2021

image of prof Nick McKeown
March 2021

Later this year, in a lab in the Durand Building, a team of researchers will demonstrate how a tight formation of computer-controlled drones can be managed with precision even when the 5G network controlling it is under continual cyberattack. The demo's ultimate success or failure will depend on the ability of an experimental network control technology to detect the hacks and defeat them within a second to safeguard the navigation systems.

On hand to observe this demonstration will be officials from DARPA, the Defense Advanced Research Projects Agency, the government agency that's underwriting Project Pronto. The $30 million effort, led by Professor Nick McKeown, is largely funded and technically supported through the nonprofit Open Networking Foundation (ONF), with help from Princeton and Cornell universities. Their goal: to make sure that the wireless world – namely, 5G networks that will support the autonomous planes, trains and automobiles of the future – remains secure and reliable as the wired networks we rely on today.

This is no small task and the consequences could not be greater. The transition to 5G will affect every device connected to the internet and, by extension, the lives of every person who relies on such networks for safe transportation. But, as recent intrusions into wired networks have shown, serious vulnerabilities exist.

The pending Pronto demo is designed to solve that problem by way of a fix that McKeown and colleagues have devised that wraps a virtually instantaneous shield around wirelessly accessible computers using a technology known as software-defined networking (SDN).

 

Excerpted from "A new Stanford initiative aims to ensure 5G networks are reliable and secure", Stanford News, March 24, 2021.

 

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image of PRONTO members

 

 

image of LaToya Powell, graduate admission officer
March 2021
Congratulations to LaToya Powell, Graduate Admissions Officer. She has been awarded the 2020 School of Engineering Staff Award for Innovation
 
LaToya is recognized for her achievements in the creation of the dEEbug peer tutoring program that started Autumn 2020. The program offers peer tutoring to current and prospective undergraduates in electrical engineering. The program's goal is to provide a welcoming and supportive environment for all students, especially those from underrepresented groups. In addition to the dEEbug program, she also helped our students create a new student group called SURG(E)2, which stands for Students for Underrepresented Groups in Electrical Engineering.

LaToya is a tireless advocate for our students and is very passionate about diversity and inclusion.  In addition to managing EE’s graduate admissions and playing an active role in the programs and student groups, she has also served on EE's Culture, Equity, and Inclusion (CEI) Committee. We thank LaToya for her dedication to students and for all of her contributions to our department.

 
Please join us in congratulating LaToya on this very well deserved award!

image of prof. David Miller
February 2021

Professor David Miller joins Professor Russ Altman on The Future of Everything podcast. David's research interests include the use of optics in switching, interconnection, communications, computing, and sensing systems, physics and applications of quantum well optics and optoelectronics, and fundamental features and limits for optics and nanophotonics in communications and information processing. 

In this podcast he explains the remarkable potential of using light instead of electricity in computation.

 

"A silicon chip these days looks like six Manhattan grids stacked atop one another," Miller says of the challenge facing today's technology. Photonics holds the promise of more powerful computing by beaming tiny packets of photons through light-bearing conduits that carry 100,000 times more data than today's comparable wires, and it can do it using far less energy, too.

Before that day can arrive, however, Miller says photonic components need to become much smaller and less expensive to compete with the sheer scale advantages silicon enjoys, and that will require investment. But, for once, a way forward is there for the asking, as Miller tells bioengineer Russ Altman, host of Stanford Engineering's The Future of Everything podcast. Listen and subscribe here.

image of our exemplary staff*
February 2021

Congratulations to Beverly Davis, John DeSilva, Kenny Green, Helen Niu, and Lisa Sickorez! They received nominations from faculty, staff and students, who appreciate their commitment and willingness to go above and beyond the ordinary! Excerpts from the nominations are below.

The staff gift card recipients make profound and positive impact in the electrical engineering department's everyday work and academic environment.
Please join us in congratulating them.

Beverly Davis

  • Beverly leads with efficiency and administrative experience.
  • She lights up our office meetings with her exuberance and kindness; making our lab group a warm and welcoming community.

John DeSilva

  • John is a "super-facilitator"!
  • He is proactive and keeps an eye our for things that need to be done in the office, whether or not related to IT. He is always willing to say 'yes'.

Kenny Green

  • He's always very responsive whenever we have any issue with our lab or office space. Glad I get to work with him!
  • Kenny's always very prompt and knowledgeable, not to mention an all around funny and nice guy!

Helen Niu

  • Once Helen undertakes a task, one can always be sure the task will be completed correctly and in time.
  • She is a thorough, knowledgeable and responsible individual.

Lisa Sickorez

  • No doubt Lisa is the hardest working person in EE! She doesn't let anything fall through the cracks.
  • Lisa monitors a great deal of department business and keeps all 120+ of us on track. I really appreciate her proactivity and thoughtfulness.

 image of our exemplary staff!*

*All photos were taken from our photo library and are pre-Covid.

