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University of California, Los Angeles - 2016

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Graduate

Research Description

Research Description By Graduate Engineering Department

Bioengineering Department

Field 1: Biomedical Instrumentation (BMI)

This field of emphasis is designed to train bioengineers interested in the applications and development of instrumentation used in medicine and biotechnology. Examples include the use of lasers in surgery and diagnostics, new micro electrical machines for surgery, sensors for detecting and monitoring of disease, microfluidic systems for cell-based diagnostics, new tool development for basic and applied life science research, and controlled drug delivery devices. The principles underlying each instrument and specific clinical or biological needs will be emphasized. Graduates of this program will be targeted principally for employment in academia, government research laboratories, and the biotechnology, medical devices, and biomedical industries.

Field 2: Molecular Cellular Tissue Therapeutics (MCTT)

This field of emphasis covers novel therapeutic development across all biological length scales from molecules to cells to tissues. At the molecular and cellular levels, this area of research encompasses the engineering of biomaterials, ligands, enzymes, protein-protein interactions, intracellular trafficking, biological signal transduction, genetic regulation, cellular metabolism, drug delivery vehicles, and cell-cell interactions, as well as the development of chemical/biological tools to achieve this. At the tissue level, this field encompasses two sub-fields which include biomaterials and tissue engineering. The properties of bone, muscles and tissues, the replacement of natural materials with artificial compatible and functional materials such as polymers, composites, ceramics and metals, and the complex interactions between implants and the body are studied at the tissue level. The emphasis of research is on the fundamental basis for diagnosis, disease treatment, and re-design of molecular, cellular, and tissue functions. In addition to quantitative experiments required to obtain spatial and temporal information, quantitative and integrative modeling approaches at the molecular, cellular, and tissue levels are also included within this field. Although some of the research will remain exclusively at one length scale, research that bridges any two or all three length scales are also an integral part of this field. Graduates of this program will be targeted principally for employment in academia, government research laboratories, and the biotechnology, pharmaceutical, and biomedical industries.

Field 3: Imaging, Informatics and Systems Engineering (IIS)

This field consists of the following four subfields: Biomedical Signal and Image Processing (BSIP), Biosystem Science and Engineering (BSSE), Medical Imaging Informatics (MII), and NeuroEngineering (NE).

Subfield 1: Biosystem Science and Engineering (BSSE)

Graduate study in Biosystem Science and Engineering (BSSE) emphasizes the systems aspects of living processes, as well as their component parts. It is intended for science and engineering students interested in understanding biocontrol, regulation, communication, measurement or visualization of biomedical systems (of aggregate parts â€" whole systems), for basic or clinical applications. Dynamic systems engineering, mathematical, statistical and multiscale computational modeling and optimization methods â€" applicable at all biosystem levels â€" form the theoretical underpinnings of the field. They are the paradigms for exploring the integrative and hierarchical dynamical properties of biomedical systems quantitatively â€" at molecular, cellular, organ, whole organism or societal levels â€" and leveraging them in applications. The academic program provides directed interdisciplinary biosystem studies in these areas â€" as well as quantitative dynamic systems biomodeling methods â€" integrated with the biology for specialized life science domain studies of interest to the student. Typical research areas include molecular and cellular systems physiology, organ systems physiology, medical, pharmacological and pharmacogenomic system studies; neurosystems, imaging and remote sensing systems, robotics, learning and knowledge-based systems, visualization and virtual clinical environments. The program fosters careers in research and teaching in systems biology/physiology, engineering, medicine, and/or the biomedical sciences, or research and development in the biomedical or pharmaceutical industry.

Subfield 2: Biomedical Signal and Image Processing (BSIP)

The Biomedical Signal and Image Processing (BSIP) graduate program prepares students for a career in the acquisition and analysis of biomedical signals; and enables students to apply quantitative methods applied to extract meaningful information for both clinical and research
applications. The BSIP program is premised on the fact that a core set of mathematical and statistical methods are held in common across signal acquisition and imaging modalities and across data analyses regardless of their dimensionality. These include signal transduction, characterization and analysis of noise, transform analysis, feature extraction from time series or images, quantitative image processing and imaging physics. Students in the BSIP program have the opportunity to focus their work over a broad range of modalities including electrophysiology, optical imaging methods, MRI, CT, PET and other tomographic devices and/or on the extraction of image features such as organ morphometry or neurofunctional signals, and detailed anatomic/functional feature extraction. The career opportunities for BSIP trainees include medical instrumentation, engineering positions in medical imaging, and research in the application of advanced engineering skills to the study of anatomy and function.

Subfield 3: Medical Imaging Informatics (MII)

Medical imaging informatics (MII) is the rapidly evolving field that combines biomedical informatics and imaging, developing and adapting core methods in informatics to improve the usage and application of imaging in healthcare. Graduate study in this field encompasses principles from across engineering, computer science, information sciences, and biomedicine. Imaging informatics research concerns itself with the full spectrum of low-level concepts (e.g., image standardization and processing; image feature extraction) to higher-level abstractions (e.g., associating semantic meaning to a region in an image; visualization and fusion of images with other biomedical data) and ultimately, applications and the derivation of new knowledge from imaging. Notably, medical imaging informatics addresses not only the images themselves, but encompasses the associated (clinical) data to understand the context of the imaging study; to document observations; and to correlate and reach new conclusions about a disease and the course of a medical problem. Research foci include distributed medical information architectures and systems; medical image understanding and applications of image processing; medical natural language processing; knowledge engineering and medical decision-support; and medical data visualization. Coursework is geared towards students with science and engineering backgrounds, introducing them to these areas in addition to providing exposure to fundamental biomedical informatics, imaging, and clinical issues. This area encourages interdisciplinary training, with faculty from multiple departments; and emphasizes the practical, translational development and evaluation of tools/applications to support clinical research and care.

Subfield 4: NeuroEngineering (NE)

The NeuroEngineering (NE) subfield is designed to enable students with a background in biological science to develop and execute projects that make use of state-of-the-art technology, including microelectromechanical systems (MEMS), signal processing, and photonics. Students with a background in engineering will develop and execute projects that address problems that have a neuroscientific base, including locomotion and pattern generation, central control of movement, and the processing of sensory information. Trainees will develop the capacity for the multidisciplinary teamwork, in intellectually and socially diverse settings, that will be necessary for new scientific insights and dramatic technological progress in the 21st century. NE students take a curriculum designed to encourage cross-fertilization of neuroscience and engineering. Our goal is for neuroscientists and engineers to speak each others’ language and move comfortably among the intellectual domains of the two fields.

