Welcome!

I am a Postdoctoral Fellow at Cornell's Digital Life Initiative (DLI) working with Helen Nissenbaum. 

Previously, I completed my PhD in Computer Science at Brown University advised by Professor Suresh Venkatasubramanian. I was also an affiliate of Brown's Center for Technological Responsibility, Reimagination and Redesign (CNTR) at the Data Science Institute.

I am interested in examining AI tools and automated decision systems to assess their long-term impacts, as well as normative questions around the unintended consequences of optimization. This interdisciplinary perspective also incorporates econometrics in the modeling and simulation of (artificial) societies

You can find my CV here (updated August 2024) and my Google Scholar page here.

Previously, I have worked on the following publications:

• Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Helen Nissenbaum, Rethinking Optimization: A Systems-Based Approach to Social Externalities, AIES 2025 (PDF here)

• Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Aditya Bhaskara, Suresh Venkatasubramanian, Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security, TMLR 2025 (PDF here)

• Pegah Nokhiz, Aravinda Kanchana, Consumer Autonomy or Illusion? Rethinking Consumer Agency in the Age of Algorithms, Journal of Social Computing (JSC) 2025 (PDF here)

• Pegah Nokhiz*, Aravinda Kanchana Ruwanpathirana*, Neal Patwari,  and Suresh Venkatasubramanian, Agent-based Simulation of Decision-making under Uncertainty to Study Financial Precarity, PAKDD 2024 (PDF here)

• Lydia Reader, Pegah Nohkiz, Cathleen Power, Neal Patwari, Suresh Venkatasubramanian, Sorelle Friedler, Models for understanding and quantifying feedback in societal systems, The 2022 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022)

• Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Neal Patwari,  and Suresh Venkatasubramanian, Precarity: Modeling the long-term effects of compounded decisions on individual instability, AAAI/ACM Conference on AI, Ethics, and Society, 2021 (the arXiv pre-print is here).

• Vivek Gupta*, Pegah Nokhiz*, Chitradeep Duttaroy*, Suresh Venkatasubramanian, Equalizing Recourse Across Groups, arXiv preprint, arXiv:1909.03166. 

• Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar, INFOTABS: Inference on Tables as Semi-structured Data, Annual Conference of the Association for Computational Linguistics (ACL), 2020

• Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, and Partha Talukdar, P-SIF: Document Embeddings using Partition Averaging, AAAI Conference on Artificial Intelligence (AAAI-20), NYC, US, 2020

•  Vivek Gupta, Ankit Saw, Pegah Nokhiz, Harshit Gupta and Partha Talukdar, Improving Document Classification with Multi-Sense Embeddings, European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain, 2020 (extension of NAACL-SRW 2019 paper)

• Vivek Gupta, Ankit Saw, Harshit Gupta, Pegah Nokhiz and Partha Talukdar, Word Polysemy Aware Document Vector Estimation, NAACL Student Research Workshop (SRW) 2019, non-archival (extension published in ECAI 2020)

• Pegah Nokhiz and Fengjun Li, Understanding Rating Behavior based on Moral Foundations: The case of Yelp Reviews, IEEE BigData, Boston, MA, 2017

• Vivek Gupta, Prerna Bharti, Pegah Nokhiz and Harish Karnick, SumPubMed: Summarization Dataset of PubMed Scientific Articles, ACL-IJCNLP SRW, 2021

• Pranshi Yadav, Priya Yadav, Pegah Nokhiz and Vivek Gupta, Unbiasing Review Ratings with Tendency based Collaborative Filtering, AACL 2020 SRW

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