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.


image of prof James Zou and PhD Amirata Ghorbani
February 2021

Each of us continuously generates a stream of data. When we buy a coffee, watch a romcom or action movie, or visit the gym or the doctor's office (tracked by our phones), we hand over our data to companies that hope to make money from that information – either by using it to train an AI system to predict our future behavior or by selling it to others.

But what is that data worth?

"There's a lot of interest in thinking about the value of data," says Professor James Zou, member of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and faculty lead of a new HAI executive education program on the subject. How should companies set prices for data they buy and sell? How much does any given dataset contribute to a company's bottom line? Should each of us receive a data dividend when companies use our data?

Motivated by these questions, James and graduate student Amirata Ghorbani have developed a new and principled approach to calculating the value of data that is used to train AI models. Their approach, detailed in a paper presented at the International Conference on Machine Learning and summarized for a slightly less technical audience in arXiv, is based on a Nobel Prize-winning economics method and improves upon existing methods for determining the worth of individual datapoints or datasets. In addition, it can help AI systems designers identify low value data that should be excluded from AI training sets as well as high value data worth acquiring. It can even be used to reduce bias in AI systems.


The data Shapley value can even be used to reduce the existing biases in datasets. For example, many facial recognition systems are trained on datasets that have more images of white males than minorities or women. When these systems are deployed in the real world, their performance suffers because they see more diverse populations. To address this problem, James and Amirata ran an experiment: After a facial recognition system had been deployed in a real setting, they calculated how much each image in the training set contributed to the model's performance in the wild. They found that the images of minorities and women had the highest Shapley values and the images of white males had the lowest Shapley values. They then used this information to fix the problem – weighting the training process in favor of the more valuable images. "By giving those images higher value and giving them more weight in the training process, the data Shapley value will actually make the algorithm work better in deployment – especially for minority populations," James says.


Excerpted from: HAI "Quantifying the Value of Data"

image of Chuan-Zheng Lee, EE PhD candidate
February 2021

Congratulations to Chuan-Zheng Lee (PhD candidate) and Leighton Pate Barnes (PhD candidate) on receiving the IEEE GLOBECOM 2020 Selected Areas of Communications Symposium Best Paper Award. Their paper is titled "Over-the-Air Statistical Estimation." Professor Ayfer Özgür is their advisor and co-author.

The award was presented by the IEEE GLOBECOM 2020 Awards Committee and IEEE GLOBECOM 2020 Organizing Committee.


Please join us in congratulating Ayfer, Chuan-Zheng, and Leighton on receiving this prestigious best paper award!

IEEE Global Communications Conference (GLOBECOM) Best Paper Award Winners

image of IEEE award

image of prof Amin Arbabian
December 2020

Professor Amin Arbabian, Aidan Fitzpatrick (PhD candidate), and Ajay Singhvi (PhD candidate) have developed an airborne method for imaging underwater objects by combining light and sound to break through the seemingly impassable barrier at the interface of air and water.

The researchers envision their hybrid optical-acoustic system one day being used to conduct drone-based biological marine surveys from the air, carry out large-scale aerial searches of sunken ships and planes, and map the ocean depths with a similar speed and level of detail as Earth's landscapes. Their "Photoacoustic Airborne Sonar System" is detailed in a recent study published in the journal IEEE Access.

"Airborne and spaceborne radar and laser-based, or LIDAR, systems have been able to map Earth's landscapes for decades. Radar signals are even able to penetrate cloud coverage and canopy coverage. However, seawater is much too absorptive for imaging into the water," reports Amin. "Our goal is to develop a more robust system which can image even through murky water."


Excerpted from "Stanford engineers combine light and sound to see underwater", Stanford News, November 30, 2020



image of prof James Zou
November 2020

Professor James Zou, says that as algorithms compete for clicks and the associated user data, they become more specialized for subpopulations that gravitate to their sites. This can have serious implications for both companies and consumers.

This is described in a paper "Competing AI: How does competition feedback affect machine learning?", written by Antonio Ginart (EE PhD candidate), Eva Zhang, and professor James Zou.

James' team recognized that there's a feedback dynamic at play if companies' machine learning algorithms are competing for users or customers and at the same time using customer data to train their model. "By winning customers, they're getting a new set of data from those customers, and then by updating their models on this new set of data, they're actually then changing the model and biasing it toward the new customers they've won over," says Antonio Ginart.

In terms of next steps, the team is looking at the effect that buying datasets (rather than collecting data only from customers) might have on algorithmic competition. James is also interested in identifying some prescriptive solutions that his team can recommend to policymakers or individual companies. "What do we do to reduce these kinds of biases now that we have identified the problem?" he says.

"This is still very new and quite cutting-edge work," James says. "I hope this paper sparks researchers to study competition between AI algorithms, as well as the social impact of that competition."


Excerpted from "When Algorithms Compete, Who Wins?"

Stanford HAI's mission is to advance AI research, education, policy and practice to improve the human condition.

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 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:[...]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 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 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]


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)

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)

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. 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 Lei Gu, PhD
July 2020

Congratulations to postdoctoral research fellow Lei Gu, PhD '19. Lei received the IEEE Power Electronics Society (PELS) PhD Thesis Talk Award. His talk, "Design Considerations for Radio Frequency Power Converters" can be viewed online at

Dr. Lei Gu is a researcher in professor Juan Rivas-Davila's SUPER Lab.

The IEEE PELS Digital Media Committee invites video submissions for the IEEE PELS Prize Ph.D. Thesis Talk. The goal of this competition is to showcase Ph.D. projects to the entire power electronics community - both in academia and industry. Five IEEE PELS P3 Talks are awarded each year. (source:

Read more about the P3 award


Please join us in congratulating Lei on his award!



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