Praveen Nair
(Nair rhymes with 🔥)
👋 Hey! I'm Praveen, a second-year master's student in Computer Science and Engineering at UC San Diego, researching machine learning fairness and the responsible use of algorithms in society.
prnair@ucsd.edu • CV • Google Scholar • GitHub • LinkedIn
I’m currently applying for PhD programs in and around computer science for Fall 2024 admission!
I’m currently advised by Professor David Danks of the Halıcıoğlu Data Science Institute (HDSI) and Department of Philosophy at UCSD. My thesis research deals with using active learning in fairness-sensitive decisions that suffer from unobserved counterfactuals, establishing how decisionmakers can benefit epistemically in the long run by observing points that were historically unlikely to be observed. I’m also currently a teaching assistant in HDSI, supervising the senior capstone course.
Prior to my graduate study, I was a data science major at UC San Diego 🔱, graduating with minors in history and liguistics, and a concentration in political science. They wouldn’t let me triple minor.
Outside the classroom, I was the sports editor of The UCSD Guardian, I was (and continue to be) in UCSD’s quizbowl club, and I spent a summer making a documentary about UCSD’s history. In my free time, I’m watching as many sports as I can, reading books at a glacial pace, and reading Wikipedia pages.
🧑💻 Research Interests
If and when we use algorithms to make important social decisions, what will go wrong, and how can we find out?
Specifically, that includes:
- Machine learning fairness: how can we ensure algorithms make decisions that match our philosophical notions of fairness? What is fair, anyway?
- Can we develop guidelines for algorithmic systems that are fair from end to end, from data collection to, ultimately, taking action?
- How do factors like temporal dynamics, missingness, or other complications affect how we assess fairness?
- How can researchers and regulators detect unfairness in the wild?
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Human-AI interaction: How do people make sense of and react to algorithmic decisionmaking that affects their lives? How do recent publicly released models affect the relationship between users and ML?
- …and plenty of other areas where people meet algorithms, such as interpretability, policy, algorithmic auditing, and participatory design.
📝 Publication(s)
Engagement in online learning: student attitudes and behavior during COVID-19. Hollister, B., Nair, P.*, Hill-Lindsay, S., & Chukoskie, L. Frontiers in Education, May 2022. https://www.frontiersin.org/articles/10.3389/feduc.2022.851019/full
* co-first-author
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