2021-2022 Computer Science Facilities

Departmental laboratories and centers for instruction and research include:

Artificial Intelligence Laboratories

Automated Reasoning Group

Adnan Y. Darwiche, Director

The Automated Reasoning Group focuses on research in automated reasoning (logical and probabilistic) and machine learning, including their application to problems in science and engineering. On the theoretical side, the group focuses on tractable circuit representations and models that combine logic and probability, in addition to new models for machine learning that can integrate background knowledge. On the practical side, the group builds scalable reasoning and learning systems that can scale to real-world problems.

Cognitive Systems Laboratory

Judea Pearl, Director

The Cognitive Systems Laboratory targets research areas concerned with evidential reasoning, the distributed interpretation of multisource data in networks of partial beliefs; learning, the structuring and parameterizing of links in belief networks to form a representation consistent with a stream of observations; constraint processing, including intelligent backtracking, learning while searching, temporal reasoning, etc.; graphoids, the characterization of informational dependencies, and their graph representations; and default reasoning, use of qualitative probabilistic reasoning to draw plausible and defeasible conclusions from incomplete information.

Computational Machine Learning Laboratory

Cho-Jui Hsieh, Director

The Computational Machine Learning Laboratory conducts research on making machine learning algorithms more efficient, scalable, robust, and interpretable. The current focuses include large-scale training algorithms, robustness evaluation and defense, AutoML, machine learning model verification, and reinforcement learning.

Large-Scale Machine Learning Group (BigML)

Baharan Mirzasoleiman, Director

The Large-Scale Machine Learning Group conducts research in machine learning focused on designing new methods that enable efficient learning from massive datasets. More specifically, the group designs techniques that can gain insights from the underlying data structure by utilizing complex and higher-order interactions between data points. The extracted information can be used to efficiently explore and robustly learn from datasets that are too large to be dealt with by traditional approaches. The developed methods have immediate application to high-impact problems where massive data volumes prohibit efficient learning and inference, such as huge image collections, recommender systems, Web and social services, video, and other large data streams.

Machine Intelligence (MINT) Group

Aditya Grover, Director

The MINT group conducts research along two main thrusts: foundational research in machine learning, including topics in probabilistic reasoning, statistical inference, graphs and network science, reinforcement learning, and deep learning; and applications of artificial intelligence for accelerating scientific discovery, with a focus on sustainable development and climate change.

Natural Language Processing Group

Kai-Wei Chang, Director

The Natural Language Processing Group focuses on developing reliable machine learning solutions for processing natural languages. Specifically, it targets design of models, algorithms, and learning mechanisms to improve the generalization ability of natural-language processing models such that they can generalize across unseen tasks, unseen inputs, and low-resource languages.

Peng’s Language Understanding and Synthesis Laboratory (PLUS)

Nanyun Violet Peng, Director

The PLUS Laboratory is a collection of researchers working on natural language processing. The laboratory’s mission is to push the frontier of natural language generation towards coherent, controllable, and creative narrative generation through natural language understanding and common-sense reasoning. Along these lines, the laboratory develops novel machine learning models, specifically deep structured models and graph neural networks to cope with challenging natural language-related problems.

Statistical and Relational Artificial Intelligence Laboratory (StarAI)

Guy Van den Broeck, Director

The StarAI Laboratory performs research on machine learning (statistical relational learning, tractable learning), knowledge representation and reasoning (graphical models, lifted probabilistic inference, knowledge compilation), applications of probabilistic reasoning and learning (probabilistic programming, probabilistic databases), and artificial intelligence in general.

Statistical Machine Learning Laboratory

Quanquan Gu, Director

The Statistical Machine Learning Laboratory conducts research on machine learning, optimization, and high-dimensional statistical inference. Its focus is on development and analysis of nonconvex optimization algorithms for machine learning to understand large-scale, dynamic, complex, and heterogeneous data; and on building the theoretical foundations of deep learning and deep reinforcement learning.

