About Me

   

photo

Here's a little bit about me:


I am currently a 3rd year Ph.D. student and NSF Graduate Research Fellowship recipient in EECS at the University of California: Berkeley in Professor Jan Rabaey's research group after receiving my B.S. degree in Electrical Engineering from Arizona State University.

My research interests are focused on neural engineering, an inter-disciplinary field focused on the interface between humans and computers; where I specifically work in digital integrated circuits and systems for biomedical applications.

Personal Info

Name
Alisha Menon


















My research thus far has focused on EMG-based gesture recognition using the computationally efficient Hyperdimensional (HD) Computing paradigm that involves representing data as large vectors and taking advantage of their orthogonality and simplistic operations to create and query a memory. This included implementing the algorithm on an FPGA and integrating it into the EMG sensing system to allow for efficient and adaptable gesture recognition on hardware.

Current work is focused on developing the system further to improve EMG prosthetic grasping through use of automatic feedback using additional sensors with a finite state machine continuing to work in the hyperdimensional realm, leveraging its properties of encoding actions and perceptions together in a memory to reduce the burden of the prosthetic on the user. Towards this goal, hyperdimensional sensor fusion has been explored with an emphasis on significant memory reduction techniques for an ASIC implementation, as well as vectorization of the algorithm to reduce cycle counts on a more generic RISC-V system using Hwacha, a vector accelerator.

My other interests include the performing arts - specifically the intersection of Indian Classical Dance with modern stories and with Western theater.

Resume

   

Summary 

EECS Ph.D. student at UC Berkeley since Fall '18

EECS Ph.D. student at UC Berkeley with a focus on Neural Engineering & Hyperdimensonal Computation research with Dr. Jan Rabaey. Awarded prestigious NSF Graduate Research Fellowship , UC Berkeley Graduate Research Fellowship, & UC Berkeley EECS Excellence Award for duration of Ph.D. program (Publications below)

Received EECS M.S. Degree at UC Berkeley in Spring '20

Received M.S. in EECS at UC Berkeley. Thesis focused on optimizing an Hyperdimensional machine learning physiological sensor fusion algorithm implemented on both ASIC and RISC-V CPU.

Hardware Engineer at CTRL-Labs in Summer '18

Hardware Engineer focused on EMG Sensors at CTRL-Labs (since acquired by Facebook), a Neural Engineering Startup, involved in designing testing protocols, developing Python scripts for data analysis, and evaluation of potential ICs for EMG sensors.

Graduated Bachelor's Degree in Electrical Engineering at ASU in Spring '18

Graduated with 4.0 GPA Summa Cum Laude as Mouer Award Recipient with B.S. in Electrical Engineering from Arizona State University (2015-2018)

FURI Research Scholar with Dr. Hugh Barnaby at ASU in Fall '17

Awarded FURI Research Scholarship to conduct research with Dr. Hugh Barnaby at ASU on designing core neuromorphic computing elements as integrated circuits to mimic spiking neurons

Research and Development Engineer with Rabaey Lab in Spring '17

Research and Development Engineer w/ Dr. Jan Rabaey at UC Berkeley focusing on a hardware interface that utilizes hyperdimensional biosignal processing for EMG-based gesture recognition including real-time filtering and processing the 96 EMG sensors' signals.

Presented Poster at Stanford Research Conference in Spring '17

Presented poster on Analog Backscatter for Implanted EMG sensing at Stanford Undergraduate Research Conference

Winner of 2017 UW Neural Engineering Hackathon in Winter '17

Developed an interactive rehabilitation system for patients with foot drop using EMG sensors, OpenBCI Cyton Biosensing Board and Arduino Uno in under 36 hours at University of Washington

Visiting Researcher with CSNE at University of Washington in Summer '16

Focused on developing automated testing system through AFG3252, software-defined radio, and interpretation and processing of data applied to JFETs with applications in implanted EMG sensing

Visiting Researcher with the Rabaey Lab at UC Berkeley in Summer '16

Focused on embedded systems software utilizing circular buffers and data structures to store and manage accelerometer data into neural data stream for hardware interface

Internship with the Blood-Brain-Barrier Lab in Summer '15

At Oregon Health and Science University under Dr. Leslie Muldoon

Graduated High School in June '15

Completed curriculum in two years with a 4.21 GPA

Founded TYE Oregon

TiE Youth Entrepreneurs program for fostering entrepreneurship among High School students w/ focus on diversity & inclusion

Graduated Stoller Middle School in '13

4.0 GPA in Accelerated Summa program

Highly Proficient in Electrical Engineering and Computer Science

Skills include Analog and digital circuits, computer architecture, hardware for machine learning, embedded software, ASICs, FPGA, machine learning, Hyperdimensional computing, Verilog, digital logic design, linear systems theory, API development, Data Structures, Algorithms, Cadence, SPICE, C, Python, Arduino, Assembly, MATLAB, HTML/CSS.

