I am currently a software engineer at Microsoft in the Azure App Services team, which supports serverless compute services such as Azure Functions and Azure Logic Apps. Outside of cool projects at work, I love learning about new areas of Machine Learning in my part-time Master's program at Columbia. When the weather's nice out, you can find me outside playing pickleball, spikeball, soccer, or bouldering; otherwise, I'll be inside immersed in a new book or conversing (and playing poker) with friends. I’m always excited to explore new, well-designed codebases and learning opportunities — nice to meet you!
Contact: alina.ying@gmail.com
I am currently a software engineer at Microsoft in the Azure App Services team, which supports serverless compute services such as Azure Functions and Azure Logic Apps. I work on scaling VMs based on workload, load balancing traffic, and maintaining data plane functionality, availability, latency, and performance.
I was a software engineer intern in the Transit / Transit Agencies team at Uber, which works to partner with agencies to obtain multimodal transportation through the Uber app.
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I looked into the acoustic-prosodic and lexical correlates of charisma in different speaker and rater demographic and personality groups, and in different speech genres. The dataset used were speech stubs from the 2019-2020 candidates for Democratic nomination for U.S. president.
I worked on path-planning with snake robotics in the ROAM Lab under Professor Matei Ciocarlie, focusing on using rapidly-exploring random trees (RRTs) to frontload path-planning computation. Experiments took into account dry, viscous, and fluid friction and were simulated on 5-link, 10-link, and 15-link snakes.
I worked on income inequality simulation in the CRIS Lab under Professor Venkat Venkatasubramanian, modeling income inequality reflected in wage distribution for a population of different classes (eg 90%, 9%, 1% or 50%, 30%, 15%, 5%) based on utility functions of compensation (alpha), effort (beta) and competition (gamma). I visualized the relative distributions and their sensitivities to alpha, beta, and gamma values after optimization.
Columbia University, Master of Science (MS), Machine Learning, 2021-2023.
Columbia University, Bachelor of Science (BS), Computer Science, 2017-2021.
During my time at Columbia, I was otherwise involved in: