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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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Departmental Research Areas

• Research Centers and Institutes
• Current Department Administered Research Funding
• Research Seminars
• Technical Reports

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)

Related Links

• Co gnitive  R obot  A utonomy and  L earning (CoRAL) lab
• MINDS: Data Science, Machine Learning, and AI
• PurPL: Center for Programming Principles and Software Systems

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

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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.

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Computer science articles from across Nature Portfolio

Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.

Latest Research and Reviews

Critical-edge based tabu search algorithm for solving large-scale multi-vehicle Chinese postman problem

• Jizhou Tang
• Hongtao Bai

Multiscale knowledge distillation with attention based fusion for robust human activity recognition

• Zhaohui Yuan
• Zhengzhe Yang
• Xiangyang Tang

Distributed constrained combinatorial optimization leveraging hypergraph neural networks

Bolstering the broad and deep applicability of graph neural networks, Heydaribeni et al. introduce HypOp, a framework that uses hypergraph neural networks to solve general constrained combinatorial optimization problems. The presented method scales and generalizes well, improves accuracy and outperforms existing solvers on various benchmarking examples.

• Nasimeh Heydaribeni
• Xinrui Zhan
• Farinaz Koushanfar

DASUNet: a deeply supervised change detection network integrating full-scale features

• Guangyu Zhang

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

• Soo Siang Teoh
• Haidi Ibrahim

A new multi-attribute group decision-making method based on Einstein Bonferroni operators under interval-valued Fermatean hesitant fuzzy environment

• Jasni Mohamad Zain

News and Comment

Who owns your voice? Scarlett Johansson OpenAI complaint raises questions

In the age of artificial intelligence, situations are emerging that challenge the laws over rights to a persona.

• Nicola Jones

Anglo-American bias could make generative AI an invisible intellectual cage

• Queenie Luo
• Michael Puett

AlphaFold3 — why did Nature publish it without its code?

Criticism of our decision to publish AlphaFold3 raises important questions. We welcome readers’ views.

Back to basics to open the black box

Most research efforts in machine learning focus on performance and are detached from an explanation of the behaviour of the model. We call for going back to basics of machine learning methods, with more focus on the development of a basic understanding grounded in statistical theory.

• Diego Marcondes
• Adilson Simonis
• Junior Barrera

Quantum computing for oncology

As quantum technology advances, it holds immense potential to accelerate oncology discovery through enhanced molecular modeling, genomic analysis, medical imaging, and quantum sensing.

• Siddhi Ramesh
• Teague Tomesh
• Alexander T. Pearson

Autonomous interference-avoiding machine-to-machine communications

An article in IEEE Journal on Selected Areas in Communications proposes algorithmic solutions to dynamically optimize MIMO waveforms to minimize or eliminate interference in autonomous machine-to-machine communications.

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Computer Science

Computer Science at the Harvard School of Engineering studies both the fundamentals of computation and computation’s interaction with the world. Computer scientists develop new algorithms, invent new systems and theories that empower people and society, and advance the science of computing while working with engineers, scientists, social scientists, lawyers, artists, and others around the university and beyond.

Computer scientists at Harvard pursue work in a wide range of areas including theoretical computer science, artificial intelligence, economics and computer science, privacy and security, data-management systems, intelligent interfaces, operating systems, computer graphics, computational linguistics, robotics, networks, architectures, program languages, machine learning, and visualization.

Computer Science at Harvard is committed to advancing the goals of the Office of Diversity, Inclusion, and Belonging (DIB) by supporting students, staff, faculty, and researchers from diverse backgrounds.

Students and researchers are involved in a number of interdisciplinary initiatives across the University, such as the Center for Research on Computation and Society , the Institute for Applied Computational Science , the Data Science Initiative , and the Berkman Klein Center for Internet & Society .

Computer Science Programs

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Research Areas

• Artificial Intelligence
• Computer Engineering and Architecture
• Computation and Society
• Computational and Data Science
• Computational Neuroscience
• Economics and Computation
• Graphics, Vision, and Visualization
• Human-Computer Interaction
• Machine Learning
• Programming Languages
• Systems, Networks, and Databases
• Theory of Computation
• Quantum Computing

Featured Stories

Alumni profile: Nick Lesica, Ph.D. '05

Building better hearing aids through deep learning

AI / Machine Learning , Alumni , Bioengineering , Computer Science , Entrepreneurship , Health / Medicine

2024 Commencement photos

Images from the 373rd Harvard Commencement on Thursday, May 23

Academics , Applied Computation , Applied Mathematics , Applied Physics , Bioengineering , Computer Science , Environmental Science & Engineering , Events , Materials Science & Mechanical Engineering , Robotics

Sustainable computing project awarded $12 million from NSF

Multi-institution research initiative aims to reduce computing’s carbon footprint by 45% within the next decade

Climate , Computer Science

Email forwarding for @cs.stanford.edu is changing. Updates and details here . CS Commencement Ceremony June 16, 2024.  Learn More .

