EE Student Information

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 EE Student Information, Spring & Summer Quarters 19-20: 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

Statistics Department Seminar presents "Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling"

Topic: 
Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling
Abstract / Description: 

Goodness-of-fit (GoF) testing is ubiquitous in statistics, with direct ties to model selection, confidence interval construction, conditional independence testing, and multiple testing. While testing the GoF of a simple null hypothesis provides an analyst great flexibility in the choice of test statistic while still ensuring validity, most GoF tests for composite null hypotheses are far more constrained, as the test statistic must have a tractable distribution over the entire null model space. A notable exception is co-sufficient sampling (CSS), which resamples. But CSS testing requires the null model to have a compact (in an information-theoretic sense) sufficient statistic, which only holds for a very limited class of models; even for a null model as simple as logistic regression, CSS testing is powerless. In this work, we leverage the concept of approximate sufficiency to generalize CSS testing to essentially any parametric model with an asymptotically-efficient estimator; we call our extension "approximate CSS" (aCSS) testing. We quantify the finite-sample Type I error inflation of aCSS testing and show that it is vanishing under standard maximum likelihood asymptotics, for any choice of test statistic. We also apply our proposed procedure both theoretically and in simulation to a number of models of interest.

This work is joint with Lucas Janson.

Date and Time: 
Tuesday, July 21, 2020 - 4:30pm
Venue: 
Meeting ID 941 3461 3493 (+password)

Probability Seminar presents "Gaussian regularization of pseudospectrum, eigenvalue gaps, and overlaps"

Topic: 
Gaussian regularization of pseudospectrum, eigenvalue gaps, and overlaps
Abstract / Description: 

Hermitian matrices are stable under small, additive perturbations, but this fact fails dramatically to generalize to the non-Hermitian case, as there are non-diagonalizable n x n matrices whose spectra move by O(ε^n) after an ε-perturbation. This issue is especially concerning for numerical linear algebra applications, where even the presence of routine machine noise can drastically alter the spectrum of a modestly sized matrix–and is mitigated only by the fact that the non-diagonalizable matrices of any dimension have measure zero.

In this talk I'll quantify this fact: a small entry-wise Gaussian perturbation εG of any n x n matrix A has a basis of eigenvectors with condition number poly(n), and eigenvalue gaps 1/poly(n). The main technique exploits the relationship between pseudospectrum and eigenvector condition number, reducing the problem to the proof of certain tail bounds on small singular values of the matrices zI – A – εG, for generic complex z. Time permitting, I'll discuss extensions to a numerical linear algebra application, where this random regularization is used as a preconditioning step in an algorithm to rapidly approximate the eigenvectors and eigenvalues of any matrix.

This is based on joint work with Jorge Garza Vargas, Archit Kulkarni, Satyaki Mukherjee, and Nikhil Srivastava, and found in these three papers:

Gaussian regularization of the pseudospectrum and Davies' conjecture
Overlaps, eigenvalue gaps, and pseudospectrum under real Ginibre and absolutely continuous perturbations
Pseudospectral shattering, the sign function, and diagonalization in nearly matrix multiplication time

Date and Time: 
Monday, July 20, 2020 - 4:00pm
Venue: 
Meeting ID 958 9191 8759 (+password)

OSA Color Technical Group presents "Modeling the Initial Steps of Human Vision"

Topic: 
Modeling the Initial Steps of Human Vision
Abstract / Description: 

Vision guides thought and action. To do so usefully it must inform us about critical features of the world around us. What we can learn about the world is limited by the initial stages of visual processing. Physicists, biologists and psychologists have created quantitative models of these stages, and these models enable us to quantify the encoded information. We have integrated these models as image computable software: the Image Systems Engineering Toolbox for Biology (ISETBio). The software is an extensible set of open-source modules that model the three-dimensional scene spectral radiance, retinal image formation (physiological optics), spatial sampling by the cone photoreceptor mosaic, fixational eye movements, and phototransduction. This webinar, hosted by the OSA Color Technical Group, will provide an overview of the ISETBio modules as well as examples of how to use the software to understand and model human visual performance.

