Faculty

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"]

image of prof emeritus Martin E. Hellman
January 2021

Congratulations to Professor Emeritus Martin Hellman. He has been selected as a 2020 Association for Computing Machinery (ACM) Fellow.

The ACM Fellows program recognizes the top 1% of ACM Members for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community. Fellows are nominated by their peers, with nominations reviewed by a distinguished selection committee.

"This year our task in selecting the 2020 Fellows was a little more challenging, as we had a record number of nominations from around the world," explained ACM President Gabriele Kotsis. "The 2020 ACM Fellows have demonstrated excellence across many disciplines of computing. These men and women have made pivotal contributions to technologies that are transforming whole industries, as well as our personal lives. We fully expect that these new ACM Fellows will continue in the vanguard in their respective fields."

 

Excerpted from ACM.org's "2020 ACM Fellows Recognized for Work that Underpins Today's Computing Innovations".

 

Please join us in congratulating Marty for this well-deserved recognition.

 

Related News

 

image of prof H. Tom Soh
January 2021

EE Professor Tom Soh, in collaboration with Professor Eric Appel, and colleagues have developed a technology that can provide real time diagnostic information. Their device, which they've dubbed the "Real-time ELISA," is able to perform many blood tests very quickly and then stitch the individual results together to enable continuous, real-time monitoring of a patient's blood chemistry. Instead of a snapshot, the researchers end up with something more like a movie.

"A blood test is great, but it can't tell you, for example, whether insulin or glucose levels are increasing or decreasing in a patient," said Professor Tom Soh. "Knowing the direction of change is important."

In their recent study, "A fluorescence sandwich immunoassay for the real-time continuous detection of glucose and insulin in live animals", published in the journal Nature Biomedical Engineering, the researchers used the device to simultaneously detect insulin and glucose levels in living diabetic laboratory rats. But the researchers say their tool is capable of so much more because it can be easily modified to monitor virtually any protein or disease biomarker of interest.

Authors are PhD candidates Mahla Poudineh, Caitlin L. Maikawa, Eric Yue Ma, Jing Pan, Dan Mamerow, Yan Hang, Sam W. Baker, Ahmad Beirami, Alex Yoshikawa, researcher Michael Eisenstein, Professor Seung Kim, and Professor Jelena Vučković.

Technologically, the system relies upon an existing technology called Enzyme-linked Immunosorbent Assay – ELISA ("ee-LYZ-ah") for short. ELISA has been the "gold standard" of biomolecular detection since the early 1970s and can identify virtually any peptide, protein, antibody or hormone in the blood. An ELISA assay is good at identifying allergies, for instance. It is also used to spot viruses like HIV, West Nile and the SARS-CoV-2 coronavirus that causes COVID-19.

The Real-time ELISA is essentially an entire lab within a chip with tiny pipes and valves no wider than a human hair. An intravenous needle directs blood from the patient into the device's tiny circuits where ELISA is performed over and over.

 Excerpted from "Stanford researchers develop lab-on-a-chip that turns blood test snapshots into continuous movies", December 21, 2020.

Related News

image of prof Stephen P. Boyd
January 2021

The Boyd group's CVXGEN software has been used in all SpaceX Falcon 9 first stage landings.  

From spacex.com: Falcon 9 is a reusable, two-stage rocket designed and manufactured by SpaceX for the reliable and safe transport of people and payloads into Earth orbit and beyond. Falcon 9 is the world's first orbital class reusable rocket. Reusability allows SpaceX to refly the most expensive parts of the rocket, which in turn drives down the cost of space access.

On December 9, Starship serial number 8 (SN8) lifted off from a Cameron County launch pad and successfully ascended, transitioned propellant, and performed its landing flip maneuver with precise flap control to reach its landing point. Low pressure in the fuel header tank during the landing burn led to high touchdown velocity resulting in a hard (and exciting!) landing. Re-watch SN8's flight here

 

Although Stephen doesn't plan to travel to Mars, he's thrilled that one day, some of his and his students' work will.

image of profs Wetzstein, Fan, Miller
December 2020

Professors Gordon Wetzstein, Shanhui Fan, and David A. B. Miller collaborated with faculty at several other institutions, to publish, "Inference in artificial intelligence with deep optics and photonics". 

Abstract: Artificial intelligence tasks across numerous applications require accelerators for fast and low-power execution. Optical computing systems may be able to meet these domain-specific needs but, despite half a century of research, general-purpose optical computing systems have yet to mature into a practical technology. Artificial intelligence inference, however, especially for visual computing applications, may offer opportunities for inference based on optical and photonic systems. In this Perspective, we review recent work on optical computing for artificial intelligence applications and discuss its promise and challenges.

Additional authors are Aydogan Ozcan, Sylvain Gigan, Dirk Englund, Marin Soljačić, Cornelia Denz, and Demetri Psaltis.

 

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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

 

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image of prof Nick McKeown
December 2020
Professor Nick McKeown will receive the 2021 IEEE Alexander Graham Bell medal, for exceptional contributions to communications and networking sciences and engineering. The IEEE Alexander Graham Bell Medal was established in 1976, in commemoration of the centennial of the telephone's invention, to provide recognition for outstanding contributions to telecommunications.
 
The award will be presented to Nick at a future IEEE Honors Ceremony.
 
Nick researches techniques to improve the Internet. Most of this work has focused on the architecture, design, analysis, and implementation of high-performance Internet switches and routers. More recently, his interests have broadened to include network architecture, backbone network design, congestion control; and how the Internet might be redesigned if we were to start with a clean slate.
 
Please join us in congratulating Nick on this well-deserved honor!
  

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