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The computing and information revolution is transforming society. Cornell Computer Science is a leader in this transformation, producing cutting-edge research in many important areas. The excellence of Cornell faculty and students, and their drive to discover and collaborate, ensure our leadership will continue to grow.

The contributions of Cornell Computer Science to research and education are widely recognized, as shown by two Turing Awards, two Von Neumann medals, two MacArthur "genius" awards, and dozens of NSF Career awards our faculty have received, among numerous other signs of success and influence.

To explore current computer science research at Cornell, follow links at the left or below.

Research Areas

Knowledge representation, machine learning, NLP and IR, reasoning, robotics, search, vision

Statistical genetics, sequence analysis, structure analysis, genome assembly, protein classification, gene networks, molecular dynamics

Computer Architecture & VLSI

Processor architecture, networking, asynchronous VLSI, distributed computing

Database systems, data-driven games, learning for database systems, voice interfaces, computational fact checking, data mining

Interactive rendering, global illumination, measurement, simulation, sound, perception

HCI, interface design, computational social science, education, computing and society

Artificial intelligence, algorithms

Programming language design and implementation, optimizing compilers, type theory, formal verification

Perception, control, learning, aerial robots, bio-inspired robots, household robots

Numerical analysis, computational geometry, physically based animation

Secure systems, secure network services, language-based security, mobile code, privacy, policies, verifiable systems

The software engineering group at Cornell is interested in all aspects of research for helping developers produce high quality software.

Operating systems, distributed computing, networking, and security

The theory of computing is the study of efficient computation, models of computational processes, and their limits.

Computer vision

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In the past five years, Computer Science faculty have had research collaborations with every other college at Purdue. The work of the computer scientist is applicable just about everywhere. Though research activity spans many broad areas, the list below reflects the interests and expertise of the faculty summarized in 14 areas.

Artificial Intelligence, Machine Learning, and Natural Language Processing

Our group members study and devise core machine learning and artificial intelligence methods to solve complex problems throughout science, engineering, and medicine. Our goal is to enhance human lives and bring advanced technologies to augment human capabilities. This research involves both deployments in real-world applications as well as development of fundamental theories in computer science, mathematics, and statistics.

List of Faculty

Aniket Bera
Simina Branzei
Brian Bullins*
Berkay Celik
Chris Clifton
David Gleich
Dan Goldwasser*
Steve Hanneke*
Jean Honorio*
Sooyeon Jeong
Rajiv Khanna*
Anuran Makur
Jennifer Neville*
Chunyi Peng
Alex Psomas
Ahmed Qureshi*
Bruno Ribeiro*
Tiark Rompf
Muhammad Shahbaz
Paul Valiant
Jianguo Wang
Yexiang Xue*
Raymond Yeh*
Ruqi Zhang*
Tianyi Zhang

(* indicates primary area of research)

Bioinformatics and Computational Biology

Faculty in the area of bioinformatics and computational biology apply computational methodologies such as databases, machine learning, discrete, probabilistic, and numerical algorithms, and methods of statistical inference to problems in molecular biology, systems biology, structural biology, and molecular biophysics.

Bedrich Benes
Petros Drineas
Ananth Grama
Majid Kazemian*
Daisuke Kihara*
Alex Pothen
Wojtek Szpankowski
Kihara Bioinformatics Lab
Kazemian Lab

Sample Projects

PrFEcT-Predict
3D-SURFER 2.0
Alex Pothen Software Artifacts
Majid Kazemian Software Artifacts

Computer Architecture

Computer Architecture research studies the interplay between computer hardware and software, particularly at the intersection of programming languages, compilers, operating systems, and security.

Changhee Jung
Xuehai Qian*
Kazem Taram*

Computational Science and Engineering

The research area of Computational Science and Engineering answers questions that are too big to address experimentally or are otherwise outside of experimental abilities. Using the latest computers and algorithms, this group addresses those questions through numerical modeling and analysis, high-performance computation, massive distributed systems, combinatorial algorithms in science applications, high-speed data analysis, and matrix-based computations for numerical linear algebra.

Petros Drineas*
David Gleich*
Ananth Grama*
Alex Pothen*
Ahmed Qureshi
Elisha Sacks
Xavier Tricoche
Yexiang Xue

CSE Research Group

David Gleich Software Artifacts
Finite Element Analysis of 9/11 Attacks

Databases and Data Mining

The data revolution is having a transformational impact on society and computing technology by making it easier to measure, collect, and store data. Our databases and data mining (big data) research group develops models, algorithms, and systems to facilitate and support data analytics in large-scale, complex domains. Application areas include database privacy and security, web search, spatial data, information retrieval, and natural language processing.

Walid Aref*
Elisa Bertino
Bharat Bhargava*
Chris Clifton*
Dan Goldwasser
Susanne Hambrusch
Jennifer Neville
Sunil Prabhakar*
Bruno Ribeiro
Jianguo Wang*
Cyber Space Security Lab (CyberS2Lab)
Conceptual Evaluation and Optimization of Queries in Spatiotemporal Data Systems
Secure Dissemination of Video Data in Vehicle-to-Vehicle Systems
Ensuring Integrity and Authenticity of Outsourced Databases
Towards Scalable and Comprehensive Uncertain DAta Management
ORION DBMS: Handling Nebulous Data

Distributed Systems

The DS group focuses on designing distributed systems that are scalable, dependable, and secure, behaving according to their specification in spite of errors, misconfigurations, or being subjected to attacks. Areas of focus include virtualization technologies with emphasis on developing advanced technologies for computer malware defense and cloud computing.

Bharat Bhargava
Pedro Fonseca
Suresh Jagannathan
Aniket Kate
Kihong Park
Vernon Rego*
Eugene Spafford
Yongle Zhang
Vassilis Zikas
Saurabh Bagchi (by courtesy)
Charlie Hu (by courtesy)
Sanjay Rao (by courtesy)

(* indicates primary area of research)

Dependable Computing Systems Lab
FRIENDS Lab
ProTracer: Practical Provenance Tracing
DCSL Projects

Graphics and Visualization

This group performs research in graphics, visualization, computational geometry, and related applications. Focus areas include model acquisition, image generalization, scientific visualization, urban modeling, robust computational geometry, and geometric computations and constraints.

Daniel Aliaga *
Bedrich Benes*
Voicu Popescu*
Elisha Sacks*
Xavier Tricoche*
Computer Graphics and Visualization Lab
High Performance Computer Graphics Laboratory

Graphics Lab Projects

Human-Computer Interaction

Sooyeon Jeong*
Tianyi Zhang*

Information Security and Assurance

Strong security and privacy is needed to defend our records, communications, finances, governments and infrastructure against all manner of threats and attacks, while also enhancing legitimate uses. Research in Information Security and Assurance focuses on the analysis, development, and deployment of technologies, algorithms, and policies to protect computing and data resources against malicious access or tampering, and to validate authenticity.

Mikhail Atallah*
Elisa Bertino*
Antonio Bianchi*
Jeremiah Blocki*
Berkay Celik*
Sonia Fahmy
Christina Garman*
Aniket Kate*
Ninghui Li*
Hemanta Maji*
Sunil Prabhakar
Vernon Rego
Eugene Spafford*
Dongyan Xu*
Vassilis Zikas*
Freedom Research Lab
Database Security Lab
Spatial-temporal Recreation of Android App Displays from Memory Images
Multiple Perspective Attack Investigation with Semantic Aware Execution Partitioning
HexHive Group Projects
Chunyi Peng Mobile Phone Projects
Freedom Lab Projects

Networking and Operating Systems

This area works on fundamental problems at different layers of the network protocol stack – from the medium access control layer up to the application layer – using theoretical models, simulation, emulation, and extensive testbed experimentation to develop and evaluate proposed solutions which leverage techniques from game theory, information theory, complexity theory, optimization, and cryptography.

Saurabh Bagchi*
Antonio Bianchi
Doug Comer*
Sonia Fahmy*
Pedro Fonseca*
Kihong Park*
Chunyi Peng*
Muhammad Shahbaz*
Yongle Zhang*

Programming Languages and Compilers

The PL group engages in research spanning all aspects of software systems design, analysis, and implementation. Active research projects exist in functional and object-oriented programming languages, both static and dynamic compilation techniques for scalable multicore systems, generative programming, assured program generation, scripting languages, distributed programming abstractions and implementations, real time and embedded systems, mobile and untrusted computing environments, and runtime systems with special focus on memory management and parallel computing environments.

Ben Delaware*
Suresh Jagannathan*
Changhee Jung*
Zhiyuan Li*
Ryan Newton*
Tiark Rompf*
Roopsha Samanta*
Xiangyu Zhang*
Yung-Hsiang Lu (by courtesy)
Milind Kulkarni (by courtesy)
PurForM - Purdue's Formal Methods research group

PurPL - Center for Programming Principles and Software Systems

Secure Software Systems Lab (S3)

Software Engineering

The software engineering area conducts research on applying advanced program analyses towards problems related to fault isolation and various kinds of bug detection, including those related to race conditions in concurrent programs, and specification inference for large-scale software systems.

Ben Delaware
Buster Dunsmore*
Xiangyu Zhang

Automatic Model Generation from Documentation for Java API Functions

Robotics and Computer Vision

The Robotics and Computer Vision area includes elements of machine learning, signal processing, and image processing to further develop robotics and computer vision systems from a computational science perspective.