Computational Systems Biology Laboratories

AI in Imaging and Neuroscience Research Laboratory

Fabien Scalzo, Director

The AI in Imaging and Neuroscience Research Laboratory aims to develop machine learning algorithms for medical images, with a special focus on vascular diseases and cancer. An important component of its research is development of computational and predictive models for neurovascular diseases based on multimodal medical imaging, including magnetic resonance imaging (MRI), computed tomography (CT), digital subtraction angiography (DSA), and transcranial Doppler ultrasound (TCD). By building models that can identify predictive factors of the patient outcome, they can help tailor treatment and improve the odds of a better recovery.

Big Data and Genomics Laboratory

Eran Halperin, Director

The Big Data and Genomics Laboratory aims to improve understanding and treatment of human disease by analysis of big data collected in relation to diseases. The main focus of the laboratory has been development of methods for analysis of genomic data—including genetics, epigenetics, RNA, and microbiome data; as well as medical records, images, and waveforms of UCLA Health medical center patients. The methods developed are typically standalone tools, often used by other researchers for analysis of specific diseases. The methodology involves a combination of machine learning, optimization algorithms, combinatorial optimization, and classical and Bayesian statistics.

Biocybernetics Laboratory

Joseph J. DiStefano III, Director

The interdisciplinary research of the Biocybernetics Laboratory typically involves integration of theory with real laboratory data, using biomodeling, computational, and biosystems approaches. Problem domains are physiological systems, disease processes, pharmacology, and some post-genomic bioinformatics. Laboratory pedagogy involves development and exploitation of the synergistic and methodologic interface between structural and computational biomodeling with laboratory data, or computational systems biology, with a focus on integrated approaches for solving complex biosystem problems from sparse biodata (e.g., in physiology, medicine, and pharmacology), as well as voluminous biodata (e.g., from genomic libraries and DNA array data).

Computational Genetics Laboratory

Eleazar Eskin, Director

The Computational Genetics Laboratory is comprised of a computational genetics group affiliated with both the Computer Science and Human Genetics departments. Research interests are in computational genetics, bioinformatics, computer science, and statistics. The laboratory focuses on developing techniques for solving the challenging computational problems that arise in attempting to understand the genetic basis of human disease.

Machine Learning and Genomics Laboratory

Sriram Sankararman, Director

The interdisciplinary Machine Learning and Genomics Laboratory research group is affiliated with UCLA departments of Computer Science, Human Genetics, and Computational Medicine. It is broadly interested in questions at the intersection of computer science, statistics, and biomedicine. It develops statistical and computational methods to make sense of complex, high-dimensional datasets generated in the fields of genomics and medicine, to answer questions ranging from how humans have evolved, to what the biological underpinnings of diseases are, to how we can improve the diagnosis and treatment of disease. A major focus of this research is understanding and interpreting human genomes. The biological questions of interest center around understanding how evolution shapes human genes, and how they modulate complex traits that include common diseases. The laboratory develops and extends tools from a diverse set of disciplines including machine learning, algorithms, optimization, high-dimensional statistics, and information theory. It also applies these tools to high-dimensional genomic and medical datasets that are publicly available or being generated by laboratory collaborators.

Computer Systems Architecture Laboratories

Architecture Specialization Laboratory (PolyArch)

Anthony J. Nowatzki, Director

The Architecture Specialization Laboratory studies how to redesign computer architectures and accelerators to continue improving performance and energy efficiency, even while technology scaling reaches its physical limits. Broadly, its approach is to consider how to reform traditional hardware/software abstractions to convey rich information that can make building efficient micro-architectures possible. These changes necessitate codesign of ISAs, architecture, execution models, and compilers.

Concurrent Systems Laboratory

Yuval Tamir, Director

The Concurrent Systems Laboratory conducts research on the design, implementation, and evaluation of computer systems that use state-of-the-art technology to achieve high performance and high reliability. Projects involve software, hardware, and networking. The focus is typically on parallel and distributed systems, and often involves fault tolerance.

