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Research Experience |
Research Associate, Digital Emotions Lab{:target="_blank"}, Harvard Business School
January 2025 - Present
- Studying human-AI interaction in emotional regulation tasks, using experimental and machine learning methods
- Building platform for human-AI trials, and developing pilot and large-scale studies
- Supporting lab's computational, data, and informational infrastructure
September 2023 - Present
- Lead author on paper introducing constraint-solving approach to integration of overlapping networks{:target="_blank"} problem with applications in neuroscience and social science, in collaboration with the TReNDS Center{:target="_blank"} and Professor Sergey Plis{:target="_blank"}
- Running simulations on compute clusters with Kubernetes and Slurm, using clingo{:target="_blank"} to solve answer-set programming formulation, establishing use on empirical data, and implementing causal learning methods to optimize inputs
- Paper in submission, currently on arXiv{:target="_blank"}
March 2023 — Present
- Finished thesis available here{:target="_blank"}
- In many common fairness settings, we can’t observe counterfactuals — e.g. would a person we denied a loan pay it back if we had granted it? — and this biases training data, creating uncertainty particularly for groups historically denied loans. Using techniques like structural equation modeling{:target="_blank"} and infomax active learning{:target="_blank"}, we show how in these situations even with optimal, unbiased models, differences in true group parameters can lead to large differences in uncertainty.
- Technically, using blavaan{:target="_blank"} for Bayesian latent-variable modeling, and formulated a maximum-likelihood problem to find points with greatest informational value
June 2021 — June 2022
- Interned at the Stowers Lab{:target="_blank"} at the Scripps Research Institute{:target="_blank"} under, providing computational assistance for neuroscience projects studying behavior
- Used ML tools such as DeepLabCut{:target="_blank"} and B-SOID{:target="_blank"} on remote compute cluster, extracting pose information from mouse behavior video
- Performed timeseries manipulation and analysis
- Worked on projects studying neurological underpinnings of physiological arousal, mouse scent marking, and olfaction
June 2021 — December 2021
- Under Professor Christine Alvarado{:target="_blank"}, studied the effects of ERSP{:target="_blank"}, an early-undergraduate CS research program, on students' senses of identity as researchers and computer scientists
- Used thematic analysis methods on open-ended survey data, as well as Python for preprocessing, analysis, and interrater reliability calculation
September 2020 — December 2021
- Along with another undergraduate, supervised by Leanne Chukoskie{:target="_blank"} at the Qualcomm Institute at UCSD, conducted self-directed research project on engagement in online learning during COVID pandemic
- Developed and distributed survey of UCSD students about their course experiences
- Analyzed and visualized data in R, wrote most of paper's Results section
- Published in Frontiers in Education as co-first author
ION-C: Integration of Overlapping Networks via Constraints. Nair, P., Bhandari, P., Abavisani, M., Plis, S., & Danks, D. (2024). arXiv preprint arXiv:2411.04243. https://arxiv.org/abs/2411.04243{:target="_blank"}
Active Learning and Epistemic Defenses of Fairness. Nair, P. (2024). (Master's thesis, University of California, San Diego). Available on escholarship.org.{:target="_blank"}
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{:target="_blank"}
* co-first-author