Aniket Bera*
Raymond Yeh

Theory of Computing, Algorithms, and Quantum Computing

Members of the group work in areas that include analysis of algorithms, parallel computation, computational algebra and geometry, computational complexity theory, digital watermarking, data structures, graph algorithms, network algorithms, distributed computation, information theory, analytic combinatorics, random structures, external memory algorithms, and approximation algorithms.

Mikhail Atallah
Saugata Basu*
Jeremiah Blocki
Simina Branzei*
Brian Bullins
Elena Grigorescu*
Susanne Hambrusch*
Steve Hanneke
Rajiv Khanna
Hemanta Maji
Anuran Makur*
Alex Psomas*
Kent Quanrud*
Eric Samperton*
Wojtek Szpankowski*
Paul Valiant*
Sabre Kais (by courtesy)

Theory Group

CGTDA: Computational Geometry & Topology for Data Analysis

Department of Computer Science, 305 N. University Street, West Lafayette, IN 47907

Purdue University Indianapolis, 723 W. Michigan St., Indianapolis, IN 46202

Phone: (765) 494-6010 • Fax: (765) 494-0739

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Bridging disciplines and accelerating discoveries in computer science.

A composite of students in the Robotorium, a student working on a laptop in the Schatz Lab, LCSR faculty and students in the Mock OR, and Suchi Saria and a collaborator.

Computer science research at the Johns Hopkins University is advancing computing technology, enabling new modes of thought, and transforming society. Our faculty conduct innovative, collaborative research aimed at solving large and complex interdisciplinary problems, drawing upon the university’s renowned strengths in areas including artificial intelligence, robotics, speech and language processing, medicine, and public health.

The department is rapidly growing, with current core research areas of theory and programming languages; systems and networking; computational biology and medicine; information security; natural language processing; machine learning, artificial intelligence, and data science; robotics, computer vision, and graphics; and human-computer interaction.

Researchers partners with colleagues in other engineering disciplines, as well as with investigators from the Johns Hopkins Krieger School of Arts and Sciences, the School of Medicine, and the Applied Physics Laboratory.

Cross-Departmental Centers and Institutes

Center for Language and Speech Processing
Laboratory for Computational Sensing and Robotics
Johns Hopkins Information Security Institute
Institute for Data Intensive Engineering and Science
Malone Center for Engineering in Healthcare
Human Language Technology Center of Excellence
Center for Computational Biology
Mathematical Institute for Data Science

Institute for Assured Autonomy

Johns Hopkins Data Science and AI Institute

Research Areas

Theory & programming languages.

Focusing on the design, implementation, and use of computer programming languages.

Systems & Networkings

Our faculty are undertaking research into all aspects of computer systems and networks.

Computational Biology & Medicine

Faculty are engaged in a wide range of computational health and biology projects, from using data-driven tools to detect early signs of sepsis to DNA sequencing technology and evolutionary genomics.

Information Security

Hopkins researchers are working to safeguard our digital world.

Natural Language Processing

Creating innovative new technologies that will enable more natural interaction between human and computers.

Machine Learning, AI, & Data Science

Applying cutting-edge machine learning techniques to new datasets and domains.

Robotics, Vision, & Graphics

Research spans the areas of computer vision, computer graphics, and augmented and virtual reality.

Human-Computer Interaction

Placing people at the center of technological innovation.

Computer-Assisted Medicine

Our faculty are shaping the digital future across all aspects of health care.

The Institute for Assured Autonomy is operating in partnership with industry, government and academia to ensure the safe, secure, reliable, and predictable integration of autonomous systems into society by covering the full spectrum of research and application across the three pillars of technology, ecosystem, and policy & governance.

Research Areas

Autonomous and cyber-physical systems.

Subareas: Real-time and Embedded Systems, Sensor Systems, Mobile Computing, Control Theory and Systems, Formal Methods, Automated Verification and Certification Faculty: Alterovitz , Anderson , Chakraborty , Duggirala , Nirjon

More on Autonomous and Cyber-Physical Systems

Bioinformatics and computational biology.

Subareas: Computational Genetics, Computational Immunology, Proteomics, Statistical Genetics, Single-Cell Bioinformatics Faculty: Ahalt , Krishnamurthy , Marron , McMillan , Snoeyink , Stanley

More on Bioinformatics and Computational Biology

Computational Immunology: Advancements in high-throughput flow and mass cytometry technologies have enabled the ability to study the immune system at an unparalleled depth. Understanding immunological adaptations to particular diseases and in aging and development offers unique opportunities to develop novel diagnostic tests or to propose specialized treatments or lifestyle interventions to optimize human health. Using single-cell flow and mass cytometry data collected across multiple individuals, our goal is to develop new computational techniques to identify and link heterogeneity in the cellular landscape to external variables of interest, such as, a clinical phenotype or diagnosis. Recent advances in imaging cytometry also enable taking images of tissues and studying the spatial organization of immune cells. Application areas of interest include pregnancy, HIV, neuroimmunology, and T-cell biology. Relevant People Natalie Stanley ; Collaborating Departments: Microbiology and Immunology , Computational Medicine Program , Department of Anesthesia

Development and Differentiation, and Metagenomics: We use novel measurement techniques as well as machine learning methods in understanding the interplay between these areas, with the aim of discovering the forces that shape the immune system throughout life. The overarching goal is to apply the insights from such analyses to propose new treatments for cancers.

Single-Cell Bioinformatics: Cellular heterogeneity, or the synergy of diverse and specialized cell-types drive a range of biological phenomena. Several technologies exist for measuring various properties (e.g. gene expression, protein expression) in individual cells, which allows for their comprehensive characterization and analysis in clinical or biological applications. Single-cell measurements can be studied in vitro to understand the etiology of disease. For example, in hypoxia of heart muscle cells, the cells become scar tissue and lose their muscle function. This process can be studied by looking at single cell transcriptomes to determine the order of events. Further, it can be possible to “reprogram” this sequence of events to avoid the adverse outcome. See some of our recent work in reprogramming scar tissue cells to recover some heat muscle cell functionality.

Technologies and Data Science Problems: Single-cell datasets produced with technologies, such as single-cell RNA sequencing (scRNA-seq) or flow and mass cytometry reveal a unique data structure where there are several high-dimensional single-cell measurements per profiled sample, which need to be efficiently integrated.

Flow and Mass Cytometry : Flow and mass cytometry are high-throughput single-cell proteomics technologies for systematic analysis of the immune system. Often applied for the analysis of human blood and tissue samples, the produced datasets can collectively contain millions of cells. We focus on developing new computational techniques for representing, dissecting, and mining this large volume of cells to identify immunological adaptations in disease and development.

Relevant People: Leonard McMillan , Natalie Stanley

Computer Architecture

Subareas: Accelerators, Clockless Logic, Energy-efficient Computing, Security Faculty: Porter , Singh , Sturton

More on Computer Architecture

Energy-Efficient Systems: With the explosive growth in mobile devices, there has been a push towards increasing energy efficiency of computation for longer battery life. Reducing power consumption is also important for desktop computing to alleviate challenges of heat removal and power delivery. A special focus in our department has been on the development of energy-efficient graphics hardware. Another area of future interest is energy-harvesting systems, which are ultra-low-power systems that operate on energy scavenged from the environment.

Asynchronous or Clockless Computing: Asynchronous VLSI design is poised to play a key role in the design of the next generation of microelectronic chips. By dispensing with global clocks and instead using flexible handshaking between components, asynchronous design offers the benefits of lower power consumption, greater ease of integration of multiple cores, and greater robustness to manufacturing and runtime variation. Our researchers work on all aspects of asynchronous design, including circuits, architectures, and CAD tools. A key area of interest is application to network-on-a-chip for integration of multiple heterogeneous cores.

Computer Graphics

Subareas: Animation & Simulation, Graphics Hardware, Modeling, Rendering, Tracking, Virtual Environments, Visualization Faculty: Alterovitz , Chakravarthula , Fuchs , Marks , Sengupta , Singh , Snoeyink , Daniel Szafir , Danielle Szafir

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Computer vision.

Subareas: Geometric Vision, Language & Vision, Recognition Faculty: Ahalt , Bansal , Bertasius , Marks , Niethammer , Sengupta

More on Computer Vision

Human-computer interaction.

Subareas: Assistive Technology, Haptics, Human Factors Analysis, Sound & Audio Display, User-Interface Toolkits, Virtual Environments Faculty: Dewan , Marks , Nirjon , Porter , Pozefsky , Srivastava , Stotts , Daniel Szafir , Danielle Szafir

More on Human-Computer Interaction

Wearable devices, such as smart watches and smart glasses, and other common sensors are increasingly facilitating new modes of interaction with modern computers—making the goal of ubiquitous computing realizable. A major research direction in HCI at UNC is exploring design techniques and system support to more easily extend desktop and phone applications onto devices with widely varying form factors and interaction modes.

Another significant research direction at UNC is exploring assistive technologies for users with impairments, such as learning disabilities, blindness, and low vision. These populations face significant barriers to education and employment that we aim to reduce, as well as study different modes of interaction with computers.

Machine Learning and Data Science

Subareas: Data Integration, Internet of Things, Knowledge Discovery, Machine Learning, Scientific Data Management, Visual Analytics Faculty: Ahalt , Bansal , Bertasius , Chaturvedi , Krishnamurthy , Marks , McMillan , Niethammer , Nirjon , Oliva , Sengupta , Srivastava , Danielle Szafir , Yao

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Machine Learning: The problems we study combine vast amounts and disparate types of measurements with equally complex prior knowledge, posing unique challenges for machine learning. Our interests include both modeling paradigms, such as Bayesian nonparametric methods, and inference methodologies, such as MCMC, variational methods and convex optimization. We also work on structured, interpretable, and generalizable deep learning models. Other topics of focus include multi-task learning, reinforcement learning, and transfer learning.

