My odyssey to sparse signal recovery, statistical signal processing, biomedical signal processing, signal separation and decomposition, wearable healthcare, and smart-home.
Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.
This blog reports latest progresses in (1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home (2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures (3) My Works
In addition to the compressed sensing talks, there are many interesting talks on Music Information Retrieval, Clustering, Learning Theory, Graphical Models and Inference, and Statistical Machine learning & Applications.
Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain-computer interface (BCI) systems can make use of such knowledge to deliver relevant feedback to the user or to an observer, or within a human-machine system to increase safety and enhance overall performance. Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may in future be increasingly ubiquitous. While performance of current BCI modeling methods is slowly increasing, current performance levels do not yet support widespread uses. Here we discuss the current neuroscientific questions and data processing challenges facing BCI designers and outline some promising current and future directions to address them.
Particularly, I found the following talks on compressed sensing/sparse signal recovery (I probably missed some):
11:20: Quick partial sparse support recovery by Vincent Poor, Princeton, Ali Tajer, Princeton
3:00: Information-theoretically optimal compressed sensing via spatial coupling and approximate message passing by David Donoho, Stanford, Adel Javanmard, Stanford, Andrea Montanari, Stanford
11:20: Faster algorithms for sparse fourier transform, by Haitham Hassanieh, MIT, Piotr Indyk, MIT, Dina Katabi, MIT, Eric Price, MIT Compressive sensing meets group testing: LP decoding for non-linear (disjunctive) measurements, by Chun Lam Chan, CUHK, Sidharth Jaggi, CUHK, Venkatesh Saligrama, BU, Samar Agnihotri, CUHK
1:35: The Big Data bootstrap, by Ariel Kleiner, UC Berkeley, Ameet Talwalkar, UC Berkeley, Purna Sarkar, UC Berkeley, Michael Jordan, UC Berkeley
In addition to these talks, there are other interesting talks on high-dimensional data analysis, information theory, and neuroscience/AI.
Next week should be a wonderful week, except an unhappy thing: this year ITA will be hold in a hotel in San Diego, not in UCSD campus as previous years. It's so inconvenient :(