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.

News

image of prof. Dorsa Sadigh
November 2020

Professor Dorsa Sadigh and her team have integrated algorithms in a novel way that makes controlling assistive robotic arms faster and easier. The team hopes their research will enable people with disabilities to conduct everyday tasks on their own– for example, cooking and eating.

Dorsa's team, which included engineering graduate student Hong Jun Jeon and computer science postdoctoral scholar Dylan P. Losey, developed a controller that blends two artificial intelligence algorithms. The first, which was developed by Dorsa's group, enables control in two dimensions on a joystick without the need to switch between modes. It uses contextual cues to determine whether a user is reaching for a doorknob or a drinking cup, for example. Then, as the robot arm nears its destination, the second algorithm kicks in to allow more precise movements, with control shared between the human and the robot.

In shared autonomy, the robot begins with a set of "beliefs" about what the controller is telling it to do and gains confidence about the goal as additional instructions are given. Since robots aren't actually sentient, these beliefs are really just probabilities. For example, faced with two cups of water, a robot might begin with a belief that there's an even chance it should pick up either one. But as the joystick directs it toward one cup and away from the other, the robot gains confidence about the goal and can begin to take over – sharing autonomy with the user to more precisely control the robot arm. The amount of control the robot takes on is probabilistic as well: If the robot has 80 percent confidence that it's going to cup A rather than cup B, it will take 80 percent of the control while the human still has 20 percent, explains Professor Dorsa Sadigh.

 

Excerpted from HAI (Human-Centered Artificial Intelligence), "Assistive Feeding: AI Improves Control of Robot Arms"

Video, "Shared Autonomy with Learned Latent Actions"

image of PhD candidate Pin Pin Tea-makorn
October 2020

PhD candidate Pin Pin Tea-makorn and Prof. Michal Kosinski have been seeking evidence to support the question of whether the faces of people in long-term relationships start to look the same over time. Their recently published article, "Spouses' faces are similar but do not become more similar with time" provides the answer in the title.

"It is something people believe in and we were curious about it," said Pin Pin Tea-makorn, an EE PhD candidate. "Our initial thought was if people's faces do converge over time, we could look at what types of features they converge on."

Pin Pin collected and analyzed thousands of public photos of couples. From these she compiled a database of pictures from 517 couples, taken within two years of tying the knot and between 20 and 69 years later.

The study has highlighted the importance of going back through past studies and checking their validity. "This is definitely something the field needs to update," said Kosinski. "One of the major problems in social sciences is the pressure to come up with novel, amazing, newsworthy theories. This is how you get published, hired, and tenured. As a result the field is filled with concepts and theories that are reclaimed, over-hyped, or not validated properly."

Kosinski praised Pin Pin for taking on the project, as he said many scientists were reluctant to "rock the boat" and reveal potential flaws in other researchers' work. "Cleaning up the field might be the most important challenge faced by social scientists today, yet she is surely not going to get as many citations or as much recognition for her work as she would get if she came up with something new and flashy," he said.

One of the researchers' next projects is to investigate claims that people's names can be predicted with any accuracy from their faces alone. "We're sceptical," Kosinski said.

 

Excerpted from The Guardian, Science, "Researchers crack question of whether couples start looking alike", October 2020

 

 

Pin Pin's research involves computational psychology, focusing on using facial recognition systems to study interpersonal relationships. Pin Pin is EE's graduate student advisor.

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

 

Related News

 

image of Cindy Nguyen (PhD candidate), Prof. Tsachy Weissman, 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".

 

Pages

January

No content classified for this term

February

February 2014

Three staff members each received a $50 Visa card in recognition of their extraordinary efforts as part of the department’s 2014 Staff Gift Card Bonus Program. The EE department received several nominations in January, and nominations from 2013 were also considered.

Following are January’s gift card recipients and some of the comments from their nominators:

Ann Guerra, Faculty Administrator

  • “She is very kind to students and always enthusiastic to help students… every time we need emergent help, she is willing to give us a hand.”
  • “Ann helps anyone who goes to her for help with anything, sometimes when it’s beyond her duty.” 

Teresa Nguyen, Student Accounting Associate

  • “She stays on top of our many, many student financial issues, is an extremely reliable source of information and is super friendly.”
  • “Teresa’s cheerful disposition, her determination, and her professionalism seem to go above and beyond what is simply required.”

Helen Niu, Faculty Administrator

  • “Helen is always a pleasure to work with.”
  • “She goes the extra mile in her dealings with me, which is very much appreciated.”

The School of Engineering once again gave the EE department several gift cards to distribute to staff members who are recognized for going above and beyond. More people will be recognized next month, and past nominations will still be eligible for future months. EE faculty, staff and students are welcome to nominate a deserving staff person by visitinghttps://gradapps.stanford.edu/NotableStaff/nomination/create.

Ann Guerra  Teresa Nguyen  Helen Niu

Pages

March

No content classified for this term

April

No content classified for this term

May

No content classified for this term

June

No content classified for this term

July

No content classified for this term

August

No content classified for this term

September

No content classified for this term

October

No content classified for this term

November

No content classified for this term

December

No content classified for this term

Story

No content classified for this term

Stanford

No content classified for this term

Test

No content classified for this term

Subscribe to RSS - News