Research Areas

• Who’s Teaching What
• Subject Updates
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• Sloan-MIT University Center for Exemplary Mentoring (UCEM)

Electrical Engineering

Computer science, artificial intelligence + decision-making.

• AI and Society
• AI for Healthcare and Life Sciences
• Artificial Intelligence and Machine Learning
• Biological and Medical Devices and Systems
• Communications Systems
• Computational Biology
• Computational Fabrication and Manufacturing
• Computer Architecture
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• Electronic, Magnetic, Optical and Quantum Materials and Devices
• Graphics and Vision
• Human-Computer Interaction
• Information Science and Systems
• Integrated Circuits and Systems
• Nanoscale Materials, Devices, and Systems
• Natural Language and Speech Processing
• Optics + Photonics
• Optimization and Game Theory
• Programming Languages and Software Engineering
• Quantum Computing, Communication, and Sensing
• Security and Cryptography
• Signal Processing
• Systems and Networking
• Systems Theory, Control, and Autonomy
• Theory of Computation
• Departmental History
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Explore all research areas

At MIT EECS, our faculty enjoy an intellectually inspiring and nurturing environment where they are empowered to ask creative questions; solve the world’s most pressing problems; and pursue inventive, innovative research.

Electrical engineers design the most sophisticated systems ever built. From computers with billions of transistors to microgrids fed by renewable energy sources, from algorithms that predict disease to solar cells and electric vehicles, electrical engineering touches all parts of modern society. We leverage computational, theoretical, and experimental tools to develop groundbreaking sensors and energy transducers, new physical substrates for computation, and the systems that address the shared challenges facing humanity.

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. 

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 environment.

The research that drives EECS forward is based out of four major labs. Most EECS faculty members are affiliated with one of these labs, where they explore challenging questions and develop innovative technological solutions every day.

Nearly 130 EECS faculty members find their research homes in four major affiliate labs:

• Computer Science and Artificial Intelligence Laboratory (CSAIL)
• Laboratory for Information and Decision Systems (LIDS)
• Microsystems Technology Laboratories (MTL)
• Research Laboratory of Electronics (RLE)

Our boundary-pushing research is often highly interdisciplinary, but it can be roughly broken down into some useful areas of inquiry. Explore them here.

UROP and SuperUROP

Students at MIT can take advantage of many opportunities to participate in research side-by-side with our world-class faculty–even on the undergraduate level.

MIT Undergraduate Research Opportunities Program (UROP)

Advanced Undergraduate Research Opportunities Program (SuperUROP) 

Research areas

Our world-class research faculty and students work in a range of areas crucial to the future of the world economy and human thriving. We bring computing innovations to the world and nuture the next generation of computer scientists. Find faculty by their research focus below.

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

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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

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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

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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

More on Machine Learning and Data Science

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

More on Networking

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

More on Theory

CS Research Areas

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• UTCS Direct

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 news explore more ».

A new framework, led by a University of Texas at Austin research team, helps a type of generative artificial intelligence that creates images to avoid replicating existing works. Credit - The University of Texas at Austin

Research Areas

Artificial Intelligence

AI addresses the challenges of machine cognition, spanning the theoretical and empirical across diverse subfields such as machine learning, computer vision, NLP, and robotics.

Bioinformatics & Computational Biology

Bioinformatics and computational biology utilize biologically inspired AI and ML methods to solve complex problems and applies data mining to biological experiments.

Computer Architecture

Computer Architecture research lies between software and hardware, exploring the foundational implementation and method of how computers function.

Computer Vision

Computer vision trains computers and systems to identify, classify, and interpret digital images and video, allowing them to “understand” the visual world.

Database research centers on addressing fundamental data management problems and developing prototype data systems to empower users in real-world scenarios.

Formal Methods

Formal methods uses mathematical techniques to assist with specification, design, implementation, and verification to make hardware and software systems more reliable.

Graphics & Visualization

Graphics and visualization studies methods for manipulating and interacting with digital images and visual content as well as processing and modeling datasets.

Human-Computer Interaction

Human-computer interaction studies the connection between humans and the design of computing technologies. UTCS HCI makes communication more effective and accessible.

Intelligent Robotics

AI roboticists create independently functioning agents that integrate perception, decision-making, and action to perform tasks in the real world relevant to a variety of applications.

Machine Learning

Machine learning is a branch of artificial intelligence (AI) focused on enabling machines to learn from data and make decisions with minimal human intervention.

Natural Language Processing

Natural language processing helps computers comprehend, decipher, and manipulate text and spoken words—bridging the gap between human language and machine communication.