Digital Arithmetic and Reconfigurable Architecture Laboratory

Milos D. Ercegovac, Director

The Digital Arithmetic and Reconfigurable Architecture Laboratory is used for fast digital arithmetic (theory, algorithms, and design) and numerically intensive computing on reconfigurable hardware. Research includes floating-point arithmetic, online arithmetic, application-specific architectures, and design tools.

eHealth Research Laboratory (ER Lab)

Majid Sarrafzadeh, Director

The ER Lab goal is to use technology in health care to reduce the cost of providing high-quality care to the chronically ill, estimated (by Milken Institute Center for Health Care Economics) to be over $1 trillion per year. The laboratory strives to improve global and local public health surveillance, with a resultant reduction in epidemics, increased control over infectious disease, and improved drug safety. Other goals are diminished rate of medical errors; ongoing preventive health, with attendant reductions in morbidity, mortality, and cost of care; and consumer engagement in health and self-management.

VAST Laboratory

Jason Cong, Director

The VAST Laboratory investigates cutting-edge research topics at the intersection of VLSI technologies, design automation, architecture, and compiler optimization at multiple scales, from micro-architecture building blocks to heterogeneous compute nodes and scalable data centers. Currently, the laboratory is focused on architecture and design automation for emerg-ing technologies such as neuromorphic computing and quantum computing; and customizable domain-specific computing with applications to multiple domains such as machine learning, big data analytics, and bioinformatics.

Graphics and Vision Laboratories

Center for Vision, Cognition, Learning, and Art

Song-Chun Zhu, Director

The Center for Vision, Cognition, Learning, and Art is affiliated with the Computer Science and Statistics departments. Research begins with computer vision and expands to other disciplines. The objective is to pursue a unified framework for representation, learning, inference, and reasoning; and to build intelligent computer systems for real-world applications. Its projects span four directions: vision (object, scene, events, etc.); cognition (intentions, roles causality, etc.); learning (information projection, stochastic grammars, etc.); and art (abstraction, expression, aesthetics, etc.).

Computer Graphics and Vision Laboratory (GraViLab)

Demetri Terzopoulos, Director

The Computer Graphics and Vision Laboratory engages in a broad spectrum of visual computing research unifying computer graphics (image synthesis), computer vision (image analysis), and related fields; with emphasis on geometric, physics-based, learning-driven, and artificial intelligence/life modeling and simulation. Major research interests include biomimetic simulation of humans and other animals, from biomechanics to sensorimotor control to intelligence; and image/video analysis combining (deep) learning and modeling paradigms, especially for application to medicine and health care.

UCLA Collective on Vision and Image Sciences

The Collective on Vision and Image Sciences brings together researchers from multiple departments at UCLA, including Brain Mapping, Computational and Systems Biology, Computer Science, Image Informatics, Mathematics, Neuroimaging, Psychology, Radiology, and Statistics.

UCLA Vision Laboratory

Stefano Soatto, Director

Researchers at the Vision Laboratory investigate how images—i.e., measurements of light—can be used to infer properties of the physical world such as shape, motion, location, and material properties of objects. This is key to developing engineering systems that can “see” and interact intelligently with the world around them. For example, images captured by a car-mounted video camera can be processed by computers to infer a model of the car’s surroundings, e.g., other vehicles, pedestrians, etc. This technology can also be used to analyze images captured in the environment, to help understand the effects of climate change by monitoring the behavior of animals and plants. Analysis of images of the human body can be used both for diagnostic purposes and for planning interventions.

Information and Data Management Laboratories

Information and Data Management Group

(Multiple Faculty)

The Information and Data Management Group is a collaboration of all UCLA faculty from this field. It is interested in multiple research areas including big data, archival information systems, knowledge discovery and data mining, Earth Science Partners’ private network, genomics graph database development, multimedia information stream system technology, Smart Space middleware architecture, and technologically based assessment of language and literacy, to name just a few.

