Galen Reeves
Associate Professor in the Department of Electrical and Computer Engineering
Current Research Interests
Information theory, high-dimensional statistical inference, statistical signal processing, compressed sensing, machine learning
Current Appointments & Affiliations
- Associate Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2020
- Associate Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2020
Contact Information
- 140 Science Dr., 321 Gross Hall, Durham, NC 27708
-
galen.reeves@duke.edu
(919) 668-4042
-
website
- 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 -
MAR 2, 2017 Pratt School of Engineering
-
- 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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
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.Full Text
-
Mayya, V., and B. Mainsah. “Information Theoretic Analysis of the Impact of Refractory Effects on the P300 Speller,” n.d.Link to Item
-
-
- 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
Some information on this profile has been compiled automatically from Duke databases and external sources. (Our About page explains how this works.) If you see a problem with the information, please write to Scholars@Duke and let us know. We will reply promptly.