December 2017

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.

The 13th IEEE Biomedical Circuits and Systems Conference (BioCAS), was held October 19-21 in Turin, Italy. The annual conference is a premier international forum for researchers and engineers to present their state-of-the-art multidisciplinary research and development activities at the frontiers of medicine, life sciences, and engineering.

Jerry's research focuses on circuits and system design for miniaturized wireless medical implantable devices with ultrasonic links. He is advised by professor Amin Arbabian, who oversees the ArbabianLab Ultrasonically Powered Implantable Devices research team.


Congratulations to Jerry Chang and co-authors: M. J. Weber, J. Charthad, S. Baltsavias, and A. Arbabian for receiving the best paper award!

Paper Abstract:
We investigate scaling of ultrasound-powered wireless receivers for efficient, miniaturized implantable medical devices. Single crystalline piezoelectric material, PMN-PT, is chosen in this study as it has low resonance frequency with scaled dimensions. For accurate modeling of sub-mm-sized receivers, we perform simulations using the finite element method, followed by validation with measurements. Results are presented for scaling of the resonance frequency, resistance at resonance, and aperture efficiency of PMN-PT receivers with thickness of 0.5 mm and widths ranging from 0.3 mm to 1.0 mm. Since optimizing the overall harvesting efficiency of an implant requires not only an efficient receiver but also an efficient interface to the power electronics, we analyze impedance matching efficiency between the receivers and the power electronics using optogenetic stimulation as an example application. Finally, we show the measurement of prototype implants with scaled receivers and discuss the trade-off between size and power harvesting efficiency of sub-mm wireless implants.



December 2017

Stephen Boyd has been elected to the Chinese Academy of Engineering, one of China's highest academic honors. The elected candidates become lifetime members of the academy. Stephen is one of 17 elected foreign academicians.

New academicians are selected every two years from academic institutions, research institutes, enterprises and hospitals, both inside and outside China.

The Chinese Academy of Engineering (CAE) – which falls under the State Council, China's top governing body – also has a role advising Beijing on the country's economic and social development, and its new members need to have "strict political clearance."

Foreigners are eligible for membership if they have contributed to the development of or played an important role in promoting China's engineering, science, and technology, the CAE said on its website.

The academy's selection of foreign members is part of this effort to strengthen China's presence and influence in engineering, science, and technology, the organisation said on its website.


Please join us in congratulating Stephen for this special and very well-deserved recognition.


Excerpts taken from "Bill Gates given one of China's highest academic honours," published in South China Morning Post.


November 2017

Congratulations to Andrea Montanari on his elevation to IEEE Fellow. IEEE Grade of Fellow is conferred by the Board of Directors upon a person with an extraordinary record of accomplishments in any of the IEEE fields of interest. Less than 0.1% of voting IEEE members are selected annually for this member recognition. IEEE Fellows will be formally announced by the IEEE at end of the 2017.

Professor Montanari's research interests include understanding patterns in complex high-dimensional data, and what mathematical and algorithmic methods can be used to disentangle them from noise. His research spans several disciplines including statistics, computer science, information theory, and machine learning. He also works on applications of these techniques to healthcare data analytics.

Congratulations to Andrea!



Andrea's EE Spotlight

Andrea Montanari's EE Spotlight


November 2017

Emeritus professor Tom Kailath has been elected a Fellow of the American Mathematical Society (AMS). The citation reads, "For contributions to information theory and related areas, and for applications."

The Fellows of the AMS designation recognizes members who have made outstanding contributions to the creation, exposition, advancement, communication, and utilization of mathematics. Among the goals of the program are to create an enlarged class of mathematicians recognized by their peers as distinguished because of their contributions to the profession, and to honor excellence.

On the 2018 Class of Fellows of the AMS, Professor Kenneth A. Ribet, President of the American Mathematical Society, states, "This year's class of AMS Fellows has been selected from a large and deep pool of superb candidates. It is my pleasure and honor as AMS President to congratulate the new Fellows for their diverse contributions to the mathematical sciences and to the mathematics profession."