Medical Image Analysis

Subareas: Biomechanical Modeling, Diffusion Imaging, Image-guided Interventions, Segmentation, Shape Analysis, Registration Faculty: Alterovitz , Marron , Niethammer , Oguz , Pizer , Styner

More on Medical Image Analysis

Natural language processing.

Subareas: Language Generation, Multimodal and Grounded NLP (with Vision and Robotics), Question Answering and Dialogue Faculty: Bansal , Chaturvedi , Srivastava

More on Natural Language Processing

Subareas: Distributed Systems, Internet Measurements, Multimedia Systems, Multimedia Transport, Network Protocols Faculty: Aikat , Dewan , Jeffay , Kaur , Mayer-Patel , Nirjon , Pozefsky

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Operating systems.

Subareas: File Systems, Virtualization, Concurrency, Software Support for Secure Hardware Faculty: Anderson , B. Berg , Jeffay , Porter

More on Operating Systems

This area has substantial overlap with a number of other research areas, including cyber-physical systems, real-time systems, mobile systems, networking, architecture, human-computer interaction, and security.

Real-Time Systems

Faculty: Anderson , Jeffay , Nirjon

More on Real-Time Systems

Subareas: Assistive Robotics, Manipulation, Medical Robotics, Motion Planning & Control, Robot Learning, Robot Perception (see: Computer Vision) Faculty: Alterovitz , Bansal , Snoeyink , Daniel Szafir

More on Robotics

Subareas: Cloud Computing Security, Cryptography, Hardware Security, Mobile Device Security, Network Security Faculty: Aikat , Eskandarian , Kwong , Porter , Sturton

More on Security

Network security: Today’s Internet infrastructure is a common target of attack and the vehicle for numerous unwanted activities in network applications (e.g., spam, phishing). We are conducting research to evaluate the extent of these vulnerabilities and to develop defenses against them. This includes research on both protecting the Internet infrastructure from attack and designing defenses within the context of network applications.

Cloud computing security: The use of cloud servers to outsource data and processing has become increasingly common. Because cloud facilities are shared, however, a customer’s data and processing may reside with those of competitors or attackers, and so privacy and integrity of the customer’s activities are paramount. We are developing technologies to better protect data and processing in such threatening environments.

Subareas: Algorithms, Automated Theorem Proving, Formal Methods Faculty: Anderson , B. Berg , Duggirala , Eskandarian , Snoeyink , Sturton

Research Research Areas

Research areas represent the major research activities in the Department of Computer Science. Faculty and students have developed new ideas to achieve results in all aspects of the nine areas of research.

Choose a research area below to learn more:

Artificial Intelligence and Machine Learning
Human-Computer Interaction and Information Visualization
Computer Engineering (in collaboration with the Electrical and Computer Engineering Department)

Contact Info

Samir Khuller Chair and Professor Phone: 847-491-2748 Email Samir

Computer Science

Research at yale cs.

At Yale Computer Science, our faculty and students are at the forefront of innovation and discoveries. We conduct ground-breaking research covering a full range of areas in theory, systems, and applications.

Our department is currently in the middle of substantial growth. Data and Computer Science is listed as one of the top five Science Priorities in Yale’s recent University Science Strategy Committee Report. Yale’s School of Engineering and Applied Science is also launching a substantial initiative in Artificial Intelligence, broadly construed, that will include research in the foundations of AI, in applications and technology, and in societal and scientific impacts.

Interdisciplinary Centers & Initiatives

Computer Science has also grown beyond its own bounds to become a multi-disciplinary field that touches many other sciences as well as arts and humanities: physics, economics, law, management, psychology, biology, medicine, music, philosophy, and linguistics. They have also led to interdisciplinary research centers.

Institute for the Foundations of Data Science

Schools/Departments: CS, S&DS, EE, Econ, Social Science, Political Science, and SOM

Wu-Tsai Institute for Interdisciplinary Neurocognition Research

Schools/Departments: CS, Psych, S&DS, SEAS, and Medicine

Yale Institute for Network Science

Schools/Departments: CS, Social Science, S&DS, and EE

Yale Quantum Institute

Schools/Departments: CS, Applied Physics, Physics, and EE

Computation and Society Initiative

Schools/Departments: CS, S&DS, Social Science

Research Areas

Algorithms and complexity theory .

Yale’s Theory group advances our understanding of the fundamental power and limits of computation and creates innovative algorithms to empower society.

Artificial Intelligence and Machine Learning

We study how to build systems that can learn to solve complex tasks in ways that would traditionally need human intelligence. Our research covers both the foundation and applications of AI: Robotics, Machine Learning Theory, Natural Language Processing, Computer Vision, Human-Computer Interactions, AI for Medicine, and AI for Social Impact.

Computer Architecture

We design the interface of software and hardware of computer systems at all scale – ranging from large-scale AI and cloud services to safety-critical embedded systems to Internet-Of-Things devices. We deliver the next-generation processors to meet performance, power, energy, temperature, reliability, and accuracy goals, by composing principled and well-abstracted hardware.

Computer Graphics

Research in computer graphics at Yale includes sketching, alternative design techniques, texture models, the role of models of human perception in computer graphics, recovering shape and reflectance from images, computer animation, simulation, and geometry processing.

Computer Music

Computer music research at Yale encompasses a range of technical and artistic endeavors.

Computer Networks

Computer networks allow computers to communicate with one another, and provide the fundamental infrastructures supporting our modern society. Research on computer networks at Yale improves on essential network system properties such as efficiency, robustness, and programmability.

Database Systems

Database systems provide an environment for storage and retrieval of both structured and semi-structured data.

Distributed Computing

Distributed computing is the field in computer science that studies the design and behavior of systems that involve many loosely-coupled components. Distributed systems research at Yale includes work in the theory of distributed computing, its programming language support, and its uses to support parallel programming.

Natural Language Processing

Yale scientists conduct cutting-edge research in NLP, including computational liguistics, semantic parsing, multilingual information retrieval, language database interfaces and dialogue systems. We also investigate how to use NLP to create transformative solutions to health care.

Operating Systems

Yale is developing new operating system architectures, application environments, and security frameworks to meet today’s challenges across the computing spectrum, including IoT devices, cyber-physical systems (such as self-driving cars and quadcopters), cloud computers, and blockchain ecosystems.

Programming Languages and Compilers

We approach Programming Languages research from several directions including language design, formal methods, compiler implementation, programming environments, and run-time systems. A major focus of the research at Yale is to build secure, error-free programs, as well as develop frameworks that help others achieve that same goal.

Quantum Computing

Yale has been at the forefront of innovation and discoveries in Quantum Science. Through interdisciplinary research and pioneering innovations, our Yale CS faculty advances the state-of-the-art in quantum computing and quantum information science, building upon insights and lessons from classical computer science.

Robotics research at Yale’s Computer Science department is currently focused on advancing Human-Robot Interaction. Applications include education, manufacturing, entertainment, and service domains. Robots are also used to advance our understanding of human behavior.

Scientific Computing and Applied Math

Scientific computing research at Yale emphasizes algorithm development, theoretical analysis, systems and computer architecture modeling, and programming considerations.

Security and Cryptography

Adequately addressing security and privacy concerns requires a combination of technical, social, and legal approaches. Topics currently under active investigation in the department include mathematical modeling of security properties, implementation and application of cryptographic protocols, secure and privacy-preserving distributed algorithms, trust management, verification of security properties, and proof-carrying code.

Societal and Humanistic Aspects of Computation

Today’s society comprises humans living in a complex and interconnected world that is intertwined with a variety of computing, sensing, and communicating devices. Yale researchers create innovative solutions to mitigate explicit and implicit biases, control polarization, improve diversity, and ensure privacy.

Email forwarding for @cs.stanford.edu is changing. Updates and details here .

Research Areas

Research Overview

We are shaping the future of computing

Here at CS@UCSB, we are shaping the future of computing by our outstanding research and education programs. Our world-renowned faculty and exceptional students conduct exciting research in all areas of computer science. From harnessing the power of machine learning in a responsible manner to ensuring the security of cloud computing, from investigating the new horizons of human-computer interaction and visual computing to improving the energy efficiency of computing, our faculty and students are making impactful contributions in all frontiers of computer science. Our teaching faculty are innovators in teaching methods that enable us to provide an outstanding education to our students at all levels and broaden participation in computing.

Areas of Research

Green background with one's and zero's in abstract pattern

Algorithms & Theory

Foundations of Computing, Geometric and Graph Algorithms, Data Structures, Quantum Computing, Cryptography, Complexity Theory, Information Theory

Faculty : Prabhanjan Ananth , Wim van Dam , Ömer Eğecioğlu , John Gilbert , Oscar H. Ibarra , Daniel Lokshtanov , Subhash Suri , Eric Vigoda , Sanjukta Krishnagopal

Computational Science and Engineering

Computational algorithms and software tools for data mining, data analysis, linear algebra, large-scale graph computations, high performance computing, partial differential equations, and multi-scale stochastic simulation. Applications to systems biology, ecology, energy, materials, fluids, and social science.

Faculty : Michael Beyeler , Frederic G. Gibou , John Gilbert , Linda Petzold , Xifeng Yan

Computer Architecture

Computer architecture, novel computing technologies, quantum computing, embedded systems, low-energy computing, network and security processors, architectural support for systems security and reliability.