Web Information Systems Laboratory

Carlo A. Zaniolo, Director

The Web Information Systems Laboratory research group investigates Web-based information systems and seeks to develop enabling technology for such systems by integrating the Web with database systems. Current research efforts include the DeAL system, a next-generation datalog system; SemScape, an NLP-based framework for mining unstructured or free text; EARL (Early Accurate Result Library) for Hadoop; Panta Rei, a study of support for schema evolution in the context of snapshot databases and transaction-time databases; Stream Mill, a complete data stream management system; and ArchIS, a powerful archival information system.

Network Systems Laboratories

Intelligent Sensing and Connect-ivity Laboratory (ICON Lab)

Omid Abari, Director

The group conducts research in the area of networked systems, with applications to the Internet of Things (IoT). It develops software-hardware systems that deliver ubiquitous sensing, efficient computing; and wireless communication at scale. Its research borrows techniques from diverse areas including computer networks, machine learning, signal processing, hardware design, and HCI to develop new algorithms and technologies that enable smart environments.

Internet Research Laboratory (IRL)

Lixia Zhang, Principal Investigator

The Internet Research Laboratory mission is to help the Internet grow. Its research efforts focus on design and development of network architecture and protocols, and the challenges in building secure networks and systems. Its past work has turned into Internet standards and successful startups. Since 2010, the laboratory has been working on design and development of named data networking (NDN), a new Internet architecture.

Network Design Automation Laboratory

George Varghese, Director

The Network Design Automation Laboratory focuses on research in this field, an effort to build a comprehensive set of design tools for networks inspired by electronic design automation for chips. A major focus is analysis and synthesis of router configuration files to avoid major outages that frequently cripple major service providers. This work involves development of new tools inspired by other fields such as programming languages, hardware design, and data mining; but targeted to incorporate the special structure and challenges of networks. It involves collaboration with multiple disciplines such as programming languages, systems, and network debugging; and includes other UCLA faculty.

Networked and Application Systems Group (NAS)

Ravi Netravali, Director

The group is focused on building practical systems to improve the performance and ease of debugging large-scale distributed applications. Such applications include web pages, mobile apps, video streaming and analytics systems, data analytics platforms, and more. The group uses a cross-layer methodology that aims to understand the impact of decisions at different layers in the end-to-end system; and designs solutions that incorporate fundamental principles at the network, operating system, and application vantage points.

UCLA Connection Laboratory

Leonard Kleinrock, Director

The Connection Laboratory offers an environment to support advanced research in technologies at the forefront of all things regarding networking and connectivity, and will deliver the benefits of that research to society globally. The laboratory’s broad-based agenda enables faculty, students, and visitors to pursue research challenges of their own choosing, without externally imposed constraints on scope or risk. It draws inspiration from the foundational role of UCLA as the birthplace of the Internet. With its open inclusive structure, the laboratory will help to realize the vision of creating high-leverage technologies, as was accomplished years ago with the Internet.

Wireless Networking Group (WiNG)

Songwu Lu, Director

The Wireless Networking Group’s research areas include wireless networking, mobile systems, and cloud computing. Its focus is on design, implementation, and experimentation of protocols, algorithms, and systems for wireless data networks. The goal is to build high-performance and dependable networking solutions for the wireless Internet.

Software Systems Laboratories

Compilers Laboratory

The Compilers Laboratory is used for research into compilers, embedded systems, and programming languages.

Large-Scale Systems Group

Harry Xu, Director

The Large-Scale Systems Group builds systems to improve the efficiency, scalability, reliability, and security of modern applications and workloads. These include graph analytics, video analytics, machine learning, smart contracts, etc. The group’s solutions cross multiple layers of the compute stack, spanning the areas of programming languages, compilers, operating systems, runtime systems, distributed systems, networking, and computer architecture.

