I am actively recruiting to fill two open postdoc positions for research in machine learning theory and methods, computational sensing/imaging, and sparse signal processing. The Rice DSP and Machine Learning groups offer an energizing environment for research; 25 recent postdocs and PhD students have been placed in top faculty positions. Apply here!
Rice University professor and engineer Richard Baraniuk has been elected to the American Academy of Arts and Sciences. He is one of 228 new members announced today by the academy, which honors some of the world’s most accomplished scholars, scientists, writers, artists and civic, business and philanthropic leaders. The academy is one of the country’s oldest learned societies and independent policy research centers. It convenes leaders from the academic, business and government sectors to respond to the challenges facing – and opportunities available to – the nation and the world.
Rice University press release
Richard Baraniuk is one of 13 Vannevar Bush Faculty Fellows announced today by the U.S. Defense Department. The fellows program provides extensive, long-term financial support for distinguished university scientists and engineers to pursue “blue sky” basic research that could produce revolutionary new technologies. The program was launched in 2008 as the National Security Science and Engineering Faculty Fellowship (NSSEFF) program and renamed this year in honor of Vannevar Bush, the famed American engineer and inventor who headed U.S. scientific research during World War II and later helped found the National Science Foundation. Baraniuk, Rice’s Victor E. Cameron Professor of Electrical and Computer Engineering, is a leading expert on compressive sensing, a branch of signal processing that enables engineers to glean useful information from far fewer data samples than would typically be required.
DOD press release
Rice University press release
Richard Baraniuk, Rice University’s Victor E. Cameron Professor of Electrical and Computer Engineering, has been elected a Fellow of the National Academy of Inventors. He is one of 175 academic inventors named this year, and is now among 757 fellows representing 229 research universities and governmental and nonprofit research institutes. NAI Fellows have demonstrated a prolific spirit of innovation to create or facilitate inventions that have made a tangible impact on quality of life, economic development and the welfare of society. Combined, they are named inventors on more than 26,000 U.S. patents.
Siemens is rolling out compressive sensing (CS) based mangetic resonance imaging (MRI) scanners featuring radically improved scan time.
Read more about their FDA clearance here and the technical details of their approach here.
The National Educational Technology Plan for Higher Ed has been published by the US Department of Education. The document is an outgrowth of the 2016 National Education Technology Plan (NETP), which presents a shared vision and call to action for transformational learning enabled by technology at all levels of our education system. The NETP recommends actions that would enable everywhere, all-the-time learning and ensure greater equity and accessibility to learning opportunities over the course of a learner’s lifetime. (Richard Baraniuk served on the Technical Working Group that reviewed drafts of the guide and provided feedback, writing, and examples from their experiences.)
Finally, a deep convolutional network that doesn’t require much “hand-holding” in the form of training examples.
Using the deep rendering mixture model, the network largely teaches itself how to distinguish handwritten digits using the MNIST dataset of 10,000 hand written digits. Results presented this month at the Neural Information Processing Systems (NIPS) conference in Barcelona, Spain, demonstrate state-of-the-art training results using just 10 correct examples of each handwritten digit plus several thousand additional unlabelled examples. The trained algorithm was more accurate at correctly distinguishing handwritten digits than almost all previous algorithms, even those trained with thousands of correct examples of each digit.
OpenStax editor-in-chief David Harris and video producer Trailer Park have won a prestigious Telly Award, the premier award honoring outstanding content for TV and Cable, Digital and Streaming, and Non-Broadcast distribution. The Telly is one of the most sought-after awards by industry leaders, from large international firms to local production companies and ad agencies.
The team won for the groundbreaking “concept trailer” Terminal Velocity that explains the idea of terminal velocity from the context of a high altitude parachute jump. The video is featured in the Rice Online AP Physics 1 MOOC.
Richard Baraniuk was one of 3000 researchers in the sciences and social sciences who authored papers that ranked among the top 1% most cited for their subject field and year of publication in Thomson Reuters’ academic citation indexing and search service, Web of Knowledge.
A. Aghazadeh, A. Y. Lin, M. A. Sheikh, A. L. Chen, L. M. Atkins, C. L. Johnson, J. F. Petrosino, R. A. Drezek, R. G. Baraniuk, “Universal Microbial Diagnostics using Random DNA Probes,” Science Advances, vol. 2, 28 September 2016.
Abstract: Early identification of pathogens is essential for limiting development of therapy-resistant pathogens and mitigating infectious disease outbreaks. Most bacterial detection schemes use target-specific probes to differentiate pathogen species, creating time and cost inefficiencies in identifying newly discovered organisms. We present a novel universal microbial diagnostics (UMD) platform to screen for microbial organisms in an infectious sample, using a small number of random DNA probes that are agnostic to the target DNA sequences. Our platform leverages the theory of sparse signal recovery (compressive sensing) to identify the composition of amicrobial sample that potentially contains novel or mutant species. We validated the UMD platform in vitro using five random probes to recover 11 pathogenic bacteria. We further demonstrated in silico that UMD can be generalized to screen for common human pathogens in different taxonomy levels. UMD’s unorthodox sensing approach opens the door to more efficient and universal molecular diagnostics.