Parallel Computing

Parallel computing researchers pursue computational efficiency by breaking down long calculation processes into smaller tasks that can be solved simultaneously.

Programming Languages & Compilers

PL and compiler research delves into novel techniques to transform the way software is expressed in written form, enhancing program efficiency and durability.

Scientific Computing

Scientific computing research is at the intersection of mathematics and computer science, using advanced computing capabilities to solve complex problems.

Security & Privacy

Security research uses theoretical and applied approaches to increase information safety in systems while simultaneously exposing security flaws.

Systems & Networking

Systems research builds large prototype software systems that convincingly demonstrate novel design principles and implementation techniques using realistic workloads.

Theoretical Computer Science

Theory focuses on the theoretical foundations of computer science and frequently relies on rigorous mathematical proofs. Potential applications include algorithm design and quantum computation.

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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

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

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 , Lei Li , 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

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

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

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 , Lei Li , Linda Petzold , Ambuj K. Singh , Misha Sra , Matthew Turk , William Wang , Xifeng Yan

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

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

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

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Research   /   Research Areas Artificial Intelligence

Artificial Intelligence (AI) research explores the nature of intelligence and the ways in which computation can be used to explain and engineer it. Work in AI combines the scale afforded by machine learning with the expressive and organizational power of semantic information-processing and knowledge-based reasoning. Our faculty research programs include work in machine learning, cognitive modeling, language understanding and generation, planning and reasoning, robotics and human-robot interaction, computational journalism, social media analysis, computer audition, computers and education, computational creativity, and legal reasoning.

Mohammed Alam

Assistant Professor of Instruction

Deputy Director of the Master of Science in Artificial Intelligence Program

Email Mohammed Alam

Emma Alexander

Assistant Professor of Computer Science and (by courtesy) Electrical and Computer Engineering

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Brenna Argall

Associate Professor of Computer Science

Associate Professor of Mechanical Engineering

Associate Professor of Physical Medicine and Rehabilitation

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Larry Birnbaum

Professor of Computer Science

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Douglas Downey

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Ken Forbus

Walter P. Murphy Professor of Computer Science

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Kristian Hammond

Bill and Cathy Osborn Professor of Computer Science

Director, Master of Science in Artificial Intelligence Program

Director, Center for Advancing Safety of Machine Intelligence (CASMI)

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Thomas Hinrichs

Research Associate Professor

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Ian Horswill

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Han Liu

Orrington Lunt Professor of Computer Science

Professor of Statistics

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Bryan Pardo

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Chris Riesbeck

Director, Master of Science in Computer Science Program

Co-Director, Center for Computer Science and Learning Sciences

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Director of Graduate Admissions in Computer Science

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Sara Owsley Sood

Professor of Instruction

Chookaszian Family Teaching Professor

Associate Chair for Undergraduate Education

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V.S. Subrahmanian

Faculty Fellow at the Northwestern Buffett Institute for Global Affairs

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Uri Wilensky

Professor of Education and Social Policy

Lorraine Morton Professor

Email Uri Wilensky

Marcelo Worsley

Karr Family Associate Professor of Computer Science

Associate Professor of Learning Sciences, School of Education and Social Policy

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7 Careers in Computer Science Fields

Discover how to start your career in various computer science fields.

The study of computer science and computer science jobs are on the rise due to the increase in technology use. When you choose to study computer science, you may explore different fields as potential career routes. Many jobs in computer science fields focus on designing and improving software to create a better overall user experience.

Read on to explore seven different computer science fields and corresponding careers in each field. Discover the salaries, job outlooks, and educational requirements for each role and how to start a career in computer science.

Careers in computer science fields

Below are popular computer science fields and careers to explore:

1. Artificial intelligence

With the rise of machine learning, artificial intelligence careers are increasingly in demand. When you work with artificial intelligence, you create and improve machine learning models to ensure they can run efficiently and provide users with accurate information.

Machine learning engineer

Average annual US salary (Glassdoor): $127,695 [ 1 ]

Job outlook (projected growth from 2022 to 2032): 23 percent [ 2 ]

Requirements: Bachelor’s degree

As a machine learning engineer, you must know programming languages to create and interact with machine learning models and applications. You test how machine learning models work and research how to improve them.

To become a machine learning engineer, you typically need a bachelor’s degree in computer science, information technology, or a related field. To expand your career options and enhance your knowledge, you may consider getting a master’s degree in machine learning.

2. Data structures and algorithms

When you work with data structures and algorithms, you analyze data and research methods to improve the functionality of computer systems. You must possess strong analytical and problem-solving skills to work with data structures and algorithms.