Please join us in congratulating Tom for this most recent recognition of his groundbreaking contributions!


Read more at the American Mathematical Fellows

October 2017

A dozen teams of EE students came together Friday afternoon to compete in EE's Annual Pumpkin Carving Contest.

This year's event was hosted in the Packard Atrium, with plenty of candy, refreshments, and music. View photo album.

Judges included student services staff Rachel Pham, graduate student Jerry Shi, and Professor Juan Rivas-Davila. Judging criteria included completeness, technical skill, creativity, and costumes.

In addition to judge's scores, a number of voting ballots were also available for attendees to vote for their favorite pumpkin. They were added to judges totals and counted toward the final result.

Third Place went to the "Eevee Evolution" of David Zeng, Tracey Hong, and Neal Jean.
Second place went to "EE42" Team, whose members are Katherine Kowalski, Amit Kohli, Justin Babauta, and Alec Preciado.
The First place team was "Pumpkin Carving Dream Team" of Nicole Grimwood, Nicolo Maganzini, Tori Fujinami, and Tong Mu.


Thanks to all of our staff, faculty, and students for your enthusiastic participation!

PhD candidate Nir Even-Chen
November 2017

PhD candidate Nir Even-Chen and his advisor, professor Krishna Shenoy, et al., share recent strides in brain-machine interface (BMI) innovation. BMIs are devices that record neural activity from the user's brain and translate it into movement of prosthetic devices. BMIs enable people with motor impairment, e.g. a spinal cord injury, to control and move prosthetic devices with their minds. They can control robotic arms for improving their independence or a computer cursor for typing and browsing the web. Even-Chen, et al's, recently published paper, "Augmenting intracortical brain-machine interface with neurally driven error detectors," describes a new system that reads users minds, detects when the user perceives a mistake, and intervenes with a corrective action. The new system allows users to control BMIs more easily, smoothly, and efficiently.

While most BMI studies focus on designing better techniques to infer the user's movement intention, Even-Chen, et al, improved the BMI performance by taking a very different approach, detecting and undoing mistakes. Their work presents both novel fundamental science and implementation of their idea. They showed for the first time that it is possible to detect key-selection errors from the motor cortex — a brain area mainly involved in movement control. Then, they used the data in real-time to undo—or even prevent—mistakes.

The need for real-time error correction

In our daily life, we all make mistakes, from typos during texting, clicking the wrong link on a web page, or knocking our cup of coffee over while reaching for the cake. Correcting these mistakes might be time-consuming, and annoying—especially when they occur frequently during challenging tasks. Imagine a system that could detect – or predict – your mistakes (e.g., typos) and automatically undo, or even prevent them from happening. This can save the time of manually correcting the mistake, especially when the errors are frequent and the actions to correct them slow you down. Error detection is not always trivial, in some cases only the person who made the mistake knows what she intended. Thus, such an error detection system needs to infer one's intention, i.e., read her mind. An automatic error detection system is most effective when the task is challenging or when our skill is limited, and errors are common. A good candidate for testing such an error detection approach is a BMI system. First, BMIs enable a readout of the user's mind. And second, it can be highly beneficial for BMI users, since BMI control is challenging and prone to errors.

Intracortical BMIs, which records neural activity directly from the brain, showed a promising result in pilot clinical trials and are the highest-performing BMI systems to date. This makes them prime candidates for serving as an assistive technology for people with paralysis. Although the performance of intracortical BMI systems has markedly improved in the last two decades, errors — such as selecting the wrong key during typing — still occur and their performance is far from able-bodied performance. 

Previously it was unknown if errors can be detected from the same brain region traditionally used for decoding BMI user's movement intention—the motor cortex. In their work, Even-Chen and colleagues found that when errors occur a characteristic brain activity can be observed. That brain activity pattern enables them to detect mistakes with high accuracy shortly after and even before they occurred.

