Jinmeng Rao

2026

1. 

   ARXIV

   TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations

   Maria Despoina Siampou, Gengchen Mai, Ni Lao, Jinmeng Rao, Neha Arora, Cyrus Shahabi, and Shushman Choudhury

   arXiv preprint arXiv:2605.06990, 2026

   Highlight: Academia–industry collaboration (USC, UT Austin, Google) on trajectory-centric urban multimodal learning with geospatially aligned neural representations

   arXiv Bib

   @article{siampou2026trajganr,
     title = {TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations},
     author = {Siampou, Maria Despoina and Mai, Gengchen and Lao, Ni and Rao, Jinmeng and Arora, Neha and Shahabi, Cyrus and Choudhury, Shushman},
     journal = {arXiv preprint arXiv:2605.06990},
     year = {2026},
     note = {Highlight: Academia–industry collaboration (USC, UT Austin, Google) on trajectory-centric urban multimodal learning with geospatially aligned neural representations},
   }

2. 

   BOOK CHAPTER

   On the Ethics of Generative GeoAI: Explainability, Bias, Hallucination, Accountability, Privacy, and Trust

   Gengchen Mai, Ni Lao, J. Zhang, L. Mao, Z. Wang, N. Wu, Krzysztof Janowicz, K. Wu, Jinmeng Rao, and  others

   In Geography According to Foundation Models, 2026

   Highlight: Book chapter in Geography According to Foundation Models; a systematic treatment of explainability, bias, hallucination, accountability, privacy, and trust in generative GeoAI

   Bib

   @incollection{mai2026ethics,
     title = {On the Ethics of Generative GeoAI: Explainability, Bias, Hallucination, Accountability, Privacy, and Trust},
     author = {Mai, Gengchen and Lao, Ni and Zhang, J. and Mao, L. and Wang, Z. and Wu, N. and Janowicz, Krzysztof and Wu, K. and Rao, Jinmeng and others},
     booktitle = {Geography According to Foundation Models},
     pages = {215--232},
     year = {2026},
     note = {Highlight: Book chapter in <em>Geography According to Foundation Models</em>; a systematic treatment of explainability, bias, hallucination, accountability, privacy, and trust in generative GeoAI},
   }

2025

1. 

   ARXIV

   Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

   Gemini Team, Google

   arXiv preprint arXiv:2507.06261, 2025

   Core contributor to Gemini 2.5, Gemini 3, and Gemini 4 Argon (post-training and evaluation for long-context, coding, and agentic capabilities); 5,400+ citations

   arXiv Bib

   @article{comanici2025gemini,
     title = {Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities},
     author = {{Gemini Team, Google}},
     journal = {arXiv preprint arXiv:2507.06261},
     year = {2025},
     note = {Core contributor to Gemini 2.5, Gemini 3, and Gemini 4 Argon (post-training and evaluation for long-context, coding, and agentic capabilities); 5,400+ citations},
   }

2. 

   JAG

   Towards the Next Generation of Geospatial Artificial Intelligence

   Gengchen Mai, Yiqun Xie, Xiaowei Jia, Ni Lao, Jinmeng Rao, Qing Zhu, Zeping Liu, Yao-Yi Chiang, and Junfeng Jiao

   International Journal of Applied Earth Observation and Geoinformation, 2025

   Highlight: Vision and roadmap paper for next-generation GeoAI; JCR Q1 journal in remote sensing; 220+ citations within two years of publication

   Bib

   @article{mai2025towards,
     title = {Towards the Next Generation of Geospatial Artificial Intelligence},
     author = {Mai, Gengchen and Xie, Yiqun and Jia, Xiaowei and Lao, Ni and Rao, Jinmeng and Zhu, Qing and Liu, Zeping and Chiang, Yao-Yi and Jiao, Junfeng},
     journal = {International Journal of Applied Earth Observation and Geoinformation},
     volume = {136},
     year = {2025},
     note = {Highlight: Vision and roadmap paper for next-generation GeoAI; JCR Q1 journal in remote sensing; 220+ citations within two years of publication},
   }

2024

1. 