Data scientist

Average annual US salary (Glassdoor): $120,496 [ 3 ]

Job outlook (projected growth from 2022 to 2032): 35 percent [ 4 ]

Data scientists research and collect data for building software programs, creating algorithms, and troubleshooting problems. You also present your data findings to other individuals at your organization and work together to create new methods to avoid problems in the future.

When you work as a data scientist, you must have strong problem-solving and analytical skills. You typically need at least a bachelor’s degree to start your career as a data scientist. However, some employers may prefer more work experience or a graduate degree before they hire you.

3. Computer networks

Computer networking is the process by which computer systems connect, communicate, and work with one another. To work with computer networks, you need a background in mathematics and strong communication and technical computer skills.

Network architect

Average annual US salary (Glassdoor): $133,377 [ 5 ]

Job outlook (projected growth from 2022 to 2032): 4 percent [ 6 ]

As a network architect, you create, test, and implement a business or organization’s computer networks. You develop networks to meet a business’s needs and continuously adjust and change them to ensure their functionality and efficiency.

Network architects typically need a bachelor’s degree in computer science, though employers’ exact qualifications may vary. As a network architect, you need to be proficient in the programs you use daily, such as Cisco or CompTIA, and may need to complete certifications in these programs to verify your proficiency. 

4. Computer architecture and organization

A career in computer architecture and organization involves creating databases and websites for computer systems. When you work with computer architecture, you ensure that the software you work on is designed and engineered to function properly.

Application architect

Average annual US salary (Glassdoor): $139,152 [ 7 ]

Job outlook (projected growth from 2022 to 2032): 25 percent [ 8 ]

Application architects design and uphold software applications for businesses and organizations. As an application architect, you use your technical knowledge and coding skills to create functional applications for other computer science roles, such as data analysts and web managers.

Application architects need strong leadership skills and the ability to work well with a team of others to reach a common goal. To become an application architect, you typically need a bachelor’s degree and some years of experience working with software.

5. Computer security

Computer security involves managing the safety of organizations' computer networks. When you work in computer security, you must have strong problem-solving and communication skills so you and your team can mitigate any issues that arise quickly and easily.

Cybersecurity analyst

Average annual US salary (Glassdoor): $103,717 [ 9 ]

Job outlook (projected growth from 2022 to 2032): 32 percent [ 10 ]

Requirements: Bachelor’s degree and certifications

As a cybersecurity analyst, you work with a business or organization to prevent compromising data and recover lost data. You protect computer networks using security tools and constantly monitor computer software to ensure safety and avoid attacks and breaches of information.

Job requirements for cybersecurity analysts vary depending on the position or your employer; however, most prefer that you have completed some higher education in the form of a bachelor’s degree. Pursuing relevant certifications may boost your resume and increase your chances of getting hired. Popular certifications for cybersecurity analysts include the Certified Information Systems Security Professional or the Certified SOC Analyst.

6. Databases

When you study databases, you learn where and how data is located and put to use. Databases collect, store, and distribute information, specifically data. When you work with databases, you typically manage data and create functional databases.

Database administrator

Average annual US salary (Glassdoor): $100,729 [ 11 ]

Job outlook (projected growth from 2022 to 2032): 8 percent [ 12 ]

Database administrators ensure systems and applications work properly for an organization. As a database administrator, you create databases and confirm their functionality so users and other members of your team have the ability to access them with ease.

To become a database administrator, you typically need a background and strong knowledge of programming languages such as SQL and NoSQL, and a bachelor’s degree in computer science or a related major. 

7. Human-computer interaction

Human-computer interaction is the study of how people interact with computers and other forms of technology. If you establish a career dealing with human-computer interaction, you’ll work to develop and improve software and databases that technology users interact with daily.

UX designer

Average annual US salary (Glassdoor): $98,529 [ 13 ]

Job outlook (projected growth from 2022 to 2032): 16 percent [ 14 ]

As a UX designer, you'll create and maintain websites and databases. Your responsibilities include designing wireframes, researching user experience, implementing feedback, and creating functional digital architecture in the form of websites or applications.

To become a UX designer, you typically need a bachelor’s degree and a computer science or software engineering background. While some employers may hire you with only completed certifications or boot camps, most prefer at least a bachelor’s degree from an interested candidate.

Learn more with Coursera.

To pursue your interest in starting a career in the computer science field, consider taking courses and certifications that will help you develop your skills and enhance your knowledge. Explore Computer Science: Programming with a Purpose offered by Princeton University or the Google Cybersecurity Professional Certificate on Coursera.

Article sources

Glassdoor, “ Salary: Machine Learning Engineer , https://www.glassdoor.com/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm.” Accessed March 19, 2024.

US Bureau of Labor Statistics, “ Computer and Information Research Scientists , https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm#tab-6.” Accessed March 19, 2024.