Software Engineering and Analysis Laboratory (SEAL)

Miryung Kim, Director

The Software Engineering and Analysis Laboratory conducts research in software engineering, in particular debugging and testing for big data systems and automated tools for data science and ML-based systems. Its overall goal is to improve software engineering productivity and correctness. To achieve it, the laboratory designs scalable software systems, software analysis algorithms, and automated development tools. It also conducts user studies with software engineers, and carries out statistical analysis of open-source project data to allow data-driven decisions for designing novel software engineering tools. With expertise in software evolution, the laboratory is known for its research on code clones—code duplication detection, management, and removal solutions. The laboratory is a leader in creation and definition of the emerging area where software engineering and data science intersect. It has conducted the most comprehensive study of industry data scientists, and developed automated debugging and testing technologies for widely-used big data systems such as Apache Spark. Through tech-transfer, several companies have used SEAL research on interactive code clone search and big data analytics debugging technologies.

Software Systems Group

(Multiple Faculty)

The Software Systems Group is a collaboration of faculty from the software systems and network systems fields. It conducts research on the design, implementation, and evaluation of operating systems, networked systems, programming languages, and software engineering tools.

Computer Science Centers

Center for Autonomous Intelligent Networked Systems (CAINS)

The Center for Autonomous Intelligent Networked Systems was established in 2001 with researchers from several laboratories in the Computer Science, and Electrical and Computer Engineering, departments. It serves as a forum for intelligent-agent researchers and visionaries from academia, industry, and government, with an interdisciplinary focus on fields such as engineering, medicine, biology, and social sciences. Information and technology are exchanged through symposia, seminars, short courses, and collaboration in joint research projects sponsored by government and industry.

Research projects include use of unmanned autonomous vehicles, coordination of vehicles into computing clouds, and integration of body sensors and smart phones into m-health systems. Ongoing research encompasses personal and body networks, cognitive radios, ad hoc multihop networking, vehicular networks, dynamic unmanned backbone, underwater unmanned vehicles, mobile sensor platforms, and network coding.

Center for Domain-Specific Computing (CDSC)

Jason Cong, Director

The Center for Domain-Specific Computing looks beyond parallelization and focuses on domain-specific customization as a disruptive technology to bring orders-of-magnitude power-performance efficiency improvement. CDSC develops a general methodology for creating novel, customizable computing platforms, and associated compilation tools and runtime management environment to support domain-specific computing. Its recent focus is on design and implementation of accelerator-rich architectures, from single chips to data centers; and actively exploring the use of emerging computing technologies, such as neuromorphic and quantum computing. It also develops highly automated compilation tools and runtime management software for customizable heterogeneous platforms, including multicore CPUs, many-core GPUs, FPGAs, and quantum computers. By combining these capabilities, CDSC researchers are able to deliver a supercomputer-in-a-box or -in-a-cluster. This approach has been successfully applied to multiple application domains such as machine learning, big data analytics, medical imaging, and bioinformatics.

Center for Encrypted Functionalities

Amit Sahai, Director

The Center for Encrypted Functionalities was established in 2014 through an NSF Secure and Trustworthy Cyberspace (SaTC) Frontier Award. The center tackles the deep and far-reaching problem of general-purpose software obfuscation. The goal of obfuscation is to enable software that can keep secrets: software that makes use of secrets, but such that they remain hidden even if an adversary can examine the software code in its entirety and analyze its behavior as it runs. The center is headquartered at UCLA with partners at Columbia, Johns Hopkins, and Stanford universities, and University of Texas at Austin.

Center for Information and Computation Security (CICS)

Rafail Ostrovsky, Director

The Center for Information and Computation Security was established in 2003 to promote all aspects of research and education in cryptography and computer security. It explores novel techniques for securing national and private-sector information infrastructures across various network-based and wireless platforms as well as wide-area networks. The inherent challenge is to provide guarantees of privacy and survivability under malicious and coordinated attacks.