Chemical and Biomolecular Engineering

Biomolecular Engineering and Systems Biology

Since the discovery of gene splicing techniques over 20 years ago, California has been the focus of the nation’s burgeoning biotechnology industry. This state hosts a third of the nation’s public biotechnology companies, and its firms account for over 50% of industry sales and better than 60% of the nation’s biotech employees. Southern California, in particular, is one of the focal areas of biotechnology research and commercialization. Highlighted by two recent Nobel prizes (Paul Boyer, 1997, for discovery of ATPase in energy metabolism; and Louis Ignarro, 1998, for discovery of roles of nitric oxide in medical physiology), UCLA is among the best campuses for research in biosciences and bioengineering.

Bioengineering, in particular, is one of the major thrust areas in the Chemical Engineering Department. Research in the Chemical Engineering Department focuses on manipulation of cells, DNA, and proteins for applications in biotechnology and clinical medicine. Both theoretical and experimental tools are developed to address current scientific and engineering issues of high societal impact. Current efforts of bioengineering research in this Department are in the areas of genomics and metabolic engineering, bioinformatics, biosensors, biomimetic systems, blood flow regulation, and artificial blood substitutes. Cutting edge technology, such as DNA microarray and nano fabrication technology are being used in conjunction with mathematical tools to advance bioscience and engineering.

Significant progress in the past few years includes the development of an artificial gene circuit for production of a novel product in E. coli, a hierarchical model for a DNA microarray, discovery of a novel thermostable, chiral-specific enzyme for L-amino acid synthesis, construction of a novel metabolic pathway in E. coli that synthesizes a large variety of important bioactive molecules, and discovery of a novel mechanism that regulates blood flow. In addition to numerous publications in top journals, the quality of research resulted in two graduate students receiving the American Chemical Society’s Peterson Award for best paper presentation and two others assuming faculty positions in highly ranked Chemical Engineering Departments.

Among the three Molecular and Cellular Bioengineering faculty members, research efforts converge to three common thrust areas with several specific applications. These common thrusts are (1) the development of functional genomic and proteomic techniques for rapid characterization of metabolic activities in microorganisms, (2) engineering of metabolic pathway and protein function to generate novel activities for biosynthesis and biosensing, and (3) liposome biomimetics for creation of artificial red blood cells and novel separations and diagnostic schemes. Specific projects are briefly described in the research description of the faculty members.
The molecular and cellular bioengineering laboratories in the Department are well equipped and supported. State-of-the-art instrumentation, such as DNA microarrays, is used to generate a large amount of gene expression data for bioinformatic analysis. HPLC coupled with a diode array detection system and electron ionization mass spectrophotometer is used in the development of functional proteomic screening methods. A spectrofluorometer and a laser light scattering instrument are used in characterizing enzymes and lipid vesicles. A unique, glass-lined steel 100-liter fermentor, which was designed and built by UCLA chemical engineers, is used for pilot-scale archaean cultures for proteomic characterization. These facilities are complemented by complete laboratories with the analytical and preparatory equipment for DNA manipulation, protein characterization, and hematology measurements. Other facilities on campus such as nuclear magnetic resonance (NMR), electron paramagnetic resonance (EPR), and electron microscopy are also being used in various projects. Software packages are developed and used for molecular simulation, DNA hybridization image analysis, DNA/protein sequence analysis, metabolic analysis, and spectra deconvolution.

The department offers a sequence of three cellular and molecular bioengineering lecture courses: Molecular Biotechnoloy, Biochemical Reaction Engineering, and Bioseparations and Bioprocess Engineering, which may be followed by a laboratory course in Bioprocess Technology, that provides hands-on training with bioreactors and protein purification to the pilot-scale. Chemical Engineering graduate students also benefit from the presence of excellent courses in chemistry, life sciences, and medicine. Most students pursue a minor in these areas to augment the bioengineering courses available in the Chemical Engineering Department.

UCLA’s NSF-supported Bioinformatics Training Program provides financial support to both life science and bioengineering graduate students, encourages collaboration among the various disciplines, and offers trainees an opportunity to intern at local biotechnology companies. The department has strong ties with the UCLA medical school, which provides the patient basis for medicine-related projects. Bioengineering students have opportunities to work together with physicians on clinically significant problems. The cutting-edge projects, excellent facilities, interdisciplinary programs, and the excellence of UCLA’s academic infrastructure provide an outstanding learning and research environment for students in the cellular and molecular bioengineering program.

Semiconductor Manufacturing

The microelectronics industry is the largest manufacturing em­ployer in the United States, generating revenues in excess of one trillion dollars in 2001. The microelectronics industry has been growing faster than the national average because of the development of revolutionary new products for communication, information storage, and computing. The world is now ringed with satellites providing world­wide voice, video (DirecTVTM), and data transmission. Many of these new technologies were developed by companies located in California and the Southwestern United States, including Intel, AMD, LSI Logic, Hewlett-Packard, Motorola, Lucent Technologies, Cirrus Logic, Silicon Graphics, Hughes Electronics, Cisco Systems, Vitesse Semiconductor, Netscape Communications, Broadcom Corp., and 3Com Corp, to name a few.

The heart of this industry is the computer chip and the tiny solid-state transistors and capacitors that comprise them. By constantly shrinking the device dimensions, integrated circuits have become more and more powerful, while the cost per function has continued to drop. At dimensions below 0.13 micrometer, microprocessors can operate at GHz and DRAM chips can store gigabits of information. All these advances hinge on developing sophisticated chemical processes for depositing, patterning, and etching thin­-film circuits onto semiconductor surfaces. The semiconductor equipment industry, which provides these process tools, gener­ated close to $70 billion in revenues annually. This industry is also a California success story, and is represented by highly prof­itable companies such as Applied Materials, Novellus Systems, Lam Research, Silicon Valley Group, KLA-Tencor, and many others.

Chemical engineers have an important role to play in micro­electronics technology. They are needed to operate and control the sophis­ticated chemical processes that fabricate the chips. Even more importantly, Ph.D. chemical engineers are needed to continu­ously research and develop new processes capable of fabri­cating the next generation of denser and denser integrated cir­cuits. These individuals have the requisite knowledge of math­ematics, physics and chemistry that is essential to define fea­tures in semiconductor materials with dimensions ranging from 10 to 1000 angstroms.