Glassdoor, “ Data Scientist Salaries , https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm.” Accessed March 19, 2024.

US Bureau of Labor Statistics, “ Data Scientists , https://www.bls.gov/ooh/math/data-scientists.htm#tab-6.” Accessed March 19, 2024.

Glassdoor, “ Network Architect Salaries , https://www.glassdoor.com/Salaries/network-architect-salary-SRCH_KO0,17.htm.” Accessed March 19, 2024.

US Bureau of Labor Statistics, “ Computer Network Architects , https://www.bls.gov/ooh/computer-and-information-technology/computer-network-architects.htm#tab-6.” Accessed March 19, 2024.

Glassdoor, “ Salary: Application Architect , https://www.glassdoor.com/Salaries/applications-architect-salary-SRCH_KO0,22.htm.” Accessed March 19, 2024.

US Bureau of Labor Statistics, “ Software Developers, Quality Assurance Analysts, and Testers , https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm#tab-6.” Accessed March 19, 2024.

Glassdoor, “ Cyber Security Analyst Salaries , https://www.glassdoor.com/Salaries/cyber-security-analyst-salary-SRCH_KO0,22.htm#.” Accessed March 19, 2024.

US Bureau of Labor Statistics, “ Information Security Analysts , https://www.bls.gov/ooh/computer-and-information-technology/information-security-analysts.htm#tab-6.” Accessed March 19, 2024.

Glassdoor, “ Database Administrator Salaries , https://www.glassdoor.com/Salaries/database-administrator-salary-SRCH_KO0,22.htm.” Accessed March 19, 2024.

US Bureau of Labor Statistics, “ Database Administrators and Architects , https://www.bls.gov/ooh/computer-and-information-technology/database-administrators.htm#tab-6. ” Accessed March 19, 2024.

Glassdoor, “ UX Designer Salaries , https://www.glassdoor.com/Salaries/ux-designer-salary-SRCH_KO0,11.htm” Accessed March 19, 2024.

US Bureau of Labor Statistics, “ Web Developers and Digital Designers , https://www.bls.gov/ooh/computer-and-information-technology/web-developers.htm#tab-6” Accessed March 19, 2024.

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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

• Computer Science
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Introduction to the Major

The Computer Science major (CS) deals with computer theory, methods of information processing, hardware and software design, and applications. The major combines a rigorous technical program with background in the liberal arts and sciences. The CS major prepares students for technical careers or graduate school programs related to EECS or CS. 

All students admitted to the College of Letters & Science are admitted as undeclared students. To declare CS, students must achieve a cumulative grade point average of 3.30 in CS61A, CS61B, & CS70. All students who meet this criteria are admitted into the major.

One Department, Two Programs 

There is no difference in the CS course content between the CS and EECS majors--the differences are what other subjects you'd like to study and the admissions processes to the university and majors.

If you prefer greater flexibility in your coursework, or have an interest double-majoring in an area outside engineering, the CS major might be a good choice. There is greater opportunity to explore other departments, like economics, business, and music.

If you have a great interest in electrical engineering or in double-majoring in another engineering major, the EECS major may be better suited for you.

Related Majors

There are many ways to get exposure to CS other than via the CS major. The following majors are avenues to study CS and to help prepare students for industry and graduate school: applied math, cognitive science, data science, & statistics.

The CS minor is also a great option that equips students for industry and graduate school.

CS isn’t something I could’ve done alone, so I’m grateful for the community here. Steven Tan, CS student and CS Peer Advisor

• Four-Year Student Timeline

Explore Your Major

• See CS requirements & declaration policies
• Take CS10 and/or CS8 before CS61A, if no coding experience.
• See math requirements & AP/IB policies . Find calculus starting point .
• Check in with a CS major advisor .

Connect and Build Community

• New to CS? Apply for the CS Scholars Program
• Get support in classes from resources and counselors
• Become familiar with Disabled Students’ Program , Gender Equity Resource Center , Undocumented Student Program , Educational Opportunity Program .

Discover Your Passions

• Enroll in a Freshman & Sophomore Seminar . Look for CS/EE 24 & 39.
• Visit the Office of Undergraduate Research and Scholarships to learn about research opportunities. 
• Take a  DeCal , a student-facilitated course.

Engage Locally and Globally

• Explore study abroad options now so you can incorporate them into your sophomore or junior year plans.
• Explore volunteer opportunities on campus.

Reflect and Plan Your Future

• Develop a plan for getting career ready.
• Join Handshake for Berkeley-specific career opportunities.
• Learn about career opportunities in CS at  Berkeley Career Engagement .
• Look for internship programs at various companies specific to first-year students.

