[PHOTO]

PARAMVEER DHILLON

Associate Professor
School of Information
University of Michigan

Affiliate Faculty
Michigan Institute for Data Science (MIDAS)
E-Health & AI Initiative (e-HAIL)


Digital Fellow
MIT Initiative on the Digital Economy

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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Research Interests   Publications   Professional Background   Teaching   Awards   Research Group   Service   Software  

Research Interests

My current research focuses on how Large Language Models (LLMs) reshape human creativity, decision-making, and information consumption. My work bridges machine learning, causal inference, and human-computer interaction (HCI) to understand and shape the societal impacts of language technologies.

Key Research Areas:

Human-LLM Collaboration/Co-writing and Creative Labor Markets: We investigate how people create with LLMs, examining questions of authorship, ownership, and market disruption [CHI '24 + under review]. 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 [WWW '24]. Our work on multi-stage policy learning enables LLMs to optimize sequential text-based interventions in domains like mental health support and educational content delivery.

Causal Methods for Language-Based Interventions: We advance causal inference techniques for high-dimensional text treatments [NAACL '24]. 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 methodology supports applications from therapeutic dialogue refinement to content moderation, where each text-based decision impacts future outcomes.

Active Research Threads:

Professional Background

I am an Associate Professor in the School of Information at the University of Michigan (tenured 2025) and a Digital Fellow at MIT's Initiative on the Digital Economy. I joined Michigan as an Assistant Professor in 2019.

I received my Ph.D. in Computer Science from the University of Pennsylvania (2015), 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). I also hold an A.M. in Statistics and M.S.E. in Computer Science from Penn.

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). This body of work established rigorous approaches for causal inference in high-dimensional, networked settings.

Much before all this, I was a carefree undergrad studying Electronics & Electrical Communication Engineering at PEC in my hometown of Chandigarh, India. I developed my interest in AI/ML and the desire to pursue a Ph.D. as a result of three memorable summer internships, before my Ph.D., at Computer Vision Center @ Barcelona [summer 2006], Max Planck Institute for Intelligent Systems @ Tuebingen [summer 2008], and Information Sciences Institute/USC @ Los Angeles [summer 2009].

Teaching

  1. ECE 4760J Data Mining: Methods and Applications @ UM-Shanghai Jiao Tong University Joint Institute (JI) @ S25.
  2. SI 671/721 Data Mining: Methods and Applications @ F[19-24].
  3. SIADS 642 [online] Deep Learning I [Developed from scratch] @ F20-present.
  4. SIADS 647 [online] Deep Learning II (Generative AI) [Developed from scratch] @ W25.
  5. SIADS 532,632 [online] Data Mining I, II @ W21-present.

Research Group

Ph.D. Students

  1. Yachuan Liu [F20-] Last Stop: BS @ UC Berkeley.
  2. Sanzeed Anwar [F21-] Last Stop: BS+MEng @ MIT.
  3. Bohan Zhang [F22-] Last Stop: MS @ University of Michigan.
  4. Siqi Liang [W25-] Last Stop: MS @ University of Southern California.

Former Students

  1. Xinyue Li[MS '23, next Ph.D. in Statistics at Boston University]
  2. Ella Li[MS '23, next Ph.D. in CS at Northeastern University]
  3. Siqi Ma [BS '23, next MS in Statistics at Stanford University]
  4. Shaochun Zheng [BS '23, next MS in CS at UC San Diego]
  5. Houming Chen [BS '23, next Ph.D. in CS at University of Michigan]
  6. Yushi She [BS '23, next MS in CS at Georgia Tech]
  7. Ted Yuan [BS '23, next MS in ECE at Carnegie Mellon University]
  8. Evan Weissburg [BS '23, next Software Engineer at Jane Street Capital]
  9. Arya Kumar [BS '23, next Software Engineer at Jane Street Capital]
  10. Jupiter Zhu [BS '22, next MS in CS at Stanford University]
  11. Tianyi Li [BS '22, next MS in INI at Carnegie Mellon University]
  12. Xianglong Li [BS '22, next MS in CS at Yale University]
  13. Florence Wu [BS '22, next MS in CS at Harvard University]
  14. Yingzhuo Yu [BS '22, next MS in CS at UIUC]
  15. Xingjian Zhang [BS '22, next Ph.D. in Information at UMSI]
  16. Bohan Zhang [MS '22, next Ph.D. in Information at UMSI]
  17. Zhengyang Shan [MS '22, next Ph.D. in CDS at Boston University]
  18. Jiapeng Guo [BS '21, next MS in CS at Columbia University]
  19. Zilu Wang [BS '21, next MS in MS&E at Stanford University]

I always have openings for strong students in my group at all levels (Postdoctoral, Ph.D, Masters, or Undergrad). I am broadly looking to supervise students who are interested in working on Human-centric AI, Information Systems, or NLP. Prior research experience in these areas is highly valued, as are strong programming skills and a solid applied math/statistics background.

Process: Masters/Undergrads (already at University of Michigan) interested in working with me can email their CV and transcripts. Prospective Postdocs can directly email me their latest CV and Research Statement. Prospective Ph.D. students need not email me directly but 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.

Awards

  1. INFORMS Information Systems Society (ISS) Gordon B. Davis Young Scholar Award, 2021.
  2. INFORMS Annual Conference (Best Paper Award), 2020.
  3. Workshop on Information Systems and Economics (WISE) (Runner-up Best Paper Award), 2016.
  4. Rubinoff Best Doctoral Dissertation Award (awarded by Penn CIS), 2015.

Service to the Profession

  1. Editorial Board JMLR [2020-].
  2. Ad-hoc Reviewer: Nature, Nature Human Behaviour, Nature Communications, PNAS, JAIR, Information Science Research (ISR), Management Science, Marketing Science, IEEE TKDE, IEEE TPAMI.
  3. Reviewer/PC/SPC Member @ Core AI/ML Conferences: [every year since 2013] NeurIPS, ICML, AISTATS, ICLR, AAAI, IJCAI.
  4. Reviewer/PC/SPC Member @ Core Information Systems Conferences: [every year since 2017] ICIS, CIST, WISE.
  5. Reviewer/PC/SPC Member @ Core NLP/Computational Social Science Conferences: [sporadically] EMNLP, NAACL, ICWSM, IC2S2.

Selected Publications

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.

*indicates alphabetical author listing.

Software

  1. Code and data for our Nature Human Behaviour 2018 paper is available here.
  2. The ANTsR toolkit for medical image analysis (including the implementation of our NeuroImage 2014 paper) is available here.
  3. The SWELL (Spectral Word Embedding Learning for Language) JAVA toolkit for inducing word embeddings (cf. JMLR 2015, ICML 2012, NeurIPS 2011) is available here.
  4. Various Eigenword (SWELL) embeddings for reproducing the results in our JMLR 2015 paper can be found below [No additional scaling required for embeddings. Use them as is]. [Based on our results, OSCCA and TSCCA embeddings are the most robust and work best on a variety of tasks.]
  5. Generic eigenwords embeddings for various languages [Trained on much larger corpora.]

Last Modified: 7.1.25