The Staff Gift Card Bonus Program is sponsored by the School of Engineering. Each year, the EE department receives several gift cards to distribute to staff members who have been recognized for going above and beyond their role. Staff are chosen from nominations received from faculty, students, and staff. Past nominations are eligible for future months.

Consider nominating a deserving staff person, or group, today. Each recipient receives a $50 Amazon gift card. Nominations can be made at any time. There are no restrictions on the quantity, persons or groups that you can nominate. Submitters are asked to include a citation of how the group or person went above and beyond. The submitter can choose to remain anonymous.

Nominate a deserving colleague today.

image of prof Hennessy
February 2021
Professor John Hennessy and Professor David Patterson (University of California, Berkeley) have received the BBVA Frontiers of Knowledge Award in Information and Communications Technologies. Their citation reads, "for turning computer architecture into a science and designing the processors that power today’s devices. […] They conceived the scientific field of computer architecture, motivated a systematic and quantitative design approach to system performance, created a style of reduced instruction set processors that has transformed how industry builds computer systems, and have made transformative advancements in computer reliability and in large-scale system coherence”. 
 
“Professors John Hennessy and David Patterson are synonymous with the inception and formalization of this field,” the citation reads. “Before their work, the design of computers – and in particular the measurement of computer performance – was more of an art than a science, and practitioners lacked a set of repeatable principles to conceptualize and evaluate computer designs. Patterson and Hennessy provided, for the first time, a conceptual framework that gave the field a grounded approach towards measuring a computer’s performance, energy efficiency, and complexity.”

The new laureates’ scientific contributions had their didactic parallel in a landmark textbook, Computer Architecture: A Quantitative Approach, which three decades on from its first release and after six editions with regularly updated content, is still considered “the bible” for the discipline in universities around the world.
 
 
Please join us in congratulating John on his extraordinary contributions to teaching, industry, and innovation.
 
 
 
Excerpted from: BBVA Foundation Frontiers of Knowledge Awards, February 2021.

image of prof. Kunle Olukotun
February 2021

Professor Kunle Olukotun has been elected to the National Academy of Engineering, "for contributions to on-chip multiprocessor architectures and advancement to commercial realization." Kunle will be formally inducted during the NAE's annual meeting on October 3.

Election to the National Academy of Engineering is among the highest professional distinctions accorded to an engineer. Academy membership honors those who have made outstanding contributions to "engineering research, practice, or education, including, where appropriate, significant contributions to the engineering literature" and to "the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education."

Hearty congratulations to Kunle on this well-deserved recognition!

 

Read National Academy of Engineering Press Release

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 prof Shanhui Fan
February 2021

Professor Shanhui Fan and his team have developed a wireless charging system that could transmit electricity even as the distance to the receiver changes. They have incorporated an amplifier and feedback resistor that allows the system to automatically adjust its operating frequency as the distance between the charger and the moving object changes.

By replacing their original amplifier with a far more efficient switch mode amplifier, they boosted efficiency. The latest iteration can wirelessly transmit 10 watts of electricity over a distance of 2 or 3 feet.

Shanhui says there aren't any fundamental obstacles to scaling up a system to transmit the tens or hundreds of kilowatts that a car would need. In fact, he claims the system is more than fast enough to resupply a speeding automobile.

 

Excerpted from "Engineers Race to Develop Wireless Charging Technology"

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 3 EE faculty: Subhasish Mitra, Mary Wootters, and H.S. Philip Wong
January 2021

Professors Subhasish Mitra, H.S. Philip Wong, Mary Wootters, and their students recently published "Illusion of large on-chip memory by networked computing chips for neural network inference", in Nature.

Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms. But efforts to build AI-capable chips for mobile devices have so far hit a wall – the so-called "memory wall" that separates data processing and memory chips that must work together to meet the massive and continually growing computational demands imposed by AI.

"Transactions between processors and memory can consume 95 percent of the energy needed to do machine learning and AI, and that severely limits battery life," said Professor Subhasish Mitra.

The team has designed a system that can run AI tasks faster, and with less energy, by harnessing eight hybrid chips, each with its own data processor built right next to its own memory storage.

This paper builds on their prior development of a new memory technology, called RRAM, that stores data even when power is switched off – like flash memory – only faster and more energy efficiently. Their RRAM advance enabled the Stanford researchers to develop an earlier generation of hybrid chips that worked alone. Their latest design incorporates a critical new element: algorithms that meld the eight, separate hybrid chips into one energy-efficient AI-processing engine.

Additional authors are Robert M. Radway, Andrew Bartolo, Paul C. Jolly, Zainab F. Khan, Binh Q. Le, Pulkit Tandon, Tony F. Wu, Yunfeng Xin, Elisa Vianello, Pascal Vivet, Etienne Nowak, Mohamed M. Sabry Aly, and Edith Beigne.

[Excerpted from "Stanford researchers combine processors and memory on multiple hybrid chips to run AI on battery-powered smart devices"]

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