Faculty : Timothy Sherwood , Chandra Krintz , Jonathan Balkind

Ones and Zeros spiraling around a center circle

Database and Information Systems

Distributed databases, fault-tolerance distributed systems, data in the cloud, multimedia databases, spatial databases, data mining, search, data-centric processes, workflow, data-aware services.

Faculty : Divyakant Agrawal , Amr El Abbadi , Ambuj K. Singh , Jianwen Su , Tao Yang , Xifeng Yan

Circle with a person pointing at you surrounded by geometric shapes

Human Centered and Social Computing

Modeling social behavior and computational systems. Proliferation of the social web into every area of business and society has brought about a need for better understanding, management and use of this valuable global resource.

Faculty : Michael Beyeler , Tobias Höllerer , Ambuj K. Singh , Misha Sra , William Wang , Xifeng Yan , Sanjukta Krishnagopal

Human brain surrounded by abstract shapes

Machine Learning and Data Mining

Machine learning and Data Mining covers a broad range of topics that include knowledge representation, natural language processing, pattern recognition, and intelligent systems, with applications in many areas including bioinformatics, business intelligence, information retrieval, security, and network science.

Faculty : Michael Beyeler , Shiyu Chang , Yu Feng , Arpit Gupta , Tobias Höllerer , Linda Petzold , Ambuj K. Singh , Misha Sra , Matthew Turk , William Wang , Xifeng Yan

Blue background with white nodes and lines connecting them

Computer networks and protocols, large-scale multimedia systems, mobile and wireless networks, quality of service, network modeling and simulation, peer-to-peer and overlay networks, dynamic spectrum and cognitive radios, high-performance mobile computing, network security, network models and protocols.

Faculty : Arpit Gupta , Elizabeth M. Belding , Subhash Suri , Trinabh Gupta

Operating Systems and Distributed Systems

Large-scale systems, cloud computing, distributed databases, distributed programming environments and runtime systems, Internet-scale analytics, social networks.

Faculty : Amr El Abbadi , Divyakant Agrawal , Elizabeth M. Belding , Peter Cappello , Trinabh Gupta , Chandra Krintz , Rich Wolski , Tao Yang

Programming Languages and Software Engineering

Static and dynamic techniques for automated software verification and program analysis, adaptive compilation and runtime, language-based security, resource and energy consumption prediction, program profiling, formal methods, web services, workflows, concurrent and distributed systems.

Faculty : Jonathan Balkind , Tevfik Bultan , Ben Hardekopf , Richard A. Kemmerer , Jianwen Su , Yu Feng , Chandra Krintz

Quantum Computing

Faculty : Murphy Niu

Robotics and Autonomy

Faculty : James A. Preiss

Security and Cryptography

Network and system security, web security, security of social networks, malware analysis, voting system security, vulnerability analysis, language-based security, specification and verification of systems, security-enhanced microprocessors.

Faculty : Prabhanjan Ananth , Tevfik Bultan , Yu Feng , Trinabh Gupta , Richard A. Kemmerer , Christopher Kruegel , Timothy Sherwood , Giovanni Vigna

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Visual Computing and Interaction

Human-computer interaction, computer vision, virtual and augmented reality, 3D modeling, multimodality, language & vision, computer graphics, visualization, scientific and information, wearable and ubiquitous computing.

Faculty : Michael Beyeler , Yu Feng , Tobias Höllerer , Misha Sra , Matthew Turk , William Wang , Yuan-Fang Wang , Lingqi Yan

Areas of Research in CSE

A unique and robust research environment

Artificial Intelligence

Experimental and applied investigations of intelligent systems, including rational decision making, machine learning and perception, natural language processing, cognitive modeling, and more.

Learn more >

Chip Design, Architecture, & Emerging Devices

Silicon chip design, computer architecture, and novel device technologies that may replace traditional CMOS transistors.

Databases & Data Mining

Building the data management infrastructure for the twenty-first century, with particular emphasis on issues surrounding Big Data.

Embedded & Mobile Systems

Designing systems for vehicles, wireless sensors, medical devices, wearable fitness devices, smartphones, and other devices not generally considered to be computers.

Formal Methods & Automated Reasoning

Developing and deploying mathematically-rigorous and algorithmically-efficient solutions to prove the correct behavior of complex hardware and software.

Human-Computer Interaction

Investigating exciting new services in educational technology, multimedia, and social computing, as well as the domains of human perception and cognition, social activity, and learning.

Languages, Compilers, & Runtime Systems

Static and run-time compiler systems are used to get more performance, robustness, and energy efficiency with program analysis, transformation, and adaptation.

Networking, Operating Systems, & Distributed Systems

Research from wireless networking and mobile computing to the Internet and datacenter networks, and tackling exciting new problems that span embedded systems, sensor networks, data centers, and cloud services.

We use artificial intelligence techniques for dealing with planning and uncertainty, localization and mapping, sensor processing and classification, and continuous learning.

Secure, Trustworthy, & Reliable Systems

Addressing key security challenges through near-term stress reduction techniques to improve the quality of silicon and longer-term technologies to detect, recover, and repair faulty systems.

Theory of Computation

Conducting research across many areas such as data structures, cryptography, quantum computing, parallel and distributed computing, algorithmic game theory, graph theory, geometry, combinatorics, and energy efficiency.

Warehouse-Scale & Parallel Systems

Pursuing the design of the hardware and software infrastructure for massive-scale computing systems. Major research topics include server architecture, GPU computing, emerging memory technologies, distributed software, and more.

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Artificial Intelligence, Machine Learning, Privacy/FATE

Theory and Computation

Systems, Databases, Software Engineering, Cyber-Physical Systems, Security

Computer Vision, Robotics, Graphics, HCI

Artificial intelligence, machine learning, privacy/fate .

Researchers in artificial intelligence (AI) seek to understand and develop machines with human-level intelligence by exploring the academic and real-world challenges surrounding AI.

At USC’s Department of Computer Science, we are pioneering breakthroughs in a full spectrum of topics related to AI, including machine learning, computer vision and image processing, human-robot interaction, speech and language analysis, information extraction and privacy-protection.

Our researchers are working in areas where artificial intelligence has been under study for decades—like language—and where the tools are just starting to make inroads—such as efforts to combat human trafficking, diagnose fetal alcohol syndrome, and prevent terrorist attacks using limited resources.

We understand that the long-term goal of building intelligent machines relies on collaboration across many fields. That’s why we also work closely with researchers across application domains, such as health care, social work and linguistics.

Affective Computing Group Automatic Coordination of Teams (ACT) Lab Center for Autonomy and AI Center on Knowledge Graphs Cognitive Architecture Cognitive Learning for Vision and Robotics Lab Collaboratory for Algorithmic Techniques and Artifical Intelligence (CATAI) Computational Linguistics Computational Neuroscience Lab (iLab) Computational Social Science Laboratory IRIS Computer Vision Lab (CV-Lab) Data, Interpretability, Language and Learning (DILL) Lab Data Science Lab Database Lab (Dblab) Haptics Robotics and Virtual ICT Natural Language Dialogue Group IDM Artificial Intelligence Laboratory Information Laboratory (InfoLab) Intelligence and Knowledge Discovery (INK) Research Lab Integrated Media Systems Center (IMSC) Interaction Lab Interactive and Collaborative Autonomous Robotic Systems (ICAROS) Lab Interactive Knowledge Capture Machine Learning and Data Mining Lab (Melody-Lab) Polymorphic Robotics Lab Privacy Research Lab Robotics and Autonomous Systems Center (RASC) Robotic Embedded Systems Lab Robotics Research Lab Semantic Information Research Speech Analysis and Interpretation Lab (SAIL) USC Brain project USC Center for Artificial Intelligence in Society USC Center for Autonomy and Artificial Intelligence

Aleksandra Korolova Andrew Gordon Aram Galstyan Barath Raghavan Bill Swartout Bistra Dilkina Craig Knoblock Cyrus Shahabi David Kempe David Pynadath David Traum Fred Morstatter Gale Lucas Gaurav Sukhatme Greg Ver Steeg Haipeng Luo Heather Culbertson Jay Pujara Jesse Thomason Jiapeng Zhang John Heidemann Jonathan Gratch Jonathan May Jose Luis Ambite Jyotirmoy Deshmukh Kallirroi Georgila Kristina Lerman Laurent Itti Leana Golubchik Maja Matarić Michael Zyda Mohammad Soleymani Muhammad Naveed Mukund Raghothaman Ning Wang Paul Rosenbloom (Emeritus) Pedro Szekely Ram Nevatia Robin Jia Satish Thittamaranahalli Saty Raghavachary Shaddin Dughmi Shang-Hua Teng Srivatsan Ravi Stefanos Nikolaidis Sven Koenig Swabha Swayamdipta Tatyana Ryutov Ulrich Neumann Victor Adamchik Weihang Wang Wei-Min Shen Wensheng Wu Xiang Ren Yan Liu Yolanda Gil

USC has a strong and active background in modern theoretical computer science, with research spanning a broad range of topics. Areas of particular interest include the theory of algorithms and optimization, graph theory, scalable algorithms, theory of machine learning, computational geometry, complex analysis, computational complexity, algorithmic number theory and cryptography.

Our researchers in this area are particularly motivated by bridging the gap between theory and practice, such as non-blockchain digital currencies, social network analysis, smoothed analysis, bilingual learning and post-quantum cryptography.

