Some of this work is funded by NSF CCF #1849883. Thanks NSF!

Publications

2021

O. Feng, R. Venkataramanan, C. Rush, and R. Samworth, “A unifying tutorial on Approximate Message Passing”, Foundations and Trends in Machine Learning (forthcoming), available online: arxiv.org/2105.02180.

Z. Bu, J. Klusowski, C. Rush, and W. Su, “Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit”, Annals of Statistics (forthcoming), available online: arxiv.org/2105.13302.

M. Avella Medina, J. Montiel Olea, C. Rush, and A. Velez, “On the Robustness to Misspecification of α-Posteriors and Their Variational Approximations”, Journal of Machine Learning (forthcoming), available online: arxiv.org/2104.08324.

K. Hsieh, C. Rush, and R. Venkataramanan, “Near-Optimal Coding for Massive Multiple Access”, Proceedings of IEEE International Symposium on Information Theory (ISIT), 2021, available online: arxiv.org/2102.04730.

C. Rush, K. Hsieh, and R. Venkataramanan, “Capacity-achieving Spatially Coupled Sparse Superposition Codes with AMP Decoding”, IEEE Transactions on Information Theory, available online: arxiv.org/2002.07844.

A. Dytso, M. Cardone, and C. Rush, “The Most Informative Order Statistic and its Application to Image Denoising”, Asilomar Conference on Signals and Systems, available online: arxiv.org/2101.11667.

V. Amalladinne, A. K. Pradhan, C. Rush, J.F. Chamberland, and K. Narayanan “Unsourced Random Access with Coded Compressed Sensing: Integrating AMP and Belief Propagation”, IEEE Transactions on Information Theory, available online: arxiv.org/2010.04364.

2020

J. Barbier, N. Macris, and C. Rush, “All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation”, NeurIPs 2020, available online: arxiv.org/2006.07971.

Z. Bu, J. Klusowski, C. Rush, and W. Su, “Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing”, IEEE Transactions on Information Theory, Vol. 67, No. 1, Jan. 2021, available online: arxiv.org/1907.07502. Short version presented at NeurIPs 2019.

A. Dytso, M. Cardone, and C. Rush, “Measuring Dependencies of Order Statistics: An Information Theoretic Perspective”, Proceedings of IEEE Information Theory Workshop (ITW), 2021, available online: arxiv.org/2009.12337.

H. Liu, C. Rush, and D. Baron, “Rigorous State Evolution Analysis for Approximate Message Passing with Side Information”, available online: arxiv.org/2003.11964.

D. Baron, C. Rush, and Y. Yapici, “mmWave Channel Estimation via Approximate Message Passing with Side Information”, IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020, available online: arxiv.org/2005.02244.

C. Rush, “An Asymptotic Rate for the LASSO Loss”, AISTATS 2020, available online: proceedings.mlr.press/rush20a.

C. Cademartori and C. Rush, “Exponentially Fast Concentration of Vector Approximate Message Passing to its State Evolution”, Proceedings of IEEE International Symposium on Information Theory (ISIT), 2020, long version prepint.

V. Amalladinne, A. K. Pradhan, C. Rush, J.F. Chamberland, and K. Narayanan “On Approximate Message Passing for Unsourced Access with Coded Compressed Sensing”, Proceedings of IEEE International Symposium on Information Theory (ISIT), 2020, available online: arxiv.org/2001.03705.

2019

Y. Ma, C. Rush, and D. Baron, “Analysis of Approximate Message Passing with Non-Separable Denoisers and Markov Random Field Priors", IEEE Transactions on Information Theory, Vol. 65, No. 11, November 2019, available online: ieeexplore.ieee.org/8793162.

C. Rush, K. Hsieh, and R. Venkataramanan, “Spatially coupled Sparse Regression Codes with Sliding Window AMP Decoding”, Proceedings of IEEE Information Theory Workshop (ITW), 2019.

H. Liu, C. Rush, and D. Baron, “State Evolution Analysis of Approximate Message Passing with Side Information”, Proceedings of IEEE International Symposium on Information Theory (ISIT), 2019, available online: arxiv.org/1902.00150

C. Rush and R. Venkataramanan, “The Error Probability of Sparse Superposition Codes With Approximate Message Passing Decoding”, IEEE Transactions on Information Theory, Vol. 65, No. 5, May 2019, available online: ieeexplore.ieee.org/8540415.

A. Ma, Y. Zhou, C. Rush, D. Baron, and D. Needell, “An Approximate Message Passing Framework for Side Information”, IEEE Transactions on Signal Processing, Vol. 67, No. 7, February 2019, available online: ieeexplore.ieee.org/8642358.

