Projects

ICSI hosts basic, pre-competitive research of fundamental importance to computer science and engineering. Projects are chosen based on the interests of the Institute’s principal investigators and the strengths of its researchers and affiliated UC Berkeley faculty.

Recent projects are listed below; the full list of each group's projects is accessible via the links listed in the sidebar.

Developing Security Science from Measurement

This project aims to define foundational data-driven methodologies and the related science to create a basis for continuous and dynamic monitoring that enables adaptive approaches to mitigate and contain the spread of attacks. The basis of the approach is data on security incidents from a real large-scale production environment at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign (UIUC).

Networking and Security
Previous Work: The Berkeley Data Analysis System

In this project, researchers at ICSI are extending and applying recent work on randomized algorithms for matrix-based machine learning problems to the computational infrastructure recently developed at the AMPLab, UC Berkeley. One of the challenges in large-scale machine learning is that MapReduce/Hadoop does not perform well for iterative algorithms that are common in matrix-based machine learning. Examples of such iterative algorithms include common algorithms for least-squares approximation, least absolute deviations approximation, low-rank matrix approximation, etc.

Big Data
Robotic Vision

To perform useful tasks in everyday human environments, robots must be able to both understand and communicate the sensations they experience during haptic interactions with objects. Toward this goal, vision researchers at ICSI augmented the Willow Garage PR2 robot with a pair of SynTouch BioTac sensors to capture rich tactile signals during the execution of four exploratory procedures on 60 household objects. In a parallel experiment, human subjects blindly touched the same objects and selected binary haptic adjectives from a predetermined set of 25 labels.

Vision
Domain Adaptation

ICSI researchers are investigating the fundamental problem of visual domain adaptation, or how to deal with the most common scenario “What you see is not what you get.” When test data and training data come from differing distributions (or unsupervised methods are employed with non-stationary distributes), conventional approaches to machine learning often perform very poorly. They have been exploring several approaches to this problem, including those based on conventional feature spaces that are transformed based on a learned adaptation to overcome a domain shift.

Vision
Fine-grained Recognition

Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories. Discriminative markings are not only subtle but often highly localized, with which traditional object recognition approaches struggle when dealing with the large pose variation often present in these domains. The ability to normalize pose based on super-category landmarks can significantly improve models of individual categories when training data is limited.

Vision
Representation Learning

Researchers at ICSI and UC Berkeley are developing new representation learning models for visual detection, leveraging advances in discriminatively trained convolutional neural networks.  In 2013, they established important results related to these models, including observations of their ability to generalize to new tasks and domains, and importantly to be applicable to detection and segmentation tasks.  They developed a new “Region-CNN” model (R-CNN), which outperformed all competing methods on the most important visual detection benchmark, the PASCAL challenge.

Vision
Bro Center of Expertise for the NSF Community

Researchers at ICSI and NCSA are operating a center to provide support and guideance to the NSF community on customized Bro installations that meet the specific needs of research environments. They are simultaneously making improvements to Bro that benefit the community, and leveraging Bro as a deployment platform for networking research results.

Networking and Security
Service Composition in Distributed Application Design and Execution

In a collaboration with the Computer Platform Research Center - CIPI (jointly established by the Universities of Genoa and Padua, Italy), researchers are investigating the service composition paradigm for distributed applications. This paradigm can be taken as a reference when a distributed application is treated as a composite service made up of atomic services. In these cases, the application designers do not need to be programmers because they can specify the distributed applications using visual Service Creation Platforms.

Research Initiatives
Previous Work: Leverage Subsampling for Regression and Dimension Reduction

In this collaborative project between UC Berkeley, University of Illinois, Urbana-Champaign, and ICSI, scientists are working toward an integrated treatment of statistical and computational issues. The first research thrust focuses on studying the statistical properties of the subsampling estimation using the statistical leverage scores in linear regression. The second research thrust generalizes the theory and methods to nonlinear regression and dimension reduction models.

Big Data
Scalable Statistics and Machine Learning for Data-Centric Science

Researchers from Lawrence Berkeley Laboratory, UC Berkeley, and ICSI are developing and applying new statistics and machine learning algorithms that can operate on real-world datasets produced by a diverse range of experimental and observational facilities. This is a critical capability in facilitating big data analysis, which will be essential for scientific progress in the foreseeable future.

