25 Jan 2016: Dongeek Shin successfully defended his PhD thesis on Computational Imaging with Small Numbers of Photons.
9 Nov 2015: Ahmed Kirmani was awarded Second Place for the 2015 Jin-Au Kong Doctoral Thesis Award for the best electrical engineering PhD thesis at MIT. See Kirmani receive his certificate and a group photograph from the award ceremony.
26 Nov 2014: Ahmed Kirmani successfully defended his PhD thesis on Computational Time-Resolved Imaging.
15 Feb 2013: Significance, the bimonthly magazine of the Royal Statistical Society and the American Statistical Association, has published an article by STIR alumnus Lav Varshney and STIR PhD student John Sun describing their work with Grace Wang and Vivek Goyal on using the optimization approach to biology to explain logarithmic numerosity. The team's paper in the Journal of Mathematical Psychology posits the theory that observed psychophysical and numerosity scaling laws arise from their Bayesian optimality in minimizing expected relative error under informational constraints.
18 Jan 2013: STIR PhD student Ahmed Kirmani has been named a 2013 Microsoft Research PhD Fellow. This is one of the most selective awards for graduate students in computer science, electrical engineering and mathematics, as only 12 fellows are selected from applicants across the US and Canada who have been nominated by their universities. Congratulations to Kirmani!
1 Jan 2013: Focus, a UK-based science and technology magazine, features a conversation with Vivek Goyal in its Breakthroughs of 2013 article in the January 2013 issue. Goyal primarily discusses gesture-controlled mobile phones as a potential near-term technology.
17 Dec 2012: The Taste project initiated and led by STIR alumnus Lav Varshney is featured by IBM as one of The 5 in 5 — innovations that will change our lives in the next five years.
22 Oct 2012: STIR's team in the MIT $100k Entrepreneurship Competition Pitch Contest, 3dim, won the Grand Prize! The pitch was delivered by Andrea Colaço, representing teammates Ahmed Kirmani, Nan-Wei Gong, and Vivek Goyal. About 300 entrants were narrowed to 60 semifinalists on October 15 and then to 12 finalists on October 18. In addition to winning the Grand Prize, the 3dim pitch also was in second place for the Audience Choice Award, determined by text-message polling. The 3dim vision of bringing users the freedom to gesture to mobile devices is rooted in STIR's CoDAC technology.
7 Aug 2012: CoDAC: Compressive Depth Acquisition Using a Single Time-Resolved Sensor, by Andrea Colaço, Ahmed Kirmani, Franco Wong, and Vivek Goyal, was a Finalist in the ACM Student Research Competition of SIGGRAPH 2012. Five finalists were selected from among 15 semifinalists based on Colaço's poster presentation. The selection as a semifinalist was out of about 400 abstracts submitted for poster presentation.
24 Jul 2012: Recent STIR alumnus Daniel Weller has been
awarded a Ruth L. Kirschstein National Research Service Award from the National Institutes of Health. His individual postdoctoral fellowship (F32) will support him at the University of Michigan. Congratulations to Dan!
10 Apr 2012: The group's third and fourth patents in the area of magnetic resonance imaging have issued. The inventions are a Method for Reducing Maximum Local Specific Absorption Rate in Magnetic Resonance Imaging, U. S. Patent number 8,148,985, and a Method for Joint Sparsity-Enforced k-Space Trajectory and Radiofrequency Pulse Design, U.S. Patent number 8,154,289.
20 Mar 2012: Andrea Colaço and Ahmed Kirmani have been named Finalists for the 2012 Qualcomm Innovation Fellowship. It is the first time that winners from a previous year have been named finalists.
2 Mar 2012: Compressive Depth Map Acquisition Using a Single Photon-Counting Detector: Parametric Signal Processing Meets Sparsity by Andrea Colaço, Ahmed Kirmani, Greg Howland, John Howell, and Vivek Goyal has been accepted for publication in the highly-selective IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012).
15 Nov 2011: Ulugbek Kamilov was named a Finalist for the Student Paper Award at 4th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2011). Ulugbek's paper (co-authored with Vivek Goyal and Sundeep Rangan) is titled Generalized Approximate Message Passing Estimation from Quantized Samples.
