Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., University of California - Berkeley 2011
• 

  Previous Appointments & Affiliations

  • Assistant Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2013 - 2020
  • Assistant Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2013 - 2020
• Recognition
• 

  In the News

  • SEP 21, 2021

    Office of Faculty Advancement

    Meet the Newly Tenured Faculty of 2021

  • MAR 2, 2017

    Pratt School of Engineering

    Modeling Traffic with Self-Driving Cars
• Research
• 

  Selected Grants

  • Collaborative Research: NSF-BSF CIF: Small: Neural Estimation of Statistical Divergences: Theoretical Foundations and Applications to Communication Systems awarded by  National Science Foundation 2023 - 2026
  • CAREER: Theoretical Foundations for Probabilistic Models with Dense Random Matrices awarded by  National Science Foundation 2018 - 2024
  • HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by  National Science Foundation 2019 - 2023
  • CIF: Small: Capacity via Symmetry awarded by  National Science Foundation 2017 - 2021
  • Theory and Methods for Community Detection with Heterogeneous Networks awarded by North Carolina State University 2019
  • LAS DO6: Theory and Methods for Coarsened Decision Making; Synthetic Data Release: The Tradeoff between Privacy and Utility of Big Data awarded by North Carolina State University 2016
  • LAS DO5: Information Theoretic Measures for Complex and Uncertain Data awarded by North Carolina State University 2015
  • Data Readiness Level - Task 2.7; Delivery Order 03 awarded by North Carolina State University 2014 - 2015
  • Data Readiness Level - Mathematical Foundations awarded by North Carolina State University 2013 - 2014
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • Van Den Boom, W., G. Reeves, and D. B. Dunson. “Erratum: Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation (Biometrika (2021) 108 (269-282) DOI: 10.1093/biomet/asaa068).” Biometrika 109, no. 1 (March 1, 2022): 275. https://doi.org/10.1093/biomet/asab019.
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    • Kipnis, A., and G. Reeves. “Gaussian Approximation of Quantization Error for Estimation from Compressed Data.” Ieee Transactions on Information Theory 67, no. 8 (August 1, 2021): 5562–79. https://doi.org/10.1109/TIT.2021.3083271.
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    • VAN DEN Boom, W., G. Reeves, and D. B. Dunson. “Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation.” Biometrika 108, no. 2 (June 2021): 269–82. https://doi.org/10.1093/biomet/asaa068.
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    • Reeves, Galen. “A Two-Moment Inequality with Applications to Rényi Entropy and Mutual Information.” Entropy (Basel, Switzerland) 22, no. 11 (November 2020): E1244. https://doi.org/10.3390/e22111244.
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    • Mathews, H., V. Mayya, A. Volfovsky, and G. Reeves. “Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise.” 2019 Ieee 8th International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Camsap 2019  Proceedings, December 1, 2019, 699–703. https://doi.org/10.1109/CAMSAP45676.2019.9022612.
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    • Reeves, G., V. Mayya, and A. Volfovsky. “The Geometry of Community Detection via the MMSE Matrix.” Ieee International Symposium on Information Theory  Proceedings 2019-July (July 1, 2019): 400–404. https://doi.org/10.1109/ISIT.2019.8849594.
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    • Reeves, G., and H. D. Pfister. “The Replica-Symmetric Prediction for Random Linear Estimation With Gaussian Matrices Is Exact.” Ieee Transactions on Information Theory 65, no. 4 (April 1, 2019): 2252–83. https://doi.org/10.1109/TIT.2019.2891664.
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    • Mainsah, B. O., G. Reeves, L. M. Collins, and C. S. Throckmorton. “Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.” Journal of Neural Engineering 14, no. 4 (August 2017): 046025. https://doi.org/10.1088/1741-2552/aa7525.
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    • Reeves, G. “The fundamental limits of stable recovery in compressed sensing.” Ieee International Symposium on Information Theory  Proceedings, January 1, 2014, 3017–21. https://doi.org/10.1109/ISIT.2014.6875388.
