Pavan Kota has been awarded a prestigious NIH NLM Training Grant in Biomedical Informatics and Data Science that will support his PhD research on new kinds of microbial diagnostics.  One particularly promising approach is based on designing coded strands of DNA that enable compressive sensing to be applied.  The upshot is that only a few coded DNA probes are required to detect and classify a much larger number of potential microbes.


Chris Metzler successfully defended his PhD thesis entitled Data-Driven Computational Imaging: With Application to Imaging Through and Around Obstacles.  He's pictured here at Rice's Valhalla pub performing the ritual "trimming of the tie" ceremony for new PhDs with Prof. Richard Baraniuk.  Chris' next stop is Stanford University for a postdoc with Prof. Gordon Wetzstein in the Stanford Computational Imaging Lab.



John Treichler, Rice DSP alum, distinguished TI visiting professor, and pioneer in the development of digital signal processing (DSP), has been awarded the Society Award by the IEEE Signal Processing Society.  The Society Award honors outstanding technical contributions in a field within the scope of the Signal Processing Society and outstanding leadership within that field.  John will present the Norbert Wiener Lecture at ICASSP 2019 in Brighton, UK.

Now the Chief Technology Officer and Senior Scientist of Raytheon Applied Signal Technology, John was cited for his “contributions and leadership in the practical use of adaptive digital signal processing and for sustained service to the Society.” Among his many creations, 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.

Rice DSP alum Tom Goldstein (postdoc 2012-2014) has received a DARPA Young Faculty Award for a project entitled Self-Assessing Network Models for Big Data Summarization.  There are many complex tasks that neither a human nor a computer can complete alone.  An example is searching a large image database: a human does not have the bandwidth to examine millions of images, while a neural network has the needed bandwidth, but lacks the intelligence to analyze complex or unusual images that lie far from its training distribution.  Tom's project will enhance human-computer teaming by giving machines the ability to assess their own performance and self confidence. This way, machines can act independently, and then ask their human collaborators for help when it is needed.

Tom is currently an Assistant Professor of Computer Science at the University of Maryland. He has previously been awarded the SIAM DiPrima Prize and a Sloan Fellowship.

DSP group members travelled en masse to Stockholm, Sweden to present seven regular papers at the International Conference on Machine Learning in July 2018:

In addition, DSP group members presented three workshop papers:

Rice DSP alum Rebecca Willett (PhD 2005) is joining the University of Chicago as a Professor of Computer Science and Statistics, where she will be developing a new machine learning initiative.  Her research interests include machine learning, network science, medical imaging, wireless sensor networks, astronomy, and social networks.  She has also held positions at the University of Wisconsin-Madison and Duke University.  Rebecca has received the NSF CAREER Award and AFOSR YIP, and has served as a member of the DARPA Computer Science Study Group.

C. A. Metzler, A. Mousavi, R. Heckel, and R. G. Baraniuk, “Unsupervised Learning with Stein’s Unbiased Risk Estimator,” https://arxiv.org/abs/1805.10531, June 2018.

Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this work, we revisit a classical idea: Stein's Unbiased Risk Estimator (SURE). We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems without any ground truth data.  Specifically, our goal is to  reconstruct an image x from a noisy linear transformation (measurement) of the image. We consider two scenarios: one where no additional data is available and one where we have measurements of other images that are drawn from the same noisy distribution as x, but have no access to the clean images. Such is the case, for instance, in the context of medical imaging, microscopy, and astronomy, where noise-less ground truth data is rarely available.  We show that in this situation, SURE can be used to estimate the mean-squared-error loss associated with an estimate of x. Using this estimate of the loss, we train networks to perform denoising and compressed sensing recovery. In addition, we also use the SURE framework to partially explain and improve upon an intriguing results presented by Ulyanov et al. in "Deep Image Prior": that a network initialized with random weights and fit to a single noisy image can effectively denoise that image.