   COLM

   Best Practices and Lessons Learned on Synthetic Data

   Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si, Yanzhe Zhang, Jinmeng Rao, Steven Zheng, Daiyi Peng, Diyi Yang, Denny Zhou, and Andrew M. Dai

   In Conference on Language Modeling (COLM), 2024

   Highlight: Google DeepMind overview of synthetic data for language models; 360+ citations; COLM 2024 (inaugural Conference on Language Modeling)

   arXiv Bib

   @inproceedings{liu2024best,
     title = {Best Practices and Lessons Learned on Synthetic Data},
     author = {Liu, Ruibo and Wei, Jerry and Liu, Fangyu and Si, Chenglei and Zhang, Yanzhe and Rao, Jinmeng and Zheng, Steven and Peng, Daiyi and Yang, Diyi and Zhou, Denny and Dai, Andrew M.},
     booktitle = {Conference on Language Modeling (COLM)},
     year = {2024},
     note = {Highlight: Google DeepMind overview of synthetic data for language models; 360+ citations; COLM 2024 (inaugural Conference on Language Modeling)},
   }

2. 

   SIGIR

   IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues

   Diji Yang, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo, Yawen Zhang, Jie Yang, and Yi Zhang

   In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

   Highlight: Top conference in information retrieval (CCF-A); 110+ citations; learns inner monologues for multi-round retrieval-augmented generation

   arXiv Bib

   @inproceedings{yang2024imrag,
     title = {IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues},
     author = {Yang, Diji and Rao, Jinmeng and Chen, Kezhen and Guo, Xiaoyuan and Zhang, Yawen and Yang, Jie and Zhang, Yi},
     booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
     year = {2024},
     note = {Highlight: Top conference in information retrieval (CCF-A); 110+ citations; learns inner monologues for multi-round retrieval-augmented generation},
   }

2023

1. 

   PH.D. THESIS

   Trajectory Privacy Protection with Geospatial Artificial Intelligence

   Jinmeng Rao

   University of Wisconsin-Madison, 2023

   Award: 2024 AAG William L. Garrison Award for Best Dissertation in Computational Geography (one recipient every two years)

   Bib

   @phdthesis{rao2023dissertation,
     title = {Trajectory Privacy Protection with Geospatial Artificial Intelligence},
     author = {Rao, Jinmeng},
     school = {University of Wisconsin-Madison},
     year = {2023},
     note = {Award: 2024 AAG William L. Garrison Award for Best Dissertation in Computational Geography (one recipient every two years)},
   }

2. 

   ACM SIGSPATIAL

   Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models

   Jinmeng Rao, Song Gao, Gengchen Mai, and Krzysztof Janowicz

   In Proceedings of the 31st International Conference on Advances in Geographic Information Systems, 2023

   Highlight: Top Conference in Geographic Information Systems; Acceptance rate 37.2%; 50+ citations; early vision for privacy and security in GeoAI foundation models

   Abs arXiv Bib

   In recent years we have seen substantial advances in foundation models for artificial intelligence, including language, vision, and multimodal models. Recent studies have highlighted the potential of using foundation models in geospatial artificial intelligence, known as GeoAI Foundation Models, for geographic question answering, remote sensing image understanding, map generation, and location-based services, among others. However, the development and application of GeoAI foundation models can pose serious privacy and security risks, which have not been fully discussed or addressed to date. This paper introduces the potential privacy and security risks throughout the lifecycle of GeoAI foundation models and proposes a comprehensive blueprint for research directions and preventative and control strategies. Through this vision paper, we hope to draw the attention of researchers and policymakers in geospatial domains to these privacy and security risks inherent in GeoAI foundation models and advocate for the development of privacy-preserving and secure GeoAI foundation models.

   @inproceedings{rao2023psgeoai,
     title = {Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models},
     author = {Rao, Jinmeng and Gao, Song and Mai, Gengchen and Janowicz, Krzysztof},
     booktitle = {Proceedings of the 31st International Conference on Advances in Geographic Information Systems},
     pages = {1--4},
     year = {2023},
     note = {Highlight: Top Conference in Geographic Information Systems; Acceptance rate 37.2\%; 50+ citations; early vision for privacy and security in GeoAI foundation models},
   }

3. 

   IJGIS

   CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches

   Jinmeng Rao, Song Gao, and Sijia Zhu

   International Journal of Geographical Information Science, 2023

   Highlight: Top Journal in Geographic Information Science; SCI Q1 in CS, IS and Geography; Acceptance rate 12%

   Abs arXiv Bib Code

   The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.

