Angad Rekhi (PhD ’20), Allen Building, Stanford EE
April 2018

Come to the Allen Building and view the original wall art designed by EE PhD candidate Angad Rekhi.

Angad Rekhi has spent many hours in the Allen Building, researching & reflecting on low power circuits and systems. As a member of the Arbabian Lab, he also helped develop a low power wake up receiver for IoT.

Angad was intrigued by the call to submit a design for the Allen Building entrance. The design challenge opened in October 2017, encouraging students, faculty & staff to create a visually representative piece that illustrates the research done within the Allen Buildings.

Angad's concept illustrates the entire electrical engineering design hierarchy – from a device-focused perspective at the far left, through chip and board design in the center, to end applications on the far right. "Design at all levels occurs in the Allen Buildings. They contain spaces and labs for the full process of design and build, from concept to product," states Angad. "For example, ExFab allows rapid prototyping of microelectronics, and the SPF (System Prototyping Facility) supports electronic sub-system design. So really, anyone on campus can go from idea to product within these walls."

Angad Rekhi (PhD ’20) posing with his wall design in the Allen Building

Please come by to view Angad's artwork!
The wall art is a creative way to greet those interested in leveraging Allen's spectrum of device development, and those who are yet to discover what's possible inside of the Allen Buildings.

About the Allen Buildings
At the time of it's construction, the building (originally named CIS) was considered "the best example of Stanford's resident architect's progressive historicism," acknowledging the blending of 1880s architectural style with 20th and 21st century Stanford architecture. The "building was designed by Antoine Predock, an Albuquerque architect with a reputation for New Age structures that rise organically from the Southwest's desert landscape." The building is named after Microsoft's co-founder, Paul G. Allen.

Together, the Allen and Gates buildings anchor the north side of the Engineering Quad. Twenty years ago, former Dean James Gibbons said "It has been a 10-year dream of ours to draw electrical engineering and computer science – the hardware and the software ­– together in an environment surrounded by such things as the biological sciences and medicine." And by spring 2019, the Neurosciences and ChEM-H research facility is expected to be complete, just on the north side of the Allen Buildings.

Related Links

Nikhil Garg, EE PhD '20 interdisciplinary research using machine-learning
April 2018

Lead author Nikhil Garg (PhD candidate '20) demonstrates that word embeddings can be used as a powerful tool to quantify historical trends and social change. His research team developed metrics based on word embeddings to characterize how gender stereotypes and attitudes toward ethnic minorities in the United States evolved during the 20th and 21st centuries starting from 1910. Their framework opens up a fruitful intersection between machine learning and quantitative social science.

Nikhil co-authored the paper with history Professor Londa Schiebinger, linguistics and computer science Professor Dan Jurafsky and biomedical data science Professor James Zou.

Their research shows that, over the past century, linguistic changes in gender and ethnic stereotypes correlated with major social movements and demographic changes in the U.S. Census data.

The researchers used word embeddings – an algorithmic technique that can map relationships and associations between words – to measure changes in gender and ethnic stereotypes over the past century in the United States. They analyzed large databases of American books, newspapers and other texts and looked at how those linguistic changes correlated with actual U.S. Census demographic data and major social shifts such as the women's movement in the 1960s and the increase in Asian immigration, according to the research.

"Word embeddings can be used as a microscope to study historical changes in stereotypes in our society," said James Zou, a courtesy professor of electrical engineering. "Our prior research has shown that embeddings effectively capture existing stereotypes and that those biases can be systematically removed. But we think that, instead of removing those stereotypes, we can also use embeddings as a historical lens for quantitative, linguistic and sociological analyses of biases."

"This type of research opens all kinds of doors to us," Schiebinger said. "It provides a new level of evidence that allow humanities scholars to go after questions about the evolution of stereotypes and biases at a scale that has never been done before."

"The starkness of the change in stereotypes stood out to me," Garg said. "When you study history, you learn about propaganda campaigns and these outdated views of foreign groups. But how much the literature produced at the time reflected those stereotypes was hard to appreciate." 

The new research illuminates the value of interdisciplinary teamwork between humanities and the sciences, researchers said.

"This led to a very interesting and fruitful collaboration," Schiebinger said, adding that members of the group are working on further research together. "It underscores the importance of humanists and computer scientists working together. There is a power to these new machine-learning methods in humanities research that is just being understood." 


Proceedings of the National Academy of Sciences, "Word embeddings quantify 100 years of gender and ethnic stereotypes" April 3,2018.  

Excerpted from Stanford News, "Stanford researchers use machine-learning algorithm to measure changes in gender, ethnic bias in U.S." April 3, 2018.