At UCLA, we have an exciting program for training graduate chemical engineers in microelectronics research. We offer students a sequence of graduate courses designed to provide in­-depth knowledge of the field: Surface and Interface Engineer­ing, Electrochemical Processing, Plasma Processing of Materi­als, and Physical and Chemical Vapor Deposition. In addition, students may pursue a minor field of study in Electrical Engineering, taking the three-course sequence of Solid-State Physics, Semiconductor Electronics and Semiconductor Devices. In addition, students may take the semiconductor processing laboratory course where in ten weeks they fabricate and test their own complementary metal-oxide-semiconductor (CMOS) devices. The UCLA School of En­gineering is world renowned for its scholarship in semiconductor physics, chemistry, materials, and engineering.

Process System Engineering

Low cost availability of computer software and hardware, and increased computing power have led to the ever increasing computerization and automation of manufacturing operations employing chemical engineers. This trend permeates both established (chemical, petroleum) and developing (microelectronics, biotechnology) industries and has led to the significant growth of process systems engineering. Indeed, process modeling and simulation have become so accurate, fast and inexpensive so as to reduce reliance on plant scale-up. The scope of process design has been expanded to include evaluation of a large number of design alternatives from economic, safety and environmental viewpoints as well as hazard and reliability analysis. Process optimization is routinely pursued in the context of both on-line and off-line applications. Finally, process control and automation projects have become a major vehicle for increasing plant efficiency and abnormal situation management.

UCLA Chemical Engineering has positioned itself to take advantage of the aforementioned trends. There are several factors that bode well for this choice. The large manufacturing base of Los Angeles with its many engineering, simulation, control and environmental design firms (e.g., Fluor, Parsons, C. F. Braun, Simulation Sciences, Profimatics, Environ); California’s forward thinking on environmental issues as it pertains to the design and control of environmentally benign plants; the strong presence of microelectronics industry in California, and UCLA’s tradition and academic recognition in the systems area.

UCLA’s efforts span the gamut of Process Systems Engineering from process modeling, simulation, design, optimization and control, to data analysis and decision support, and to computational and applied mathematics. They have five focal points:

Fundamental studies on the theory of Process Systems Engineering.
Process Systems Engineering aiming at green manufacturing.
Process Systems Engineering for the microelectronics industry.
Intelligent decision support systems in process operations and design.
Computational modeling and simulation of complex biological systems, advanced materials processing, and fluid flows.

In the first area, studies are pursued on nonlinear and robust process control, process monitoring and identification, model reduction, optimization and control of nonlinear distributed parameters systems, hybrid control and hybrid systems, and analysis and synthesis of constrained control systems and global optimization of nonconvex nonlinear programs. Green manufacturing studies include the synthesis of thermodynamically feasible reaction clusters; the synthesis of molecules with environmentally benign properties; the design of energy efficient distillation networks; the development of heat and mass integration techniques and the integration of process design and control.

The microelectronics research focuses on the development of comprehensive and simplified models for the simulation, design and control of plasma reactors, rapid thermal processing systems, chemical vapor deposition reactors and crystal growth processes. In the intelligent systems area, studies are directed toward the development, application, and integration of knowledge-based and neural reasoning techniques into data analysis and decision support systems. Finally, research also focuses on computational modeling and simulation of complex biological systems, and dynamics and control of fluid flows for drag reduction in aerospace vehicles.
The above research activities are supported from a variety of governmental and industrial sources. Research assistantships are available from various sources including NSF, ONR NASA, EPRI, PRF AND AFOSR. Industrial internships, from a three to six month period, from engineering companies as well as internships at US Air Force research labs are available.

In addition to books and numerous publications in high-quality journals, the research activities of the UCLA group have been recognized with three Ted Peterson awards from the Computing and Systems Technology division of the AIChE, the O. Hugo Schuck award from the American Automatic Control Council, NSF and ONR Young Investigator awards, and the consistent placement of doctoral graduates in faculty positions in highly-ranked chemical engineering departments in the United States and abroad. UCLA’s group is widely regarded today as one of the leading Process Systems Engineering groups in the world.

Civil and Environmental Engineering

Structural Engineering and Mechanics

Research in structural mechanics is directed toward improving the ability of engineers to understand and interpret structural behavior through experiments and computer analyses. Areas of special interest include computer analysis using finite-element techniques, computational mechanics, structural dynamics, nonlinear behavior, plasticity, micromechanics of composites, damage and fracture mechanics, structural optimization, probabilistic static and dynamic analysis of structures, and experimental stress analysis.

Designing structural systems capable of surviving major earthquakes is the goal of experimental studies on the strength of full-scale reinforced concrete structures, computer analysis of soils/structural systems, design of earthquake resistant masonry, and design of seismic-resistant buildings and bridges.

Teaching and research areas in structural/earthquake engineering involve assessing the performance of new and existing structures subjected to earthquake ground motions. Specific interests include assessing the behavior of reinforced concrete buildings and bridges, as well as structural steel, masonry, and timber structures. Integration of analytical studies with laboratory and field experiments is emphasized to assist in the development of robust analysis and design tools, as well as design recommendations. Reliability-based design and performance assessment methodologies are also an important field of study.

Geotechnical Engineering

Research in geotechnical engineering focuses on understanding and advancing the state of knowledge on the effects that soils and soil deposits have on the performance, stability, and safety of civil engineering structures. Areas of research include laboratory investigations of soil behavior under static and dynamic loads, constitutive modeling of soil behavior, behavior of structural foundations under static and dynamic loads, soil improvement techniques, response of soil deposits and earth structures to earthquake loads, and the investigation of geotechnical aspects of environmental engineering.

Environmental Engineering

Research in environmental engineering focuses on the understanding and management of physical, chemical, and biological processes in the environment and in engineering systems. Areas of research include process development for water and wastewater treatment systems and the investigation of the fate and transport of contaminants in the environment.

Hydrology and Water Resources

Ongoing research in hydrology and water resources deals with surface and ground-water processes, hydrometeorology and hydroclimatology, watershed response to disturbance, remote sensing, data assimilation, hydrologic modeling and parameter estimation, multiobjective resources planning and management, numerical modeling of solute transport in groundwater, and optimization of conjunctive use of surface water and groundwater.

Civil Engineering Materials

Ongoing research is focused on inorganic, random porous materials and incorporates expertise at the interface of chemistry and materials science to develop the next generation of sustainable construction materials. The work incorporates aspects of first principles and continuum scale simulations and integrated experiments, ranging from nano-to-macro scales. Special efforts are devoted toward developing low-clinker factor cements and concretes, reducing the carbon footprint of construction materials, and increasing the service life of civil engineering infrastructure.

