student

image of professor Eric Pop
June 2021

Professor Eric Pop and team describe the ability to produce nanoscale flexible electronics In their paper, "High-Performance Flexible Nanoscale Transistors Based on Transition Metal Dichalcogenides," published in Nature Electronics. Flexible electronics promise bendable, shapeable, yet energy-efficient computer circuits that can be worn on or implanted in the human body to perform myriad health-related tasks. Future variations future of the circuits will communicate wirelessly with the outside world – another large leap toward viability for flextronics, particularly those implanted in the human body or integrated deep within other devices connected to the internet of things.

[...]
With a prototype and patent application complete, postdoc Alwin Daus and Professor Eric Pop have moved on to their next challenges of refining the devices. They have built similar transistors using two other atomically thin semiconductors (MoSe2 and WSe2) to demonstrate the broad applicability of the technique.

Meanwhile, Alwin said that he is looking into integrating radio circuitry with the devices, which will allow future variations to communicate wirelessly with the outside world – another large leap toward viability for flextronics, particularly those implanted in the human body or integrated deep within other devices connected to the internet of things.

Eric reports, "This is more than a promising production technique. We've achieved flexibility, density, high performance and low power – all at the same time. This work will hopefully move the technology forward on several levels."

Co-authors include postdoctoral scholars Sam Vaziri and Kevin Brenner, EE doctoral candidates Victoria Chen, Çağıl Köroğlu, Ryan Grady, Connor Bailey and Kirstin Schauble, and research scientist Hye Ryoung Lee. Pop Lab People

 

Excerpted from "Stanford researchers develop new manufacturing technique for flexible electronics" Stanford News

June 2021
Capstone Course Award

Design Award, Capstone Courses

This award is given to the outstanding design project in one of the capstone design courses. This year the award goes to Ryan Ressmeyer for his EE 264 project: "Orthogonal Frequency Division Multiplexer".

Centennial Award

Centennial Teaching Award

The School of Engineering's Centennial TA Award is given to students to recognize their outstanding contributions to teaching. The 2021 Centennial TA award recipients are: Shubham ChandakAmy FritzSiavash KananianAnna NunesErik Van.

JEDI award

Justice, Equity, Diversity & Inclusion (JEDI) Graduation Awards

The School of Engineering's Justice, Equity, Diversity & Inclusion Graduation Awards recognizes the exceptional work done by graduating graduate students in outreach and mentorship for underserved and underrepresented communities with the goal of improving the accessibility of STEMM. This includes the fields of Science, Technology, Engineering, Math, and Medicine.
The JEDI Graduation Award recipients are Crystal NattooCindy NguyenSean Peters.

Gibbons Award

James F. Gibbons Outstanding Student Teaching Award 2021

The James F. Gibbons Award for Outstanding Student Teaching Award highlights students who have been nominated by faculty and peers for their extraordinary service as teaching assistants. Thank you for your tremendous work in our department – Anna NunesElizabeth Chen, and Erik Van.

Phil Levis Chairs Award 2021

Chair's Award for Outstanding Contributions to Undergraduate Education

Congratulations to Professor Phil Levis - Over the last few years, Phil has led a team of students on the FLIGHT project, it's a large-scale electromechanical art installation for the Packard [building] stairwell, right behind me. It consists of 76 Fractal Flyers, each of which is a programmable shape inspired by the geometry of the stairwell, with hundreds of LEDs and moving dichroic surfaces that cast colored reflections and shadows.

Terman Award

Frederick E. Terman Engineering Scholastic Award

The Terman Award is presented to the top 5% of each senior class in the School of Engineering. We are pleased to congratulate our 2021 Terman Scholars for their outstanding work. Joaquin BorggioRahul Lall, and Ryan Ressmeyer.

TBP Teaching Award

Tau Beta Pi (TBP) Teaching Honor Roll (The Engineering Society)

Professor John Pauly and Assistant Professor Mary Wootters - This award recognizes outstanding faculty instructors in the School of Engineering. These faculty instructors were nominated by Stanford students to recognize their distinguished teaching, superior mentorship, and/or any other notable contribution to engineering education at Stanford.

TBP Student Honor Roll

Tau Beta Pi (TBP) Honor Roll (The Engineering Society)

The California Gamma chapter of Tau Beta Pi at Stanford University serves the Stanford community by acting as a representative entity for academic excellence, leadership, and continued service to our community. Tau Beta Pi is the only engineering honor society representing the entire engineering profession. Congratulations to
  • Vineet Edupuganti, EE-BS
  • Yong (Collin) Kwon, EE-BS
  • Rahul Lall, EE-BSH
  • Michael Oduoza, EE-BS
  • Kangrui Xue, EE-BS

image of PhD candidate Riley Culberg
April 2021

Research by EE PhD candidate Riley Culberg and Prof. Dustin Schroeder is revealing the long-term impact of vast ice melt in the Arctic.

Using a new approach to ice-penetrating radar data, researchers show that this melting left behind a contiguous layer of refrozen ice inside the snowpack, including near the middle of the ice sheet where surface melting is usually minimal. Most importantly, the formation of the melt layer changed the ice sheet's behavior by reducing its ability to store future meltwater. The research appears in Nature Communications.

"When you have these extreme, one-off melt years, it's not just adding more to Greenland's contribution to sea-level rise in that year – it's also creating these persistent structural changes in the ice sheet itself," said lead author Riley Culberg, EE PhD candidate. "This continental-scale picture helps us understand what kind of melt and snow conditions allowed this layer to form."

Airborne radar data, a major expansion to single-site field observations on the icy poles, is typically used to study the bottom of the ice sheet. But by pushing past technical and computational limitations through advanced modeling, the team was able to reanalyze radar data collected by flights from NASA's Operation IceBridge from 2012 to 2017 to interpret melt near the surface of the ice sheet, at a depth up to about 50 feet.

"Once those challenges were overcome, all of a sudden, we started seeing meltwater ice layers near the surface of the ice sheet," EE courtesy professor, Dustin Schroeder said. "It turns out we've been building records that, as a community, we didn't fully realize we were making."

Melting ice sheets and glaciers are the biggest contributors to sea-level rise – and the most complex elements to incorporate into climate model projections. Ice sheet regions that haven't experienced extreme melt can store meltwater in the upper 150 feet, thereby preventing it from flowing into the ocean. A melt layer like the one from 2012 can reduce the storage capacity to about 15 feet in some parts of the Greenland Ice Sheet, according to the research.

 

 

Excerpted from "Stanford researchers reveal the long-term impacts of extreme melt on Greenland Ice Sheet", Stanford News, April 20, 2021

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

 

Related

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.


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