Excellent Presentation and Communication skills

Drawing from extensive experience in theater and performing arts (over 200 main stage performances)

Education and Experience 

Special Skills

Python
A/D IC
C/C++
MATLAB
Verilog

Personal Statement

   

Science fiction writer Arthur C. Clarke stated that “Any sufficiently advanced technology is indistinguishable from magic.” Researchers have made technological advances in the last century that have changed lives in truly magical ways, but I strongly believe that the next step in technology and human interfaces revolves around machine learning, advanced signal processing and their applications in neural engineering. Medically, there are patients who are in need of treatment involving technology that does not yet exist. Commercially, there are many initiatives utilizing neural engineering technology to enhance the way society functions and solve other real-world problems that will make a difference in the lives of those in need. Machine learning, signal processing and neural engineering are rapidly growing fields that are on the cutting edge of technology. I believe that in the next few decades, neural engineering will become as commonplace as smart devices are today and I want to be a part of this futuristic field as it expands and thrives.

My interest in neural engineering began in high school when I came across a news article about a prosthetic hand that could be controlled by the human brain. I thought that kind of technology only existed in a galaxy far, far away, but seeing engineers and neuroscientists working on this new solution for veteran amputees and paralyzed patients completely changed my perspective. My curiosity led me to discover the infinite applications of machine learning and signal processing, including in the nascent field of neural engineering. As I read more about the interdisciplinary field and its infinite applications, neural engineering quickly became my primary passion as it involves advances in CS and EE - two fields I have always had an interest in - along with fascinating research in neuroscience and mechanical and biomedical engineering to create the future of technology.

I began reading BCI (Brain-Computer Interface) literature to gain a better understanding for the applications of machine learning and signal processing in neural engineering. This taught me a lot about the key components involved in neural engineering including electroencephalography and electrocorticography which read electrical impulses in the brain through external electrodes and invasive electrodes, respectively. Once the signals are recorded, extensive signal processing is required to gain any insight into the data due to the considerable volume of artifacts that are observed and the variation from person-to- person. This is a huge challenge and, as neural signal processing is involved in almost all neural engineering research, it must become more advanced and adept as the field continues to grow. There is huge potential in this field for machine learning which could make it possible for BCIs to learn to recognize an individual’s neural data patterns allowing the field to take huge steps forward medically and commercially.

With that knowledge in mind, I decided to attend Arizona State University as an undergraduate with a major in Electrical Engineering in order to create a solid engineering foundation. I received my B.S. in Electrical Engineering from ASU in Spring 2018 having taken EE/CS courses covering digital design, computer organization, assembly language programming, engineering electromagnetics, signals and systems, properties of electromagnetic materials, analog and digital circuits, and random signal analysis. Of the many pathways my school offers, I am focusing on computer science and signal processing with the plan to pursue machine learning and signal processing at the graduate level. Because my degree focused on electrical engineering, I worked hard to learn new skills and knowledge outside of my coursework particularly in algorithms, data structures, and operating systems.

In Summer 2016, I was offered a Visiting Researcher position with Dr. Jan Rabaey, Distinguished EECS Professor at UC Berkeley, whose work focuses brain-machine interfaces, neuro-inspired computing and sensory swarms. During this 8-week internship, I collaborated with graduate students and faculty of the Rabaey lab on the development of a Brain-Machine Interface for prosthetic applications. I focused on writing embedded system software for the processing of neural signals acquired by the hardware interface, and in the interpretation thereof. I had the opportunity to write, test and integrate an API for the processing of data collected by an accelerometer (motion detector) placed on the system to interpret motion and corresponding neural data. To accomplish this, I developed C code utilizing circular buffers & data structures to store and manage accelerometer data into the neural data stream. The internship gave me the chance to apply my software development skills with an embedded system and showed me the value of such immersive experiences. Courses and lectures are essential to understanding the concepts, but an environment in which knowledge must be applied in real-world problems in order to achieve results and push the boundaries of existing solutions, is where the learning really happens. After this experience, my interest in machine learning and signal processing only grew.