Computer Science

Artificial Intelligence
Artificial intelligence (AI) is the study of intelligent behavior. While other fields such as philosophy, psychology, neuroscience, and linguistics are also concerned with the study of intelligence, the distinguishing feature of AI is that it deals primarily with information processing models. Thus the central scientific question of artificial intelligence is how intelligent behavior can be reduced to information processing. Since even the simplest computer is a completely general information processing device, the test of whether some behavior can be explained by information processing mechanisms is whether a computer can be programmed to produce the same behavior. Just as human intelligence involves gathering sensory input and producing physical action in the world, in addition to purely mental activity, the computer for AI purposes is extended to include sense organs such as cameras and microphones, and output devices such as wheels, robotic arms, and speakers.

The predominant research paradigm in artificial intelligence is to select some behavior that seems to require intelligence on the part of humans, to theorize about how the behavior might be accounted for, and to implement the theory in a computer program to produce the same behavior. If successful, such an experiment lends support to the claim that the selected behavior is reducible to information processing terms, and may suggest the program’s architecture as a candidate explanation of the corresponding human process.

The UCLA Computer Science Department has active research in the following major subfields of artificial intelligence:

Problem Solving. Analysis of tasks, such as playing chess or proving theorems, that require reasoning about relatively long sequences of primitive actions, deductions, or inferences
Knowledge representation and qualitative reasoning. Analysis of tasks such as commonsense reasoning and qualitative physics. Here the deductive chains are short, but the amount of knowledge that potentially may be brought to bear is very large
Expert systems. Study of large amounts of specialized or highly technical knowledge that is often probabilistic in nature. Typical domains include medical diagnosis and engineering design
Natural language processing. Symbolic, statistical, and artificial neural network approaches to text comprehension and generation
Computer vision. Processing of images, as from a TV camera, to infer spatial properties of the objects in the scene (three-dimensional shape), their dynamics (motion), their photometry (material and light), and their identity (recognition)
Robotics. Translation of a high-level command, such as picking up a particular object, into a sequence of low-level control signals that might move the joints of a robotic arm/hand combination to accomplish the task; often this involves using a computer vision system to locate objects and provide feedback
Machine learning. Study of the means by which a computer can automatically improve its performance on a task by acquiring knowledge about the domain
Parallel architecture. Design and programming of a machine with thousands or even millions of simple processing elements to produce intelligent behavior; the human brain is an example of such a machine
Computational Systems Biology
The computational systems biology (CSB) field can be selected as a major or minor field for the Ph.D. or as a specialization area for the M.S. degree in Computer Science.

Graduate studies and research in the CSB field are focused on computational modeling and analysis of biological systems and biological data.

Core coursework is concerned with the methods and tools development for computational, algorithmic, and dynamic systems network modeling of biological systems at molecular, cellular, organ, whole organism, or population levelsâ€"and leveraging them in biosystem and bioinformatics applications. Methodological studies include bioinformatics and systems biology modeling, with focus on genomics, proteomics, metabolomics, and higher levels of biological/physiological organization, as well as multiscale approaches integrating the parts.

Typical research areas with a systems focus include molecular and cellular systems biology, organ systems physiology, medical, pharmacological, pharmacokinetic (PK), pharmacodynamic (PD), toxicokinetic (TK), physiologically based PBPK-PD, PBTK, and pharmacogenomic system studies; neurosystems, imaging and remote sensing systems, robotics, learning and knowledge-based systems, visualization, and virtual clinical environments. Typical research areas with a bioinformatics focus include development of computational methods for analysis of high-throughput molecular data, including genomic sequences, gene expression data, protein-protein interaction, and genetic variation. These computational methods leverage techniques from both statistics and algorithms.

Computer Networks
The computer networks field involves the study of computer networks of different types, in different media (wired, wireless), and for different applications. Besides the study of network architectures and protocols, this field also emphasizes distributed algorithms, distributed systems, and the ability to evaluate system performance at various levels of granularity (but principally at the systems level). In order to understand and predict systems behavior, mathematical models are pursued that lead to the evaluation of system throughput, response time, utilization of devices, flow of jobs and messages, bottlenecks, speedup, power, etc. In addition, students are taught to design and implement computer networks using formal design methodologies subject to appropriate cost and objective functions. The tools required to carry out this design include probability theory, queueing theory, distributed systems theory, mathematical programming, control theory, operating systems design, simulation methods, measurement tools, and heuristic design procedures. The outcome of these studies provides the following:

An appropriate model of the computer system under study
An adequate (exact or approximate) analysis of the behavior of the model
The validation of the model as compared to simulation and/or measurement of the system
Interpretation of the analytical results in order to obtain behavioral patterns and key parameters of the system
Design methodology
Resource Allocation
A central problem in the design and evaluation of computer networks deals with the allocation of resources among competing demands (e.g., wireless channel bandwidth allocation to backlogged stations). In fact, resource allocation is a significant element in most of the technical (and nontechnical) problems we face today.

Most of our resource allocation problems arise from the unpredictability of the demand for the use of these resources, as well as from the fact that the resources are geographically distributed (as in computer networks). The computer networks field encounters such resource allocation problems in many forms and in many different computer system configurations. Our goal is to find allocation schemes that permit suitable concurrency in the use of devices (resources) so as to achieve efficiency and equitable allocation. A very popular approach in distributed systems is allocation on demand, as opposed to prescheduled allocation. On-demand allocation is found to be effective, since it takes advantage of statistical averaging effects. It comes in many forms in computer networks and is known by names such as asynchronous time division multiplexing, packet switching, frame relay, random access, and so forth.

Computer Science Theory
Computer science is in large measure concerned with information processing systems, their applications, and the corresponding problems of representation, transformation, and communication. The computer science fields are concerned with different aspects of such systems, and each has its own theoretical component with appropriate models for description and analysis, algorithms for solving the related problems, and mathematical tools. Thus in a certain sense computer science theory involves all of computer science and participates in all disciplines.

The term theoretical computer science has come to be applied nationally and intentionally to a certain body of knowledge emphasizing the interweaving themes of computability and algorithms , interpreted in the broadest sense. Under computability, one includes questions concerning which tasks can and cannot be performed by information systems of different types restricted in various ways, as well as the mathematical analysis of such systems, their computations, and the languages for communication with them. Under algorithms, one includes questions concerning (1) how a task can be performed efficiently under reasonable assumptions on available resources (e.g., time, storage, type of processor), (2) how efficiently a proposed system performs a task in terms of resources used, and (3) the limits on how efficiently a task can be performed. These questions are often addressed by first developing models of the relevant parts of an information processing system (e.g., the processors, their interconnections, their rules of operation, the means by which instructions are conveyed to the system, or the way the data is handled) or of the input/output behavior of the system as a whole. The properties of such models are studied both for their own interest and as tools for understanding the system and improving its performance or applications.