Second Year

• Complete the CS prerequisite coursework to declare your major. Recommended: Apply to CS by the end of your 2nd year.
• Use the EECS website to help guide your B.A. program, and the HKN course guide to think about future classes in CS/EE.
• Consider a minor .
• Learn about EECS student organizations
• Consider becoming an Academic Intern, Reader, or Tutor for a lower-division CS/EE class.
• Seek CS Peer Advising and ask questions on the EECS 101 on Edstem
• Go to office hours of professors and GSIs.
• Assist a professor in their research through the Undergraduate Research Apprenticeship Program
• Attend the EECS Department Colloquium Series to learn more about the field.
• Learn more about research opportunities available at UC Berkeley.
• Explore study abroad options for CS and meet with both a CS major advisor and your L&S advisor to confirm requirement fulfillment.
• Join Bridging Berkeley to become a math mentor to middle schoolers.
• Subscribe to the eecs-ugrad-jobs list-serv to learn about CS Info-sessions and Tech Talks
• Attend the EECS Internship Fair , EECS & STEM Career Fairs
• Meet with Berkeley Career Engagement or UPE for resume help and interview practice.
• Complete CS lower-division requirements ; begin taking upper-division courses
• Check-in with a CS major advisor
• Participate in faculty advising each semester once declared.
• If eligible and interested in research, consider the  EECS Honors Program .
• Enjoy teaching and/or mentoring? Become an EE/CS DeCal facilitator or CS Mentor . Learn about how to become an Undergraduate Student Instructor in future semesters.
• Consider applying to the Accel Scholars Program .
• Explore Beehive and other EECS research opportunities for undergraduates.
• Learn about upper-division technical electives for your major outside CS.
• Join CalTeach to gain teaching skills and explore a career in education.
• Interested in community outreach? Check out the opportunities available in community outreach programs for engineering students.
• Get matched with a graduate student mentor through Berkeley Connect .
• Attend the Engineering and Tech Career Conference to prepare for recruiting season.
• Utilize job board tools in your job search.
• Explore graduate school options by speaking with faculty members and advisors .

Fourth Year

• Complete remaining CS upper-division requirements
• Consider getting faculty permission to take CS graduate courses.
• Meet with a CS advisor to ensure CS requirements will be completed.
• Check-in with an L&S advisor to stay on track to graduate.
• Give back by becoming a CS peer advisor or tutor at the Student Learning Center .
• Volunteer for EECS Departmental events like CS Education Day and Cal Day .
• See ways to stay in touch with the EECS Department after you graduate.
• Carry out your own research project funded by scholarships
• Attend events at the Sutardja Center for Entrepreneurship & Technology or the Jacobs Institute for Design and Innovation .
• Consider researching and applying for scholarships available to recent Berkeley graduates.
• If interested in graduate school, explore gap year opportunities prior to embarking on your next academic or career adventure.
• Continue to attend industry-related events.
• Take the GRE & seek letters of recommendation if interested in graduate school.
• View the First Destination Survey to find out what recent grads are doing.

What Can I Do With My Major?

Jobs and employers.

• Analyst, Axioma
• Application Developer, Workday
• ASIC Engineer, Nvidia
• Assoc. Publishing Producer, Google
• Care Coordinator, YoDerm
• Consultant, Bain and Company
• Cyber Security Consultant, Deloitte
• Data Analyst, Apple
• Data Scientist, Nerdwallet
• Front End Developer, HealthTap
• Hardware Engineer, Apple
• Infrastructure Engineer, Capital One
• Investment Engineer, Bridgewater
• iOS Engineer, Mozilla
• Machine Learning Engineer, eBay
• Mobile Developer, Sony
• Performance Engineer, Splunk
• Program Manager, Microsoft
• Programmer, Intl CS Institute
• R&D Engineer, Glint Photonics
• Site Reliability Engineer, Google
• Software Developer, Expedia
• Software Engineer, AirBnB
• Surface Warfare Officer, U.S. Navy
• Systems Specialist, Salesforce
• Teacher, Teach for India
• Technology Analyst, Goldman Sachs
• UX Designer, GoDaddy

Graduate Programs

• Algebra & Numbers Theory
• Artificial Intelligence & Robotics
• Audiology & Hearing Sciences
• Biological Sciences
• Biostatistics
• Computational Mathematics
• Computer Engineering
• Computer Graphics
• Electrical Engineering
• Industrial & Org. Psychology
• Interdisciplinary Studies
• Physical Chemistry

Examples from the First Destination Survey of recent Berkeley graduates.

Connect With Us

Come to Berkeley’s annual Open House in April for information sessions, campus tours, special talks, and more. See what events the EECS Department offers at eecs.berkeley.edu/academics/undergraduate/calday .

Golden Bear Orientation

Join your peers in the campus-wide UC Berkeley orientation program for all new students.

Attend department events with students, staff, and faculty. Visit eecs.berkeley.edu for news and updates.