   @article{rao2023cats,
     title = {CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches},
     author = {Rao, Jinmeng and Gao, Song and Zhu, Sijia},
     journal = {International Journal of Geographical Information Science},
     year = {2023},
     volume = {37},
     number = {12},
     pages = {1-33},
     note = {Highlight: Top Journal in Geographic Information Science; SCI Q1 in CS, IS and Geography; Acceptance rate 12\%},
   }

2021

1. 

   TGIS

   A privacy-preserving framework for location recommendation using decentralized collaborative machine learning

   Jinmeng Rao, Song Gao, Mingxiao Li, and Qunying Huang

   Transactions in GIS, 2021

   Highlight: Flagship Journal in Geographic Information Systems; Recognized as the first work on Federated Learning + Spatial Data Sciences (Graser, A. et al 2022)

   Abs Bib HTML Code

   The nowadays ubiquitous location-aware mobile devices have contributed to the rapid growth of individual-level location data. Such data are usually collected by location-based service platforms as training data to improve their predictive models’ performance, but the collection of such data may raise public concerns about privacy issues. In this study, we introduce a privacy-preserving location recommendation framework based on a decentralized collaborative machine learning approach: federated learning. Compared with traditional centralized learning frameworks, we keep users’ data on their own devices and train the model locally so that their data remain private. The local model parameters are aggregated and updated through secure multiple-party computation to achieve collaborative learning among users while preserving privacy. Our framework also integrates information about transportation infrastructure, place safety, and flow-based spatial interaction to further improve recommendation accuracy. We further design two attack cases to examine the privacy protection effectiveness and robustness of the framework. The results show that our framework achieves a better balance on the privacy–utility trade-off compared with traditional centralized learning methods. The results and ensuing discussion offer new insights into privacy-preserving geospatial artificial intelligence and promote geoprivacy in location-based services.

   @article{rao2021privacy,
     title = {A privacy-preserving framework for location recommendation using decentralized collaborative machine learning},
     author = {Rao, Jinmeng and Gao, Song and Li, Mingxiao and Huang, Qunying},
     journal = {Transactions in GIS},
     year = {2021},
     note = {Highlight: Flagship Journal in Geographic Information Systems; Recognized as the first work on Federated Learning + Spatial Data Sciences <a href="https://publications.ait.ac.at/en/publications/on-the-role-of-spatial-data-science-for-federated-learning">(Graser, A. et al 2022)</a>},
   }

2. 

   PNAS

   Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

   Xiao Hou, Song Gao, Qin Li, Yuhao Kang, Nan Chen, Kaiping Chen, Jinmeng Rao, Jordan S Ellenberg, and Jonathan A Patz

   Proceedings of the National Academy of Sciences, 2021

   Highlight: PNAS; models intracounty COVID-19 spread with business-traffic mobility, age, and race to inform reopening policies; 190+ citations

   Abs Bib HTML Code

   The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

   @article{hou2021intracounty,
     title = {Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race},
     author = {Hou, Xiao and Gao, Song and Li, Qin and Kang, Yuhao and Chen, Nan and Chen, Kaiping and Rao, Jinmeng and Ellenberg, Jordan S and Patz, Jonathan A},
     journal = {Proceedings of the National Academy of Sciences},
     volume = {118},
     number = {24},
     pages = {e2020524118},
     year = {2021},
     publisher = {National Academy of Sciences},
     note = {Highlight: <em>PNAS</em>; models intracounty COVID-19 spread with business-traffic mobility, age, and race to inform reopening policies; 190+ citations},
   }

2020

1. 

   GISCIENCE

   LSTM-TrajGAN: A deep learning approach to trajectory privacy protection

   Jinmeng Rao, Song Gao, Yuhao Kang, and Qunying Huang

   In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I, 2020

   Highlight: Top Conference in Geographic Information Sciences; Acceptance rate 34%; 270+ citations; open-source model extended, adapted, and used as the benchmark in many later trajectory-privacy studies

   Abs Bib HTML Code

   The prevalence of location-based services contributes to the explosive growth of individual-level location trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection for human mobility studies.

