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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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image of Grayson Zulof, PhD and Thaibao Peter Phan, PhD
November 2020

Congratulations to Thaibao Phan (PhD candidate) and Grayson Zulauf (PhD '20)! Their paper was one of three selected to receive the Best Paper Award at the Control and Modeling in Power Electronics (COMPEL) Workshop 2020.

Grayson Zulauf (PhD '20) was a member of professor Juan Rivas-Davila's SUPER Lab, and Thaibao Phan (PhD candidate) is a member of professor Jonathan Fan's Fan Lab.

 

Their collaborative paper, "1 kW, Multi-MHz Wireless Charging for Electric Transportation" will be published on IEEExPLORE in the upcoming weeks.

 

Please join us in congratulating them on this wonderful accomplishment!

image of prof Kwabena Boahen
November 2020

Professor Kwabena Boahen builds highly efficient "neuromorphic" supercomputers modeled on the human brain.

He hopes they will drive the artificial intelligence future. He uses an analogy when describing the goal of his work: "It's LA versus Manhattan."

Kwabena means structurally. Today's chips are two dimensional — flat and spread out, like LA. Tomorrow's chips will be stacked, like the floors of the skyscrapers on a New York block. In this analogy, the humans are the electrons shuffling data back and forth. The shorter distances they have to travel to work, and the more they can accomplish before traveling home, will drive profound leaps in energy efficiency. The consequences could not be greater. Kwabena says that the lean chips he imagines could prove tens-of-thousands times less expensive to operate than today's power hogs.

To learn how it works, listen in as Kwabena Boahen describes neuromorphic computing to fellow bioengineer Russ Altman in a recent episode of Stanford Engineering's The Future of Everything podcast.

 

Excerpted from Stanford Engineering's Research & Ideas

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 prof. Chelsea Finn
November 2020

Congratulations to Professor Chelsea Finn. She has been awarded an inaugural Samsung AI Researcher of the Year award. Presented at Samsung AI Forum 2020, the five recipients are AI researchers from around the world.

At the event, Chelsea's lecture was titled, "From Few-Shot Adaptation to Uncovering Symmetries". In her lecture, she introduced meta learning technologies in which AI, in spite of changes in data, can adapt swiftly to untrained data, and proceeded to share success stories of the application of these technologies in the areas of robotics and new drug candidate material design.

Chelsea's research interests lie in the ability to enable robots and other agents to develop broadly intelligent behavior through learning and interaction. Her work lies at the intersection of machine learning and robotic control, including topics such as end-to-end learning of visual perception and robotic manipulation skills, deep reinforcement learning of general skills from autonomously collected experience, and meta-learning algorithms that can enable fast learning of new concepts and behaviors.

Please join us in congratulating Chelsea on this well-deserved distinction! Additional awards went to Prof. Kyunghyun Cho (New York University), Prof. Seth Flaxman (Imperial College London), Prof. Jiajun Wu (Stanford), and Prof. Cho-Jui Hsieh (UCLA).

Excerpted from Samsung Newsroom, "[Samsung AI Forum 2020] Day 1: How AI Can Make a Meaningful Impact on Real World Issues"

 

Related News

image of prof. Dorsa Sadigh
November 2020

Professor Dorsa Sadigh and her team have integrated algorithms in a novel way that makes controlling assistive robotic arms faster and easier. The team hopes their research will enable people with disabilities to conduct everyday tasks on their own– for example, cooking and eating.

Dorsa's team, which included engineering graduate student Hong Jun Jeon and computer science postdoctoral scholar Dylan P. Losey, developed a controller that blends two artificial intelligence algorithms. The first, which was developed by Dorsa's group, enables control in two dimensions on a joystick without the need to switch between modes. It uses contextual cues to determine whether a user is reaching for a doorknob or a drinking cup, for example. Then, as the robot arm nears its destination, the second algorithm kicks in to allow more precise movements, with control shared between the human and the robot.

In shared autonomy, the robot begins with a set of "beliefs" about what the controller is telling it to do and gains confidence about the goal as additional instructions are given. Since robots aren't actually sentient, these beliefs are really just probabilities. For example, faced with two cups of water, a robot might begin with a belief that there's an even chance it should pick up either one. But as the joystick directs it toward one cup and away from the other, the robot gains confidence about the goal and can begin to take over – sharing autonomy with the user to more precisely control the robot arm. The amount of control the robot takes on is probabilistic as well: If the robot has 80 percent confidence that it's going to cup A rather than cup B, it will take 80 percent of the control while the human still has 20 percent, explains Professor Dorsa Sadigh.

 

Excerpted from HAI (Human-Centered Artificial Intelligence), "Assistive Feeding: AI Improves Control of Robot Arms"

Video, "Shared Autonomy with Learned Latent Actions"

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.

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February 2014

Three staff members each received a $50 Visa card in recognition of their extraordinary efforts as part of the department’s 2014 Staff Gift Card Bonus Program. The EE department received several nominations in January, and nominations from 2013 were also considered.

Following are January’s gift card recipients and some of the comments from their nominators:

Ann Guerra, Faculty Administrator

  • “She is very kind to students and always enthusiastic to help students… every time we need emergent help, she is willing to give us a hand.”
  • “Ann helps anyone who goes to her for help with anything, sometimes when it’s beyond her duty.” 

Teresa Nguyen, Student Accounting Associate

  • “She stays on top of our many, many student financial issues, is an extremely reliable source of information and is super friendly.”
  • “Teresa’s cheerful disposition, her determination, and her professionalism seem to go above and beyond what is simply required.”

Helen Niu, Faculty Administrator

  • “Helen is always a pleasure to work with.”
  • “She goes the extra mile in her dealings with me, which is very much appreciated.”

The School of Engineering once again gave the EE department several gift cards to distribute to staff members who are recognized for going above and beyond. More people will be recognized next month, and past nominations will still be eligible for future months. EE faculty, staff and students are welcome to nominate a deserving staff person by visitinghttps://gradapps.stanford.edu/NotableStaff/nomination/create.

Ann Guerra  Teresa Nguyen  Helen Niu

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