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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.

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.

Faculty

image of prof. Andrea Montanari
October 2020

Professor Andrea Montanari, along with researchers from other institutions, have launched their first project: the Collaboration on the Theoretical Foundations of Deep Learning. The project is led by UC Berkeley researchers and has received five years of funding from NSF and Simons Foundation.

The project aims to gain a theoretical understanding of deep learning, which is making significant impacts across industry, commerce, science, and society.

Although deep learning is a widely used artificial intelligence approach for teaching computers to learn from data, its theoretical foundations are poorly understood, a challenge that the project will address. Understanding the mechanisms that underpin the practical success of deep learning will allow researchers to address its limitations, including its sensitivity to data manipulation.

The other institutions include UC Berkeley, the Massachusetts Institute of Technology, UC Irvine, UC San Diego, Toyota Technological Institute at Chicago, EPFL in Lausanne, Switzerland, and the Hebrew University in Jerusalem.

Professor Andrea Montanari's research spans several disciplines including statistics, computer science, information theory, and machine learning.

 

Excerpted from "UC Berkeley to lead $10M NSF/Simons Foundation program to investigate theoretical underpinnings of deep learning", August 2020

 

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image of prof Gordon Wetzstein and EE PhD candidate David Lindell
September 2020

Professor Gordon Wetzstein and EE PhD candidate David Lindell, have created a system that reconstructs shapes obscured by 1-inch-thick foam. Their tests are detailed in, "Three-dimensional imaging through scattering media based on confocal diffuse tomography", published in Nature Communications.

Gordon Wetzstein reports, "A lot of imaging techniques make images look a little bit better, a little bit less noisy, but this is really something where we make the invisible visible. This is really pushing the frontier of what may be possible with any kind of sensing system. It's like superhuman vision."

"We were interested in being able to image through scattering media without these assumptions and to collect all the photons that have been scattered to reconstruct the image," said David Lindell, EE PhD candidate and lead author of the paper. "This makes our system especially useful for large-scale applications, where there would be very few ballistic photons."

In order to make their algorithm amenable to the complexities of scattering, the researchers had to closely co-design their hardware and software, although the hardware components they used are only slightly more advanced than what is currently found in autonomous cars. Depending on the brightness of the hidden objects, scanning in their tests took anywhere from one minute to one hour, but the algorithm reconstructed the obscured scene in real-time and could be run on a laptop.

"You couldn't see through the foam with your own eyes, and even just looking at the photon measurements from the detector, you really don't see anything," said David. "But, with just a handful of photons, the reconstruction algorithm can expose these objects – and you can see not only what they look like, but where they are in 3D space."

Excerpted from Stanford News, "Stanford researchers devise way to see through clouds and fog", September 2020.


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image of Prof Dan Boneh
September 2020

Professor Dan Boneh's Hidden Number Problem helped academic researchers identify and resolve a vulnerability. Dan leads the Applied Cryptography Group.

The attack – known as Raccoon – affects TLS 1.2 and previous versions, which specify that any leading bytes beginning with zero in the premaster secret are stripped out. The premaster secret is the shared key used by the client and server to compute the subsequent TLS keys for each session.

"Since the resulting premaster secret is used as an input into the key derivation function, which is based on hash functions with different timing profiles, precise timing measurements may enable an attacker to construct an oracle from a TLS server. This oracle tells the attacker whether a computed premaster secret starts with zero or not," the description of the attack says.

"Based on the server timing behavior, the attacker can find values leading to premaster secrets starting with zero. In the end, this helps the attacker to construct a set of equations and use a solver for the Hidden Number Problem (HNP) to compute the original premaster secret established between the client and the server."

Excerpted from "Raccoon Attack can Compromise Some TLS Connections", by Dennis Fisher


In addition to leading the applied cryptography group, Dan co-directs the computer security lab. His research focuses on applications of cryptography to computer security. His work includes cryptosystems with novel properties, web security, security for mobile devices, and cryptanalysis.

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image of prof. Shanhui Fan
August 2020

Professor Shanhui Fan's rooftop cooling system could eventually help meet the need for nighttime lighting in urban areas, or provide lighting in developing countries.

