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.

Multimodal Feature Learning for Understanding Consumer Produced Multimedia Data

ICSI is working with LLNL on ongoing work on feature extraction and analytic techniques that map raw data from multiple input modalities (e.g., video, images, text) into a joint semantic space. This requires cutting edge research in multiple modalities, as well as in the mathematical methods to learn the semantic mappings.

Audio and Multimedia
Variable Precision Computing LDRD Project

Large-scale physics simulations pose a significant challenge on the currently available computational resources, because of the costs of both communication and storage largely exceeding the cost of the actual computation. The efficient management of the Exascale data flows generated by a large-scale simulation is still an unsolved problem. This project aims to provide an initial solution to this problem.

Research Initiatives
Theory and Practice of Randomized Algorithms for Ultra-Large-Scale Signal Processing

The dramatic increase in our abilities to observe massive amounts of measurements coming from distributed and disparate high-resolution sensors have been instrumental in enhancing our understanding of many physical phenomena. Signal processing (SP) has been the primary driving force in this knowledge of the unseen from observed measurements. However, in the last decade, the exponential increase in observations has outpaced our computing abilities to process, understand, and organize this massive but useful information.

Big Data
Usable Security of Emerging Healthcare Technologies for Seniors

Older adults (65+) are becoming primary users of technologies, including emerging smart systems, especially in health care. However, such technologies are often not designed for older users and can pose serious privacy and security concerns due to their novelty, complexity, and propensity to collect vast amounts of sensitive information.

Usable Security and Privacy
Towards Automated Testing and Discovery of Interoperability

The difficulty of automated testing and discovery in interoperability depends on information explicitly known. Interoperability remains a challenging unsolved problem that depends on manual error-prone solutions and costs billions annually. The goal of this research is to investigate automated approach to verification and discovery of interoperability based on recently developed theory of property-based interoperability. This may enable the next generation of automatically composable and reconfigurable systems.

Funding provided by DARPA

Research Initiatives
Scaling Contextual Privacy to MDM Environments

It has long been understood that privacy and usability are often in tension: the privacy controls that are often mandated by workplace environments are difficult to use, which results in either low rates of compliance, negative impacts on job performance (e.g., being unable to perform various tasks due to access control restrictions), or inadvertent disclosure of sensitive information (i.e., privacy breaches).

Usable Security and Privacy
Mobile Dynamic Privacy and Security Analysis at Scale

Current approaches for detecting suspicious application activity on mobile platforms rely on static analysis: reverse-engineering and examining sequences of program code to infer application behavior. This method invariably falls short in that it can only detect what behaviors or capabilities a program might have, and not whether and to what extent a program actually engages in these behaviors. It is also susceptible to code obfuscation techniques commonly used by many mobile applications.

Usable Security and Privacy
Increasing Users' Cyber-Security Compliance by Reducing Present Bias

Despite recent advances in increasing computer security through automation, there are still situations in which humans must manually perform computer security tasks. These tasks may include enabling automatic updates, rebooting machines to apply those updates, configuring automatic backups, or enrolling in two-factor authentication. However, despite viewing these tasks as important for security, many people still choose to ignore them. Two decades of usable security research have shown that these tasks are often seen as annoyances because they are almost never the user's primary objective.

Usable Security and Privacy
Combining stochastics and numerics for more meaningful matrix computations

The amount of data in our world has exploded, with data being at the heart of modern economic activity, innovation, and growth. In many cases, data are modeled as matrices, since an m x n matrix A provides a natural structure to encode information about m objects, each of which is described by n features. As a result, linear algebraic algorithms, and in particular matrix decompositions, have proven extremely successful in the analysis of datasets in the form of matrices.

Big Data
The Science of Privacy: Implications for Data Usage

The International Computer Science Institute (ICSI) in Berkeley, CA is the home to one of six NSA-funded lablets focused on security and privacy research. ICSI's lablet is led by Dr. Serge Egelman, head of the Usable Security and Privacy Research group at ICSI, and includes collaborators at Cornell Tech and UC Berkeley. Other lablets are centered at University of Kansas, Vanderbilt University, Carnegie-Mellon University, University of Illinois-Champaign, and North Carolina State University.