The center has raised federal, state, and private-sector funding, including collaboration with Israel through multiple U.S.–Israel Binational Science Foundation grants. It has also attracted multiple international visiting scholars. ClCS explores and develops state-of-the-art cryptographic algorithms, definitions, and proofs of security; novel cryptographic applications such as new electronic voting protocols and identification, data-rights management schemes, and privacy-preserving data mining; security mechanisms underlying a clean-slate design for a next-generation secure Internet; biometric-based models and tools, such as encryption and identification schemes based on fingerprint scans; and the interplay of cryptography and security with other fields such as bioinformatics, machine learning, complexity theory, etc.

Scalable Analytics Institute (ScAI)

The Scalable Analytics Institute was established in 2013 with a focus on the continuing growth of data and demand for smart analytics to mine that data. Such analytics are creating major transformative opportunities in science and industry. To fully capitalize on these opportunities, computing technology must solve the three-pronged challenge created by the exploding size of big data, the growing complexity of big data, and the increased sophistication of analytics that can be used to extract patterns and trends from the data.

Wireless Health Institute (WHI)

Benjamin M. Wu (Bioengineering), Director; Bruce Dobkin (Medicine/Neurology), William Kaiser (Electrical and Computer Engineering), Gregory J. Pottie (Electrical and Computer Engineering), Co-Directors

WHI is leading initiatives in health care solutions in the fields of disease diagnosis, neurological rehabilitation, optimization of clinical outcomes for many disease conditions, geriatric care, and many others. WHI also promotes this new field in the international community through the founding and organization of the leading Wireless Health conference series.

WHI technology always serves the clinician community through jointly developed innovations and clinical trial validation. Each WHI program is focused on large-scale product delivery in cooperation with manufacturing partners. WHI collaborators include the UCLA schools of Medicine, Nursing, and Engineering and Applied Sciences; Clinical Translational Science Institute for medical research; Ronald Reagan UCLA Medical Center; and faculty from many departments across UCLA. WHI education programs span high school, undergraduate, and graduate students, and provide training in end-to-end product development and delivery for WHI program managers.

WHI develops innovative, wearable biomedical monitoring systems that collect, integrate, process, analyze, communicate, and present information so that individuals become engaged and empowered in their own health care, improve their quality of life, and reduce burdens on caregivers. WHI products appear in diverse areas including motion sensing, wound care, orthopaedics, digestive health and process monitoring, advancing athletic performance, and many others. Clinical trials validating WHI technology are underway at 10 institutions. WHI products developed by the UCLA team are now in the marketplace in the U.S. and Europe. Physicians, nurses, therapists, other providers, and families can apply these technologies in hospital and community practices. Academic and industry groups can leverage the organization of WHI to rapidly develop products in complete-care programs, and validate in trials. WHI welcomes new team members, and continuously forms new collaborations with colleagues and organizations in medical science and health care delivery.

Computing Resources

In summarizing the resources now available to conduct experimentally based research in the UCLA Computer Science Department, it is useful to identify the major components of the research environment: the departmental computing facility, other hardware and software systems, administrative structure, and technical support staff.


Computing facilities range from large campus-operated supercomputers to a major local network of servers and workstations that are administered by the department computing facilities (DCF) or school network (SEASnet).

The departmental research network includes Oracle servers and shared workstations, on the school Ethernet TCP/IP local network. A wide variety of peripheral equipment is also part of the facility, and many more research-group workstations share the network; the total number of machines exceeds 1000, the majority running the Linux operating system. The network consists of switched gigabit Ethernet to the desktop with a gigabit backbone connection. The department LAN is connected to the campus gigabit backbone. An 802.11ac wireless network is also available to faculty, staff, and graduate students.

Administrative Structure

The central facilities and wide-area networking are operated by the campuswide Information Technology Services. Access to departmental and SEASnet machines is controlled so as to maximize the usefulness of these computers for education and research, but no direct charges are involved.

Technical Support Staff

The support staff consists of hardware and software specialists. The hardware laboratory supports network connections, configures routers, switches, and network monitoring tools. The software group administers the department UNIX servers, providing storage space and backup for department users.