Ana Klimovic, EE PhD '19
April 2018

Congratulations to Ana Klimovic (PhD candidate '19), Professor Christos Kozyrakis, and postdoc Heiner Litz. They won the 2018 Memorable Paper Award for System Architecture and Applications at the 9th Annual Non-Volatile Memories Workshop (NVMW) hosted by the University of California, San Diego. Their paper, "ReFlex: Remote Flash == Local Flash" was one of six finalists for the award selected from over 80 papers submitted to the workshop.

About The Memorable Paper Award

The Memorable Paper Award recognizes the best recent research on non-volatile memories published throughout the world. It is given annually to outstanding research published in the last two years that is expected to have substantial impact on the study of non-volatile memories. To be eligible, the paper must have been published in peer-reviewed venue in the last two years and the lead researcher must have been a student at the time.

About the Non-Volatile Memories Workshop

The Non-Volatile Memories Workshop is the world's premier venue for research into how to use non-volatile memory technology to improve the performance, reliability, and efficiency of computing systems. It was founded in 2010 by Dr. Paul Siegel and Dr. Steven Swanson of the University of California, San Diego's Jacob School of engineering. The workshop is a co-production of the Center for Magnetic Recording Research and the Non-Volatile Systems Laboratory at UC San Diego. More information, including a detailed program, is available at

Please join us in congratulating Ana, Christos, and Heiner on their award! 

Award winner Ana Kilmovic (center) with general chairs of NVMW'18 Professor Steven Swanson (left) and Professor Paul Siegel (right), both of UCSD.

Paper Summary:

Internet companies such as Facebook and Google host trillions of messages, photos, and videos for their users. Hence, they need storage systems that are massive in scale, fast to access, and cost effective. Scale is achieved by hosting internet services in datacenters with thousands of machines, each contributing its local storage to the global data pool. Speed is achieved by selectively replacing slow hard disks in machines with Flash storage devices that can serve data accesses with 100x lower latency and 10,000x higher throughput.

However, Flash makes it difficult to build a cost-effective storage system. Flash devices are typically underutilized in terms of capacity and throughput due to the imbalance in the compute and storage requirements of the internet services running on each machine. In the past, datacenter operators dealt with the same challenge for disks by allowing services running on each machine to allocate storage over the network on any disk with spare capacity and bandwidth in the datacenter. Remote (over the network) access to disks enables utilizing all available capacity and throughput. Past efforts to implement similar remote access systems for Flash devices have run into significant challenges. Network protocol processing at the throughput of Flash devices requires a large number of processor cores and adds overheads that cancel out the latency advantages of using Flash. Moreover, when two remote machines access the same Flash device, interference between the two access streams can lead to unpredictable performance degradation.

To address these challenges, researchers Ana Klimovic, Heiner Litz and Christos Kozyrakis developed a software system called ReFlex. ReFlex enables high performance access to remote Flash storage with minimal compute resources and provides predictable performance for multiple services sharing a Flash device over the network. Using a single processing core, the system can process up to 850,000 requests per second which is 11x more than a traditional Linux network storage system. ReFlex makes remote Flash look like local Flash to applications, making it easy for a service running on a particular machine to use spare Flash capacity and bandwidth on other machines in the datacenter. To provide predictable performance when multiple remote machines access the same Flash device, ReFlex uses a novel scheduler to process incoming requests in an interference-aware manner.

ReFlex is having an increasing impact in industry and, in collaboration with IBM Research, has been integrated into the Apache Crail distributed storage system. This integration allows popular data analytics frameworks to leverage ReFlex to improve their resource efficiency while maintaining high, predictable performance. ReFlex is also being ported to a system on chip (SoC) platform by Broadcom Limited. ReFlex is open-source software and available at:


Excerpted from the full NVMW'18 press release.

April 2018

The 2018 EE Admit Weekend welcomed 60 newly admitted PhD graduate students. The 2-day event connected admitted students with current students, faculty and staff. Several student-faculty sessions occurred throughout the 2 days, fostering a range of discussions from research topics to housing. Admitted students also participated in research lab and campus tours. Friday concluded with a PhD student research poster session and dinner with faculty.

Twenty-two PhD research projects were presented in this year's poster session. The posters are judged on oral presentation, visual quality, and clarity of presentation – all within a one minute timeframe. Judges include staff, faculty and students, and select one entry from EE's core research areas.