2018

C. Rush, K. Hsieh, and R. Venkataramanan, “Capacity-achieving sparse regression codes via spatial coupling”, Proceedings of IEEE Information Theory Workshop (ITW), 2018, available online: https://ieeexplore.ieee.org/8613392

K. Hsieh, C. Rush, and R. Venkataramanan, “Spatially Coupled Sparse Regression Codes: Design and State Evolution Analysis”, Proceedings of IEEE Int. Symp. on Information Theory (ISIT), 2018, ieeexplore.ieee.org/8437615

C. Rush and R. Venkataramanan, “Finite Sample Analysis of Approximate Message Passing", IEEE Transactions on Information Theory, Vol. 64, No. 11, November 2018, available online: arxiv.org/1606.01800.

2017

C. Rush and R. Venkataramanan, “The Error Exponent of Sparse Regression Codes with AMP Decoding”, Proceedings of IEEE Int. Symp. on Information Theory (ISIT), 2017, ieeexplore.ieee.org/8006975.

D. Baron, A. Ma, D. Needell, C. Rush, and T. Woolf “Conditional Approximate Message Passing with Side Information”, Proceedings of Asilomar Conference on Signals, Systems and Computers, 2017, ieeexplore.ieee.org/8335374.

Y. Ma, C. Rush, D. Baron, “Analysis of Approximate Message Passing with a Class of Non-Separable Denoisers", Proceedings of IEEE Int. Symp. on Information Theory (ISIT), 2017, ieeexplore.ieee.org/8006524.

C. Rush, A. Greig, and R. Venkataramanan, “Capacity-achieving Sparse Superposition Codes with Approximate Message Passing Decoding," IEEE Transactions on Information Theory, Vol. 63, No. 3, March 2017, available online: ieeexplore.ieee.org/7809025/.

X. Wang, C. Rush and N. Horton, “Data Visualization on Day One: Bringing Big Ideas into Intro Stats Early and Often”, Technology Innovations in Statistics Education, Volume 10, Issue 1, 2017, available online: https://escholarship.org/.

2016 and before

C. Rush and R. Venkataramanan, “Finite Sample Analysis of Approximate Message Passing", Proceedings of IEEE Int. Symp. on Information Theory (ISIT), 2016 (JKW Student Paper Award Finalist), ieeexplore.ieee.org/7541400.

C. Rush, A. Greig, and R. Venkataramanan, “Capacity-achieving Sparse Regression Codes with Approximate Message Passing Decoding", Proceedings of IEEE Int. Symp. on Information Theory (ISIT), 2015, ieeexplore.ieee.org/7282809.

B. Wexler, M. Iselli, S. Leon, W. Zaggle, C. Rush, A. Goodman, and E. Ahmet, “Cognitive Priming and Cognitive Training: Immediate and Far Transfer to Academic Skills in Children", Scientific Reports 6 (2016): 32859, available online: http://www.nature.com/srep32859. Press: NPR, Yale News,WTNH.

C. Rush and A. Barron, “The Method of Nearby Measures", Proceedings of Workshop on Information Theoretic Methods in Science and Engineering (WITMSE), 2013, www.me.inf.kyushu-u.ac.jp/witmse2013.

G. Carter, C. Rush, F. Uygun, N. Sakhanenko, D. Galas, and T. Galitski, “A systems-biology approach to modular genetic complexity", Chaos 20.2 (2010): 026102, available online: http://www.ncbi.nlm.nih.gov/pmc.

Selected Invited Talks

2022

  • UPenn Statistics Seminar, Philadelphia, PA, April 2022.
  • 2022 International Zurich Seminar on Information and Communication, Zurich, Switzerland, March 2022.
  • Duke Statistics Seminar, Durham, NC, March 2022.
  • Conference on Information Sciences and Systems, Princeton, NJ, March 2022.
  • Google Research Seminar, February 2022.
  • Workshop on Recent Directions in Machine Learning at FOCS2021, Denver, CO, February 2022.
  • 2021

    • Banff International Research Station, Workshop on Mathematical Statistics and Learning, Alberta, Canada, December 2021.
    • MIT, Stochastics and Statistics Seminar, Cambridge, MA, November 2021.
    • Texas A&M, Statistics Seminar, College Station, TX, November 2021.
    • Stanford University, Statistics Seminar, Stanford, CA, September 2021.
    • Simons Institute for the Theory of Computing Workshop on Rigorous Evidence for Information-Computation Trade-offs, Berkeley, CA, August 2021.
    • Simons Institute for the Theory of Computing Program on Probability, Geometry, and Computation in High Dimensions, Bootcamp, Berkeley, CA, August 2021.
    • COLT 2021 Planned Mentorship Workshop, Learning Theory Alliance, Boulder, CO, August 2021.
    • Two Sigma PhD Research Symposium, New York, NY, July 2021.
    • Youth in High Dimensions, Trieste, Italy, June 2021.
    • Harvard University, Probability Seminar, Cambridge, MA, April 2021.
    • Young Statisticians' Meet: Data Science in Action, Kolkata, India, March 2021.
    • Conference on Information Sciences and Systems, Baltimore, Maryland, March 2021.

