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.

Previous Work: Implement and Evaluate Matrix Algorithms in Spark on High Performance Computing Platforms for Science Applications

The overall goal of this project is to enable the Berkeley Data Analytics Stack (BDAS) to run efficiently on the Cray XC30 and Cray XC40 supercomputer platforms. BDAS has a rich set of capabilities and is of interest as a computational environment for very large-scale machine learning and data analysis applications. To extend the capabilities of BDAS, ICSI researchers will consider the performance of deterministic and randomized matrix algorithms for problems such as least-squares approximation and low-rank matrix approximation that underlie many common machine-learning algorithms.

Big Data
Previous Work: Local Algorithms for Large Informatics Graphs

A serious problem with many existing machine learning and data analysis tools in the complex networks area is that they are often very brittle and/or do not scale well to larger networks. As a consequence, analysts often develop intuition on small networks, with 102 or 103 nodes, and then try to apply these methods on larger networks, with 105 or 107 or more nodes. Larger networks, however, often have very different static and dynamic properties than smaller networks.

Big Data
Liquid Data Networking

Packet loss often occurs when transmitting over wireless links, due to interference, intermittent obstacles, routing changes as conditions vary, etc. As examples, interference and the resulting packet loss are major concerns as the Internet is extended using wireless mesh networks, 5G millimeter wave transmission is known to be prone to packet loss with even the slightest obstruction, and slight variations in atmospheric conditions cause packet loss in laser communications between ground stations and drones or satellites.

TCS
Exploring the Boundaries of Passive Listening in Voice Assistants

Various forms of voice assistants—stand-alone devices or those built into smartphones—are becoming increasingly popular among consumers. Currently, these systems react when you directly speak to them using a specific wake-word, such as “Alexa,” “Siri,” “Ok Google.” However, with advancements in speech recognition, the next generation of voice assistants is expected to always listen to the acoustic environment and proactively provide services and recommendations based on human conversations or other audio signals, without being explicitly invoked.

Usable Security and Privacy
Low latency/high reliability streaming video prototype based on RaptorQ

We are happy to announce the availability of prototype software implementing low latency/high reliability video streaming based on RaptorQ.  The prototype uses the ROUTE protocol, which in turn uses RaptorQ to provide protection against packet loss. ROUTE and RaptorQ are specified in A/331, “Signaling, Delivery, Synchronization, and Error Protection”, as part of the ATSC 3.0 standard for delivery of media and non-timed data.

TCS
Intelligent Channel Management for Wireless Mesh Networks

ICSI's TCS team is working with Facebook to build and evaluate a system wide network channel management tool for a wireless mesh network. The aim of the collaboration is to increase wireless capacity, decrease interference between devices (self and external), and provide more automated and intelligent network channel management and planning.

Funding being provided by Facebook Connectivity.

TCS
Enhancements for Wireless Mesh Networks

ICSI's TCS team is working with Facebook to simulate, understand and enhance wireless mesh networks. The main goal of the effort is to build a high fidelity simulator for wireless mesh networks to understand the performance scaling of such networks and improve coverage, density, throughput. Potential enhancements include extending the reach and performance of wireless mesh networks i.e. to support more hops and/or provide superior internet connectivity, using technology which avoids deep changes to the network or wireless technology.

TCS
Codornices

The RaptorQ erasure code (specified in IETF RFC 6330) enables reliable wireless communication systems, including high quality streaming and delivery of large data packages.

Under the auspices of the International Computer Science Institute, the Codornices project aims to spur further adoption and deployment of RaptorQ by developing a high performance software implementation of RaptorQ and developing application software that supports different use cases and standards that leverage RaptorQ.

TCS
Co-Design of Network, Storage and Computation Fabrics for Disaggregated Datacenters

Traditional datacenters are built using servers, each of which tightly integrates a small amount of CPU, memory and storage onto a single motherboard. The slowdown of Moore's Law has led to surfacing of several fundamental limitations of such server-centric architectures (e.g, the memory-capacity wall making CPU-memory co-location unsustainable). As a result, a new computing paradigm is emerging --- a disaggregated datacenter architecture, where each resource type is built as a standalone "blade" and a network fabric interconnects the resource blades within and across datacenter racks.

Networking and Security
Universal Packet Scheduling

This project addresses a seemingly simple question: Is there a universal packet scheduling algorithm? More precisely, researchers are analyzing whether there is a single packet scheduling algorithm that, at a network-wide level, can perfectly match the results of any given scheduling algorithm. The question of universal packet scheduling is being investigated from both a theoretical and empirical perspective.

Networking and Security
Bro Intrusion Detection System Refinements

ICSI is working with LBNL on refinements to Zeek (formerly known as Bro). The work includes troubleshooting and resolving the most complex problems with the Zeek network monitor, development/integration of the communication framework, development and implementation of new features for the Input framework, and development of a persistence solution for the NetControl and Catch-and Release frameworks of Zeek/Bro. Zeek/Bro is an open-source network intrustion detection system developed at ICSI and LBNL which is currently in use at Fortune 500 companies, universities, and governments.

Networking and Security
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
When do Computers Discriminate? Toward Informing Users About Algorithmic Discrimination

In this collaborative project with University of Maryland, ICSI researchers are tackling the challenge of explaining what constitutes unacceptable algorithmic discrimination. Getting the answer to this question right is key to unlocking the potential of automated decision systems without eroding the ability of people to get a fair deal and advance in society.

Networking and Security
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
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
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
Creating an Evolvable, Diverse, and Dynamic Internet

The Internet has ushered in a new era of communication, and has supported an ever-growing set of applications that have transformed our lives. It is remarkable that all this has taken place with an Internet architecture that has remained unchanged for over forty years. While unfortunate, some view this architectural stagnation as inevitable. After all, it has long been a central tenet that the Internet needs a "narrow waist" at the internetworking layer (L3), a single uniform protocol adopted by everyone; given this assumption, changing this layer is inevitably hard.

Networking and Security
Towards Programming Datacenters

Datacenters have redefined the nature of high-end computing, but harnessing their computing power remains a challenging task. Initially, programming frameworks such as MapReduce, Hadoop, Spark, TensorFlow, and Flink provided a way to run large-scale computations. These frameworks took care of the difficult issues of scaling, fault-tolerance, and consistency, freeing the developer to focus on the logic of their particular application. However, each of these frameworks were aimed at a specific computational task (e.g., machine learning, data analytics, etc.), and are not fully general.

Networking and Security

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