Emphasis of Computer Science Theory

•Design and analysis of algorithms
•Distributed and parallel algorithms
•Models for parallel and concurrent computation
•Online and randomized algorithms
•Computational complexity
•Automata and formal languages
•Cryptography and interactive proofs



Computer System Architecture
Computer system architecture deals with the design, implementation, and evaluation of computer systems and their building bIocks. It deals with general-purpose systems as well as embedded special-purpose systems. The field also encompasses the development of tools to enable system designers to describe, model, fabricate, and test highly complex computer systems.

Computer systems are implemented as a combination of hardware and software. Hence, research in the field of computer architecture often involves both hardware and software issues. The requirements of application software and operating systems, together with the capabilities of compilers, play a critical role in determining the features implemented in hardware. At the same time, the computer architect must also take into account the capabilities and limitations of the underlying implementation technology as well as of the design tools.

The goal of research in computer architecture is to develop building blocks, system organizations, design techniques, and design tools that lead to improved performance and reliability as well as reduced power consumption and cost.

Corresponding to the richness and diversity of computer systems architecture research at UCLA, a comprehensive set of courses is offered in the areas of advanced processor architecture, arithmetic processor systems. parallel and distributed architectures, fault-tolerant systems, reconfigurable systems, embedded systems, and computer-aided design of VLSI circuits and systems.

Novel architectures encompass the study of computations that are performed in ways that are quite different than those used by conventional machines. Examples include various domain-specific architectures characterized by high computational rates, low power, and reconfigurable hardware implementations.
The study of high-performance processing algorithms deals with algorithms for very high-performance numerical processing. Techniques such as redundant-digit representations of number systems, fast arithmetic, and the use of highly parallel arrays of processing elements are studied with the goal of providing the extremely high processing speeds required in a number of upcoming computer applications.
The study of computational algorithms and structures deals with the relationship between computational algorithms and the physical structures that can be employed to carry them out. It includes the study of interconnection networks, and the way that algorithms can be formulated for efficient implementation where regularity of structure and simplicity of interconnections are required.
Computer-aided design of VLSI circuits and systems is an active research area that develops techniques for the automated synthesis and analysis of large-scale systems. Topics include high-level and logic-level synthesis, technology mapping, physical design, interconnect modeling, and optimization of various VLSI technologies such as full-custom designs, standard cells, programmable logic devices (PLDs), multichip modules (MCMs), system-on-a-chip (SaCs), network-on-a-chip (NoC), system-in-a-package (SIPs), and design for nanotechnologies.
VLSI architectures and implementation is an area of current interest and collaboration between the Electrical Engineering and Computer Science Departments that addresses the impact of large-scale integration on the issues of computer architecture. Application of these systems in medicine and healthcare, multimedia, and finance is being studied in collaboration with other schools on campus.
Graphics and Vision
See http://www.cs.ucla.edu/magix/ and http://vision.ucla.edu for more information.

Information and Data Management
The information and data management field focuses on basic problems of modeling and managing data and knowledge, and their relation with other fundamental areas of computer science, such as operating systems and networking, programming languages, and human-computer interface design.

A data management system embodies a collection of data, devices in which the data are stored, and logic or programs used to manipulate that data. Information management is a generalization of data management in which the data being stored are permitted to be arbitrarily complex data structures, such as rules and trees. In addition, information management goes beyond simple data manipulation and query and includes inference mechanisms, explanation facilities, and support for distributed and web-based access.

The need for rapid, accurate information is pervasive in all aspects of modern life. Modern systems are based on the coordination and integration of multiple levels of data representation, from characteristics of storage devices to conceptual and abstract levels. As human enterprises have become more complex, involving more complicated decisions and trade-offs among decisions, the need for sophisticated information and data management has become essential.

Software Systems
The programming languages and systems field is concerned with the study of theory and practice in the development of software systems. Well-engineered systems require appreciation of both principles and architectural trade-offs. Principles provide abstractions and rigor that lead to clean designs, while systems-level understanding is essential for effective design.

Principles here encompass the use of programming systems to achieve specified goals, the identification of useful programming abstractions or paradigms, the development of comprehensive models of software systems, and so forth. The thrust is to identify and clarify concepts that apply in many programming contexts.

Development of software systems requires an understanding of many methodological and architectural issues. The complex systems developed today rely on concepts and lessons that have been extracted from years of research on programming languages, operating systems, database systems, knowledge-based systems, real-time systems, and distributed and parallel systems.

Electrical Engineering

Electrical Engineering MS

Areas of Study
Students may pursue specialization across three major areas of study: (1) circuits and embedded systems, (2) physical and wave electronics, and (3) signals and systems. These areas cover a broad spectrum of specializations in, for example, communications and telecommunications, control systems, electromagnetics, embedded computing systems, engineering optimization, integrated circuits and systems, microelectromechanical systems (MEMS), nanotechnology, photonics and optoelectronics, plasma electronics, signal processing, and solid-state electronics. Students must select a number of formal graduate courses to serve as their major and minor fields of study according to the requirements listed below for the thesis (seven courses) and non-thesis (eight courses) options. The selected courses must be approved by the faculty adviser.

Electrical Engineering Ph.D.
Areas of Study
Students may pursue specialization across three major areas of study: (1) circuits and embedded systems, (2) physical and wave electronics, and (3) signals and systems. These areas cover a broad spectrum of specializations in, for example, communications and telecommunications, control systems, electromagnetics, embedded computing systems, engineering optimization, integrated circuits and systems, microelectromechanical systems (MEMS), nanotechnology, photonics and optoelectronics, plasma electronics, signal processing, and solid-state electronics.

Circuits and Embedded Systems Area Tracks

Embedded Computing Track. Courses deal with the engineering of computer systems as may be applied to embedded devices used for communications, multimedia, or other such restricted purposes.

Integrated Circuits Track. Courses deal with the analysis and design of analog and digital integrated circuits; architecture and integrated circuit implementations of large-scale digital processors for communications and signal processing; hardware-software codesign; and computer-aided design methodologies.

Physical and Wave Electronics Area Tracks

Electromagnetics Track. Courses deal with electromagnetic theory; propagation and scattering; antenna theory and design; microwave and millimeter wave circuits; printed circuit antennas; integrated and fiber optics; microwave-optical interaction, antenna measurement, and diagnostics; numerical and asymptotic techniques; satellite and personal communication antennas; periodic structures; genetic algorithms; and optimization techniques.