Hosted By: Color Technical Group

Date and Time: 
Tuesday, July 21, 2020 - 9:00am

Student / Faculty Roundtable Discussion

Topic: 
Lunch and AMA conversation
Abstract / Description: 

These informal & informational lunches consist of several students and faculty. There is no set agenda during the discussion and it is completely informal. Students are encouraged to share their experience within the department, research, their plans, and feedback.

Registration is required.

Sign up as soon as you can because the roundtable discussion session is very popular and fills up quickly! Space is limited; you will receive a confirmation email if you are confirmed to attend. Please contact Tiffany, Student Life Coordinator if you have any questions, tdtran@stanford.edu. Thank you!

The student/faculty roundtable discussions are organized by the Student Life Committee.

Zoom Link: Provided once confirmed

Date and Time: 
Thursday, July 23, 2020 - 12:00pm
Venue: 
REGISTRATION REQUIRED

Q-FARM presents "Quantum Metrology in the Era of Quantum Information"

Topic: 
Quantum Metrology in the Era of Quantum Information
Abstract / Description: 

I will review the most recent advances in the theoretical methods of quantum metrology, focusing on the quantum information related concepts such as quantum error-correction and matrix product states formalism. The aim of the talk is to show how the two fields, quantum metrology and quantum information, are closely connected and how they can benefit from each other. The talk will be based on three papers:

[1] R. Demkowicz-Dobrzanski, J. Czajkowski, P. Sekatski, Adaptive quantum metrology under general Markovian noise, Phys. Rev. X 7, 041009 (2017)

[2] K. Chabuda, J. Dziarmaga, T. J. Osborne, R.Demkowicz-Dobrzanski, Tensor-Network Approach for Quantum Metrology in Many-Body Quantum Systems, Nat. Commun. 11, 250 (2020)

[3] A. Kubica, R. Demkowicz-Dobrzanski, Using Quantum Metrological Bounds in Quantum Error Correction: A Simple Proof of the Approximate Eastin-Knill Theorem, arXiv:2004.11893 (2020)

 

Password via qfarm-contact@stanford.edu

Date and Time: 
Wednesday, July 22, 2020 - 10:00am
Venue: 
Meeting ID: 987 676 025; + password)

Robotics Today Series presents "New Connections between Motion Planning and Machine Learning"

Topic: 
New Connections between Motion Planning and Machine Learning
Abstract / Description: 

Any time a robot needs to move, a motion needs to be planned. But yet roboticists often treat motion planning as a black box, and barely understand fundamental algorithms like A*. In this talk, I'll start with some intuition about search, via the absurd analogy of amoebas. I'll use the analogy to describe the first-ever edge-optimal A*-like search algorithm we invented. I'll then cast anytime search with experience (what we call the Experienced Piano Movers' Problem) as an instance of Bayesian Reinforcement Learning, enabling us to derive the first-ever sublinear regret bounds for anytime motion planning. I'll end with some open problems I want you all to solve, so I can retire in peace.

Date and Time: 
Friday, July 24, 2020 - 10:00am
Venue: 
roboticstoday.github.io/watch.html

Statistics Department Seminar presents "Statistical frameworks for mapping 3D shape variation onto genotypic and phenotypic variation"

Topic: 
Statistical frameworks for mapping 3D shape variation onto genotypic and phenotypic variation
Abstract / Description: 

The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global-patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). In this talk, we present SINATRA: a statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our method takes in two classes of shapes and highlights the physical features that best describe the variation between them.

The SINATRA pipeline implements four key steps. First, SINATRA summarizes the geometry of 3D shapes (represented as triangular meshes) by a collection of vectors (or curves) that encode changes in their topology. Second, a nonlinear Gaussian process model, with the topological summaries as input, classifies the shapes. Third, an effect size analog and corresponding association metric is computed for each topological feature used in the classification model. These quantities provide evidence that a given topological feature is associated with a particular class. Fourth, the pipeline iteratively maps the topological features back onto the original shapes (in rank order according to their association measures) via a reconstruction algorithm. This highlights the physical (spatial) locations that best explain the variation between the two groups.

