I'm a fourth year Ph.D. student in the Computer Science and Engineering Department at the University at Buffalo. My research interests are in developing robust and reliant mobile, cyber-physical, and mixed criticality systems, particularly for robotics and computer vision applications.
In the summer of 2019, I interned at Google, where I developed a machine learning model to inform prefetching decisions on Chrome to decrease in user-perceived page load latency. Before starting the Ph.D. program, I was a software engineer at edX for a year, where I worked on all the layers of the "full stack" - from creating an entirely accessible essay-writing module in Javascript for keyboard-only users, to countless management commands and APIs in Python and Django, to running and analyzing A/B tests with Optimizely, to devops and system administration for VEDA, edX's video encoding and delivery pipeline.
I graduated from Brandeis University with a degree in computer science in 2017.
Publications
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Sofiya Semenova, Steven Y. Ko, Yu David Liu, Lukasz Ziarek, and Karthik Dantu. A Quantitative Analysis of System Bottlenecks in Visual SLAM. HotMobile 2022. Best Poster Award
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Ali J. Ben Ali, Sofiya Semenova, and Karthik Dantu. Platform Variability in Edge-Cloud Vision Systems. HotMobile 2019.
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Ryan Marcus, Olga Papaemmanouil, Sofiya Semenova, and Solomon Garber. NashDB: An Economic Approach to Fragmentation, Replication and Provisioning for Elastic Databases. SIGMOD 2018.
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Ryan Marcus, Sofiya Semenova, and Olga Papaemmanouil. A Learning-based Service for Cost and Performance Management of Cloud Databases (Demonstration). ICDE 2017.
Projects
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For older projects, see the projects page of this website.