Photonics and Plasma Electronics Track. Courses deal with laser physics, optical amplification, electro-optics, acousto-optics, magneto-optics, nonlinear optics, photonic switching and modulation, ultrafast phenomena, optical fibers, integrated waveguides, photodetection, optoelectronic integrated circuits, optical microelectromechanical systems (MEMS), analog and digital signal transmission, photonics sensors, lasers in biomedicine, fundamental plasma waves and instability; interaction of microwaves and laser radiation with plasmas; plasma diagnostics; and controlled nuclear fusion.

Solid-State and Microelectromechanical Systems (MEMS) Devices Track. Courses deal with solid-state physical electronics, semiconductor device physics and design, and microelectromechanical systems (MEMS) design and fabrication.

Signals and Systems Area Tracks

Communications Systems Track. Courses deal with communication and telecommunication principles and engineering applications; channel and source coding; spread spectrum communication; cryptography; estimation and detection; algorithms and processing in communication and radar; satellite communication systems; stochastic modeling in telecommunication engineering; mobile radio engineering; and telecommunication switching, queuing system, communication networks, local-area, metropolitan-area, and wide-area computer communication networks.

Control Systems and Optimization Track. Courses deal with state-space theory of linear systems; optimal control of deterministic linear and nonlinear systems; stochastic control; Kalman filtering; stability theory of linear and nonlinear feedback control systems; computer-aided design of control systems; optimization theory, including linear and nonlinear programming; convex optimization and engineering applications; numerical methods; nonconvex programming; associated network flow and graph problems; renewal theory; Markov chains; stochastic dynamic programming; and queuing theory.

Signal Processing Track. Courses deal with digital signal processing theory, statistical signal processing, analysis and design of digital filters, digital speech processing, digital image processing, multirate digital signal processing, adaptive filtering, estimation theory, neural networks, and communications signal processing.

Materials Science and Engineering

Materials Science and Engineering M.S.
Areas of Study
There are three main areas in the M.S. program: ceramics and ceramic processing, electronic and optical materials, and structural materials. Students may specialize in any one of the three areas, although most students are more interested in a broader education and select a variety of courses. Basically, students select courses that serve their interests best in regard to thesis research and job prospects.

Materials Science and Engineering Ph.D.
Fields of Study
Ceramics and Ceramic Processing
The ceramics and ceramic processing field is designed for students interested in ceramics and glasses, including electronic materials. As in the case of metallurgy, primary and secondary fabrication processes such as vapor deposition, sintering, melt forming, or extrusion strongly influence the microstructure and properties of ceramic components used in structural, electronic, or biological applications. Formal course and research programs emphasize the coupling of processing treatments, microstructure, and properties.

Electronic and Optical Materials
The electronic and optical materials field provides an area of study in the science and technology of electronic materials that includes semiconductors, optical ceramics, and thin films (metal, dielectric, and multilayer) for electronic and optoelectronic applications.

Course offerings emphasize fundamental issues such as solid-state electronic and optical phenomena, bulk and interface thermodynamics and kinetics, and applications that include growth, processing, and characterization techniques. Active research programs address the relationship between microstructure and nanostructure and electronic/optical properties in these materials systems.

Structural Materials
The structural materials field is designed primarily to provide broad understanding of the relationships between processing, microstructure, and performance of various structural materials, including metals, intermetallics, ceramics, and composite materials. Research programs include material synthesis and processing, ion implantation-induced strengthening and toughening, mechanisms and mechanics of fatigue, fracture and creep, structure/property characterization, nondestructive evaluation, high-temperature stability, and aging of materials.

Mechanical and Aerospace Engineering

Fields of Study

Design, Robotics, and Manufacturing

The program is developed around an integrated approach to design, robotics, and manufacturing. It includes research on manufacturing and design aspects of mechanical systems, material behavior and processing, robotics and manufacturing systems, CAD/CAM theory and applications, computational geometry and geometrical modeling, composite materials and structures, automation and digital control systems, microdevices and nanodevices, radio frequency identification (RFID), and wireless systems.

Dynamics

Features of the dynamics field include dynamics and control of physical systems, including spacecraft, aircraft, helicopters, industrial manipulators; analytical studies of control of large space structures; experimental studies of electromechanical systems; and robotics.

Fluid Mechanics

The graduate program in fluid mechanics includes experimental, numerical, and theoretical studies related to a range of topics in fluid mechanics, such as turbulent flows, hypersonic flows, microscale and nanoscale flow phenomena, aeroacoustics, bio fluid mechanics, chemically reactive flows, chemical reaction kinetics, numerical methods for computational fluid dynamics (CFD), and experimental methods. The educational program for graduate students provides a strong foundational background in classical incompressible and compressible flows, while providing elective breadth courses in advanced specialty topics such as computational fluid dynamics, microfluidics, bio fluid mechanics, hypersonics, reactive flow, fluid stability, turbulence, and experimental methods.

Heat and Mass Transfer

The heat and mass transfer field includes studies of convection, radiation, conduction, evaporation, condensation, boiling and two-phase flow, chemically reacting and radiating flow, instability and turbulent flow, reactive flows in porous media, as well as transport phenomena in support of micro-scale and nanoscale thermosciences, energy, bioMEMS/NEMS, and microfabrication/nanofabrication.

Nanoelectromechanical/Microelectromechanical Systems

The nanoelectromechanical/microelectromechanical systems (NEMS/MEMS) field focuses on science and engineering issues ranging in size from nanometers to millimeters and includes both experimental and theoretical studies covering fundamentals to applications. The study topics include microscience, top-down and bottom-up nanofabrication/microfabrication technologies, molecular fluidic phenomena, nanoscale/microscale material processing, biomolecular signatures, heat transfer at the nanoscale, and system integration. The program is highly interdisciplinary in nature.

Structural and Solid Mechanics

The solid mechanics program features theoretical, numerical, and experimental studies, including fracture mechanics and damage tolerance, micromechanics with emphasis on technical applications, wave propagation and nondestructive evaluation, mechanics of composite materials, mechanics of thin films and interfaces, analysis of coupled electro-magneto-thermomechanical material systems, and ferroelectric materials. The structural mechanics program includes structural dynamics with applications to aircraft and spacecraft, fixed-wing and rotary-wing aeroelasticity, fluid structure interaction, computational transonic aeroelasticity, biomechanics with applications ranging from whole organs to molecular and cellular structures, structural optimization, finite element methods and related computational techniques, structural mechanics of composite material components, structural health monitoring, and analysis of adaptive structures.

Systems and Control

The program features systems engineering principles and applied mathematical methods of modeling, analysis, and design of continuous- and discrete-time control systems. Emphasis is on modern applications in engineering, systems concepts, feedback and control principles, stability concepts, applied optimal control, differential games, computational methods, simulation, and computer process control. Systems and control research and education in the department cover a broad spectrum of topics primarily based in aerospace and mechanical engineering applications. However, the Chemical and Biomolecular Engineering and Electrical Engineering Departments also have active programs in control systems, and collaboration across departments among faculty members and students in both teaching and research is common.

