ward identity with an algebraic uniqueness condition for Diophantine equations derived from the Newton-Girard identities.

This is joint work with Marius Lemm.

Date and Time: 
Monday, October 5, 2020 - 4:00pm
Venue: 
Zoom

Probability Seminar & Applied Math present "Shapes of equilibrium capillary drops on a rough surface"

Topic: 
Shapes of equilibrium capillary drops on a rough surface
Abstract / Description: 

I will discuss some simplified models for the shape of liquid droplets on rough solid surfaces. These are elliptic free boundary problems with oscillatory coefficients. I will talk about the large-scale effects of small-scale surface roughness, e.g., contact line pinning, hysteresis, and formation of flat parts (facets) in the contact line, and how to understand these by homogenization theory. I will also mention a connection with the continuum scaling limit of an abelian sandpile-type model and some results in that setting.

Date and Time: 
Wednesday, September 30, 2020 - 12:00pm
Venue: 
Zoom

Workshop in Biostatistics presents "Evidence-Based Elections"

Topic: 
Evidence-Based Elections
Abstract / Description: 

Elections rely on people, hardware, and software, all of which are fallible and subject to manipulation. Well resourced nation-states continue to attack U.S. elections and domestic election fraud is not unheard of. Voting equipment is built by private vendors--some foreign, but all using foreign parts. Many states even oursource election reporting to foreign firms. How can we conduct and check elections in a way that provides evidence that the reported winners really won--despite malfunctions and malfeasance? Evidence-based elections require voter-verified (generally, hand-marked) paper ballots kept demonstrably secure throughout the canvass and manual audits of election results against the trustworthy paper trail. Hand-marked paper ballots are far more trustworthy than machine-marked ballots for a variety of reasons. Two kinds of audits are required to provide affirmative evidence that outcomes are correct: _compliance audits_ to establish whether the paper trail is complete and trustworthy, and _risk-limiting audits_ (RLAs). RLAs test the hypothesis that an accurate manual tabulation of the votes would find that one or more reported winners did not win. To reject that hypothesis means there is convincing evidence that a full hand tally would confirm the reported results. For a broad variety of social choice functions, including plurality, multi-winner plurality, supermajority, proportional representation rules such as D'Hondt, Borda count, approval voting, and instant-runoff voting (aka ranked-choice voting), the hypothesis that one or more outcomes is wrong can be reduced to the hypothesis that the means of one or more lists of nonnegative numbers is not greater than 1/2. Martingale methods for testing such nonparametric hypotheses sequentially are especially practical. RLAs are in law in several states and have been piloted in more than a dozen; there have been roughly 60 pilots in jurisdictions of all sizes, including roughly 10 audits of statewide contests. Open-source software to support RLAs is available.

Suggested Readings:
● "Sets of Half-Average Nulls Generate Risk-Limiting Audits: SHANGRLA"
● "Ballot-marking devices cannot assure the will of the voters"
● "Evidence-Based Elections: Create a Meaningful Paper Trail, Then Audit"
● "Testing Cannot Tell Whether Ballot-Marking Devices Alter Election Outcomes"

* Subscribe to the BIODS260 Workshop list via their abstracts page to receive one-click access details directly!

Date and Time: 
Thursday, September 24, 2020 - 2:30pm
Venue: 
Zoom ID 926 9609 8893 (+password)*

Probability Seminar presents "A Gaussian particle distribution for branching Brownian motion with an inhomogeneous branching rate"

Topic: 
A Gaussian particle distribution for branching Brownian motion with an inhomogeneous branching rate
Abstract / Description: 

Motivated by the goal of understanding the evolution of populations undergoing selection, we consider branching Brownian motion in which particles independently move according to one-dimensional Brownian motion with drift, each particle may either split into two or die, and the difference between the birth and death rates is a linear function of the position of the particle. We show that, under certain assumptions, after a sufficiently long time, the empirical distribution of the positions of the particles is approximately Gaussian. This provides mathematically rigorous justification for results in the Biology literature indicating that the distribution of the fitness levels of individuals in a population over time evolves like a Gaussian traveling wave.

This is joint work with Matt Roberts.