   @inproceedings{rao2020lstm,
     title = {LSTM-TrajGAN: A deep learning approach to trajectory privacy protection},
     author = {Rao, Jinmeng and Gao, Song and Kang, Yuhao and Huang, Qunying},
     booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
     volume = {177},
     pages = {12--1},
     year = {2020},
     organization = {Schloss Dagstuhl--Leibniz-Zentrum f$\{$$\backslash$"u$\}$r Informatik},
     note = {Highlight: Top Conference in Geographic Information Sciences; Acceptance rate 34\%; 270+ citations; open-source model extended, adapted, and used as the benchmark in many later trajectory-privacy studies},
   }

2. 

   ACM SIGSPATIAL

   Mapping county-level mobility pattern changes in the United States in response to COVID-19

   Song Gao, Jinmeng Rao, Yuhao Kang, Yunlei Liang, and Jake Kruse

   SIGSpatial Special, 2020

   Highlight: One of the first social distancing maps during COVID-19; 410+ citations; Media Coverage NBC, FOX, ABC

   Abs Bib HTML Code Website

   To contain the COVID-19 epidemic, one of the non-pharmacological epidemic control measures is reducing the transmission rate of SARS-COV-2 in the population through social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancing guidelines was developed by the GeoDS Lab @UW-Madison with the support of the National Science Foundation RAPID program. The web portal integrates geographic information systems (GIS) and daily updated human mobility statistical patterns (median travel distance and stay-at-home dwell time) derived from large-scale anonymized and aggregated smartphone location big data at the county-level in the United States, and aims to increase risk awareness of the public, support data-driven public health and governmental decision-making, and help enhance community responses to the COVID-19 pandemic.

   @article{gao2020mapping,
     title = {Mapping county-level mobility pattern changes in the United States in response to COVID-19},
     author = {Gao, Song and Rao, Jinmeng and Kang, Yuhao and Liang, Yunlei and Kruse, Jake},
     journal = {SIGSpatial Special},
     volume = {12},
     number = {1},
     pages = {16--26},
     year = {2020},
     publisher = {ACM New York, NY, USA},
     note = {Highlight: One of the first social distancing maps during COVID-19; 410+ citations; Media Coverage <a href="https://www.nbc15.com/2020/10/07/tracking-movement-in-wisconsin-leading-up-to-latest-health-order/">NBC</a>, <a href="http://fox47.com/news/local/uw-madison-researchers-map-travel-data-to-combat-pandemic">FOX</a>, <a href="https://www.wisn.com/article/uw-madison-scientists-develop-social-distancing-tracker-map/32163419#">ABC</a>},
   }

3. 

   JAMA

   Association of mobile phone location data indications of travel and stay-at-home mandates with covid-19 infection rates in the us

   Song Gao, Jinmeng Rao, Yuhao Kang, Yunlei Liang, Jake Kruse, Dorte Dopfer, Ajay K Sethi, Juan Francisco Mandujano Reyes, Brian S Yandell, and Jonathan A Patz

   JAMA network open, 2020

   Highlight: JAMA Network Open (JCR Q1, general and internal medicine); early nationwide evidence linking stay-at-home mandates, mobility, and COVID-19 infection rates; 270+ citations

   Abs Bib HTML

   Importance A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed. Objective To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection. Design, Setting, and Participants This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases. Exposure State-level stay-at-home orders during the COVID-19 pandemic. Main Outcomes and Measures The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state. Results Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was –0.586 (95% CI, –0.742 to –0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results. Conclusions and Relevance These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.

   @article{gao2020association,
     title = {Association of mobile phone location data indications of travel and stay-at-home mandates with covid-19 infection rates in the us},
     author = {Gao, Song and Rao, Jinmeng and Kang, Yuhao and Liang, Yunlei and Kruse, Jake and Dopfer, Dorte and Sethi, Ajay K and Reyes, Juan Francisco Mandujano and Yandell, Brian S and Patz, Jonathan A},
     journal = {JAMA network open},
     volume = {3},
     number = {9},
     pages = {e2020485--e2020485},
     year = {2020},
     publisher = {American Medical Association},
     note = {Highlight: <em>JAMA Network Open</em> (JCR Q1, general and internal medicine); early nationwide evidence linking stay-at-home mandates, mobility, and COVID-19 infection rates; 270+ citations},
   }