20 May 2011: Ahmed Kirmani and Andrea Colaço have won a 2011 Qualcomm Innovation Fellowship (providing $100,000 in support) for their project on
Single Pixel Depth Sensing and 3D Camera. The competition was open to students from eleven top universities. Eight proposals were chosen for funding from among 146 applications.
1 Apr 2011: Daniel Weller was named a Finalist in the Student Paper Competition at IEEE International Symposium on Biomedical Imaging. Dan's paper (co-authored with
Jonathan Polimeni, Leo Grady, Lawrence Wald, Elfar Adalsteinsson, and Vivek Goyal) is titled Evaluating Sparsity Penalty Functions for Combined Compressed Sensing and Parallel MRI.
23 Mar 2011: John Sun has been awarded a Claude E. Shannon Research Assistantship by the Research Laboratory of Electronics. These RAs support students doing basic research in communication, in memory of Claude Shannon.
The recent and ongoing work of the STIR group has included key innovations in a broad range of fields. Our work is generally organized around conceptual themes, including quantization, sampling, and sparsity. Alongside the invention of theoretical frameworks for source coding, we work with great collaborators to maximize our technological impact in several areas of information acquisition.
The newest focus area for the STIR group is optical imaging. We have invented new ways to relate optical imaging to spatiotemporal sampling, and this has led to some surprising new capabilities.
Demonstrated that the capture of transient light field properties, beyond mere time of flight, enables dramatic effects such as forming an image of a surface that is in the line of sight of neither the illumination source nor the sensor—without a mirror.
MRI requires sophisticated signal processing both to create the magnetization conditions needed to reveal tissue properties and to encode and interpret the magnetization. MRI measurements are essentially uninterpretable without firm grounding in both signal processing and physics. The STIR group has made contributions to both excitation design and image reconstruction.
Demonstrated that our formulation enables B1+ inhomogeneity mitigation for 7T brain imaging, reducing a major impediment to clinical use of ultra-high main field MRI (Mag. Res. Med. paper)
Provided a method to analyze and minimize specific absorption rate, providing optimum imaging under safety constraints (J. MRI paper)
Introduced a joint reconstruction technique for multiple contrast preparations using a hierarchical Bayesian model (Mag. Res. Med. paper)
Introduced the SpRING algorithm to improve upon GRAPPA and compressed sensing used separately for image construction from multiple receive coils (ISMRM paper; full paper in revision)
Sparse Signal Estimation and Detection (Including Compressed Sensing)
Exploiting sparsity has become a central theme in signal processing over the past two decades, and this trend has accelerated greatly with the introduction of compressed sensing. The STIR group has made foundational contributions in understanding the limits of estimation and detection and the performances of algorithms.
Proved necessary and sufficient conditions for sparse signal support recovery that were the first to establish the importance of signal-to-noise ratio and dynamic range in understanding the relative performance of the (intractable) optimal detector, lasso, and a (very simple) thresholding-based detector (IEEE Trans. Inform. Theory paper).
Provided the first analytical framework to enable computation of the exact asymptotic performance of a large class of estimators, including the lasso estimator. This is based on generalizing Guo and Verdú's replica method analysis of high-dimensional estimation problems with linear mixing (full paper).
Presented a new class of simultaneous sparsity problems, along with a variety of algorithms for solving these problems; these arise in MRI excitation design (SIAM J. Sci. Comput. paper).
Constructively demonstrated that conditional rank information (the relative sizes of the nonzero entries of a sparse vector) is tremendously valuable by proving that a simple algorithm using conditional ranks can have performance approaching maximum likelihood at high SNR. This is presented in the context of random access communication, where the interpretation is that knowledge of conditional ranks mitigates the near-far effect and can in the best case asymptotically eliminate multiple access interference. This work also supports previously-unproven observations in sparse Bayesian learning (full paper).
Source Coding and Quantization
The STIR group has introduced several new ways to think about fundamental limits of source coding and the effects of quantization, as well as new techniques for source coding.
Developed optimal quantizer designs for low relative error, which has self-evident importance but is often less convenient than absolute error (DCC paper won the 2011 Capocelli Prize).
Introduced a framework for understanding team decision making on ensembles of problems, where optimal categorization of hypothesis testing problems can be seen as quantization of the prior probabilities of the hypotheses. This has many intriguing implications for human decision making, apparent statistical biases, and team or committee formation (DCC paper 1, DCC paper 2).