Topological properties of chromosome conformation graphs reflect spatial proximities within chromatin. - Hao Wang 024; Geet Duggal; Rob Patro; Michelle Girvan; Sridhar Hannenhalli; Carl Kingsford (BCB, 2013)

Predictability of User Behavior in Social Media: Bottom-Up v. Top-Down Modeling. - David Darmon; Jared Sylvester; Michelle Girvan; William Rand (SocialCom, 2013)

Robustness of Network Measures to Link Errors. - John Platig; Edward Ott; Michelle Girvan (CoRR, 2013)

Spatially embedded growing small-world networks. - Ari Zitin; Alex Gorowora; Shane Squires; Mark Herrera; Thomas M. Antonsen Jr.; Michelle Girvan; Edward Ott (CoRR, 2013)

Competing Opinions and Stubborness: Connecting Models to Data. - Keith Burghardt; William Rand; Michelle Girvan (CoRR, 2014)

The Impact of Imperfect Information on Network Attack. - Andrew Melchionna; Jesus Caloca; Shane Squires; Thomas M. Antonsen Jr.; Edward Ott; Michelle Girvan (CoRR, 2014)

Forecasting High Tide: Predicting Times of Elevated Activity in Online Social Media. - Jimpei Harada; David Darmon; Michelle Girvan; William Rand (ASONAM, 2015)

Complexity-Regularized Regression for Serially-Correlated Residuals with Applications to Stock Market Data. - David Darmon; Michelle Girvan (Entropy, 2015)

Finding New Order in Biological Functions from the Network Structure of Gene Annotations. - Kimberly Glass; Michelle Girvan (PLoS Comput. Biol., 2015)

The Myopia of Crowds: A Study of Collective Evaluation on Stack Exchange. - Keith Burghardt; Emanuel F. Alsina; Michelle Girvan; William Rand; Kristina Lerman (CoRR, 2016)

Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach. - Sanjukta Krishnagopal; Yiannis Aloimonos; Michelle Girvan (Complex., 2018)

Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model. - Jaideep Pathak; Alexander Wikner; Rebeckah Fussell; Sarthak Chandra; Brian R. Hunt; Michelle Girvan; Edward Ott (CoRR, 2018)

Encoding of a Chaotic Attractor in a Reservoir Computer: A Directional Fiber Investigation. - Sanjukta Krishnagopal; Garrett E. Katz; Michelle Girvan; James A. Reggia (IJCNN, 2019)

Computational landscape of user behavior on social media. - David Darmon; William Rand; Michelle Girvan (CoRR, 2019)

Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms. - Pantelis-Rafail Vlachas; Jaideep Pathak; Brian R. Hunt; Themistoklis P. Sapsis; Michelle Girvan; Edward Ott; Petros Koumoutsakos (CoRR, 2019)

Separation of Chaotic Signals by Reservoir Computing. - Sanjukta Krishnagopal; Michelle Girvan; Edward Ott; Brian R. Hunt (CoRR, 2019)

Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems. - Alexander Wikner; Jaideep Pathak; Brian R. Hunt; Michelle Girvan; Troy Arcomano; Istvan Szunyogh; Andrew Pomerance; Edward Ott (CoRR, 2020)

Hybrid Backpropagation Parallel Reservoir Networks. - Matthew Evanusa; Snehesh Shrestha; Michelle Girvan; Cornelia Fermüller; Yiannis Aloimonos (CoRR, 2020)

Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics. - Pantelis-Rafail Vlachas; Jaideep Pathak; Brian R. Hunt; Themistoklis P. Sapsis; Michelle Girvan; Edward Ott; Petros Koumoutsakos (Neural Networks, 2020)

Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components. - Alexander Wikner; Jaideep Pathak; Brian R. Hunt; Istvan Szunyogh; Michelle Girvan; Edward Ott (CoRR, 2021)

Parallel Machine Learning for Forecasting the Dynamics of Complex Networks. - Keshav Srinivasan; Nolan Coble; Joy Hamlin; Thomas M. Antonsen Jr.; Edward Ott; Michelle Girvan (CoRR, 2021)

A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data. - Daniel Canaday; Andrew Pomerance; Michelle Girvan (CoRR, 2021)

Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization. - Alexander Wikner; Brian R. Hunt; Joseph Harvey; Michelle Girvan; Edward Ott (CoRR, 2022)

Deep-Readout Random Recurrent Neural Networks for Real-World Temporal Data. - Matthew Evanusa; Snehesh Shrestha; Vaishnavi Patil; Cornelia Fermüller; Michelle Girvan; Yiannis Aloimonos (SN Comput. Sci., 2022)

t-ConvESN: Temporal Convolution-Readout for Random Recurrent Neural Networks. - Matthew S. Evanusa; Vaishnavi Patil; Michelle Girvan; Joel Goodman; Cornelia Fermüller; Yiannis Aloimonos (ICANN (6), 2023)

Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems. - Ravi Chepuri; Dael Amzalag; Thomas M. Antonsen Jr.; Michelle Girvan (CoRR, 2024)

Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing. - Alexander Wikner; Joseph Harvey; Michelle Girvan; Brian R. Hunt; Andrew Pomerance; Thomas M. Antonsen Jr.; Edward Ott (Neural Networks, 2024)

Tailored Forecasting from Short Time Series via Meta-learning. - Declan A. Norton; Edward Ott; Andrew Pomerance; Brian R. Hunt; Michelle Girvan (CoRR, 2025)

Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics. - Keshav Srinivasan; Dietmar Plenz; Michelle Girvan (CoRR, 2025)

Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing. - Declan A. Norton; Yuanzhao Zhang; Michelle Girvan (CoRR, 2025)