Hi, I’m Alina.
Software Engineer

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
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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.

LinkedIn Github Devpost


Speech Lab

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.

Robotic Manipulation and Mobility Lab

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.

Complex Resilient Intelligent Systems Lab

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:

  • ESC, the Engineering Student Council, as the President to assist in policy, finances, communication, and student life.
  • WikiCU, the Columbia Wiki, as a sysadmin to assist in maintaining institutional memory written by students, for students.
  • Speech Lab, the Spoken Language Processing Group, as a Research Assistant under Professor Julia Hirschberg.
  • ROAM Lab, the Robotic Manipulation and Mobility Lab, as a Research Assistant under Professor Matei Ciocarlie.
  • TAing for courses Data Structures and Algorithms and Introduction to Computer Science

Awards & Honors

Uber Fellowship
Weekend-long workshops, mentorship, and insight into engineering practices.
Rewriting the Code Fellowship
Industry exploration as part of the Rewriting the Code national program.
Grace Hopper Conference Scholarship
Awarded through Womxn in CS at Columbia University, with sponsored access to GHC.
PennApps Top-30 Award
Top 30 projects in a PennApps Hackathon.
PennApps Docusign Grand Prize
$1000 awarded for best use of the Docusign API in a PennApps Hackathon.