Date and Time: 
Monday, September 21, 2020 - 4:00pm
Venue: 
Zoom ID 984 0476 9786 (+password)

Statistics Seminar presents "Scaled minimax optimality in high-dimensional linear regression: A non-convex algorithmic regularization approach"

Topic: 
Scaled minimax optimality in high-dimensional linear regression: A non-convex algorithmic regularization approach
Abstract / Description: 

The question of fast convergence in the classical problem of high-dimensional linear regression has been extensively studied. Arguably, one of the fastest procedures in practice is Iterative Hard Thresholding (IHT). Still, IHT relies strongly on the knowledge of the true sparsity parameters. In this talk, we present a novel fast procedure for estimation in high-dimensional linear regression. Taking advantage of the interplay between estimation, support recovery and optimization, we achieve both optimal statistical accuracy and fast convergence. The main advantage of our procedure is that it is fully adaptive, making it more practical than state-of-the-art IHT methods. Our procedure achieves optimal statistical accuracy faster than, for instance, classical algorithms for the Lasso. Moreover, we establish sharp optimal results for both estimation and support recovery. As a consequence, we present a new iterative hard thresholding algorithm for high-dimensional linear regression that is scaled minimax optimal (achieves the estimation error of the oracle that knows the sparsity pattern if possible), fast and adaptive.

Date and Time: 
Tuesday, September 29, 2020 - 4:30pm
Venue: 
Zoom ID 973 5368 4241 (+password)

Statistics Seminar presents "Some recent insights on transfer-learning"

Topic: 
Some recent insights on transfer-learning
Abstract / Description: 

A common situation in Machine Learning is one where training data is not fully representative of a target population due to bias in the sampling mechanism or due to prohibitive target sampling costs. In such situations, we aim to "transfer" relevant information from the training data (a.k.a. source data) to the target application. How much information is in the source data about the target application? Would some amount of target data improve transfer? These are all practical questions that depend crucially on "how far" the source domain is from the target. However, how to properly measure "distance" between source and target domains remains largely unclear.

In this talk we will argue that much of the traditional notions of distance (e.g., KL-divergence, extensions of TV such as D_A discrepancy, density-ratios, Wasserstein distance) can yield an over-pessimistic picture of transferability. Instead, we show that some new notions of "relative dimension" between source and target (which we simply term transfer-exponents) capture a continuum from easy to hard transfer. Transfer-exponents uncover a rich set of situations where transfer is possible even at fast rates; they encode relative benefits of source and target samples, and have interesting implications for related problems such as multi-task or multi-source learning.

In particular, in the case of transfer from multiple sources, we will discuss (if time permits) a strong dichotomy between minimax and adaptive rates: no adaptive procedure exists that can achieve the same rates as minimax (oracle) procedures.

The talk is based on earlier work with Guillaume Martinet, and ongoing work with Steve Hanneke.

Date and Time: 
Tuesday, September 22, 2020 - 4:30pm
Venue: 
Zoom ID 973 5368 4241 (+password)

Stanford EE and Apple joint initiative in hardware education

Topic: 
Stanford EE and Apple joint initiative in hardware education
Abstract / Description: 

Announcing a new joint initiative in hardware education

Find out what's possible in silicon engineering. Discover classes that will allow you to bring your ideas to life. Learn about new Coterm scholarships and PhD fellowships.

Following the presentation, there will be breakout sessions with Stanford EE faculty and Apple for Q&A and discussion.

Date and Time: 
Wednesday, September 23, 2020 - 5:30pm

SCIEN and EE292E present "Retinal topography using stripe illumination in a fundus camera"

Topic: 
Retinal topography using stripe illumination in a fundus camera
Abstract / Description: 

Retinal topography is affected by pathology such as drusen and tumors, and it may be useful to determine topography with fundus imaging when three-dimensional imaging is not available. In this talk, I will present a novel method of retinal topography scanning using the stripe projection technology of the CLARUSTM 700 (ZEISS, Dublin, CA) wide-field fundus camera. The camera projects stripes onto the retina and records images of the returned light while maintaining a small angle between illumination and imaging. We make use of this structured illumination, analyzing neighboring stripes to determine depth -i.e. the retinal topography – from both relative defocus and stripe displacements. The resulting topography maps are finally compared to three-dimensional data from optical coherence tomography imaging.

Date and Time: 
Wednesday, September 23, 2020 - 4:30pm
Venue: 
Zoom registration required

Pages

Subscribe to RSS - Graduate