Siemens is rolling out compressive sensing (CS) based mangetic resonance imaging (MRI) scanners featuring radically improved scan time.
DSP alum Christoph Studer (postdoc 2010-2012, Assistant Professor at Cornell University) has received an NSF CAREER award for his project "Hardware Accelerated Bayesian Inference via Approximate Message Passing: A Bottom-Up Approach." The project will bridge the ever-growing gap between theory and practice in Bayesian signal processing using a holistic approach that spans the circuit design, algorithm, and theory levels. In addition to improving the efficiency and quality of Bayesian inference in real-time applications, the project will advance future wireless systems through collaboration with the telecommunications industry, along with the development of new tools that are accessible to experts on all levels.
DSP group alum Aswin Sankaranarayanan (Postdoc 2009-2012, Assistant Professor at CMU) has received an NSF CAREER Award for his project "Plenoptic Signal Processing — A Framework for Sampling, Detection, and Estimation using Plenoptic Functions." He will be exploring how light interacts with objects in a scene by studying characterizations of light that go beyond images. A key objective is to study light-object interactions at unprecedented space and time resolutions, thereby advancing research in many disciplines including computer vision, graphics as well as 3D acquisition and printing. Congratulations!
DSP group alums Mark Davenport (PhD, 2010, Assistant Professor at Georgia Tech) and Tom Goldstein (Postdoc, 2012-14, Assistant Professor at the University of Maryland) have been named 2017 Sloan Research Fellows. The 2017 class of fellows comprises 126 early-career scholars representing the most promising scientific researchers working today (not all have beards). Their achievements and potential place them among the next generation of scientific leaders in the U.S. and Canada. Since 1955, Sloan Research Fellows have gone on to win 43 Nobel Prizes, 16 Fields Medals, 69 National Medals of Science, 16 John Bates Clark Medals, and numerous other distinguished awards. Congratulations!
Thanks for making the Rice Machine Learning Workshop on January 24, 2017 a great success! Over 400 attendees participated in a range of sessions on not just new machine learning theory and algorithms but their applications in the energy, medical, financial, and legal industries.
Rice University faculty speakers: Genevera Allen on sparse learning, Richard Baraniuk on advanced data analytics, Paul Hand on machine vision, Ankit Patel on deep learning, and Anshumali Shrivastava on large-scale learning.
Industry speakers: Thomas Halsey (ExxonMobil) on machine learning in the energy industry, Satyam Priyadarshy (Halliburton) on machine learning in the upstream oil and gas industry, Craig Rusin (Medical Informatics Corp.) on patient informatics; Hardeep Singh (VA Hospital Houston) on reducing medical misdiagnosis through machine learning, and Alan Lockett (CS Disco) on machine learning in the legal domain.
Keynote speaker: Alfred Spector (Two Sigma)
See you next year at ML@RICE 2018!
FlatCam, invented by the labs of Rice DSP faculty members Richard Baraniuk and Ashok Veeraraghavan, is little more than a thin sensor chip with a mask that replaces the lenses in a traditional camera.
Making it practical are the sophisticated computer algorithms that process what the sensor detects and converts the sensor measurements into images and videos.
John Treichler, Rice DSP alum, distinguished visiting professor, and pioneer in the development of digital signal processing (DSP), has been elected to the National Academy of Engineering (NAE).
Now the president of Raytheon Applied Signal Technology of Sunnyvale, Calif., Treichler was cited by the NAE for his “contributions to digital signal processing and its applications to national intelligence gathering.” John is celebrated for inventing the “constant modulus” adaptive filtering algorithm, which is used to compensate for interference, such as multipath echoes, on communication signals.