In addition, we have many strong connections to other fields, including economics and game theory, pure mathematics, applied mathematics and scientific computing, network science, sociology, as well as evolution of concepts, ideas and organisms.

Collaboratory for Advanced Computing and Simulations (CACS) Collaboratory for Algorithmic Techniques and Artifical Intelligence (CATAI) CS Theory Group

Aaron Cote Aiichiro Nakano Aleksandra Korolova Bistra Dilkina David Kempe Haipeng Luo Jeffrey Miller Jiapeng Zhang Jonathan May Len Adleman Ming-Deh A. Huang Satish Thittamaranahalli Shaddin Dughmi Shang-Hua Teng Shawn Shamsian Srivatsan Ravi Victor Adamchik

The demands on modern computing systems are increasingly complex, from small embedded systems in phones, laptops and wearables, to large-scale cloud computing and high-performance networks.

Systems, databases and software engineering research at USC aims to develop innovative hardware and software across the computing spectrum for existing technologies and to support future power-efficient, sustainable and secure computer systems.

From smart cities and intelligent transportation systems to personalized medicine, next-generation computer systems will require new, innovative and visionary approaches to hardware, wired and wireless communication.

At USC, researchers investigate various issues in the design and analysis of infrastructures for large networks. We focus on fundamental aspects of information acquisition, processing, security, privacy, storage, and communication.

Our research interests include crowd-sensing, program analysis, privacy-preserving systems, network design and management, software-defined networking, cloud computing, internet measurement, software verification and synthesis, advanced 5G wireless networks and data center design.

ANT (The Analysis of Network Traffic Lab) Autonomous Networks Research Group Center for Computer Systems Security Center for Systems and Software Engineering (CSSE) Center on Knowledge Graphs Collaboratory for Advanced Computing and Simulations (CACS) FPGA/Parallel Computing Group Information Laboratory / USC Integrated Media Systems Center (IMSC) Networked Systems Lab Quantitative Evaluation & Design Lab (QED ) Safe Autonomy and Intelligent Distributed Systems (SAIDS) Lab STEEL Security Research Lab The Postel Center USC Database Lab

Andrew Goodney Barath Raghavan Bill Cheng Bill Swartout Chao Wang Claire Bono Clifford Neuman Craig Knoblock Cyrus Shahabi Ellis Horowitz Ewa Deelman Fred Morstatter Jelena Mirkovic Jeffrey Miller John Heidemann Jose Luis Ambite Jyotirmoy Deshmukh Lars Lindemann Leana Golubchik Mark Redekopp Mukund Raghothaman Nenad Medvidovic Pedro Szekely Ramesh Govindan Saty Raghavachary Shahram Ghandeharizadeh Shawn Shamsian Srivatsan Ravi Tatyana Ryutov Weihang Wang Wensheng Wu William G.J. Halfond Xiang Ren

Computer Vision, Robotics, Graphics and HCI

At USC, the areas of computer vision, robotics and graphics represent the interface between computers and the rest of the world.

Robotics at USC focuses on developing effective, robust, human-centric, and scalable robotic systems. In this area, our expertise ranges from socially assistive robotic and novel haptics technology for virtual touch to complex human-robot interaction and multi-robot systems.

In computer vision and graphics, our researchers bridge physical and digital worlds with powerful recognition and analysis algorithms, as well as immersive technologies, such as augmented and virtual reality.

In computer vision, our strengths include object detection and recognition, face identification, activity recognition, video retrieval and integrating computer vision with natural language queries.

Our graphics researchers focus on interactive techniques and the simulation and synthesis of multimedia, 3D content and virtual worlds, including image-based modeling and reconstruction, shape analysis, 3D face processing, human digitization, efficient physics simulation, image and video-based rendering techniques.

Last but not least, USC Games, a collaboration between the Department of Computer Science and the School of Cinematics Arts, is recognized as one of North America’s top game design programs, according to Princeton Review.

Cognitive Learning for Vision and Robotics Lab Computer Graphics Group Computer Graphics, Animation and Simulation Laboratory Computer Graphics and Immersive Technologies (CGIT) Geometry and Graphics Group Haptics Robotics and Virtual Interaction (HaRVI) Lab Autonomous Robotic Systems (ICAROS) Lab Interaction Lab Polymorphic Robotics Lab Robotics and Autonomous Systems Center (RASC) Robotic Embedded Systems Lab Safe Autonomy and Intelligent Distributed Systems (SAIDS) Lab USC Geometric Capture Group Vision & Graphics Lab at USC ICT

Andrew Goodney David Pynadath David Traum Gale Lucas Gaurav Sukhatme Heather Culbertson Jernej Barbic Jesse Thomason Jonathan Gratch Kallirroi Georgila Lars Lindemann Laurent Itti Maja Mataric Mark Redekopp Michael Zyda Mohammad Soleymani Ning Wang Oded Stein Paul Rosenbloom Ram Nevatia Satish Thittamaranahalli Saty Raghavachary Stefanos Nikolaidis Sven Koenig Ulrich Neumann Wein-Min Shen Yolanda Gil

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Explore all research areas

Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

Research areas

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

Latest news

Precision home robots learn with real-to-sim-to-real.

CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.

Study: When allocating scarce resources with AI, randomization can improve fairness

Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.

Study across multiple brain regions discerns Alzheimer’s vulnerability and resilience factors

Genomics and lab studies reveal numerous findings, including a key role for Reelin amid neuronal vulnerability, and for choline and antioxidants in sustaining cognition.

The Department of EECS announces new Career Development chairs

The career development chair recipients are Cheng-Zhi Anna Huang, Kuikui Liu, Marzyeh Ghassemi, Kaiming He, and Alexander Rives.

Department of EECS names new chair recipients

The new chairs became effective July 1.

Upcoming events

Doctoral thesis: automated and provable privatization for black-box processing, doctoral thesis: representation learning for control: lessons from partially observable linear dynamical systems, doctoral thesis: leveraging mechanics for multi-step robotic manipulation planning.

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Assistive Technologies and Learning with Disabilities

Biomedical Informatics

Biomed imaging and visualization, cloud computing, cybersecurity, cyber-physical systems, databases and data mining.

Data Science and Analytics

Multimedia Systems and Apps

Semantic, Social and Sensor Web
Machine Learning and Artificial Intelligence

Wireless Networking and Security

Assistive technologies and learning with disabilities.

"Disabilities can be very traumatic, leading to frustration and depression," according to the American Foundation for the Blind. The rate of unemployment among legally blind individuals of working age residing in the United States greatly exceeds the unemployment rate for individuals with no functional limitations. Clever devices and information technology engineering strategies can be developed to help people overcome barriers to pursue educational and professional opportunities that will allow them to become productive members of the society.

Current Research Projects

Reading devices for the blind and visually impaired
Navigation devices for the blind
Multimodal forms of representation for virtual learning environments
Rehabilitation Assistants

Researchers

Nikolaos Bourbakis

Research Labs

Center of Assistive Research Technologies (CART)

Bioinformatics advances fundamental concepts in molecular biology, biochemistry, and computer science to help further understanding of basic DNA, genes, and protein structures it relates to mechanisms for drug development and treatment of diseases.

Metabolomics and toxicology
Trends in molecular evolution
Automation of forensic DNA analysis
Indexing genomic databases
Stochastic reaction modeling
Search optimization
National model for bioinformatics education
Disease analysis
Travis Doom
Guozhu Dong
Michael Raymer
Tanvi Banerjee
T.K. Prasad
Bioinformatic Research Group

Biomedical imaging and visualization research has become a very active research field during the last two decades, offering unique solutions for a great variety of biological and biomedical problems. Analysis and visualization of medical images facilitates diagnosis and treatment planning. Visualization systems used as surgical navigation systems enable precise and minimally invasive surgery.

Image registration in surgical navigation
Segmentation of MR and CT images for spinal surgery
Design of a surgical robot assistance for biopsy
Detection and visualization of brain shift during brain surgery
Automated endoscopic imaging
EEG+fMRI Modeling of the Brain
Ultrasound Modeling of Human organs (heart, liver)
Bio-signatures of in-vivo cells
Thomas Wischgoll
Advanced Visual Data Analysis (AViDA)

Cloud computing is a major step toward organizing all aspects of computation as a public utility service. It embraces concepts such as software as a service and platform as a service, including services for workflow facilities, application design and development, deployment and hosting services, data integration, and management of software. The cloud platform increases in importance as our industry makes the phase change from in-house data management to cloud-hosted data management to improve efficiency and focus on core businesses. However, like any new technology, there are formidable problems, from performance issues to security and privacy, from metadata management to massively parallel execution.

This is a major part of the Kno.e.sis Research Center.

Cloud infrastructure for data management
Privacy and security in cloud data management
Cloud-based mining and learning algorithms
Cloud support for text mining and web search
Large-scale natural language modeling and translation
Parallel and distributed algorithms for bioinformatics
Performance evaluation and benchmarking
Database Research Laboratory
Bioinformatics Research Group

The Department of Computer Science and Engineering of Wright State University recently received a grant, titled "REU Site: Cybersecurity Research at Wright State University", from the National Science Foundation. This NSF REU site offers a ten-week summer program that aims at providing a diverse group of motivated undergraduates with competitive research experiences in cyber-security research. A variety of projects will be offered in Network Security, Intrusion Detection, Wireless Sensor Network Security, Internet Malware Detection, Analysis, and Mitigation, Software Reverse Engineering and Vulnerability Discovery, and Privacy-Preserving Data Mining. More information of this REU Site can be found at http://reu.cs.wright.edu .

