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.

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.

research

image of Prof. Tsachy Weissman and program coordinators Cindy Nguyen (PhD candidate) and Suzanne Sims
September 2020

In July and August, Professor Tsachy Weissman and the Stanford Compression Forum hosted the 2020 STEM to SHTEM (Science, Humanities, Technology, Engineering and Mathematics) internship program for high schoolers.

The summer program welcomed 64 high school students. The students were matched with one of nineteen projects ranging from financial exchanges to narratives of science and social justice – a full list follows. Each of the project groups were supervised by mentors from the Compression Forum.

The 8-week STEM to SHTEM Program culminates in final reports that often weave an entirely new perspective. As a team, the students' interests and knowledge are combined with traditional research methodology. Several mentors provide guidance during the experience and encourage exploration of the interns' strengths and interests.

Special thanks to program coordinators Cindy Nguyen and Suzanne Sims.

Congratulations to all the 2020 STEM to SHTEM Program interns! We enjoyed working with you and look forward to hearing from you in the future.


Students' final reports describe new insights and broaden knowledge of the topics. A few takeaways from the 2020 projects include,

  • the use of animation to improve the quality and efficiency of video communication;
  • theatrical performance as technology and a pandemic create new boundaries;
  • how might today's "science" and world be different If history had been more inclusive of the sciences that exist but aren't well-known?

Complete list of projects from STEM to SHTEM Program. Source: theinformaticists.com[...]journal-for-high-schoolers-2020

1. Applications of Astrophysics to Multimedia Art-Making In Parallel to Narratives of Science and Social Justice
2. Artificial Neural Networks with Edge-Based Architecture
3. COVerage: Region-Specific SARS-CoV-2 News Query Algorithm
4. Developing and Testing New Montage Methods in Electroencephalography
5. Fundamental Differences Between The Driving Patterns of Humans and Autonomous Vehicles
6. Identifying and Quantifying Differences Among SARS-CoV-2 Genomes Using K-mer Analysis
7. Improving the Infrastructure of a Financial Exchange System in the Cloud
8. Journal for High Schoolers in 2020
9. Keypoint-Centric Video Processing for Reducing Net Latency in Video Streaming
10. Olfaction Communication System
11. Optimizing the Measurement of SPO2 With a Miniaturized Forehead Sensor
12. Properties and effects of ion implantation into silicon and wide bandgap materials
13. ProtographLDPC: Implementation of Protograph LDPC error correction codes
14. RF/mm-Wave Semiconductor Technology for 5G Applications and Beyond
15. The Price of Latency in Financial Exchanges
16. Understanding COVID-19 Through Sentiment Analysis on Twitter and Economic Data
17. Virtual Reality for Emotional Response
18. Vision-Based Robotic Object Manipulation: Using a Human-Mimicking Hand Design with Pure
19. Object Recognition Algorithms to Intelligently Grasp Complex Items
20. YOU ARE HERE (AND HERE AND THERE): A Virtual Extension of Theatre

Summer 2021 application notification

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.


Related News

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.

Related News

 

 

image of REU 2020 cohort
August 2020

Congratulations to the 26 undergraduate students who participated in Electrical Engineering's Research Experience for Undergraduates (REU) program, 2020. Students worked remotely to participate in their selected research projects. Faculty advisors and graduate student mentors met with and guided the program participants during research team meetings and one on one. The undergraduate researchers also attended weekly seminars featuring faculty, industry experts, and a graduate student panel to explore advanced degrees and research.

 
Thursday will be the student’s final presentations, demonstrating their findings. The community is invited to attend. 2020’s research projects are grouped into three main areas: circuits and physical systems, signals and information systems, and materials and devices. 
 
The project and researchers are listed below in order of presentation. 
Please email reu@ee.stanford.edu if you have questions, or plan to attend the event (password is required). 
 

Research Experience for Undergraduates (REU) program participants 2020

[Photo credit: Marisa Cheng]

 

CIRCUITS AND PHYSICAL SYSTEMS
1. Understanding and Measuring Troubleshooting Ability in Regards to Physical Circuits (ATINDRA JHA)
2. MOCHA: A Modular and Open-source Control and Hardware Library for Power Electronics (BRIAN KAETHER)
3. Modular FPGA and Programmable SoC Environment for ASIC Verification and Evaluation (JOHN KUSTIN)
4. Communicating Olfaction Using Frugal Device Design (ERIK LUNA)
5. Creating a Cost Function for Optimizing Loop Fusion in Clockwork (ISABELA DAVID RODRIGUES)
6. Running Accelerated Halide Programs End-to-End on an SoC (CHARLES TSAO)