Big Data
CESR: The Center for Evidence-based Security Research

The Center for Evidenced-based Security Research (CESR) is a joint project among researchers at UC San Diego, the International Computer Science Institute, and George Mason University. This interdisciplinary effort takes the view that, while security is a phenomenon mediated by the technical workings of computers and networks, it is ultimately a conflict driven by economic and social issues that merit a commensurate level of scrutiny.

Networking and Security
Network Virtualization for OpenCloud

Researchers are working to implement a network virtualization infrastructure to allow the academic community to explore the fundamental technical challenges that underlie the cloud.

Networking and Security
Previous Work: COrtical Separation Models for Overlapping Speech (COSMOS)

In this collaborative project among ICSI, UCSF, and Columbia, researchers are measuring brain activity to understand in detail how human listeners are able to separate and understand individual speakers when more than one person is talking at the same time. This information can then be used to design automatic systems capable of the same feat.

Speech
Semantic Security Monitoring for Industrial Control Systems

Industrial control systems differ significantly from standard, general-purpose computing environments, and they face quite different security challenges. With physical "air gaps" now the exception, our critical infrastructure has become vulnerable to a broad range of potential attackers. In this project we develop novel network monitoring approaches that can detect sophisticated semantic attacks: malicious actions that drive a process into an unsafe state without exhibiting any obvious protocol-level red flags.

Networking and Security
SMASH - Scalable Multimedia content AnalysiS in a High-level language

This big data project develops tools to support researchers and developers in the task of prototyping multimedia content analysis algorithms on a large scale. Typically, scientists and engineers prefer to use high-level programming languages such as Python or MATLAB to conduct experiments, as they allow for a quick implementation of a novel idea.

Audio and Multimedia
Censorship Counterstrike via Measurement, Filtering, Evasion, and Protocol Enhancement

This project studies Internet censorship as practiced by some of today's nation-states. The effort emphasizes analyzing the technical measures used by censors and the extent to which their operations inflict collateral damage (unintended blocking or blocking of activity wholly outside the censoring nation). Researchers also study the vulnerabilities that arise because of how censorship operates by analyzing flaws in either how the censorship monitoring detects particular network traffic to suppress, or in how the monitor then attempts to block or disrupt the target traffic.

Networking and Security
Understanding and Exploiting Parallelism in Deep Packet Inspection on Concurrent Architectures

Researchers are developing a comprehensive approach to introducing parallelism across all stages of the complex deep packet inspection (DPI) pipeline. DPI is a crucial tool for protecting networks from emerging and sophisticated attacks. However, it is becoming increasingly difficult to implement DPI effectively due to the rising need for more complex analysis, combined with the relentless growth in the volume of network traffic that these systems must inspect.

Networking and Security
The Design and Implementation of a Consolidated MiddleBox Architecture

Researchers are designing infrastructures for specialized network appliances, called middleboxes, that consolidate their management, reducing the cost of deploying new middleboxes and simplifying network management. Middleboxes fill a number of needs and include network intrusion detection systems and WAN optimizers. They are typically added to a network as a need arises, and each has its own management interface. In this project, researchers will explore architectures that provide centralized control.

Networking and Security
Previous Work: Towards Modeling Human Speech Confusions in Noise

Researchers are studying how background noise and speaking rate affect the ability of humans to recognize speech. In this project, they evaluate components of a model of human speech perception. Researchers look at the effect of incorporating spectro-temporal filters, which operate in the human auditory cortex and are sensitive to particular modulations in auditory frequency. The results from this project will improve our understanding of how humans perceive sound, and they could be used to improve artificial systems for speech processing, such as hearing aids.

Speech
Limiting Manipulation in Data Centers and the Cloud

Researchers are designing algorithms to allocate resources in datacenters and clouds that can't be manipulated by users. In datacenters and clouds, computing resources or individual machines are allocated to users based on the requirements of the jobs they want to run. Users can manipulate allocations by misreporting their requirements. In this project, researchers design algorithms that are less susceptible to such manipulation. They will also use algorithmic mechanism design and game theory to develop general procedures for converting protocols so that they can't be manipulated.

Research Initiatives, Algorithms

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