Using commercially available technology, the research team has designed an off-grid, low-cost modular energy source that can efficiently produce power at night.

Although solar power brings many benefits, its use depends heavily on the distribution of sunlight, which can be limited in many locations and is completely unavailable at night. Systems that store energy produced during the day are typically expensive, thus driving up the cost of using solar power.

To find a less-expensive alternative, researchers led by professor Shanhui Fan looked to radiative cooling. Their approach uses the temperature difference resulting from heat absorbed from the surrounding air and the radiant cooling effect of cold space to generate electricity.

In The Optical Society (OSA) journal Optics Express, the researchers theoretically demonstrate an optimized radiative cooling approach that can generate 2.2 Watts per square meter with a rooftop device that doesn't require a battery or any external energy. This is about 120 times the amount of energy that has been experimentally demonstrated and enough to power modular sensors such as ones used in security or environmental applications.

"We are working to develop high-performance, sustainable lighting generation that can provide everyone–including those in developing and rural areas–access to reliable and sustainable low cost lighting energy sources," said Lingling Fan, EE PhD candidate and first author of the paper. "A modular energy source could also power off-grid sensors used in a variety of applications and be used to convert waste heat from automobiles into usable power."

Additional authors include Wei Li (EE PhD candidate), and post-doctoral researcher Weiliang Jin, PhD, and Meir Orenstein (Technion-Israel Institute of Technology).

 

 

Excerpted from Science Daily, "Efficient low-cost system for producing power at night".

 

image of prof. Gordon Wetzstein
August 2020

Professor Gordon Wetzstein and team use AI to revolutionize real-time holography.

"The big challenge has been that we don't have algorithms that are good enough to model all the physical aspects of how light propagates in a complex optical system such as AR eyeglasses," reports Gordon. "The algorithms we have at the moment are limited in two ways. They're computationally inefficient, so it takes too long to constantly update the images. And in practice, the images don't look that good."


Gordon says the new approach makes big advances on both real-time image generation and image quality. In heads-up comparisons, he says, the algorithms developed by their "Holonet" neural network generated clearer and more accurate 3-D images, on the spot, than the traditional holographic software.

That has big practical applications for virtual and augmented reality, well beyond the obvious arenas of gaming and virtual meetings. Real-time holography has tremendous potential for education, training, and remote work. An aircraft mechanic, for example, could learn by exploring the inside of a jet engine thousands of miles away, or a cardiac surgeon could practice a particularly challenging procedure.

In addition to professor Gordon Wetzstein, the system was created by Yifan Peng, a postdoctoral fellow in computer science; Suyeon Choi, an EE PhD candidate; Nitish Padmanaban, EE PhD '20; and Jonghyun Kim, a senior research scientist at Nvidia Corp.



Excerpted from: "Using AI to Revolutionize Real-Time Holography", August 17, 2020

image of professor emeritus James F. Gibbons
August 2020

James Gibbons has always been ahead of the times.

 

In a Q&A conversation with Stanford Engineering, Professor Emeritus James Gibbons shares lessons in remote learning experiments from the 1970s.

At the time of the research, James was asked to join President Nixon's Science Advisory Council, which was studying the effectiveness of televised education – dubbed "Tutored Video Instruction, or TVI".

A subset of the Science Advisory Council started by reviewing a very large study comparing televised classes with live classes. The study covered every subject matter from math to arts, from kindergarten to a baccalaureate degree. It was a huge study, 363 different experiments.

The overall answer was: There is no significant difference in student learning between TV and live instruction.

As the technology evolved, James and his colleagues began working with Sun Microsystems to create what was called distributed tutored video instruction – DTVI. He reports:

 

"We imagined the students to be remote from each other. We provided each of them with a microphone and a video camera to support remote communication within the group. We did an experiment at two campuses of the California State University system where we had 700 students at the two universities. We ran a regular lecture, a TVI group and a DTVI group for every class. The DTVI students were in their own rooms, connected to each other through our early version of the internet."

image of Gibbons' TVI research

"Sound familiar? Well, it should. This is exactly what Zoom does, right? In fact, it looked just like Zoom in the gallery view, with everyone wearing headsets and so forth. The results showed about the same performance academically, between TVI and DTVI, with each of them being superior to the live lecture class over a range of subjects."