This finding encouraged them to develop and implement first-of-its-kind error "detect-and-act" system. This system reads the user's mind, detects when the user thinks an error occurred, and can automatically "undo" or "prevent" them. The detect-and-act system works independently and in parallel to a traditional movement BMI that estimate user's movement intention (see figure). In a challenging BMI task that resulted in substantial errors, this approach improved the performance of a BMI. Using the detect-and-act system, hard tasks will have fewer errors and become easier, the use of a BMI will become smoother, and be less frustrating.

A detect-and-act system can potentially be used to improve how fast people with paralysis can type or control a robotic arm using a BMI. For example, automatically correcting a mistake when they type, or stopping the movement of a robotic arm when they are about to knock over their coffee. While this work has been done in pre-clinical trial with monkeys, Even-Chen and colleagues also presented encouraging preliminary results of a clinical trial (BrainGate2) at a conference, and showed the potential translation to humans.


Read more: Journal of Neural Engineering, "Augmenting intracortical brain-machine interface with neurally driven error detectors."
Additional authors include Sergey Stavisky, Jonathan Kao, Stephen Ryu, and Krishna Shenoy. 


October 2017

Ruishan Liu (PhD candidate) has received the Best Poster Award at the Bay Area Machine Learning Symposium, October 19, 2017. 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. She is also interested in applications in genomics and healthcare.


Poster Title:
"The Effects of Memory Replay in Reinforcement Learning"

Poster Abstract:
Experience replay is a key technique behind many recent advances in deep reinforcement learning. Despite its wide-spread application, very little is understood about the properties of experience replay. How does the amount of memory kept affect learning dynamics? Does it help to prioritize certain experiences?

In our work, we address these questions by formulating a dynamical systems ODE model of Q-learning with experience replay. We derive analytic solutions of the ODE for a simple setting. We show that even in this very simple setting, the amount of memory kept can substantially affect the agent's performance. Too much or too little memory both slow down learning.

We also proposed a simple algorithm for adaptively changing the memory buffer size which achieves consistently good empirical performance.


Congratulations to Ruishan!

October 2017

 Professor Andrea Goldsmith has been selected as the recipient of the 2017 WICE Mentorship Award from the IEEE Communications Society. She will be presented with a plaque at the IEEE Globecom'17 in Singapore.

The WICE Mentorship Award recognizes members of IEEE ComSoc who have made a strong commitment to mentoring WICE members, have had a significant positive impact on their mentees' education and career, and who, through their mentees, have advanced communications engineering.

The IEEE (Institute of Electrical and Electronics Engineers, Inc.) is the world's largest technical professional society. Through its more than 400,000 members in 150 countries, the organization is a leading authority on a wide variety of areas ranging from aerospace systems, computers and telecommunications to biomedical engineering, electric power and consumer electronics. Dedicated to the advancement of technology, the IEEE publishes 30 percent of the world's literature in the electrical and electronics engineering and computer science fields, and has developed nearly 900 active industry standards. The organization annually sponsors more than 850 conferences worldwide.

The IEEE Communications Society (IEEE ComSoc) is a leading global community comprised of a diverse set of professionals with a common interest in advancing all communications and networking technologies.


Congratulations to Andrea on this well-deserved recognition!



July 2017

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 in July.

All student preesenters were ranked by a committee comprised of members of the International Advisory Committee. More than 25 presentations and posters were evaluated by this committee. Kirby's award is accompanied by $300 and a glass commemorative trophy.


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 part of the Pop Lab research group, advised by Professor Eric Pop.


Congratulations to Kirby!



July 2017

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."

The awards candidates were nominated by program committee together with award committee based on the rating of the abstract. The awards winners were selected from the candidates by the award committee based on both the recommendation of excellent final papers by track chairs and the rating of the overall quality of the final paper and the presentation by session chairs and invited speakers.

Saurabh and Alex are part of the Pop Lab.

Congratulations Alex & Saurabh! 



The paper's authors are Saurabh Vinayak Suryavanshi, Alexander Joseph Gabourie, Amir Barati Farimani, Eilam Yalon and Eric Pop.



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