Network and Information Sciences Lab aims at developing provably efficiently algorithms for practically-relevant problem spanning from the control of large-scale stochastic networks to distributed computation and machine learning. The lab’s current main areas of research focus are:
Distributed Computation and Machine Learning
Distributed machine learning algorithms that dominate the use of modern large-scale computing platforms face several types of randomness, uncertainty and system noise. These include straggler nodes, system failures, maintenance outages, and communication bottlenecks. In this project, we view distributed machine learning algorithms through an information-theoretic and coding-theoretic lens. Our goal is to understand fundamental trade-offs in distributed computation between latency of computation, redundancy in computation, communication bottlenecks and storage capacities, and to develop distributed algorithms with robustness against various sources of uncertainty in the system.
Analysis and Control of Large-Scale Stochastic Networks Large-scale
Networked infrastructures such as transportation systems and data centers are rapidly growing in size and demand. Given the new challenges brought about by such networks, we design novel scalable tools for the analysis and control of them. Our goal is to develop mathematical models for complex networks that span diverse application areas from transportation networks with mixed autonomy to data centers, and to design simple, distributed, and robust scheduling algorithms that maximize the efficiency of the network, and are robust to the unknown and time-varying parameters of the network.
Sparse Recovery of High-Dimensional Signals
The past several years have seen a new approach to the recovery of high-dimensional signals, where a few sketches of the signal retain sufficient information for an approximate sparse recovery. This approach has found numerous applications in the areas of signal processing, imaging, data stream computing, etc. Our focus is to exploit tools from modern coding theory to design fast and robust reconstruction algorithms for a variety of problems including compressed sensing, compressive phase retrieval and group testing.
SCL research covers a wide spectrum of topics in signal compression and related areas, and is a confluence of both theoretical foundations, and practical algorithms and applications.
Theory and Methods for Source Coding and Networking: Research under this topic spans both information and estimation theories, with recent emphasis on joint source-channel coding and distributed source coding. Specific problems we are currently focused on include: Achievable rate-distortion region and communication cost minimization for a general multi-hop network with correlated sources and multiple sinks (as in the figure), via joint compression-routing scheme called dispersive information routing (DIR); Developing new communication schemes for emerging network applications with low delay and low energy constraints based on the general "analog networking” methods which achieve optimality at low delay and robustness to varying channel conditions.
Image/Video Coding and Processing: Research under this topic spans image/video coding and transmission, bio-image informatics, and image/video tracking. Specific focus problems include: Jointly optimal spatial transform and intra-prediction (quality improvement depicted in the figure); Spatio-temporal prediction based on non-separable Markov models; Optimization frameworks, adaptive to time varying network conditions, for optimal scalability in video coders (covering bit-rate, encoding/decoding delay and spatial resolution scalability); Object tracking & 3D tracing in bio-image data via probabilistic graphical models.
Audio Coding and Processing: Research under this topic spans audio coding and networking. Specific focus problems include: Unified coding paradigms for diverse types of aural signals; Cascaded long term prediction (illustrated in figure) for compression and frame loss concealment; Optimization algorithms for efficient coder design and resource allocation (bitrate, encoding/decoding delay and complexity); Common Information framework-based optimal coding for layered storage and transmission
Research in the ViVoNets Lab is concerned with all aspects of the transmission and processing of voice, audio, still images, and video for communications over multihop, wireless, heterogeneous networks, with a particular emphasis on handheld devices and highly mobile broadband networks.
Our lab’s main focus is on new system concepts and architectures for wireless communication and sensor networks, with smaller efforts in areas such as multimedia security and neuroimaging. Some examples of recent and ongoing research efforts are as follows:
Our research often involves interdisciplinary collaborations, since we tackle hard problems requiring diverse expertise. Current collaborators include faculty from computer science, controls, electronics, and psychology.
Dr. Visell's research focuses on haptic engineering, robotics, and the mechanics and neuroscience of touch. His work is motivated by creative applications in haptic human-computer interaction, sensorimotor augmentation, and interaction in virtual reality.
The lab's main areas of research include:
At the Advanced Graphics Lab, we work on problems in computer graphics and computational imaging. These include:
Improved Algorithms for Image Synthesis: Image synthesis is the problem of generating an image from scene data that includes geometrical models, surface textures, and camera and light properties. In our lab, we work on new algorithms for image synthesis, from high-end algorithms that could be used to produce photorealistic images for feature films, to extremely fast algorithms that could be used to render scenes in videogames or other interactive applications. Our work combines ideas from signal processing, applied math, and computational methods to improve the speed and quality of rendered images.
Better Imaging Techniques: Digital photography is changing the way we communicate, socialize, and document events around us, but it still has several shortcomings. In our lab, we develop new technology and algorithms that improve photography or break its traditional paradigms. One example is our recent work to improve the acquisition of high-dynamic range (HDR) images with a conventional camera. Standard digital cameras have a small dynamic range and cannot capture the wide range of illumination of a scene in the way our eyes can (we can see the bright and dark parts of a scene simultaneously). Although researchers have developed techniques to compute HDR images from a set of sequential images at different exposures, these techniques usually only work robustly for static scenes. In our lab, we have developed a new optimization-based framework that reconstructs high-quality HDR images from a set of standard images of a dynamic scene. Algorithms like this could change the way people take pictures in the future.
Novel modes of Interaction: As we develop new technologies for sensing, display, and interaction, we need to study new ways to use them to solve real-world problems. In this research thrust, we explore ways to leverage new technologies for applications in visualization, training, and entertainment. These new techniques are more immersive and more intuitive than traditional interaction.
Brain Science has attracted the attention of many biology research groups around the world. With recent advances in imaging, it is possible to harvest large amounts of image data through in vitro and in vivo procedures at multiple scales. As a result the need to develop computational techniques that help biologists interpret the data is crucial.
The Center for Bio-Image Informatics is an interdisciplinary research effort between Biology, Neuroscience, Computer Science, Statistics, Multimedia and Engineering. The overarching goal of the center is the advancement of human knowledge of the complex biological processes which occur at various resolutions. To achieve this core objective, the center employs and develops cutting edge techniques in the fields of imaging, pattern recognition and data mining for analyzing data using different modalities.
We pursue research on optical microscopy, image processing and image analysis to study biological systems.
Our group develops optical microscopy procedures to image live samples at high framerates and over extended periods of time. In particular, we study the development of dynamic organs, such as the heart in zebrafish embryos, with high temporal and spatial, single-cell, resolution.
A central aspect of our lab is the tight integration of image processing (3D-reconstruction, noise reduction, alignment) and image analysis (tracking, flow estimation) algorithms into our image acquisition system, so as to increase the amount of information extracted from images while limiting the invasiveness of the imaging procedure.