SIGNALS AND INFORMATION SYSTEMS
7. Complex multiple feedback filter feasibility (BURCU ALICI)
8. Generative Adversarial Networks for Vehicle Models (EVA BATELAAN)
9. Producing digital puppetry (RACHEL CAREY)
10. Enabling selective neuron stimulation for brain machine interfaces (ISAAC CHERUIYOT)
11. Optimization of LiCoRICE: A Realtime Computational Platform for Systems Neuroscience (HELEN GORDAN)
12. Human Inspired Music Compression Through Transcription (ZACHARY HOFFMAN)
13. Visualizing the Effects of Polarity on Persistent Scatterers and Land Cover (PARKER KILLION)
14. Neural Network Confidence Intervals with Unbiased Risk Estimators (KAO KITICHOTKUL)
15. Julia on Embedded Systems (ALBERT LANDA)
16. Developing user interfaces for reinforcement learning tasks (NIKESH MISHRA)
17. Performance of Monostatic and Bistatic Radar Imaging Modalities with Varying Target Geometries (ANNIE NGUYEN)
18. AI-Assisted Wearable Multimodal Lung Monitoring System for Remote and Early Stage Diagnosis (ADRIAN SALDANA)
19. Learning Effective Image Reconstruction through Patch-wise Singular Value Decomposition (BRUCE XU)
20. Quasistatic Simulation for Data-Driven Clothing: from T-Shirts to Capes (KANGRUI XUE)
21. ML Fairness for ML API Joint Optimization (EVA ZHANG)
22. Nanopore FASTQ File Compression (YIFAN ZHU)

MATERIALS AND DEVICES
23. A Detection System for Continuous, Multiplexed Biomarker Monitoring (HAGOP CHINCHINIAN)
24. Motorized Stage Configurations for Optimal Straining of Flexible Electronics (NOOR FAKIH)
25. Using Python to Manipulate and Analyze Atomistic Simulations (SIDRA NADEEM)
26. Towards high specific power transition metal dichalcogenide (TMD) solar cells (FREDERICK NITTA)

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.

July 2020

To advance quantum research during this exciting time, and help bridge Stanford's physics and engineering departments, the university is launching a new postdoctoral fellows program named after Felix Bloch, who was a theoretical physicist at Stanford and the university's first Nobel Prize winner.

The Bloch fellowship is awarded by the Q-FARM (Quantum Fundamentals, ARchitecture and Machines) initiative, which launched last year. Q-FARM emerged from Stanford's long-range planning process as part of a team focused on understanding the natural world. The initiative seeks to utilize the resources of both Stanford and the SLAC National Accelerator Laboratory to accelerate quantum research.

The first cohort of Bloch fellows was appointed this year and includes postdocs studying topics ranging from the fundamentals of quantum theory to computing and sensing applications.

Up to six fellows will be selected each year for a 2-3 year appointment, based on strong research proposals and previous accomplishments in the field. They are then jointly advised by at least two faculty members, which in most cases hail from different departments and schools, to foster interdisciplinary collaborations. The first cohort of Bloch fellows was appointed this year and includes postdocs studying topics ranging from the fundamentals of quantum theory to computing and sensing applications.

"These first five fellows had innovative proposals that connect research groups and establish collaborations that didn't exist previously," reports Prof. Jelena Vuckovic. "We also picked candidates who span all areas of Q-FARM: from theory to experiment, from algorithms to devices and circuits, from science to engineering."

The Q-FARM directors hope this diversity of interests and collaboration between departments will seed more creative projects and build lasting connections. The result will be a new generation of quantum scientists and engineers for academia and industry that honors the fellowship's namesake.

 

To read more about the 2020 Q-FARM Bloch Fellows, see Q-FARM news article, "Bloch Fellowship in Quantum Science and Engineering"

Congratulations to the 2020 Q-FARM Fellows!

  • Shahriar Aghaeimeibodi
  • Vahid Ansari
  • Anirudh Krishna
  • Tibor Rakovszky
  • Yijian Zou

 

Excerpted from Stanford News, "First Stanford Bloch Fellowship in quantum science and engineering announced", July 27, 2020

image of prof. Gordon Wetzstein and Isaac Kauvar, EE /PhD
June 2020

Professor Gordon Wetzstein and first authors Isaac Kauvar (EE PhD candidate) and postdoctoral researcher Tim Machado, have developed an optical technique that can simultaneously record the activity of neurons spread across the entire top surface of a mouse's cerebral cortex, a key part of the brain involved in making decisions. Their article, "Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions" was published in the journal Neuron.

The researchers call their system Cortical Observation by Synchronous Multifocal Optical Sampling, or COSMOS. In addition to studying motor control and decision making, the team is also using COSMOS to study sensory perception in animals and as a screening technique to develop better psychiatric drugs.

The prototype COSMOS system is relatively simple to build and costs less than $50,000, which is hundreds of thousands of dollars cheaper than other optical systems for recording neural population dynamics. To encourage further adoption and development of the technique, the authors have built a website with instructions to help other researchers build their own COSMOS systems.

The bifocal microscope uses a single camera to capture two movies of neural activity at the same time: one focused on the sides of the brain, and the other focused on the middle, to provide a side-by-side view shown in a video. The researchers then computationally extract signals – reflecting the timing, intensity and duration of when neurons fire – from both of these movies to obtain a comprehensive measurement of neural activity across the whole surface.

Excerpted from Stanford News, "Stanford researchers develop an inexpensive technique to show how decisions light up the brain", June 2, 2020.

 

"COSMOS Reveals Widespread Population Encoding of Actions", first authors Isaac Kauvar, EE PhD candidate (photo credit: Daphna Spivack) and Tim Machado, Bioengineering postdoctoral researcher.

 

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