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

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)

SystemX presents "Simulating Realistic Human Motion for Robotics "

Topic: 
Simulating Realistic Human Motion for Robotics
Abstract / Description: 

Creating realistic virtual humans has traditionally been considered a research problem in Computer Animation primarily for entertainment applications. With the recent breakthrough in collaborative robots and deep reinforcement learning, accurately modeling human movements and behaviors has become a common challenge also faced by researchers in robotics and artificial intelligence. In this talk, I will first discuss our recent work on developing efficient computational tools for simulating and controlling human movements. By learning a differentiable kinematic constraints from the real world human motion data, we enable existing multi-body physics engines to simulate more humanlike motion. In a similar vein, we learn task-agnostic boundary conditions and energy functions from anatomically realistic neuromuscular models, effectively defining a new action space better reflecting the physiological constraints of the human body. The second part of the talk will focus on two different yet highly relevant problems: how to teach robots to move like humans and how to teach robots to interact with humans. While Computer Animation research has shown that it is possible to teach a virtual human to mimic real athletes' movements, the current techniques still struggle to reliably transfer a basic locomotion control policy to robot hardware in the real world. We developed a series of sim-to-real transfer methods to address the intertwined issue of system identification and policy learning for challenging locomotion tasks. Finally, I will showcase our effort on teaching robot to physically interact with humans in the scenarios of robot-assisted dressing and walking assistance.

Date and Time: 
Thursday, May 21, 2020 - 4:30pm
Venue: 
Zoom id: 865 305 030

AMA EE IT Open Office Hours

Topic: 
Ask Me Anything!
Abstract / Description: 

Bring your IT-related ponderings and enjoy the company of our IT team!

Every Friday, Zoom link available upon request via eamil to action@ee.stanford.edu.

SEE YOU THERE!

 

Date and Time: 
Friday, May 15, 2020 - 9:00am to 10:00am
Friday, May 22, 2020 - 9:00am to 10:00am
Friday, May 29, 2020 - 9:00am to 10:00am
Venue: 
Zoom (link by request only)

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