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.

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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: 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 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. 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 IEEE-PELS.org.

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: ieee-pels.org/images/files/pdf/P3_Talks_Write_Up_4-27.pdf

Read more about the P3 award

 

Please join us in congratulating Lei on his award!

 

Image credit: Courtesy Bechtel International Center
July 2020

Congratulations to EE MS candidate Nicolo Zulaybar! He is a recipient of the David L. Boren Fellowship.

As a Boren Fellow, he will study Mandarin at the Inter-University Program for Chinese Studies at Tsinghua University in Beijing. Through the fellowship, he intends to further improve his language skills by auditing Chinese lectures and getting involved with student organizations.

"It's an honor and privilege to receive this Boren Fellowship," Zulaybar said. "In this time of global challenges, it feels all the more urgent that people get involved with government to solve the problems facing their communities. I appreciate how Boren both supports my placement in federal service and prepares me for my role with cultural skills I can use to problem-solve with America's international partners. It complements my technical education in this way."

Zulaybar is from Los Angeles. He graduated from Stanford in 2018 with a bachelor's degree in chemistry. As an undergraduate, he was a research associate in Assistant Professor of Chemistry Yan Xia's Polymer Chemistry Lab, as well as a member of the Alpha Chi Sigma professional fraternity.

Nicolo is one of five Stanford students who are the recipients of the 2020 Boren Awards. Two are graduate students, and will receive David L. Boren Fellowships and three are undergraduates who will receive David L. Boren Scholarships.

Congratulations to Nicolo and his Boren Awards colleagues!

 

Excerpted from "Five Stanford students receive 2020 Boren Awards," June 25, 2020

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.

 

RELATED RESOURCES

Diversity in EE statement 
Black Lives Matter, Stanford Student Affairs
Coalition of Black Student Organizations Asks to University Administration on Campus Police 

Educational resources for anti-racism:

Support for Stanford students: 

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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