    2020

    • Washington University in St. Louis, Department of Electrical & Systems Engineering, Seminar, October 2020.
    • Simons Institute for the Theory of Computing Program on Probability, Geometry, and Computation in High Dimensions, Fellows Talk, September 2020.
    • International Symposium on Nonparametric Statistics, Paphos, Cyprus, Greece, June 2020. (Postponed)
    • Conference on Statistical Learning and Data Science/Nonparametric Statistics 2020, Irvine, California, May 2020. (Cancelled)
    • 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, Atlanta, Georgia, May 2020.
    • Conference on Information Sciences and Systems, Princeton, New Jersey, March 2020. (Cancelled)
    • 2020 Information Theory and Applications Workshop, San Diego, CA, February 2020.
    • University of Washington, Department of Statistics Seminar, February 2020.
    • 3rd Berkeley-Columbia Meeting in Engineering and Statistics, Berkeley, Ca, February 2020.
    • International Centre for Theoretical Sciences Statistical Physics and Machine Learning Workshop, Bangalore, India, January 2020.

    2019

    • The XV Latin American Congress of Probability and Mathematical Statistics (CLAPEM), Visby, Merida-Yucatan, Mexico, December 2019.
    • Columbia University, Department of Statistics, Student Seminar, September 2019.
    • Information Theory Workshop, Visby, Gotland, Sweden, August 2019.
    • Workshop on Higher-Order Asymptotics and Post-Selection Inference, St. Louis, Missouri, August 2019.
    • IMS China International Conference on Statistics and Probability, Dalian, China, July 2019.
    • London Symposium on Information Theory, May 2019.
    • The 33rd New England Statistics Symposium, Hartford, Connecticut, May 2019.
    • Optimization Methods in Computer Vision and Image Processing, ICERM, Providence, Rhode Island, April 2019.
    • Columbia University, Department of IEOR, Applied Probability and Risk Seminar, April 2019.
    • Conference on Information Sciences and Systems, Baltimore, Maryland, March 2019.
    • Information Theory and Applications Workshop, San Diego, California, February 2019.

    2018

    • Information Theory Workshop, Guangzhou, China, December 2018.
    • Hong Kong University of Science and Technology, Department of Statistics, Colloquium, December 2018.
    • International Conference on Big Data and Information Analytics, Houston, Texas, December 2018.
    • International Symposium on Turbo Codes & Iterative Information Processing, Hong Kong, December 2018.
    • Pattern Theory Group, Division of Applied Mathematics, Brown University, October, 2018.
    • Statistical Physic and Machine Learning Back Together, Cargese, France, August 2018.
    • IMS Asia Pacific Rim Meeting, June 2018.
    • 2nd Berkeley-Columbia Meeting in Engineering and Statistics, April 2018.
    • Vanderbilt University, Department of Biostatistics Seminar Series, April 2018.
    • AT&T NYC Seminar Series, February 2018.

    2017

    • International Indian Statistical Association International Conference on Statistics, Hyderabad, India, December 2017.
    • Harvard University, Department of Statistics, Colloquium, November 2017.
    • Rutgers University, Department of Statistics, Statistics Seminar, October 2017.
    • Columbia University, Department of Statistics, Student Seminar, October 2017.
    • Participant, American Institute of Math, Entropy Power Inequalities Workshop, April 2017.

    2016 and before

    • University of Illinois, Urbana-Champagne, Coordinated Science Laboratory, Colloquium, October 2016.
    • Yale University, Department of Biostatistics, Colloquium, September 2016.
    • Columbia University, Department of Statistics, Colloquium, March 2016.
    • University of California, Los Angeles, Department of Mathematics and Department of Statistics, Colloquium, February 2016.
    • Carnegie Mellon University, Department of Statistics, Colloquium, February 2016.
    • University of Florida, Department of Statistics, Colloquium, January 2016.
    Setting for the ISIT 2016 Banquet in Barcelona, Spain