The 2018 poster award winners are:

  • Spyridon Baltsavias (PhD candidate '21), Hardware/Software Systems, for his poster, "Ingestible and Implantable Ultrasonic Sensors for GI-Tract Real-Time Monitoring"
  • Joseph Landry (PhD candidate '19), Physical Technology & Science, for his poster, "Structured Illumination Light Sheet Microscopy for High Throughput Imaging of Thick Tissue"
  • David Lindell (PhD candidate '22), Information Systems & Science, for his poster titled, "Confocal Non-Line-of-Sight Imaging based on the Light Cone Transform"

The winning student researchers were presented with a gift card and certificate from the EE Student Life Committee.

Congratulations and thanks to everyone for participating in the 2018 EE Admit Weekend and research poster session! Additional thanks to the EE Admissions, GSEE, and the EE Student Life Committee for sponsoring the poster contest and generous prizes. 


March 2018

In November 2017, the EE and CS departments hosted the Rising Stars Workshop. We welcomed 70 women from around the world for two days of workshops, panels, and discussions aimed at helping them navigate an academic career.

Rising Stars – now in it's 6th year – was started at MIT with the sole intention of helping women interested in academic careers navigate the process.

At the welcome dinner, Provost Persis Drell encouraged the participants to "always remember that the diversity you bring to the conversation is of enormous value. It's not about them having accepted or allowed you into the room, it's that they desperately need you to be there."

Co-chairs of the event were professors Moses Charikar, Andrea Goldsmith and Fei-Fei Li. They were joined by more than 30 faculty, as well as industry leaders to organize and run the event. The participants were selected from nearly 400 applications.

For the young scholars, hearing from a range of panelists with a variety of backgrounds helped give them the tools and the mindset they need to succeed. Umashanthi Pavalanathan, a doctoral candidate in social computing and natural language processing at Georgia Tech, said that as an international student from Sri Lanka, hearing the experiences of faculty members with similar histories gave her confidence: "When I see role models, I get inspired." Adds Sara Mouradian, a doctoral candidate in quantum information processing at MIT: "When you go to conferences, I'm usually the only one or one of two women in any given room of 50 to 100 people, so it's been great to see all these women." Being here, she says, has been "mind-blowing."

Thanks to all of our participants and support for the 2017 Rising Stars event.


Excerpted from "'Rising Stars' workshop raises visibility for women in engineering," Stanford Engineering, March 14, 2018.

Rising Stars 2017 website.

image credit: L. Cicero
February 2018

Krishna Shenoy and team have been researching the use of brain machine interfaces (BMI) to assist people with paralysis. Recently, one of the researchers changed the task, requiring physical movement from a change in thought. He realized that the BMI would allow study of the mental rehearsal that occurs before the physical expression.

Although there are some important caveats, the results could point the way toward a deeper understanding of what mental rehearsal is and, the researchers believe, to a future where brain-machine interfaces, usually thought of as prosthetics for people with paralysis, are also tools for understanding the brain.

"Mental rehearsal is tantalizing, but difficult to study," said Saurabh Vyas, a graduate student in bioengineering and the paper's lead author. That's because there's no easy way to peer into a person's brain as he imagines himself racing to a win or practicing a performance. "This is where we thought brain-machine interfaces could be that lens, because they give you the ability to see what the brain is doing even when they're not actually moving," he said.

"We can't prove the connection beyond a shadow of a doubt," Krishna said, but "this is a major step in understanding what mental rehearsal may well be in all of us." The next steps, he and Vyas said, are to figure out how mental rehearsal relates to practice with a brain-machine interface – and how mental preparation, the key ingredient in transferring that practice to physical movements, relates to movement.

Meanwhile, Krishna said, the results demonstrate the potential of an entirely new tool for studying the mind. "It's like building a new tool and using it for something," he said. "We used a brain-machine interface to probe and advance basic science, and that's just super exciting."

Additional Stanford authors are Nir Even-Chen, a graduate student in electrical engineering, Sergey Stavisky, a postdoctoral fellow in neurosurgery, Stephen Ryu, an adjunct professor of electrical engineering, and Paul Nuyujukian, an assistant professor of bioengineering and of neurosurgery and a member of Stanford Bio-X and the Stanford Neurosciences Institute.

Funding for the study came from the National Institutes of Health, the National Science Foundation, a Ric Weiland Stanford Graduate Fellowship, a Bio-X Bowes Fellowship, the ALS Association, the Defense Advanced Research Projects Agency, the Simons Foundation and the Howard Hughes Medical Institute.

Excerpted from Stanford News, "Mental rehearsal prepares our minds for real-world action, Stanford researchers find," February 16, 2018.


Related News:

Research by PhD candidate and team detects errors from Neural Activity, November 2017.

Krishna Shenoy's translation device; turning thought into movement, March 2017.

Brain-Sensing Tech Developed by Krishna Shenoy and Team, September 2016.

