Paramveer Dhillon

PARAMVEER DHILLON

Office:5544 Leinweber, 2200 Hayward St., Ann Arbor, MI 48109
Phone: 734-764-5876
Email: lastname followed by the letter 'p' at umich dot edu
Twitter: @dhillon_p
Google Scholar: https://goo.gl/FEsnE8

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My current research focuses on how Large Language Models (LLMs) reshape human creativity, decision-making, and information consumption. My work bridges the disciplines of AI, Human-Computer Interaction (HCI), and Information Systems and is broadly situated in Human-Centered AI.

Human-LLM Collaboration/Co-writing and Creative Labor Markets: We investigate how people create with LLMs, examining questions of authorship, ownership, and market disruption. Our empirical work provides the first systematic evidence that fine-tuned LLMs can produce undetectable, professional-quality writing that competes directly with human authors.

Personalization and Human-Centric Recommender Systems: We design LLM-based systems that respect human agency while providing personalized experiences. This includes developing temptation-aware recommendation algorithms that help users navigate between immediate desires and long-term goals, and creating personalization methods that adapt to individual preferences without reinforcing filter bubbles.

Causal Methods for Language-Based Interventions: We advance causal inference techniques for high-dimensional text treatments. Our recent work introduces policy learning frameworks for natural language action spaces, enabling LLMs to learn optimal intervention strategies through gradient-based optimization on language embeddings. This line of work supports a variety of applications ranging from therapeutic dialogue refinement to content moderation, where each text-based decision impacts future outcomes.

I hold an A.M. in Statistics and an M.S.E. and a Ph.D. (2015) in Computer Science all from the University of Pennsylvania, where I was advised by Professors Lyle Ungar, Dean Foster, and James Gee. My dissertation, "Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging," received the Morris and Dorothy Rubinoff Award for outstanding doctoral dissertation. This work introduced theoretically-grounded spectral methods for learning word embeddings (JMLR 2015, ICML 2012, NeurIPS 2011) and brain image segmentation (NeuroImage 2014), achieving both computational efficiency and provable convergence guarantees. My contributions to spectral decomposition and context-dependent representation learning provided early theoretical foundations for understanding how distributed representations capture semantic relationships, principles that remain central to modern transformer architectures. I also did other research in my Ph.D. on establishing connections between PCA and ridge regression (JMLR 2013) and on provably faster row and column subsampling algorithms for least squares regression (NeurIPS 2013a,b).

Following my Ph.D., I completed a postdoctoral fellowship at MIT with Professor Sinan Aral, where I worked on problems at the intersection of Machine Learning, Causal Inference, Network Science, and Information Systems. This research program produced several foundational contributions: establishing tractable methods for influence maximization under empirically-grounded network models (Nature Human Behaviour 2018); designing optimal digital paywall strategies that balance subscription revenue with content demand leveraging quasi-experiments (Management Science 2020); developing neural matrix factorization techniques for modeling temporal dynamics in user preferences (Marketing Science 2021); quantifying the information advantages of network brokers through novel diversity metrics (Management Science 2023); and creating surrogate-index methods for optimizing long-term outcomes in sequential decision problems (Management Science 2024).

Research Group

Ph.D. Students

Yachuan Liu [F20–] — Last Stop: BS @ UC Berkeley
Sanzeed Anwar [F21–] — Last Stop: BS+MEng @ MIT
Bohan Zhang [F22–] — Last Stop: MS @ University of Michigan

Former Students

Xiaochun Wei [BS ’25, next M.S. in Data Science at Harvard University]
Jiyu Chen [BS ’23, MS ’24, next Software Engineer at Stripe]
Xinyue Li [MS ’23, next Ph.D. in Statistics at Boston University]
Ella Li [MS ’23, next Ph.D. in CS at Northeastern University]
Siqi Ma [BS ’23, next MS in Statistics at Stanford University]
Shaochun Zheng [BS ’23, next MS in CS at UC San Diego]
Houming Chen [BS ’23, next Ph.D. in CS at University of Michigan]
Yushi She [BS ’23, next MS in CS at Georgia Tech]
Ted Yuan [BS ’23, next MS in ECE at Carnegie Mellon University]
Evan Weissburg [BS ’23, next Software Engineer at Jane Street Capital]
Arya Kumar [BS ’23, next Software Engineer at Jane Street Capital]
Jupiter Zhu [BS ’22, next MS in CS at Stanford University]
Tianyi Li [BS ’22, next MS in INI at Carnegie Mellon University]
Xianglong Li [BS ’22, next MS in CS at Yale University]
Florence Wu [BS ’22, next MS in CS at Harvard University]
Yingzhuo Yu [BS ’22, next MS in CS at UIUC]
Xingjian Zhang [BS ’22, next Ph.D. in Information at UMSI]
Bohan Zhang [MS ’22, next Ph.D. in Information at UMSI]
Zhengyang Shan [MS ’22, next Ph.D. in CDS at Boston University]
Jiapeng Guo [BS ’21, next MS in CS at Columbia University]
Zilu Wang [BS ’21, next MS in MS&E at Stanford University]

I supervise students interested in Human-centric AI and Information Systems. For Fall ’26, I’m specifically recruiting Ph.D. students with strong AI/HCI skills—e.g., building polished front-ends (React/TypeScript), rapid prototyping and performing online user studies, fine-tuning and evaluating LLMs, and solid data/ML engineering/programming skills. The nature of the research project will be similar in spirit to this paper.

Masters/Undergrads (already at University of Michigan) may email their CV and transcripts. Prospective Ph.D. students are encouraged to apply to our Ph.D. program here and mention my name as a potential advisor. The deadline is December 1 each year.

Below is a list of selected publications that highlight my core research interests and contributions. A complete list of all my publications is available here.