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LIZ IZHIKEVICH

 

ABOUT ME

I'm a Ph.D Student/ NSF Fellow/Stanford Graduate Fellow at the Computer Science Department at Stanford University and a graduate of UC San Diego (B.S. '17, M.S. '18). My current research interests lie at the intersection of systems and security. My hobbies are breakfast, hiking, skiing and traveling. My younger sister, Katherine, is the reason why this website exists and doesn't look like garbage. 

My important mottos:

  • It's all about the "lern" 'cause it's not about the "A."

  • Sleep is for death.

 

PUBLICATIONS

(visit my Google Scholar Page for more details)

Ao, L., Izhikevich, L., Voelker, G., Porter, G. Sprocket: A Serverless Video Processing Framework for the Cloud. In Proceedings of the ninth ACM Symposium on Cloud Computing (Socc’18), Carlsbad, Calif, October 2018.

Moshiri, N., Izhikevich L. Design and Analysis of Data Structures. 2018. Print. 

Moshiri N., Izhikevich L.  Data Structures. Stepik, 2016. Online. 

[Pre-Print; Under Submission] L.Izhikevich, R.Gao, E.Peterson, B.Voytek. Measuring the average power of neural oscillations. bioRxiv 441626; Online.

L.Izhikevich, E.Peterson, B.Voytek. Neural oscillatory power is not Gaussian distributed across time. Program No. 271.03. 2016 Neuroscience Meeting Planner. San Diego, CA:

Society for Neuroscience, 2016. Online.

 

PROJECTS

EMPIRICAL SECURITY RESEARCH GROUP

January 2019 - Now

Collaborating with Prof. Zakir Durumeric and Stanford Visiting Scholar Renata Teixeira on computer network analysis and security. 

July 2017- September 2018

Collaborated with Prof. Geoff Voelker and Prof. George Porter on serverless distributed systems and security related research. Projects include:

  • Sprocket: A Serverless Video Processing Framework for the Cloud (paper to appear at SOCC'18).

  • investigating vulnerabilities in AWS Lambda (to appear in upcoming published masters thesis)

To learn more about our projects: https://burst-parallel.sysnet.ucsd.edu

Jan 2015 – September 2018

Collaborated with Prof. Brad Voytek, Erik Peterson and Richard Gao on electrophysiological data analysis. Project includes: 

  • analyzing the distribution of EEG, ECOG, MEG and LFP data at different frequencies to invalidate a statistical assumption of a commonly used estimating method in neuroscience (Welch's Method). Presented research results at SFN 2016 (Society For Neuroscience). Pre-print on BioRxiv; Paper submitted to Journal of Neuroscience Methods. 

 

AWARDS

  • Undergraduate Award for Excellence in Teaching @UC San Diego

  • NSF Graduate Research Fellowship 

  • Stanford Graduate Fellowship in Science and Engineering

  • Ledell Family Endowed Research Scholarship for Science and Engineering @UC San Diego

  • Warren Undergraduate Research Scholarship Recipient @UC San Diego

  • UCSD Big Pixel Initiative Hackathon Winner

  • Teradata Datathon Finalist

  • Warren Undergraduate Research Scholarship Recipient @UC San Diego

  • Yelp Dataset Challenge Grand Prize Winner

  • Society for Women Engineers Admiral Grace Murray Hopper Scholarship Recipient

 
 
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