Prospective students can make an appointment to meet with a CS advisor at berkeleycs.youcanbook.me . Current students should make a CS advising appointment through CalCentral.

Drop-in CS advising is available. Please check eecs.berkeley.edu/resources/undergrads/cs/advising   for the latest schedule.

Letters & Science College advising services can be found at lsadvising.berkeley.edu

How to Use this Map

Use this map to help plan and guide your experience at UC Berkeley, including academic, co-curricular, and discovery opportunities. Everyone’s Berkeley experience is different and activities in this map are suggestions. Always consult with your advisors whenever possible for new opportunities and updates.

• What Can I Do with My Major?

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Home » Shifting the Frame: The Labors of ImageNet and AI Data

Shifting the Frame: The Labors of ImageNet and AI Data

Recorded on April 17, this video features a talk by Dr. Alex Hanna , Director of Research at the Distributed AI Research Institute (DAIR). The talk was part of a symposium series presented by the UC Berkeley Computational Research for Equity in the Legal System Training Program (CRELS), which trains doctoral students representing a variety of degree programs and expertise areas in the social sciences, computer science and statistics.

The talk was co-sponsored by the UC Berkeley Department of Sociology , the  Criminal Law & Justice Center , and the  Berkeley Institute of Data Sciences  (BIDS).

Artificial intelligence (AI) technologies like ChatGPT, Stable Diffusion, and LaMDA have led a multi-billion dollar industry in generative AI, and a potentially much larger industry in AI more generally. However, these technologies would not exist were it not for the immense amount of data mined to make them run, low-paid and exploited annotation labor required for labeling and content moderation, and questionable arrangements around consent to use these data. Although datasets used to train and evaluate commercial models are often obscured from view under the shroud of trade secrecy, we can learn a great deal about these systems by interrogating certain publicly available datasets which are considered foundational in academic AI research.

In this talk, I investigate a single dataset, ImageNet. It is not an understatement to say that without ImageNet, we may not have the current wave of deep learning techniques which power nearly all modern AI technologies. I begin from three vantage points: the histories of ImageNet from the perspective of its curators and its linguistic predecessor WordNet, the testimony of the data annotators which labeled millions of ImageNet images, and the data subjects and the creators of the images within ImageNet. Academically, I situate this analysis within a larger theory and practice of infrastructure studies. Practically, I point to a vision for technology which is not based on practices of unrestricted data mining, exploited labor, and the use of images without meaningful consent.

About the Speaker

Dr. Alex Hanna is Director of Research at the Distributed AI Research Institute (DAIR). A sociologist by training, her work centers on the data used in new computational technologies, and the ways in which these data exacerbate racial, gender, and class inequality. She also works in the area of social movements, focusing on the dynamics of anti-racist campus protest in the US and Canada. She holds a BS in Computer Science and Mathematics and a BA in Sociology from Purdue University, and an MS and a PhD in Sociology from the University of Wisconsin-Madison. Dr. Hanna has published widely in top-tier venues across the social sciences, including the journals Mobilization, American Behavioral Scientist, and Big Data & Society, and top-tier computer science conferences such as CSCW, FAccT, and NeurIPS. Dr. Hanna serves as a Senior Fellow at the Center for Applied Transgender Studies, and sits on the advisory board for the Human Rights Data Analysis Group and the Scholars Council for the UCLA Center for Critical Internet Inquiry.

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A Dear Colleague Letter (DCL) is an informal correspondence which is written by a Requesting Office and distributed to communities within a specific program area, to attract individuals eligible under a Visiting Scientist, Engineer, and Educator (VSEE) appointment, an Intergovernmental Personnel Act (IPA) assignment and/or a Federal Temporary appointment. These letters may be circulated in paper form through internal mail, distributed electronically using listservs or accessed through NSF.gov’s Career Page.

Computer Scientist (Program Director)

Application timeline, position summary.

The Division of Computing and Communication Foundations (CCF) in the Directorate for Computer and Information Science and Engineering announces a nationwide search to fill a Program Director position(s) whose expertise would cover topics within Education and Workforce Development (EWF) in computing-related fields. 

For more information on CISE please click here:  https://new.nsf.gov/cise

For more information on CCF please click here:  https://new.nsf.gov/cise/ccf

For more information on EWF please click here:  https://new.nsf.gov/funding/opportunities/education-workforce-program .

This position is in the Division of Computing and Communication Foundations (CCF) within the Directorate for Computer and Information Science and Engineering (CISE). CCF supports research and education activities in the mathematical, scientific and technological foundations of computing, communication, hardware, software and emerging technologies such as quantum information science and bio-inspired systems. CCF seeks to foster innovative and transformative research that contributes to the development of future computing and communication technologies. CCF also coordinates cross-divisional activities that foster the integration of research, education, and workforce development.