Krishna Shenoy receives Inaugural Professorship, February 2017.


February 2018

Angad Rekhi (PhD candidate) and Amin Arbabian have developed a wake-up receiver that turns on a device in response to incoming ultrasonic signals – signals outside the range that humans can hear. By working at a significantly smaller wavelength and switching from radio waves to ultrasound, this receiver is much smaller than similar wake-up receivers that respond to radio signals, while operating at extremely low power and with extended range.

This wake-up receiver has many potential applications, particularly in designing the next generation of networked devices, including so-called "smart" devices that can communicate directly with one another without human intervention.

"As technology advances, people use it for applications that you could never have thought of. The internet and the cellphone are two great examples of that," said Rekhi. "I'm excited to see how people will use wake-up receivers to enable the next generation of the Internet of Things."

Excerpted from Stanford News, "Stanford researchers develop new method for waking up small electronic devices", February 12, 2018


Related news:

Amin's Research Team Powers Tiny Implantable Devices, December 2017.

Stanford Team led by Amin Arbabian receives DOE ARPA-E Award, January 2017.

Amin Arbabian receives Tau Beta Pi Undergrad Teaching Award, June 2016.

EE's excellent teachers: Boyd, Mahalati, Prabala
December 2017

The Stanford chapter of Tau Beta Pi, an engineering honor society, is proud to announce the inaugural "Teaching Honor Roll," which recognizes the extraordinary teaching of 12 educators in the School of Engineering, three are from Electrical Engineering.

Selection criteria include great teaching, extraordinary inspiration to study a topic, outstanding mentoring and particularly creative lecturing, but are by no means limited to these characteristics. Any undergraduate in the School of Engineering can nominate an instructor.

The 2017 honorees in the Tau Beta Pi Teaching Honor Roll include Electrical Engineering's Stephen Boyd, Reza Mahalati, and Rahul Prabala (BS '16, MS '17).

"I'm so glad to be able to make an impact with EE108," said Rahul Prabala (BS '16, MS '17) on hearing the news of his inclusion. "And I'm honored to be part of the first TBP Teaching Honor Roll."

The honor roll will be displayed in the Jen-Hsun Huang Engineering Center, with plaques bearing the names and short quotes from this year's 12 recipients. The Teaching Honor Roll wall can be found on the ground floor of Huang, near NVIDIA Auditorium. In subsequent years, a list of previous winners will be maintained on the Tau Beta Pi Honor Roll website.

Tau Beta Pi is the nation's second oldest honor society. Founded in 1885, it has chapters in at least 242 U.S. colleges and universities and a membership of well over 550,000. Tau Beta Pi promotes academic excellence, civic leadership and community service for students. In their duties, members organize panel discussions, host industry dinners and conduct math and science programs at local K-12 schools, among many other activities.


Congratulations Stephen, Reza, and Rahul!

Excerpted from Stanford Engineering's, "Tau Beta Pi engineering honor society debuts its "Teaching Honor Roll"" Dec. 6, 2017.

December 2017

We are very proud of the research being done by our graduate and undergraduate students.

Throughout the academic year, we encourage students to present at conferences and related interdisciplinary events. The practice of sharing and speaking about research to a variety of audiences is a quality we encourage. We are pleased to again acknowledge electrical engineering students who have been recognized for their presentation, poster, and/or paper awards.

Jerry Chang (EE PhD candidate)
Ting Chia (Jerry) Chang (PhD candidate '20) is the lead author of "Scaling of Ultrasound-Powered Receivers for Sub-Millimeter Wireless Implants." He and his co-authors received the Best Paper Award at the 2017 IEEE BioCAS Conference.

Ruishan Liu (EE PhD candidate)
Ruishan Liu (PhD candidate) received the Best Poster Award at the Bay Area Machine Learning Symposium. Ruishan belongs to the Stanford Laboratory for Machine Learning group, advised by Professor James Zou. Ruishan develops algorithms and theories in machine learning and reinforcement learning, and is interested in applications in genomics and healthcare.

Her poster title is, "The Effects of Memory Replay in Reinforcement Learning."

PhD candidates Connor McClellan and Fiona Ching-Hua Wang
PhD candidates Connor McClellan and Fiona Ching-Hua Wang each received the Best in Session Award at the TechCon 2017.

  • Connor's paper, "Effective n-type Doping of Monolayer MoS2 by AlO(x)" was presented in the 2-D and TMD Materials and Devices: I session. Professor Eric Pop is Connor's advisor 
  • Fiona's paper, "N-type Black Phosphorus Transistor with Low Work Function Contacts," was presented in the 2-D and TMD Materials and Devices: III session. Professor H.-S. Philip Wong is Fiona's advisor. 
Read More

David Hallac EE PhD candidate
David Hallac, EE PhD candidate, is the lead author of "Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data," which has been selected to receive the KDD 2017 Conference Best Paper Runner-Up Award and the Best Student Paper runner-up Award.