Usable Security and Privacy
AppCensus: Learn the Privacy Costs of Free Apps

There exists a mature ecosystem of developers and service providers that produce mobile applications, often offering them at zero up-front cost. These free apps are supported by advertising networks, who distribute software libraries that developers use for drop-in integration of ad delivery and audience tracking functionality. However, integrated advertiser code and core application code run with the same access privileges, a security and privacy risk not readily apparent to end-users and developers alike.

Usable Security and Privacy
Accountable Information Use: Privacy and Fairness in Decision-Making Systems

Increasingly, decisions and actions affecting people's lives are determined by automated systems processing personal data. Excitement over the positive contributions of these systems has been accompanied by serious concerns about their opacity and the threats that they pose to privacy, fairness, and other values. Recognizing these concerns, this project seeks to enable real-world automated decision-making systems to be accountable for privacy and fairness.

Networking and Security
Deep Learning-Based Self-Organizing Network for B5G Communications with Massive IoT Devices

Self-organizing network (SON) algorithms that were designed for the self-configuration, self-optimization, and self-healing of today's 4G networks exhibit various drawbacks. The two most severe drawbacks are (1) so-called SON use case coordination - the coordination of conflicting network parameter changes - which can lead to sub-optimal network configurations and, more likely, to a worsening of network performance, and (2) a qualitative and quantitative lack of input information to the SON, making reliable network management cumbersome.

Research Initiatives
Next Generation Methods and Workflow for Automated Optimal Multi-Functional Design

This collaborative project led by LLNL is part of the DARPA TRAnsforming DESign (TRADES) program, and focuses on modeling, analysis and synthesis of complex parameterized multi-scale material structures.

Research Initiatives
Robust, Efficient, and Local Machine Learning Primitives

The large-scale data being generated in many application domains promise to revolutionize scientific discovery, engineering and technological development, social science understanding, and our ability to monitor masses and influence behavior in subtle ways. In most applications, however, this promise has yet to be fulfilled. One major reason for this is the dificulty of using, in a low-friction manner, cutting-edge algorithmic and statistical tools to explore the data and develop domain-informed models of the processes generating the data.

Big Data
Counter Power Lab

In this collaborative project with UC Berkeley, ICSI PIs are working with the lead developer of the "Snowflake" censorship circumvention system to refine the code for production deployment in both the Tor Browser Bundle and as a stand-alone application. The work includes developing instrumentation to measure the usage of Snowflake as its deployment rolls out and analyzing the results to assess Snowflake's impact on enabling circumvention.

Networking and Security
Exploring Internet Balkanization through the Lens of Regional Discrimination

One of the Internet’s greatest strengths is the degree to which it facilitates access to any of its resources from users anywhere in the world. Various forces, however, have arisen that restrict particular users from accessing particular destinations, resulting in a "balkanization" of the network. This project explores apt methodologies for understanding such balkanization, an effort we will undertake in the context of examining "regional discrimination," i.e., the degree to which certain web services deny or degrade access to users from particular geographic regions.

Networking and Security
Automatic Recognition of Camera Speech (ARCS)

In this ICSI project, researchers are working to improve speech recognition from noisy, often distorted audio taken from the body cameras of working police officers during traffic stops. This is part of a larger project at Stanford to extract information from these data. The Stanford project is focused on the analysis of the interactions between the officers and the communities they serve, in the hope that they could help to transform the relationship between the police and communities, produce solid data on officer-community interaction, and inform officer training programs.

Speech
Interoperability Challenges and Scenarios in Computational Design and Manufacturing

A common informal definition of interoperability refers to the ability of a system, whose interfaces are completely understood, to work with other products or systems, present or future, without any restricted access or implementation. In the context of computational design and manufacturing, interoperability subsumes the problems of data sharing, exchange, and translation, as well as the problems of systems integration.

Research Initiatives
Effective and Economical Protection for High-Performance Research and Education Networks

As scientific research requires free exchange of information and ideas among collaborators world-wide, scientists depend critically on full and open access to the Internet. Yet in today’s world, such open access also exposes sites to incessant network attacks. Some of the most powerful networks today remain particularly hard to defend: for the 100G environments and backbones that facilitate modern data-intensive sciences, classic inline firewalls remain infeasible options.

Networking and Security

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