In June 2016, I was accepted into the highly competitive CSNE Research Experience for Undergraduates Program with Dr. Joshua Smith, a Computer Science and Engineering Professor at University of Washington whose work focuses include Ubiquitous Computing, Robotics and Machine learning. During this 8-week internship, I worked with the Sensor Systems lab on Analog Backscatter for Implanted EMG Sensing. I focused on developing a test system to determine which of the JFETs (junction gate field-effect transistors) the lab had acquired for the project had the highest signal-to-noise ratio. To accomplish this, I wrote, tested and implemented an automated testing algorithm in Python to upload 100 test signals of frequencies between 1Hz and 10 kHz to a function generator, an AFG3252, which was connected to the JFETs. The algorithm also processed and interpreted data on each trial. Using the test system, the signal-to-noise ratios for all 24 JFETs at all frequencies were calculated and compared allowing the project to move forward with the successful JFETs. At the end of the internship, I presented a poster and summary of my work for leaders and experts in the field.

In Summer 2017, Dr. Jan Rabaey invited me back to work on another project, hyperdimensional biosignal processing for EMG-based gesture recognition. My research focused on adapting the previously mentioned neuromodulator for EMG signals, specifically on the interface between neuromodulator and software. I worked on the transfer of data from the neuromodulator to the processing software as well as real-time filtering, processing and selection of the 96 EMG sensors' signals. I was also involved in the implementation of the hyperdimensional computing algorithm for real-time gesture recognition and utilization of parallel computing for simultaneous storage of data and interpretation thereof.

Given my work in an analog circuitry coursework and previous research experience with technology involving the brain, Dr. Hugh Barnaby at ASU invited me to work with his group on designing neuromorphic computing hardware. Due to the need for greater computing capabilities, neuromorphic hardware, or circuitry designed to function like biological neurons, is becoming increasingly recognized as the next step in handling large-scale computing processes. This has been evidenced by Intel’s recent release of Loihi, a chip based on neuromorphic computing that is estimated to increase the speed of pattern recognition algorithms by 1000 and decrease the need for power and memory – two heavily limited resources in neural devices. My research there included designing and refining leaky integrate-and-fire circuits in CMOS and memristor-based synaptic elements to create a neuromorphic interface. I received the highly competitive FURI research fellowship and scholarship to work on this project for the 2017-2018 school year and made an oral and poster presentation on my work at the Fulton Engineering Research Symposium in November 2017 and May 2018.

After spending so much time in research, I wanted to explore the neural interface development process in other environments so, after completing my undergraduate degree, in Summer of 2018, I applied and was offered an internship at CTRL-Labs - a neural technology start-up in NYC (since acquired by Facebook) as a Hardware Engineer working on EMG Sensors involved in designing and implementing testing protocols, developing Python scripts for analysis and assessment of testing data, and evaluation of potential ICs for EMG sensor board. I very much enjoyed this experience, it allowed me to compare the research environment in the start-up world with that in the academic world. However, I still felt that I wanted to build my skills in specific areas more deeply to be able to better contribute to this field.

In Fall 2018, I joined the Ph.D. program in EECS at UC Berkeley (receiving my M.S. in Spring 2020). I was honored to receive the NSF Graduate Research Fellowship and UC Berkeley Fellowship to support my graduate work. My first year focused on Analog and Digital Integrated Circuits, but subsequently I have also built knowledge in Linear Systems Theory, Computer Architecture and Hardware for Machine Learning. My research work thus far has focused on EMG-based gesture recognition using the computationally efficient Hyperdimensional (HD) Computing paradigm that involves representing data as large vectors and taking advantage of their orthogonality and simplistic operations to create and query a memory. This included implementing the algorithm on an FPGA and integrating it into the EMG sensing system to allow for efficient and adaptable gesture recognition on hardware.