Iliana Erteza Bray EE PhD candidate
Iliana Erteza Bray, EE PhD candidate, received The Firestone Medal for Excellence in Undergraduate Research. Her paper is titled, “Frequency Shifts and Depth Dependence of Beta Band Activity in Rhesus Premotor Cortex Perceptual Decision-Making.” She ia advised by Krishna Shenoy (Electrical Engineering).

 JULY 2017 PhD candidates Alex Gabourie and Saurabh Suryavanshi
PhD candidates Alex Gabourie and Saurabh Suryavanshi received Best Paper Award at the 17th IEEE International Conference on Nanotechnology (IEEE NANO 2017). Their paper is titled, "Thermal Boundary Conductance of the MoS2-SiO2 Interface."

Kirby Smithe EE B.S. candidate
Kirby Smithe (PhD candidate) received first place for his presentation, "High-field transport and velocity saturation in CVD monolayer MoS2" at the EDISON 20 Conference.
Kirby's research involves growth and material characterization of 2D semiconductors and engineering 2D electronic devices for circuit-level applications. He is the recipient of the Stanford Graduate Fellowship as well as the NSF Graduate Fellowship. Kirby is advised by Professor Eric Pop.

Yuanfang Li (M.S. candidate) and Dr. Ardavan Pedram
Co-authors Yuanfang Li (MS candidate) and Dr. Ardavan Pedram received the Best Paper Award at the 28th annual IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP).

Yuanfang Li is an M.S. candidate and Dr. Ardavan Pedram is a senior research associate who manages the PRISM Project. The PRISM project enables the design of reconfigurable architectures to accelerate the building blocks of machine learning, high performance computing, and data science routines.

EE Admit Weekend poster winners
EE Admit Weekend hosts a competitive poster session. The presenting students are judged by faculty, peers and staff and scored on their presentation, poster, and professionalism. The awards went to:
  • Leighton Barnes winner in Information Systems and Science for poster titled "Geometry and the Relay Channel,” 
  • Adrian Alabi in Hardware/Software Systems for poster titled "915 MHz FSK Detection for Wireless Ultrasonic Imaging Data Reception,” 
  • Max Wang in Physical Technology and Science for poster titled "Minimally Invasive Ultrasonically Powered Implants for Next-Generation Therapies and Neuromodulation” 
Read More

December 2017

From desktop to laptop to mobile devices and wearables, personal computing platforms continue to evolve. Virtual reality (VR) and augmented reality (AR) are among the fastest evolving such platforms. VR is an immersive experience that replaces the user's real world with a simulated one. With VR, the user typically wears a headset and/or other wearables that provide simulated interaction through sound, haptics, and graphics. Augmented reality (AR) is not immersive, although it does add elements to the user's reality. An example of AR is the real time translation of traffic signs and restaurant menus while traveling in another country. Applications of VR and AR systems have been gaining in popularity and span entertainment, education, communication, training, behavioral therapy, and basic vision research.

VR and AR provide a host of opportunities for engineers to design new sensors, displays, algorithms, and embedded systems, as well as develop new applications. Stanford students interested in learning about VR and AR systems have been flocking to a new course developed by professor Gordon Wetzstein. The course, EE 267: Virtual Reality, emphasizes aspects of VR systems such as rendering, tracking, haptics, inertial measurement units, depth perception, and presence (or immersion).

EE 267, now in its third year, continues to appeal to undergrad and graduate students both within and outside of the electrical engineering department. The primary course objective is to build a head mounted display (HMD) from scratch and to create a final project of the student's own virtual environment. Past student projects have included innovative combination of 2d and 3d inputs; collection of user data via VR interaction; and developing VR immersive viewing options for medical scans.

"Many final projects are extraordinarily creative and provide novel solutions to current problems," states professor Wetzstein. "The students are enthusiastic to share their work and usually a few interested Silicon Valley companies attend our final presentations."


From a past student – "It became clear within my first week [of my internship] that everything in the EE 267 syllabus is relevant to what I'm doing here at Google and I would have been completely lost if I had not taken your class before starting this internship. There could not have been a better primer for working in VR/AR than your class and I hope that you will continue teaching it for many years!

When I tell my coworkers that I got to take a VR class at Stanford where we built our own HMDs, they are all very jealous and wish they could have had an opportunity like that when they were in grad school."


Subscribe to RSS - student