In addition there are two ongoing projects sponsored by DARPA and ONR for Deepfake techniques, Deep Understanding of Technical Documents, and Computer Security (like memory attacks).

Junjie Zhang
WSU Cybersecurity Lab

Related Programs

Master of Science in Cybersecurity
Undergraduate

Cyber-Physical Systems are jointly physical and computational and are characterized by complex loops of cause and effect between the computational and physical components. We focus on the creation of methods by which such systems can self-adapt to repair damage and exploit opportunities and methods by which we can explain and understand how they operate even after having diverged from their original forms. Our current application area the creation of control systems for insect-like flapping-wing air vehicles that repair themselves, in flight, after suffering wing damage.

Click here for more information about Cyber Physical Systems at Wright State University

Data mining is the process of extracting useful knowledge from a database. Data mining facilitates the characterization, classification, clustering, and searching of different databases, including text data, image and video data, and bioinformatics data for various applications. Text, multimedia, and bioinformatics databases are very large and so parallel/distributed data mining is essential for scalable performance.

Parallel/distributed data mining
Text/image clustering and categorization
Metadata for timelining events
XML database
Data warehousing
Biological/medical data mining
Data Mining Research Lab

Data Science and Analytics

Mathematical, statistical, and graphical methods for exploring large and complex data sets. Methods include statistical pattern recognition, multivariate data analysis, classifiers, modeling and simulation, and scientific visualization.

Topological Data Analysis
Predictive Analytics
Michelle Cheatham
Machine Learning and Complex Systems Lab
Data Science for Healthcare

Multimedia systems offer synergistic and integrated solutions to a great variety of applications related to multi-modality data, such as automatic target recognition, surveillance, tracking human behavior, etc.

Object recognition in digital images and video
Multimedia content classification and indexing
Integrated search and retrieval in multimedia repositories
Background elimination in live video
Modeling and visualization
Biometrics and cyber security
Network and security visualization

Semantic, Social and Sensor Webs

The World Wide Web contains rapidly growing amount of enterprise, social, device/sensor/IoT/WoT data in unstructured, semistructured and structured forms. The Semantic Web initiative by the World Wide Web consortium (W3C) of which Wright State University is a member (represented by Kno.e.sis) has developed standards and technologies to associate meaning to data, to make data more machine and human understandable, and to apply reasoning techniques for intelligent processing leading to actionable information, insights, and discovery. Kno.e.sis has one of the largest academic groups in the US in Semantic Web, and its applications for better use and analysis of social and sensor data.

Computer assisted document interpretation tools
Information extraction from semi-structured documents
Semantic Web knowledge representation
Semantic sensor web
Linked and Big Data

Machine Learning and Artificial Intelligence

Machine learning and artificial intelligence aim to develop computer systems that exhibit intelligent behavior in decision making, object recognition, planning, learning, and other applications that require intelligent assessment of complex information. Our faculty apply modern tools such as deep neural networks, evolutionary algorithms, statistical inference, topological analysis, and graphical inference models to a wide variety of problems from engineering, science, and medicine.

Knowledge Representation and Reasoning
Intelligent agents
Natural language understanding
Evolutionary algorithms and evolvable hardware
Autonomous robotic systems
Machine learning
Fuzzy and neural systems
Intelligent control systems
Deep Neural Networks

Wireless communication and networking have revolutionized the way people communicate. Currently, there are more than two billion cellular telephone subscribers worldwide. Wireless local area networks have become a necessity in many parts of the globe. With new wireless enabled applications being proposed every day, such as wireless sensor networks, telemedicine, music telepresence, and intelligent web, the potential of this discipline is just being unleashed.

Ultra-high speed optical network
Wireless sensor network
Music telepresence
Cognitive radio and dynamic spectrum access
Secure protocol and secure processors authentication
Cyber-physical systems
Network coding

Take the Next Step

Finding the right college means finding the right fit. See all that the College of Engineering and Computer Science has to offer by visiting campus.

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The computer science department at the University of Virginia attracts federal research support in excess of $6 million annually, with total external research funding of more than $19.8 million each year. In addition to excelling in traditional research areas within computer science, we believe that many important research challenges lie at the boundary of computer science and other disciplines.

Our Research

Computer systems, architecture, and networks.

Strength in computer systems has been a pillar of our department since its earliest days. This focus has led to notable contributions, for example, in cyber-physical systems, compilers, grid computing, and computer architecture.

Cyber-physical Systems

Cyber-physical Systems (CPS) and the Internet of Things have been identified by the National Academy of Sciences as national research priorities, critical to educating scientists and engineers for an increasingly cyber-enabled future. In response, we have created the multi-disciplinary Link Lab, bringing together researchers from the departments of CS, Electrical & Computer Engineering, Mechanical & Aerospace Engineering, and Engineering Systems & Environment.

Artificial Intelligence (AI)

The recent explosive growth in artificial intelligence (AI) and data mining demonstrates their vast potential to radically transform the norms and practices in every industry and every aspect of our society. Our department is advancing the state of the art in both fundamental algorithms as well as new algorithms to help users benefit from this revolution and provide new capabilities for the benefit of society.

UVA’s Department of Computer Science has a rapidly growing group of faculty working in the area of cyber security, including hardware, software, operating systems, networks, and cryptographic theory, working in close collaboration with other research groups such as the Link Lab, Software Engineering, and AI.

Software Engineering

Software-intensive systems play a variety of critical roles throughout society: they support human decision making in medical, transportation, and legal domains and, over the coming years they will increasingly operate autonomously – without a human in the loop. Our department’s strength in formal methods and program analysis allows us to advance the state of the art in all these areas, ensuring that these systems operate correctly and securely.

With our recent successful faculty hires in the CS department, the areas of security/cryptography, algorithmic game theory, as well as network science have achieved critical mass that puts CS@UVA in a unique position to differentiate itself and serve as a catalyst for rapid growth in this area.

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12 Most Emerging Research Areas in Computer Science in 2021

By: P. Chaudhary, B. Gupta

Artificial Intelligence and Robotics

Artificial Intelligence and Robotics [1, 2] field aims at developing computational system that are intelligent in decision making, planning, object recognition, and other complex computational tasks that require minimum human intervention. This field emphasizes upon the development of cognitive algorithms for a variety of domains including e-commerce, healthcare, transport, manufacturing, gaming, defense industry, logistics, to name a few. It includes the application of popular emerging technologies such as Deep leaning, machine learning, Natural language processing (NLP), robotics, evolutionary algorithms, statistical inference, probabilistic methods, and computer vision. Some of the eminent research areas includes the following:

Knowledge representation and reasoning
Estimation theory
Mobility mechanisms
Multi-agent negotiation
Intelligent agents
Semantic segmentation
Assistive robotics in medical diagnosis
Robot perception and learning
Motion planning and control
Autonomous vehicles
Personal assistive robots
Search and information retrieval
Speech and language recognition
Fuzzy and neural system
Intelligent embedded system in industries
Object detection and capturing
Intelligent information systems

2. Big Data Analytics

Big data analytics [3, 4] research field involves design and development of techniques/algorithms/frameworks to explore the large amount of data to fulfill organization’s objectives. This area includes mathematical, statistical and graphical approaches to mine useful knowledge patterns from heterogeneous raw data. It is one of the potential and emerging research domains as almost every organization is attempting to utilize available data to enhance their productivity and services to their customers. Some of the distinguished research areas are following:

Predictive analysis
Data capturing and transmission
Parallel Data processing
Uncertainty in data
Data anonymization methods
Data processing in distributed environment
Privacy protecting techniques
Semantic analysis on social media
Intelligent traffic surveillance
Topological data analysis

3. Biometrics and Computational Biology

This field embraces enormous potential for researchers as it amalgamates multiple research areas including big data, image processing, biological science, data mining, and machine learning. This field emphasizes on the designing and development of computational techniques for processing biological data [5, 6]. Some of the potential research areas includes:

Structure and sequence analysis algorithms
Protein structure anticipation
Data modeling of scientific applications
Virtual screening
Brain image analysis using data mining approaches
Design predictive models for severe disease analysis
Molecular structure modeling and analysis
Brain-machine interfaces
Computational neuroscience

4. Data Mining and Databases

This field motivates research on designing vital methods, prototype schemes and applications in data mining and databases. This field ensembles all methods, techniques, and algorithms used for extracting knowledgeable information from the available heterogenous raw data [7, 8]. It enables classification, characterization, searching and clustering different datasets from wide range of domains including e-commerce, social media, healthcare, to name a few. This field demands parallel and distributed processing of data as it operates on massive quantity of data. It integrates various research domains including artificial intelligence, big data analytics, data mining, database management system, and bioinformatics. Some of the eminent research areas comprises as follows:

Distributed data mining
Multimedia storage and retrieval
Data clustering
Pattern matching and analysis
High-dimensional data modeling
Spatial and scientific data mining for sensor data
Query interface for text/image processing
Scalable data analysis and query processing
Metadata management
Graph database management and analysis system for social media
Interactive data exploration and visualization
Secure data processing

5. Internet of Things (IoTs)

Internet of Things has transformed the lives of people through exploring new horizons of networking. It connects physical objects with the internet as per the application to serve the user. This field carries enormous potential in different research areas related to the IoT and its interrelated research domains [9, 10]. These areas include as follows:

IoT network infrastructure design
Security issues in IoT
Architectural issues in Embedded system
Adaptive networks for IoT
Service provisioning and management in IoT
Middleware management in IoT
Handling Device Interoperability in IoT
Scalability issues in IoT
Privacy and trust issues in IoT
Data storage and analysis in IoT networks
Integration of IoT with other emerging technologies such as fog computing, SDN, Blockchain, etc.
Context and location awareness in IoT networks
Modeling and management of IoT applications
Task scheduling in IoT networks
Resource allotment among smart devices in IoT networks.