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    • Reeves, G. “Beyond sparsity: Universally stable compressed sensing when the number of 'free' values is less than the number of observations.” 2013 5th Ieee International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Camsap 2013, December 1, 2013, 17–20. https://doi.org/10.1109/CAMSAP.2013.6713996.
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    • Reeves, G., and M. C. Gastpar. “Approximate sparsity pattern recovery: Information-theoretic lower bounds.” Ieee Transactions on Information Theory 59, no. 6 (May 23, 2013): 3451–65. https://doi.org/10.1109/TIT.2013.2253852.
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    • Donoho, D., and G. Reeves. “Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown.” Ieee International Symposium on Information Theory  Proceedings, January 1, 2013, 101–5. https://doi.org/10.1109/ISIT.2013.6620196.
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    • Reeves, G., and D. Donoho. “The minimax noise sensitivity in compressed sensing.” Ieee International Symposium on Information Theory  Proceedings, January 1, 2013, 116–20. https://doi.org/10.1109/ISIT.2013.6620199.
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    • Reeves, G., and M. Gastpar. “Compressed sensing phase transitions: Rigorous bounds versus replica predictions.” 2012 46th Annual Conference on Information Sciences and Systems, Ciss 2012, November 12, 2012. https://doi.org/10.1109/CISS.2012.6310927.
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    • Donoho, D., and G. Reeves. “The sensitivity of compressed sensing performance to relaxation of sparsity.” Ieee International Symposium on Information Theory  Proceedings, October 22, 2012, 2211–15. https://doi.org/10.1109/ISIT.2012.6283846.
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    • Reeves, G., and M. Gastpar. “The sampling rate-distortion tradeoff for sparsity pattern recovery in compressed sensing.” Ieee Transactions on Information Theory 58, no. 5 (May 1, 2012): 3065–92. https://doi.org/10.1109/TIT.2012.2184848.
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    • Reeves, G., N. Goela, N. Milosavljevic, and M. Gastpar. “A compressed sensing wire-tap channel.” 2011 Ieee Information Theory Workshop, Itw 2011, December 21, 2011, 548–52. https://doi.org/10.1109/ITW.2011.6089562.
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    • Reeves, G., and M. Gastpar. “On the role of diversity in sparsity estimation.” Ieee International Symposium on Information Theory  Proceedings, October 26, 2011, 119–23. https://doi.org/10.1109/ISIT.2011.6033723.
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    • Reeves, G., and M. Gastpar. “"Compressed" compressed sensing.” Ieee International Symposium on Information Theory  Proceedings, August 23, 2010, 1548–52. https://doi.org/10.1109/ISIT.2010.5513517.
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    • Reeves, G., and M. Gastpar. “A note on optimal support recovery in compressed sensing.” Conference Record  Asilomar Conference on Signals, Systems and Computers, December 1, 2009, 1576–80. https://doi.org/10.1109/ACSSC.2009.5470153.
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    • Reeves, G., J. Liu, S. Nath, and F. Zhao. “Managing massive time series streams with multi-scale compressed trickles.” Proceedings of the Vldb Endowment 2, no. 1 (January 1, 2009): 97–108. https://doi.org/10.14778/1687627.1687639.
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    • Reeves, G., and M. Gastpar. “Sampling bounds for sparse support recovery in the presence of noise.” Ieee International Symposium on Information Theory  Proceedings, September 29, 2008, 2187–91. https://doi.org/10.1109/ISIT.2008.4595378.
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    • Reeves, G., and M. Gastpar. “Differences between observation and sampling error in sparse signal reconstruction.” Ieee Workshop on Statistical Signal Processing Proceedings, December 1, 2007, 690–94. https://doi.org/10.1109/SSP.2007.4301347.
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  • Conference Papers

