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Richard G. Baraniuk, PhD

Full Affiliate Member, Research Institute
Houston Methodist


Biography

Dr. Baraniuk grew up in Winnipeg, Canada and received his B.Sc. degree in 1987 from the University of Manitoba. During his undergraduate studies he was a research engineer with Omron Tateisi Electronics in Kyoto, Japan (1986) and a Research Assistant with the National Research Council of Canada (1987). He received his M.Sc. degree in 1988 from the University of Wisconsin-Madison, and a Ph.D. degree in 1992 from the University of Illinois at Urbana-Champaign. While at the University of Illinois, he held a joint appointment with the CERL Sound Group and the Coordinated Science Laboratory.

After spending 1992-1993 at Ecole Normale Supérieure in Lyon, France, he joined Rice University in Houston, Texas, where he is currently the Victor E. Cameron Professor of Engineering and a sporadic DJ for KTRU. He spent sabbaticals at Ecole Nationale Supérieure de Télécommunications in Paris in 2001 and Ecole Fédérale Polytechnique de Lausanne in Switzerland in 2002. Dr. holds eight U.S. patents and has published more than 120 manuscripts. He Joined TMHRI in 2010.

Dr. Baraniuk received a NATO postdoctoral fellowship from NSERC in 1992, the National Young Investigator award from the National Science Foundation in 1994, a Young Investigator Award from the Office of Naval Research in 1995, the Rosenbaum Fellowship from the Isaac Newton Institute of Cambridge University in 1998, the C. Holmes MacDonald National Outstanding Teaching Award from Eta Kappa Nu in 1999, the Charles Duncan Junior Faculty Achievement Award from Rice in 2000, the University of Illinois ECE Young Alumni Achievement Award in 2000, the George R. Brown Award for Superior Teaching at Rice in 2001, 2003, and 2006, the Hershel M. Rich Invention Award from Rice in 2007, the Wavelet Pioneer Award from SPIE in 2008, the Internet Pioneer Award from the Berkman Center for Internet and Society at Harvard Law School in 2008, and the World Technology Award for Education in 2009.

He was selected as one of Edutopia Magazine's Daring Dozen educators in 2007. Connexions received the Tech Museum Laureate Award from the Tech Museum of Innovation in 2006. His work with Kevin Kelly on the Rice single-pixel compressive camera was selected by MIT Technology Review Magazine as a TR10 Top 10 Emerging Technology in 2007. He was co-author on a paper with Matthew Crouse and Robert Nowak that won the IEEE Signal Processing Society Junior Paper Award in 2001 and another with Vinay Ribeiro and Rolf Riedi that won the Passive and Active Measurement (PAM) Workshop Best Student Paper Award in 2003. He received the IEEE Signal Processing Society Magazine Column Award in 2009. He was elected Fellow of the IEEE in 2001 and Fellow of the AAAS in 2009.

Description of Research

Dr. Baraniuk's research interests lie in the areas of signal and image processing and include compressive sensing (compressed sensing), sensor networks, and pattern recognition and learning. In a bygone era he has worked in multiscale natural image modeling using hidden Markov models and time-frequency analysis.

In 1999, Dr. Baraniuk launched Connexions, a non-profit publishing project that aims to bring textbooks and learning materials into the Internet Age. Connexions makes high-quality educational content available to anyone, anywhere, anytime for free on the web and at very low cost in print by inviting authors, educators, and learners worldwide to "create, rip, mix, and burn" textbooks, courses, and learning materials from its global open-access repository. Each month, Connexions' free educational materials are used by over 2 million people from nearly 200 countries.

Areas Of Expertise

Signal and imaging processing Compressive sensing Sensor networks Pattern recognition Learning
Education & Training

PhD, University of Illinois at Urbana-Champaign
MSc, University of Wisconsin-Madison
Publications

Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
Seydoux, L, Balestriero, R, Poli, P, Hoop, MD, Campillo, M & Baraniuk, R 2020, , Nature Communications, vol. 11, no. 1, 3972. https://doi.org/10.1038/s41467-020-17841-x

CANOPIC: Pre-digital privacy-enhancing encodings for computer vision
Tan, J, Khan, SS, Boominathan, V, Byrne, J, Baraniuk, R, Mitra, K & Veeraraghavan, A 2020, . in 2020 IEEE International Conference on Multimedia and Expo, ICME 2020., 9102956, Proceedings - IEEE International Conference on Multimedia and Expo, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc. 2020 IEEE International Conference on Multimedia and Expo, ICME 2020, London, United Kingdom, 7/6/20. https://doi.org/10.1109/ICME46284.2020.9102956

To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics
Elworth, RAL, Wang, Q, Kota, PK, Barberan, CJ, Coleman, B, Balaji, A, Gupta, G, Baraniuk, RG, Shrivastava, A & Treangen, TJ 2020, , Nucleic Acids Research, vol. 48, no. 10, pp. 5217-5234. https://doi.org/10.1093/nar/gkaa265

Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference
Wang, Y, Shen, J, Hu, TK, Xu, P, Nguyen, T, Baraniuk, R, Wang, Z & Lin, Y 2020, , IEEE Journal on Selected Topics in Signal Processing, vol. 14, no. 4, 9028245, pp. 623-633. https://doi.org/10.1109/JSTSP.2020.2979669

Deep-inverse correlography: Towards real-time high-resolution non-line-of-sight imaging
Metzler, CA, Heide, F, Rangarajan, P, Balaji, MM, Viswanath, A, Veeraraghavan, A & Baraniuk, RG 2020, , Optica, vol. 7, no. 1, pp. 63-71. https://doi.org/10.1364/OPTICA.374026

Erratum: Deep-inverse correlography: Towards real-Time high-resolution non-line-of-sight imaging (Optica (2020) 7 (63) DOI: 10.1364/OPTICA.374026)
Metzler, CA, Heide, F, Rangarajan, P, Balaji, MM, Viswanath, A, Veeraraghavan, A & Baraniuk, RG 2020, , Optica, vol. 7, no. 3. https://doi.org/10.1364/OPTICA.391291

Adaptive estimation for approximate k-nearest-neighbor computations
LeJeune, D, Baraniuk, RG & Heckel, R 2020, , Paper presented at 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, Naha, Japan, 4/16/19 - 4/18/19.

A Multi-sensory Approach to Present Phonemes as Language through a Wearable Haptic Device
Dunkelberger, N, Sullivan, JL, Bradley, J, Manickam, I, Dasarathy, G, Baraniuk, RG & Omalley, MK 2020, , IEEE Transactions on Haptics. https://doi.org/10.1109/TOH.2020.3009581

Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes
Sen, D, Aghazadeh, A, Mousavi, A, Nagarajaiah, S, Baraniuk, RG & Dabak, A 2019, Mechanical Systems and Signal Processing, vol. 131, pp. 524-537. https://doi.org/10.1016/j.ymssp.2019.06.003

IdeoTrace: A framework for ideology tracing with a case study on the 2016 U.S. presidential election
Manickam, I, Lan, AS, Dasarathy, G & Baraniuk, RG 2019, . in F Spezzano, W Chen & X Xiao (eds), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019. Association for Computing Machinery, Inc, pp. 274-281, 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019, Vancouver, Canada, 8/27/19. https://doi.org/10.1145/3341161.3342887

Introduction to this special section: Machine learning applications
Shaw, S, Sharma, A, Baraniuk, RG & Roy, B 2019, Leading Edge, vol. 38, no. 7. https://doi.org/10.1190/tle38070510.1

Do open educational resources improve student learning? Implications of the access hypothesis
Grimaldi, PJ, Basu Mallick, D, Waters, AE & Baraniuk, RG 2019, , PLoS ONE, vol. 14, no. 3, e0212508. https://doi.org/10.1371/journal.pone.0212508

Sparsity-based approaches for damage detection in plates
Sen, D, Aghazadeh, A, Mousavi, A, Nagarajaiah, S & Baraniuk, RG 2019, , Mechanical Systems and Signal Processing, vol. 117, pp. 333-346. https://doi.org/10.1016/j.ymssp.2018.08.019

Preface
Sosnovsky, S, Brusilovsky, P, Agrawal, R, Baraniuk, RG & Lan, AS 2019, CEUR Workshop Proceedings, vol. 2384, pp. 1-3.

Representing formal languages: A comparison between finite automata and recurrent neural networks
Michalenko, JJ, Shah, A, Verma, A, Baraniuk, RG, Chaudhuri, S & Patel, AB 2019, , Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States, 5/6/19 - 5/9/19.

From hard to soft: Understanding deep network nonlinearities via vector quantization and statistical inference
Balestriero, R & Baraniuk, RG 2019, Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States, 5/6/19 - 5/9/19, .

Representing formal languages: A comparison between finite automata and recurrent neural networks
Michalenko, JJ, Shah, A, Verma, A, Baraniuk, RG, Chaudhuri, S & Patel, AB 2019, Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States, 5/6/19 - 5/9/19, .

A max-affine spline perspective of recurrent neural networks
Wang, Z, Balestriero, R & Baraniuk, RG 2019, , Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States, 5/6/19 - 5/9/19.

A meta-learning augmented bidirectional transformer model for automatic short answer grading
Wang, Z, Lan, AS, Waters, AE, Grimaldi, P & Baraniuk, RG 2019, . in CF Lynch, A Merceron, M Desmarais & R Nkambou (eds), EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining. International Educational Data Mining Society, pp. 667-670, 12th International Conference on Educational Data Mining, EDM 2019, Montreal, Canada, 7/2/19.

A data-driven and distributed approach to sparse signal representation and recovery
Mousavi, A, Dasarathy, G & Baraniuk, RG 2019, , Paper presented at 7th International Conference on Learning Representations, ICLR 2019, New Orleans, United States, 5/6/19 - 5/9/19.