6. High-Performance Computing

This field encourage the research in designing and development of parallel algorithms/techniques for multiprocessor and distributed systems. These techniques are efficient for data and computationally exhaustive programs like data mining, optimization, super computer application, graph portioning, to name a few [11, 12]. Some of the eminent research challenges includes the following:

Information retrieval methods in cloud storage
Graph mining in social media networks
Distributed and parallel computing methods
Development of architecture aware algorithms
Big data analytics methods on GPU system
Designing of parallel algorithms
Designing of algorithms for Quantum computing

7. Blockchain and Decentralized Systems

This field [13, 14] revolutionize the digital world through processing network information without any central authority. This field is an emerging computing paradigm and motivates the design and development of algorithms that operate in decentralized environment. These techniques provide security, robustness and scalability in the network. Some of the eminent research areas includes the following:

Enhancing IoT security using blockchain
Precision agriculture and blockchain
Social blockchain networks
Blockchain based solutions for intelligent transportation system
Security and privacy issues in blockchain networks
Digital currencies and blockchain
Blockchain and 5G/6G communication networks
Integration of cloud/fog computing with blockchain
Legislation rules and policies for blockchain
Artificial Intelligence for blockchain system

8. Cybersecurity

With the development of new technology such as IoT, attackers have wider attack surface to halt the normal functioning of any network. Attackers may have several intentions to trigger cyber-attacks either against an individual person, organization, and/or a country. Now-a-days, we are living in a digital world where everything is connected is to the internet, so we are prone to some form of security attacks [15, 16]. This field carries massive potential for research on different techniques/methods to defend against these attacks. Some of the emerging research areas comprise the following:

Intrusion detection system
Applied cryptography
Privacy issues in RFID system
Security challenges in IoT system
Malware detection in cloud computing
Security and privacy issues in social media
Wireless sensor network security
Mobile device security
Lawa and ethics in cybersecurity
Cyber physical system security
Software defined network security
Security implications of the quantum computing
Blockchain and its security
AI and IoT security
Privacy issues in big data analytics
Phishing detection in finance sector

9. AI and Cyber Physical System

Specifically, Cyber physical system integrates computation and physical methods whose functionalities is determined by both physical and cyber component of the system. Research in this area motivates the development of tools, techniques, algorithms and theories for the CPS and other interrelated research domains [17, 18]. Research topics includes the following:

Human computer interaction
Digital design of CPS interfaces
Embedded system and its security
Industrial Interne to things
Automation in manufacturing industries
Robotics in healthcare sector
Medical informatics
AI, robotics and cyber physical system
Robot networks
Cognitive computing and CPS

10. Networking and Embedded Systems

This field [19, 20] encourages research on the designing of contemporary theories and approaches, effective and scalable methods and protocols, and innovative network design structure and services. These mechanisms improve the reliability, availability, security, privacy, manageability of current and future network and embedded systems. Research in this domain comprises of following topics:

Cyber physical system
Design of novel network protocols
Cognitive radio networks
Network security for lightweight and enterprise networks
Resource allocation schemes in resource-constrained networks
Network coding
Energy efficient protocols for wireless sensor networks
AI and embedded system
Embedded system for precision agriculture

11. Computer Vision and Augmented Reality

Computer vision [21, 22] is a multidisciplinary field that make computer system to understand and extract useful information from digital images and videos. This field motivates the research in designing the tools and techniques for understanding, processing, extracting, and storing, analyzing the digital images and videos. It embraces multiple domains such as image processing, artificial intelligence, pattern recognition, virtual reality, augmented reality, semantic structuring, statistics, and probability. Some of the eminent research topics includes the following:

Computer vision for autonomous robots
Object detection in autonomous vehicles
Object detection and delineation in UAVs network.
Biomedical image analysis
Augmented reality in gaming
Shape analysis in digital images
Computer vision for forensics
Robotics navigation
Deep learning techniques for computer vision
Automation in manufacturing sector
3D object recognition and tracking

12. Wireless Networks and Distributed Systems

The research in this field emphasizes on the developments of techniques that facilitate communication and maintain coordination among distributed nodes in a network [23, 24]. It is a broad area that embraces numerous domains including cloud computing, wireless networks, mobile computing, big data, and edge computing. Some of the eminent research topics includes the following:

Message passing models in distributed system
Parallel distributed computing
Fault tolerance and load balancing
Dynamic resource allocation in distributed system
Resource discovery and naming
Low-latency consistency protocols
Designing of consensus protocols
Efficient communication protocols in distributed system
Security issues in distributed networks
Privacy and trust models
Optimization of distributed storage
Distributed and federated machine learning

[1] Wisskirchen, G., Biacabe, B. T., Bormann, U., Muntz, A., Niehaus, G., Soler, G. J., & von Brauchitsch, B. (2017). Artificial intelligence and robotics and their impact on the workplace . IBA Global Employment Institute, 11(5), 49-67. [2] Kortenkamp, D., Bonasso, R. P., & Murphy, R. (Eds.). (1998). Artificial intelligence and mobile robots: case studies of successful robot systems. MIT Press. [3] Dai, H. N., Wang, H., Xu, G., Wan, J., & Imran, M. (2020). Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies . Enterprise Information Systems, 14(9-10), 1279-1303. [4] Müller, O., Junglas, I., Vom Brocke, J., & Debortoli, S. (2016). Utilizing big data analytics for information systems research: challenges, promises and guidelines . European Journal of Information Systems, 25(4), 289-302. [5] Waterman, M. S. (2018). Introduction to computational biology: maps, sequences and genomes. Chapman and Hall/CRC. [6] Imaoka, H., Hashimoto, H., Takahashi, K., Ebihara, A. F., Liu, J., Hayasaka, A., … & Sakurai, K. (2021). The future of biometrics technology: from face recognition to related applications. APSIPA Transactions on Signal and Information Processing, 10. [7] Zhu, X., & Davidson, I. (Eds.). (2007). Knowledge Discovery and Data Mining: Challenges and Realities: Challenges and Realities . Igi Global. [8] Tseng, L., Yao, X., Otoum, S., Aloqaily, M., & Jararweh, Y. (2020). Blockchain-based database in an IoT environment: challenges, opportunities, and analysis. Cluster Computing, 23(3), 2151-2165. [9] Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., & Markakis, E. K. (2020). A survey on the internet of things (IoT) forensics: challenges, approaches, and open issues. IEEE Communications Surveys & Tutorials, 22(2), 1191-1221. [10] Nižetić, S., Šolić, P., González-de, D. L. D. I., & Patrono, L. (2020). Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. Journal of Cleaner Production, 274, 122877. [11] Hager, G., & Wellein, G. (2010). Introduction to high performance computing for scientists and engineers. CRC Press. [12] Wang, G. G., Cai, X., Cui, Z., Min, G., & Chen, J. (2017). High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm . IEEE Transactions on Emerging Topics in Computing, 8(1), 20-30. [13] Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352-375. [14] Nguyen, D. C., Ding, M., Pham, Q. V., Pathirana, P. N., Le, L. B., Seneviratne, A., … & Poor, H. V. (2021). Federated learning meets blockchain in edge computing: Opportunities and challenges . IEEE Internet of Things Journal. [15] Tawalbeh, L. A., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020). IoT Privacy and security: Challenges and solutions. Applied Sciences, 10(12), 4102. [16] Boubiche, D. E., Athmani, S., Boubiche, S., & Toral-Cruz, H. (2021). Cybersecurity Issues in Wireless Sensor Networks: Current Challenges and Solutions. Wireless Personal Communications, 117(1). [17] Gupta, R., Tanwar, S., Al-Turjman, F., Italiya, P., Nauman, A., & Kim, S. W. (2020). Smart contract privacy protection using ai in cyber-physical systems: Tools, techniques and challenges. IEEE Access, 8, 24746-24772. [18] Kravets, A. G., Bolshakov, A. A., & Shcherbakov, M. V. (2020). Cyber-physical Systems: Industry 4.0 Challenges . Springer. [19] Duan, Q., Wang, S., & Ansari, N. (2020). Convergence of networking and cloud/edge computing: Status, challenges, and opportunities. IEEE Network, 34(6), 148-155. [20] Wang, C. X., Di Renzo, M., Stanczak, S., Wang, S., & Larsson, E. G. (2020). Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges. IEEE Wireless Communications, 27(1), 16-23. [21] Chen, C. H. (Ed.). (2015). Handbook of pattern recognition and computer vision . World Scientific. [22] Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., … & Socher, R. (2021). Deep learning-enabled medical computer vision. NPJ digital medicine, 4(1), 1-9. [23] Farahani, B., Firouzi, F., & Luecking, M. (2021). The convergence of IoT and distributed ledger technologies (DLT): Opportunities, challenges, and solutions. Journal of Network and Computer Applications, 177, 102936. [24] Alfandi, O., Otoum, S., & Jararweh, Y. (2020, April). Blockchain solution for iot-based critical infrastructures: Byzantine fault tolerance. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium (pp. 1-4). IEEE.