Current work is focused on developing the system further to improve neural prosthetic grasping through use of automatic feedback using additional sensors with a finite state machine continuing to work in the hyperdimensional realm, leveraging its properties of encoding actions and perceptions together in a memory to reduce the burden of the prosthetic on the user. Towards this goal, hyperdimensional sensor fusion has been explored with an emphasis on significant memory reduction techniques for an ASIC implementation, as well as vectorization of the algorithm to reduce cycle counts on a more generic RISC-V system using Hwacha, a vector accelerator.

I believe very firmly that the future of technology and interfaces belongs with neural engineering. The applications of this field are infinite. Already we see successes such as cochlear implants and initiatives involving neural prosthetics, seizure prediction and immediate treatment through BCI monitoring, deep brain stimulation for Parkinson's disease, accelerating rehabilitation through a closed-loop neurofeedback system, communication systems for locked-in patients, brain-controlled wheelchairs, and stimulating the brain to induce memory recovery; these are just the medical applications! Commercial applications include gaming through the brain to enhance user experience, robotic avatars, harnessing the brain's image processing capabilities, lie detection, monitoring awareness at the wheel, identification, cognitive amplification, even physical enhancements with exoskeletons. All of these technologies involve recording and translating brain activity through reliable and precise neural engineering technology that doesn't yet exist.

Today these are ideas straight out of comic books or sci-fi movies, but in the next few decades, they may quickly become a reality. I see this field becoming absolutely huge with the need for advancements in neural interfaces growing along with it and I want to be on the cutting edge of this technology as this happens.

Resources      

Performing Arts

   

Experience  

Title Character "Chitra" February 2018

Northwest Children's Theater production of "Chitra: The Girl Prince" - 30+ shows

Principal Character "Rann" February 2015

Northwest Children's Theater production of "Jungle Book" - 30+ shows

Principal Character "Janaki Dev Balan" September 2014

Anjali School of Dance production of "Murder on the Ganges".

Principal Character "Mermaid" Dec 2013 - Jan 2014

Northwest Children's Theater production of "Peter Pan" - 30+ shows

"Citizen of Who" and Ensemble Jan 2013 - Feb 2013

Northwest Children's Theater production of "Seussical" - 35+ shows

Principal Character "Puck" Sep 2012, June 2013

Bollywood Musical production of "A Midsummer Night's Dream" - Portland & Seattle

Principal Character "Queen of Hearts" April 2012

Stoller Theatre Arts production of "Alice in Wonderland Jr." - 5 shows

"Joseph and the Amazing Technicolor Dreamcoat" Aug 2011

Christian Community Theatre - 8 shows

Principal Character "Sophie Greedly" (British Accent) May 2011

Stoller Theatre Arts production of "Murder By Indecision"

Understudy to the Principal Character "Nassrin" Feb 2011 - Mar 2011

Oregon Children's Theatre production of "On the Eve of Friday Morning" at the Winningstad Theatre - 30+ shows

Principal Character "JoJo" Jan 2011

Stoller Theatre Arts production of "Broadway Musical - Seussical Jr." - 4 shows

Solo 2 hour Indian Classical dance graduation performance Aug 2009

Performing Arts Center, PCC Sylvania Campus. Featuring a rendition of "The Wizard of Oz" set to dance

Print Ad Modeling Feb 2009

For Intel Classmate PC - Photographer: Steve Bloch

Education 













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TYE Oregon

   
Encouraging leadership and the entrepreneurial mindset in high school students
TYE Oregon has had over 200 participated from over 19 Portland high schools

In December 2013, I began working with the TiE board to found TiE Young Entrepreneurs (TYE) Oregon, a TiE Oregon initiative, to inspire, challenge and empower students to become the next generation of entrepreneurs, business and community leaders. The program is run in 23 cities around the world. Seasoned entrepreneurs and mentors coach high school students based on a business-focused curriculum, which runs from October through May.

Since then, TYE Oregon has had over 200 students from over 19 Portland high schools. The TYE Steering Committee is committed to diversity and inclusion. As a result, in the 2015-2016 program, 48% of the students had a minority background and 42% were girls. TYE Oregon also hosted the TYE Global Competition in June 2016.

            TYE Oregon has continued to grow and has expanded to include in-school classes at low-income schools providing internship opportunities to all students.

Please visit the links below for more information about the program.

TYE Oregon Social Media      

Contact Me

   

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Name
Alisha Menon