School of Engineering and Applied Science

In this program students can earn a Master of Science in Engineering degree with the same program of study as in our departmental programs, with the same courses, same instructors, and same grading standards as on campus. You can also participate in one or several of our certificate programs by completing certain course sequences. These represent specialized areas of study offered by departments, or broader school wide areas of study such as systems engineering. The MS Engineering degree also offers the flexibility to build your own program of study to best suit your professional needs. Beginning Summer 2013, all of the HSSEAS-MSOL courses will be available in three formats for viewing on a computer, a tablet, or a smart phone.


Areas of Study
Engineering Management Program

The engineering management program focuses on providing entering and current engineering management personnel an opportunity to expand their business-related knowledge base and skills to enhance employment performance to the benefit of both the employee and employer. The program offers similar curriculum to that currently offered on campus by the professional schools.

The program has a strong on-campus component to enhance social networking, communications, and team building skills. All Internet-available lecturers are offered 24/7, with a weekly homeroom time to enhance the taped lectures and promote class interaction. The homerooms are held in early evenings to facilitate nonimpact with employee work schedules. All on-campus events are held on Saturday mornings.
System Engineering Program


System engineering has broad applications that include software, hardware, materials, and electrical and mechanical systems. A set of four core courses is offered that form the foundation of the system engineering program. The sequence of courses is designed for working professionals who are faced with design, development, support, and maintenance of complex systems.

For students who already hold an M.S. degree, a separate certificate of completion of the system engineering program can be earned by completing three of the core courses. See http://www.msol.ucla.edu/system-engineering/ for further information.
M.S. in Engineeringâ€"Aerospace


The main objective of the program is to provide students with broad knowledge of the major technical areas of aerospace engineering to fulfill the current and future needs of the aerospace industry. Major technical areas include aerodynamics and computational fluid dynamics (CFD), systems and control, and structures and dynamics. Courses cover fundamental concepts of science and engineering of aerodynamics, compressible flow, computational aerodynamics, digital control of physical systems, linear dynamic systems, linear optimal control, design of aerospace structures, and dynamics of structures. Through a graduate course, students also gain skills in the development and application of CFD codes for solving practical aerospace problems.

If students have taken Mechanical and Aerospace Engineering 150B, 154B, and 171B or the equivalent at their undergraduate institutions, they can take other online-offered courses, approved by the area director, as substitute courses. In addition, students are required to complete a project on a topic related to the three major areas of this program.
M.S. in Engineeringâ€"Computer Networking



Three undergraduate elective courses complement the basic background of the undergraduate electrical engineering or computer science degree with concepts in security, sensors, and wireless communications. The graduate courses expose students to key applications and research areas in the network and distributed systems field. Two required graduate courses cover the Internet and emerging sensor embedded systems. The electives probe different applications domains, including wireless mobile networks, security, network management, distributed P2P systems, and multimedia applications.
M.S. in Engineeringâ€"Electrical


The electrical engineering program covers a broad spectrum of specializations in communications and telecommunications, control systems, electromagnetics, embedded computing systems, engineering optimization, integrated circuits and systems, microelectromechanical systems (MEMS), nanotechnology, photonics and optoelectronics, plasma electronics, signal processing, and solid-state electronics.
M.S. in Engineeringâ€"Electronic Materials


The electronic materials program provides students with a knowledge set that is highly relevant to the semiconductor industry. The program has four essential attributes: theoretical background, applied knowledge, exposure to theoretical approaches, and introduction to the emerging field of microelectronics, namely organic electronics. All faculty members have industrial experience and are currently conducting active research in these subject areas.
M.S. in Engineeringâ€"Integrated Circuits


The integrated circuits program includes analog integrated circuit (IC) design, design and modeling of VLSI circuits and systems, RF circuit and system design, signaling and synchronization, VLSI signal processing, and communication system design. Summer courses are not yet offered in this program; therefore it cannot currently be completed in two calendar years.
M.S. in Engineeringâ€"Manufacturing and Design


The manufacturing and design program covers a broad spectrum of fundamental and advanced topics, including mechanical systems, digital control systems, microdevices and nanodevices, wireless systems, failure of materials, composites, and computational geometry. The program prepares students with the higher educational background that is necessary for today’s rapidly changing technology needs.
M.S. in Engineeringâ€"Materials Science


Materials engineering is concerned with the design, fabrication, and testing of engineering materials that must simultaneously fulfill dimensional properties, quality control, and economic requirements. Several manufacturing steps may be involved: (1) primary fabrication, such as solidification or vapor deposition of homogeneous or composite materials, (2) secondary fabrication, including shaping and microstructural control by operations such as mechanical working, machining, sintering, joining, and heat treatment, and (3) testing, which measures the degree of reliability of a processed part, destructively or nondestructively.
M.S. in Engineeringâ€"Mechanical


The mechanical engineering program offers students advanced study in a number of areas, including mechanical behavior of materials, structures, fluids, controls, and manufacturing.
M.S. in Engineeringâ€"Signal Processing and Communications


The program provides training in a set of related topics in signal processing and communications. Students receive advanced training in multimedia systems from the fundamentals of media representation and compression through transmission of signals over communications links and networks.
M.S. in Engineeringâ€"Structural Materials


The program provides students with a broad knowledge of structural materials. Courses cover fundamental concepts of science and engineering of lightweight advanced metallic and composite materials, fracture mechanics, damage tolerance and durability, failure analysis and prevention, nondestructive evaluation, structural integrity and life prediction, and design of aerospace structures. Students are required to complete a project on a topic related to structural materials.

Research Description By Engineering Research Center

B. John Garrick Institute for the Risk Sciences

The B. John Garrick Institute for the Risk Sciences aims to advance the application of the risk sciences to save lives, protect the environment and improve system performance. The Institute’s mission: 1. identifying ways to minimize society’s most serious risks based on rigorous quantitative analysis, and 2. improving system performance in terms of its capability to perform its intended functions in a safe way.

Center for Function Accelerated nanoMaterial Engineering

The Center for Function Accelerated nanoMaterial Engineering (FAME) aims to incorporate nonconventional materials and nano-structures with their quantum properties for enabling analog, logic, and memory devices for beyond-Boolean computation. Its main focus is nonconventional material solutions ranging from semiconductors and dielectrics to metallic materials as well as their correlated quantum properties. FAME creates and investigates new, nonconventional, atomic-scale engineered materials and structures of multifunction oxides, metals, and semiconductors to accelerate innovations in analog, logic, and memory devices for revolutionary impact on the semiconductor and defense industries.