Cite this article:

P. Chaudhary, B. Gupta (2021) 12 Most Emerging Research Areas in Computer Science in 2021 , Insights2Techinfo, pp. 1

FAQ on this topic

Artificial Intelligence and Robotics, Big Data Analytics, Biometrics and Computational Biology, Data Mining and Databases, Internet of Things (IoTs), High-Performance Computing, Blockchain and Decentralized Systems,Cybersecurity

Big data research field involves design and development of techniques/algorithms/frameworks to explore the large amount of data to fulfill organization’s objectives. Some of the distinguished research areas are following: Data capturing and transmission, Parallel Data processing,Data anonymization methods,Data processing in distributed environment

Artificial Intelligence field aims at developing computational systems that are intelligent in decision making, planning, object recognition, and other complex computational tasks that require minimum human intervention. Some of the eminent research areas includes the following: Knowledge representation and reasoning Autonomous vehicles, Fuzzy and neural system, Intelligent information systems

Some of the eminent research areas comprises as follows:Distributed data mining, Multimedia storage and retrieval, Data clustering, Pattern matching and analysis, High-dimensional data modeling, Spatial and scientific data mining for sensor data.

The research areas in IoT include as follows: IoT network infrastructure design, Security issues in IoT,Architectural issues in Embedded system, Service provisioning and management in IoT, Middleware management in IoT

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UT Computer Science is proud to announce that Professors Brent Waters and Hovav Shacham have both been honored with the prestigious Test-of-Time Award by the International Association for Cryptologic Research (IACR).

Brent Waters: Dual-System Encryption

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B.S. in Computer Science

Carnegie Mellon's undergraduate major in computer science combines a solid core of computer science courses with the ability to gain substantial depth in another area through a required minor in a second subject. The curriculum also gives you numerous choices for science and humanities courses. Computing is a discipline with strong links to many fields, and our program gives you unparalleled flexibility to pursue these fields. Our mathematics and probability component ensures that you'll have the formal tools to remain current as technologies and systems change, but at the same time you'll gain insight into the practical issues of building and maintaining systems by participating in intensive project-oriented courses.

Unlike other universities, where research rarely occurs at the undergraduate level, CMU CS students often have part-time or summer jobs — or receive independent study credit — working on research while pursuing their bachelor's degree. If you're interested in a research/graduate school career, we offer an intensive course of research, equivalent to four classroom courses, culminating in the preparation of a senior research honors thesis.

Requirements

Current Computer Science Undergraduate Curriculum

Computer Science Undergraduate curriculum information for prior years are available on the Previous Course Catalogs webpage .

How to Apply

SCS Undergraduate Majors

Including the B.S. in CS, the School of Computer Science offers five bachelor's degrees.

Information on the other four degrees can be found on the respective websites for the degree:

Artificial Intelligence
Computational Biology
Human-Computer Interaction
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Computer Science Fields Of Study
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Computer Science Fields Of Study Subjects In Computer Science
Computer Science Fields Of Study Subjects In Computer Science
What is Computer Science ?

COMMENTS

Research
Research. The computing and information revolution is transforming society. Cornell Computer Science is a leader in this transformation, producing cutting-edge research in many important areas. The excellence of Cornell faculty and students, and their drive to discover and collaborate, ensure our leadership will continue to grow.
Explore all research areas
Explore all research areas EECS' research covers a wide variety of topics in electrical engineering, computer science, and artificial intelligence and decision-making.
Departmental Research Areas
Departmental Research Areas In the past five years, Computer Science faculty have had research collaborations with every other college at Purdue. The work of the computer scientist is applicable just about everywhere. Though research activity spans many broad areas, the list below reflects the interests and expertise of the faculty summarized in 14 areas.
Research
Computer science research at the Johns Hopkins University is advancing computing technology, enabling new modes of thought, and transforming society. Our faculty conduct innovative, collaborative research aimed at solving large and complex interdisciplinary problems, drawing upon the university's renowned strengths in areas including ...
Research Areas
Autonomous and Cyber-Physical Systems. Subareas: Real-time and Embedded Systems, Sensor Systems, Mobile Computing, Control Theory and Systems, Formal Methods, Automated Verification and Certification. Faculty: Alterovitz , Anderson, Chakraborty, Duggirala , Nirjon. More on Autonomous and Cyber-Physical Systems.
Research Areas
Research areas represent the major research activities in the Department of Computer Science. Faculty and students have developed new ideas to achieve results in all aspects of the nine areas of research.
Artificial Intelligence and Machine Learning
Artificial Intelligence and Decision-making combines intellectual traditions from across computer science and electrical engineering to develop techniques for the analysis and synthesis of systems that interact with an external world via perception, communication, and action; while also learning, making decisions and adapting to a changing ...
Research at Yale CS
Research at Yale CS At Yale Computer Science, our faculty and students are at the forefront of innovation and discoveries. We conduct ground-breaking research covering a full range of areas in theory, systems, and applications.
Research & Impact
Stanford Computer Science faculty members work on the world's most pressing problems, in conjunction with other leaders across multiple fields. Fueled by academic and industry cross-collaborations, they form a network and culture of innovation.
Research
UT Computer Science is among the world's leading centers of excellence across all major research thrusts in computer science as well as in key application areas.
Research Areas
Gates Computer Science Building. 353 Jane Stanford Way. Stanford, CA 94305. United States. Contact Us. Directions to the Gates Building. Campus Map. Follow us on LinkedIn. Links to.
Research Overview
Our world-renowned faculty and exceptional students conduct exciting research in all areas of computer science. From harnessing the power of machine learning in a responsible manner to ensuring the security of cloud computing, from investigating the new horizons of human-computer interaction and visual computing to improving the energy ...
Research Areas
Home > Research > Research Areas. Experimental and applied investigations of intelligent systems, including rational decision making, machine learning and perception, natural language processing, cognitive modeling, and more. Silicon chip design, computer architecture, and novel device technologies that may replace traditional CMOS transistors.
Top Trends in Computer Science and Technology
Computer science is constantly evolving. Learn more about the latest trends in AI, cybersecurity, regenerative agritech, and other developing areas of the field.
Research Areas and Labs
At USC, the areas of computer vision, robotics and graphics represent the interface between computers and the rest of the world. Robotics at USC focuses on developing effective, robust, human-centric, and scalable robotic systems. In this area, our expertise ranges from socially assistive robotic and novel haptics technology for virtual touch ...
Research Areas
Research Areas Computer Science at U of T is known for its work in neural networks, computer graphics, machine learning, theory, human-computer interaction (HCI), scientific computation, computer performance evaluation, and more. Our faculty's innovative approaches and paradigms have had widespread international impact.
Computer Science
Computer Science. Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day. Primary subareas of this field include: theory, which uses rigorous math to test algorithms' applicability to certain ...
Areas of Research
The Department of Computer Science and Engineering of Wright State University recently received a grant, titled "REU Site: Cybersecurity Research at Wright State University", from the National Science Foundation.
Research
The computer science department at the University of Virginia attracts federal research support in excess of $6 million annually, with total external research funding of more than $19.8 million each year. In addition to excelling in traditional research areas within computer science, we believe that many important research challenges lie at the boundary of computer science and other disciplines.
Research
Research Highlights Interdisciplinary research at the intersection of computer science and other fields.
MSc and PhD Research Interests
MSc and PhD Research Interests. Below is a listing of research areas represented in the Department of Computer Science. For some areas, their parent branch of Computer Science (such as Scientific Computing) is indicated in parentheses. Artificial Intelligence (AI) natural language processing (NLP), speech processing, information retrieval ...
12 Most Emerging Research Areas in Computer Science in 2021
This field embraces enormous potential for researchers as it amalgamates multiple research areas including big data, image processing, biological science, data mining, and machine learning.
Nils Murrugarra-Llerena
Dr. Murrugarra' s teaching and research interests encompass computer vision, machine learning, and natural language processing, with a focus on attribute recognition and projects involving deep learning, gaze prediction, transfer learning, reinforcement learning, and metric learning. Education . PhD, University of Pittsburgh ; Research Areas
Interview with Computer Science Experts: Answering ...
The job market for computer science graduates is strong but can vary depending on your specialization. Different areas within computer science offer various levels of demand and opportunities. If you specialize in cybersecurity, you'll find many openings.
Department of Computer Science
Since 1966, the Rutgers Computer Science Department has been paving the way for innovative thinkers. Whether you're starting your journey with a bachelor's degree or delving deeper into research with our graduate programs, you'll be part of a vibrant community within the Rutgers School of Arts and Sciences.
Brent Waters and Hovav Shacham Honored with Test-of-Time Awards for
UT Computer Science is proud to announce that Professors Brent Waters and Hovav Shacham have both been honored with the prestigious Test-of-Time Award by the International Association for Cryptologic Research (IACR) for their influential papers presented at Crypto 2009.
Thijs Vogels at Microsoft Research
I am a research engineer in Microsoft Research AI for Science, working on the intersection of deep learning and quantum chemistry. Before joining MSR, I worked in computer graphics and distributed optimization.
Computer Science Major Frequently Asked Questions
The Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley offers one of the strongest research and instructional programs in this field anywhere in the world.
Carolyn Rosé
My research toward this end has birthed and substantially contributed to the growth of two thriving interrelated research areas: automated analysis of collaborative learning processes and dynamic support for collaborative learning. Both areas use intelligent conversational agents to support collaborative learning in a context-sensitive way.
Bachelor's Programs
B.S. in Computer Science. Carnegie Mellon's undergraduate major in computer science combines a solid core of computer science courses with the ability to gain substantial depth in another area through a required minor in a second subject.

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