    • Barbier, J., and G. Reeves. “Information-theoretic limits of a multiview low-rank symmetric spiked matrix model.” In Ieee International Symposium on Information Theory  Proceedings, 2020-June:2771–76, 2020. https://doi.org/10.1109/ISIT44484.2020.9173970.
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    • Reeves, G., J. Xu, and I. Zadik. “All-or-Nothing Phenomena: From Single-Letter to High Dimensions.” In 2019 Ieee 8th International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Camsap 2019  Proceedings, 654–58, 2019. https://doi.org/10.1109/CAMSAP45676.2019.9022473.
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    • Mayya, V., and G. Reeves. “Mutual Information in Community Detection with Covariate Information and Correlated Networks.” In 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019, 602–7, 2019. https://doi.org/10.1109/ALLERTON.2019.8919733.
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    • Kipnis, A., and G. Reeves. “Gaussian Approximation of Quantization Error for Estimation from Compressed Data.” In Ieee International Symposium on Information Theory  Proceedings, 2019-July:2029–33, 2019. https://doi.org/10.1109/ISIT.2019.8849826.
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    • Bertran, M., N. Martinez, A. Papadaki, Q. Qiu, M. Rodrigues, G. Reeves, and G. Sapiro. “Adversarially learned representations for information obfuscation and inference.” In 36th International Conference on Machine Learning, Icml 2019, 2019-June:960–74, 2019.
    • Kipnis, A., G. Reeves, and Y. C. Eldar. “Single Letter Formulas for Quantized Compressed Sensing with Gaussian Codebooks.” In Ieee International Symposium on Information Theory  Proceedings, 2018-June:71–75, 2018. https://doi.org/10.1109/ISIT.2018.8437761.
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    • Reeves, G., H. D. Pfister, and A. Dytso. “Mutual Information as a Function of Matrix SNR for Linear Gaussian Channels.” In Ieee International Symposium on Information Theory  Proceedings, 2018-June:1754–58, 2018. https://doi.org/10.1109/ISIT.2018.8437326.
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    • Reeves, G. “Additivity of information in multilayer networks via additive Gaussian noise transforms.” In 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, 2018-January:1064–70, 2018. https://doi.org/10.1109/ALLERTON.2017.8262855.
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    • Kipnis, A., G. Reeves, Y. C. Eldar, and A. J. Goldsmith. “Compressed sensing under optimal quantization.” In Ieee International Symposium on Information Theory  Proceedings, 2148–52, 2017. https://doi.org/10.1109/ISIT.2017.8006909.
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    • Reeves, G. “Two-moment inequalities for Rényi entropy and mutual information.” In Ieee International Symposium on Information Theory  Proceedings, 664–68, 2017. https://doi.org/10.1109/ISIT.2017.8006611.
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    • Reeves, G. “Conditional central limit theorems for Gaussian projections.” In Ieee International Symposium on Information Theory  Proceedings, 3045–49, 2017. https://doi.org/10.1109/ISIT.2017.8007089.
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    • Mainsah, B. O., L. M. Collins, G. Reeves, and C. S. Throckmorton. “A performance-based approach to designing the stimulus presentation paradigm for the P300-based BCI by exploiting coding theory.” In Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing  Proceedings, 3026–30, 2017. https://doi.org/10.1109/ICASSP.2017.7952712.
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    • Mayya, V., B. Mainsah, and G. Reeves. “Modeling the P300-based brain-computer interface as a channel with memory.” In 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016, 23–30, 2017. https://doi.org/10.1109/ALLERTON.2016.7852206.
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    • Renna, F., L. Wang, X. Yuan, J. Yang, G. Reeves, R. Calderbank, L. Carin, and M. R. D. Rodrigues. “Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information.” In Ieee Transactions on Information Theory, 62:6459–92, 2016. https://doi.org/10.1109/TIT.2016.2606646.
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    • Reeves, G., and H. D. Pfister. “The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact.” In Ieee International Symposium on Information Theory  Proceedings, 2016-August:665–69, 2016. https://doi.org/10.1109/ISIT.2016.7541382.
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    • Llull, P., G. Reeves, L. Carin, and D. J. Brady. “Performance assessment of image translation-engineered point spread functions.” In Optics Infobase Conference Papers, 2016. https://doi.org/10.1364/COSI.2016.CW2D.4.
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    • Renna, F., L. Wang, X. Yuan, J. Yang, G. Reeves, R. Calderbank, L. Carin, and M. R. D. Rodrigues. “Classification and reconstruction of compressed GMM signals with side information.” In Ieee International Symposium on Information Theory  Proceedings, 2015-June:994–98, 2015. https://doi.org/10.1109/ISIT.2015.7282604.
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    • Van Den Boom, W., D. Dunson, and G. Reeves. “Quantifying uncertainty in variable selection with arbitrary matrices.” In 2015 Ieee 6th International Workshop on Computational Advances in Multi Sensor Adaptive Processing, Camsap 2015, 385–88, 2015. https://doi.org/10.1109/CAMSAP.2015.7383817.
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    • Mayya, V., and B. Mainsah. “Information Theoretic Analysis of the Impact of Refractory Effects on the P300 Speller,” n.d.
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• Teaching & Mentoring
• 

  Recent Courses

  • ECE 587: Information Theory 2023
  • ECE 741: Compressed Sensing and Related Topics 2023
  • STA 563: Information Theory 2023
  • STA 711: Probability and Measure Theory 2023
  • STA 741: Compressed Sensing and Related Topics 2023
  • ECE 587: Information Theory 2022
  • STA 563: Information Theory 2022
  • STA 711: Probability and Measure Theory 2022
  • MATH 228L: Probability for Statistical Inference, Modeling, and Data Analysis 2021
  • STA 240L: Probability for Statistical Inference, Modeling, and Data Analysis 2021
  • STA 693: Research Independent Study 2021
  • STA 711: Probability and Measure Theory 2021

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