FAME is one of six university-based research centers established by SRC through its Semiconductor Technology Advanced Research network (STARnet). Funded by DARPA and the U.S. semiconductor and supplier industries as a public-private partnership, STARnet projects help maintain U.S. leadership in semiconductor technology vital to U.S. prosperity, security, and intelligence. FAME expects to receive a total of $35 million in funding through 2018.

Center for Translational Applications of Nanoscale Multiferroic Systems

The Center for Translational Applications of Nanoscale Multiferroic Systems (TANMS) is a 10-year program focused on miniaturizing electromagnetic devices using a three-pillar strategy involving research, translation, and education. The research strategy engages the best researchers from the five TANMS campuses (UCLA, UC Berkeley, Cornell University, California State University, Northridge, and Northeastern University) to understand and develop new nanoscale multiferroic devices. The fundamental research activities work synergistically with the center’s industrial partners to translate the concepts into applications such as memory, antennae, and motors. These research and translational efforts rely on a workforce of postgraduate, graduate, undergraduate, and K-12 students while educating the next generation of engineering leaders. TANMS fosters an inclusive atmosphere, producing a more innovative and diverse research environment compared to monolithic center cultures.

Institute for Technology Advancement

The Institute of Technology Advancement (ITA) is an off-campus technology development center established by the UCLA Henry Samueli School of Engineering and Applied Science to accelerate the transition of high-impact innovative research from UCLA to technology development and commercialization.

ITA provides operational flexibility for university researchers, industry, government, and investment partners alike. The flexible organizational structure of ITA complements existing capabilities of the university and industry, allowing the center to movie quickly to promote the transition of discoveries to development and commercialization.

ITA Supports Three Types of Research Activities:
Transitional Research

Transitional research focuses on demonstrating technical feasibility of technologies and reducing risk in the transition from the laboratory to market. Transitional research can be funded by either government or industry.

Developmental Research

Developmental research focuses on innovative ideas for new applications and emerging markets. Ultimately, developmental research could result in a spin-off company funded by leveraging various sources such as SBIR/STTR grants, development contracts, debt financing, and venture capital.

Near-Term R&D Support to Industry

ITA can also facilitate near-term research and development projects that support industry needs by leveraging its flexible infrastructure to enable access to university expertise. ITA can enable industry research support through service agreements.
The ITA Team

ITA, as an organization and through its business-savvy Technology Strategists, is a conduit to local, state, government agencies, industry, and venture capital investors. Each Technology Strategist has a specialized domain of expertise and relationships with various funding resources.

Smart Grid Energy Research Center

The UCLA Smart Grid Energy Research Center (SMERC) performs research, creates innovations, and demonstrates advanced technologies to enable the development of the next generation of the electric utility gridâ€"the smart grid. SMERC is currently working on electric vehicle-to-grid integration (V1G and V2G), automated demand response, microgrids, distributed renewable integration including solar and wind, energy storage integration within microgrids, cybersecurity, and consumer behavior. SMERC also provides thought leadership through partnership between utilities, renewable energy companies, technology providers, electric vehicle and electric appliance manufacturers, DOE research labs, and universities, so as to collectively work on vision, planning, and execution towards a grid of the future. It is expected that this smart grid would enable integration of renewable energy sources, allow for integration of electric vehicles and energy storage, improve grid efficiency and resilience, reduce power outages, allow for competitive energy pricing, and overall become more responsive to market, consumer, and societal needs. SMERC is a participant in the Los Angeles Department of Water and Power (LADWP) Regional Smart Grid Demonstration Project, which has been funded by DOE at an estimated $60 million for LADWP and its partners combined.

Western Institute of Nanoelectronics

WIN aims to be the leading nanoelectronics research institute able to support the semiconductor, aerospace & defense, health care, biotechnology and telecommunication industries. One major effort underway is research focusing on next generation nanoelectronic systems. Nanoelectronic systems beyond CMOS must meet the following major challenges: to continue scaling with low power dissipation, high functional throughput, and to achieve robustness with fault tolerance in nanosystems. WIN is taking the reserach lead in addressing power dissipation, by examining alternate state variables other than the use of electron charge. In particular, spin appears of interest for a number of reasons including new functionalities; potentially lower power dissipation and potentially greater noise immunity. Our research is a holistic approach to spintronics, in terms of understanding device performance, device-device interactions and benchmarking performance to more conventional devices such as CMOS. Our research is conveniently divided up into major themes that highlight the aforementioned challenges.

Theme 1: Nano Magnetic Circuits

Nanomagnetic Logic (NML) is one of the promising candidate technologies for ultra-low power logic with integrated nonvolatile memory. NML involves the use of closely spaced, single-domain, nanoscale magnetic islands with aligned shape anisotropy that interact via nearest-neighbor interactions. The interaction is "clocked" in that a group of magnets are magnetized along their hard axes by an external magnetic field, and then allowed to relax as the field is removed. The clocking field lowers the energy barrier between the 1 and 0 states, and as a result of nearest-neighbor interactions, the magnets flip into the lowest energy configuration resulting in a logical computation. Increased efforts will be placed on clock circuits and clocking mechanisms, coupling to the electronic domain via BFO and enhancing the metrology capability via PEEM studied.

Theme 2: Spin Wave Devices

Spin Wave Logics utilize phases in addition to amplitudes for building wave-based logic devices with functional capabilities far beyond the limits of the traditional Von Neumann architecture. We consider magnetization as a vector computational state variable, which lets us take advantage of phases and amplitudes for logic. The combination of these new ideas may lead to the development of novel circuits and architectures taking advantage of magnetic and wave-based devices (e.g. non-volatile circuits, high fan-in logic devices, devices for parallel data processing, systolic array architectures) for general and special type data processing. In this theme we plan to implement spin wave based circuits where we plan on fabricating majority gate structures. Together with these devices, we plan to incorporate essential elements of spin wave based logic and signal processing, spin wave generators, detectors and “gates”.

Theme 3: Spin Torque Logic
In this theme we plan to construct STTRAM logic and circuits to perform digital read, write and logic operations in the GHz frequency operation. A large effort here will be in fabricating the circuits and also utilizing careful design, taking into account the high speed and dynamic nature of this system.

Theme 4: SpinFET

We plan to amalgamate the DMS room temperature operation semiconductor with the Ge/MgO tunnel barrier to demonstrate room temperature spin FET operations. The main goal of this project is to demonstrate spin-dependent effect in spin-FET based on the phase transistor at room temperature.