The Education and Workforce (EWF) program mission is to transform computing education at all levels and on a national scale to meet the challenges and opportunities of a world where computing is increasingly essential to all sectors of society and overturn the longstanding underrepresentation in computing. Ultimately, EWF envisions the development of a diverse workforce well prepared for careers in computing-related fields.

Position Description

The responsibilities of the positions will be in support of CISE education programs (Education and Workforce Development) and would also include support for other NSF-wide programs involving computing R&D components. The range of EWF activities are described at https://new.nsf.gov/funding/opportunities/education-workforce-program . That page includes links to program pages with listings of current NSF Program Education Workforce Development Program Directors, who may be contacted for additional information regarding this employment opportunity.

Specifically, we are looking for a candidate to fill the position in the following program area:

• Education and Workforce (EWF)

The programs covered by EWF are:

• Broadening Participation in Computing (BPC) 
• Computer Science for All (CSforAll: Research and RPPs) 
• CISE Graduate Fellowships (CSGrad4US)
• CISE Research Expansion Program (CISE-MSI)
• Improving Undergraduate STEM Education:   Computing in Undergraduate Education (IUSE: CUE)
• Research Experience for Teachers (RET) Sites
• Research Experience for Undergraduates (REU) Sites

Formal consideration of applications begins immediately and will continue until selections are made against all open vacancies.

Program Directors are responsible for specific research areas. They solicit, receive, and review research and education proposals; make funding recommendations; administer awards; use data to monitor and improve the impact of programs, and interact with relevant research and education communities. They also provide service to NSF-wide activities and often work across government agencies to enable interagency collaborations. Program Directors contribute to the accomplishment of NSF's strategic goals to (1) Expand knowledge in science, engineering, and learning; (2) Advance the capability of the Nation to meet current and future challenges; and (3) Enhance NSF's performance of its mission.

Appointment options

The position recruited under this announcement will be filled under the following appointment option(s):

Intergovernmental Personnel Act (IPA) Assignment: Individuals eligible for an IPA assignment with a Federal agency include employees of State and local government agencies or institutions of higher education, Indian tribal governments, and other eligible organizations in instances where such assignments would be of mutual benefit to the organizations involved. Initial assignments under IPA provisions may be made for a period up to two years, with a possible extension for up to an additional two-year period. The individual remains an employee of the home institution and NSF provides the negotiated funding toward the assignee's salary and benefits. Initial IPA assignments are made for a one-year period and may be extended by mutual agreement. 

Eligibility information

It is NSF policy that NSF personnel employed at or IPAs detailed to NSF are not permitted to participate in foreign government talent recruitment programs.  Failure to comply with this NSF policy could result in disciplinary action up to and including removal from Federal Service or termination of an IPA assignment and referral to the Office of Inspector General. https://www.nsf.gov/careers/Definition-of-Foreign-Talent-HRM.pdf .

Applications will be accepted from U.S. Citizens. Recent changes in Federal Appropriations Law require Non-Citizens to meet certain eligibility criteria to be considered. Therefore, Non-Citizens must certify eligibility by signing and attaching this Citizenship Affidavit to their application. Non-Citizens who do not provide the affidavit at the time of application will not be considered eligible. Non-Citizens are not eligible for positions requiring a security clearance.

To ensure compliance with an applicable preliminary nationwide injunction, which may be supplemented, modified, or vacated, depending on the course of ongoing litigation, the Federal Government will take no action to implement or enforce the COVID-19 vaccination requirement pursuant to Executive Order 14043 on Requiring Coronavirus Disease 2019 Vaccination for Federal Employees. Federal agencies may request information regarding the vaccination status of selected applicants for the purposes of implementing other workplace safety protocols, such as protocols related to masking, physical distancing, testing, travel, and quarantine.

Qualifications

Candidates must have a Ph.D. in computer science, computer engineering, computing education, or a related discipline plus, after the award of the Ph.D., six or more years of successful research, research administration, and/or managerial experience pertinent to the position OR a Master’s degree in computer science, computer engineering, computing education, or a related discipline plus after award of the degree, eight or more years of successful research, research administration, and/or managerial experience pertinent to the position. 

Outstanding knowledge of the relevant research area and related areas, a commitment to fairness, ethical conduct, and personal integrity, receptivity to new ideas, a strong work ethic and initiative, excellent interpersonal skills, expert communication ability, and familiarity with NSF activities are highly desirable.

How to apply

Applications should be submitted electronically and must include contact information, a current CV, a letter briefly describing the candidate's background that specifically relates to the appropriate program objectives, availability time frame, and a list of references.

Please make electronic submissions to the Division of Computing and Communication Foundations, at [email protected] with “EWF Program Director Position” in the subject line.