We use a rigorous simulation framework to assess our approach, which themselves are a novel contribution to 3D image analysis. Lastly, as a case study, we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.

Date and Time: 
Tuesday, July 14, 2020 - 4:30pm
Venue: 
Meeting ID 941 3461 3493 (+password)

Probability Seminar presents "Limit theorems for descents of Mallows permutations"

Topic: 
Limit theorems for descents of Mallows permutations
Abstract / Description: 

The Mallows measure on the symmetric group gives a way to generate random permutations which are more likely to be sorted than not. There has been a lot of recent work to try and understand limiting properties of Mallows permutations. I'll discuss recent work on the joint distribution of descents, a statistic counting the number of "drops" in a permutation, and descents in its inverse, generalizing work of Chatterjee and Diaconis, and Vatutin. The proof is new even in the uniform case and uses Stein's method with a size-bias coupling as well as a regenerative representation of Mallows permutations.

Date and Time: 
Monday, July 13, 2020 - 4:00pm
Venue: 
Meeting ID 916 4174 2729 (+password

SCIEN and EE292E present "ThinVR: A VR display approach providing wide FOV in a compact form factor"

Topic: 
ThinVR: A VR display approach providing wide FOV in a compact form factor
Abstract / Description: 

This talk describes ThinVR: An approach to build near-eye VR displays that simultaneously provides a very wide (180 degree horizontal) FOV and a compact form factor. The key is to replace traditional large optics with curved microlens arrays and to place the optics in front of curved displays. We had to design custom heterogeneous optics to make this approach work, because many lenslets are viewed off the central axis. To ensure the existence of an adequate eyebox and to minimize pupil swim distortions, we had to design and build a custom optimizer to produce an acceptable heterogeneous lenslet array. We prove this approach works through prototypes with both static and dynamic displays. To our knowledge, this is the first work to both convincingly demonstrate and analyze the potential for curved, heterogeneous microlens arrays to enable compact, wide FOV near-eye VR displays.

Date and Time: 
Wednesday, July 15, 2020 - 4:30pm
Venue: 
Zoom - register for link + password

SCIEN and EE292E present "Fluorescence Guided Precision Surgery TM – Illuminating Tumors and Nerves"

Topic: 
Fluorescence Guided Precision Surgery TM – Illuminating Tumors and Nerves
Abstract / Description: 

Although treatment algorithms vary, surgery is the primary treatment modality for most solid cancers. In oncologic tumor resection, the preferred outcome is complete cancer removal as residual tumor left behind is considered treatment failure. However complete tumor removal needs to be balanced with functional preservation and minimizing patient morbidity including prevention of inadvertent nerve injury. The inability of surgeons to visually distinguish between tumor and normal tissue including nerves leads to residual cancer cells being left behind at the edges of resection, i.e. positive surgical margins (PSM). PSM can be as high as 20-40% in breast cancer lumpectomy, 21% for radical prostatectomy, and 13% for HNSCC. Similarly, using white light reflectance alone which is the current standard of care in operating rooms, nerve dysfunction following surgery has been reported to be as high as ~2-40% ranging from immediate post op to long-term dysfunction.

Molecular imaging with fluorescence provides enhanced visual definition between diseased and normal tissue and have been shown to decrease PSM in both animal models and patients. Molecular imaging with fluorescence can also provide enhanced visualization of important structures such as nerves to improve preservation and minimize inadvertent injury. Our laboratory has extensive experience in development of both nerve and tumor injectable markers for surgical visualization. In presentation we will discuss the development of nerve and tumor markers combinations to improve intraoperative visualization – aka Precision Surgery TM.


The SCIEN Colloquia are now offered via Zoom - Please register by going to https://stanford.zoom.us/meeting/register/tJctd-utrT4rHtT5OO34glASg1vol-PCGuXR

Date and Time: 
Wednesday, July 8, 2020 - 4:30pm
Venue: 
Zoom

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