 


In these days of COVID-19, everyone from parents to teachers to school administrators, not to mention the students themselves, is worried how this nationwide experiment in online learning is going to work out.

And from James' research findings, there should be no significant difference between online and in-person learning.

 

To read Stanford Engineering's Q&A article in its entirety, see "Lessons in remote learning from the 1970s: A Q&A with James Gibbons".

 

image of professors Shenoy and Murmann
August 2020

The current generation of neural implants record enormous amounts of neural activity, then transmit these brain signals through wires to a computer. But, so far, when researchers have tried to create wireless brain-computer interfaces to do this, it took so much power to transmit the data that the implants generated too much heat to be safe for the patient. A new study suggests how to solve his problem -- and thus cut the wires.

Research led by Professors Krishna Shenoy, Boris Murmann and Dr. Jaimie Henderson, have shown how it would be possible to create a wireless device, capable of gathering and transmitting accurate neural signals, but using a tenth of the power required by current wire-enabled systems. These wireless devices would look more natural than the wired models and give patients freer range of motion.

Graduate student Nir Even-Chen and postdoctoral fellow Dante Muratore, PhD, describe the team's approach in a Nature Biomedical Engineering paper.

The next step will be to build an implant based on this new approach and proceed through a series of tests toward the ultimate goal.

 

 

Excerpted from Science News, "How thoughts could one day control electronic prostheses, wirelessly", August 5, 2020.

image of prof. Tom Lee
July 2020

Congratulations to Professor Thomas Lee. He has been awarded the IEEE Gustav Robert Kirchhoff Award for "pioneering CMOS technology for high-performance wireless circuits and systems."

The IEEE Gustav Robert Kirchhoff Award recognizes an outstanding contribution to the fundamentals of any aspect of electronic circuits and systems that has a long-term significance or impact.

Tom is the principal investigator of the SMIrC Lab, which has been a driving force in developing the theory of radio frequency (RF) CMOS integrated circuit design as well as in educating tomorrow's RFIC designers.

Please join us in congratulating Tom for this well-deserved recognition.

 

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image of prof Andrea Goldsmith
July 2020

Congratulations to Professor Andrea Goldsmith, Stephen Harris Professor of Engineering. She has been awarded the IEEE Leon K. Kirchmayer Graduate Teaching Award. Her citation reads, "For educating, developing, guiding, and energizing generations of highly successful students and postdoctoral fellows."

The IEEE Leon K. Kirchmayer Graduate Teaching Award recognizes inspirational teaching of graduate students in the IEEE fields of interest.

Andrea's research interests are in information theory, communication theory, and signal processing, and their application to wireless communications, interconnected systems, and neuroscience. She is the director of Stanford's Wireless Systems Lab.

Please join us in congratulating Andrea!

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June 2020

We are proud of our history of innovation and entrepreneurship, and of our ongoing mission to address major societal challenges. This includes diversity in research interests, research styles, and providing supportive mentorship. EE recognizes that we have significant work to do in these areas and we look to the entire EE community to improve our future.


The Department of Electrical Engineering supports Black Lives Matter, inclusion, and diversity. 

As engineers, engineers-in-training, and staff, we build upon and apply systems-thinking to major societal challenges, including climate change, health, and better communication.

 The Department of Electrical Engineering strives to continue its success in innovation and research through the participation and inclusion of students, post-docs, and faculty from diverse backgrounds, experiences, religions, ethnicities, identities, genders, sexual orientation, and perspectives. We recognize diversity as central and critical to our mission to provide an inclusive environment and culture where all are welcomed, respected, and valued. Diversity in EE.

 

The following links are from our students, colleagues and friends. We include them to provide education and support to our community. 

If you have questions, insights, or edits, please contact us via info@ee.stanford.edu.

 

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