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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 and education on 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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Computer architecture, educational technology.

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Theory of Computation

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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

Using spatial learning to transform math and science education.

PrismsVR, founded by Anurupa Ganguly ’07, MNG ’09, takes students to virtual worlds to learn through experiences and movement.

Combining next-token prediction and video diffusion in computer vision and robotics

A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.

MIT team takes a major step toward fully 3D-printed active electronics

By fabricating semiconductor-free logic gates, which can be used to perform computation, researchers hope to streamline the manufacture of electronics.

New 3D printing technique creates unique objects quickly and with less waste

By using a 3D printer like an iron, researchers can precisely control the color, shade, and texture of fabricated objects, using only one material.

How AI is improving simulations with smarter sampling techniques

MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.

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Bridging disciplines and accelerating discoveries in computer science.

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; human-computer interaction; and computer-assisted medicine.

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 & Networking

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 Johns Hopkins Data Science and AI Institute

Johns Hopkins has made a transformational investment in the power and promise of data science and artificial intelligence and is building the nation’s foremost destination for emerging applications, opportunities and challenges presented by data science, machine learning, and AI.

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The Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT pioneers research in computing and AI that improves how people live, work, and learn. CSAIL’s mission is to push the boundaries of knowledge, train brilliant students in research, collaborate with like-minded organizations, and create technology with widespread societal benefits. CSAIL engages in cutting-edge work that covers a comprehensive range of topics.

Research Focus Areas

• Artificial Intelligence (AI): CSAIL explores the frontiers of AI, developing systems that exhibit intelligent reasoning, perception, behavior, and learning. Research areas include computational biology, computer graphics, natural language processing, computer vision, machine learning, medical informatics, and robotics. 
• Systems: CSAIL’s focus is on understanding the fundamental principles that underlie computer hardware and software. Research areas include compilers, computer architecture, chip design, operating systems, programming languages, and computer networks.
• Theoretical Computer Science: CSAIL’s work aims to develop the foundations of computation, including algorithms, complexity theory, computational geometry, cryptography, distributed computing, information security, and quantum computing.

Educational Opportunities and Collaborations

CSAIL is deeply invested in fostering the next generation of technology leaders. Undergraduate students have the opportunity to engage in research through the Undergraduate Research Opportunities Program (UROP), while graduate students may participate in research assistantships. CSAIL’s graduate community is diverse, with students from Electrical Engineering and Computer Science, Mathematics, Aeronautics and Astronautics, Brain and Cognitive Sciences, Mechanical Engineering, and the MIT-Harvard Health Sciences and Technology Program.

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Computer Science

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The computer science department continues to lead the world in computer science research and education.

Throughout the past four decades, the department has influenced society at levels that remain without parallel among academic institutions. Its spin-offs are among the most successful corporate ventures in the world, and many of the leaders in the academic and corporate research world are our graduates.

What are we researching?

Strong research groups exist in areas of artificial intelligence, robotics, foundations of computer science, scientific computing and systems. 

What is it like for undergraduate students?

The CS curriculum provides knowledge that is applicable across many fields, including many areas of engineering, science, and medicine. Students receive a strong foundation in computer science as well as specialized knowledge through the student’s choice of track.

What is it like for graduate students?

With faculty and resources that are among the strongest in the world, students have the opportunity to participate in leading-edge academic research carried out at Stanford. The main educational goal is to prepare students for research and teaching careers either in universities or in industry

Information For

• Prospective Graduate Students
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The future of ultra fast electronics

What Starts Here

   , ph.d. program, master's programs, portfolio program in robotics, admissions & incoming students, current students, online programs & degrees, master's degrees, student experience, 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 ».

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.

Upcoming Seminars explore more »

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Join the community, trending research, lcb-net: long-context biasing for audio-visual speech recognition.

The growing prevalence of online conferences and courses presents a new challenge in improving automatic speech recognition (ASR) with enriched textual information from video slides.

Sound Multimedia Audio and Speech Processing

FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications

Then, we propose a language-model-based foundation TTS system.

Sound Audio and Speech Processing

Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU

We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra.

Data Structures and Algorithms Distributed, Parallel, and Cluster Computing

FOX: Coverage-guided Fuzzing as Online Stochastic Control

FOX-Fuzz/FOX • 6 Jun 2024

6 CPU-years of extensive evaluations on the FuzzBench dataset and complex real-world programs (a total of 38 test programs) demonstrate that FOX outperforms existing state-of-the-art fuzzers, achieving average coverage improvements up to 26. 45% in real-world standalone programs and 6. 59% in FuzzBench programs over the state-of-the-art AFL++.

Cryptography and Security

DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving

LLMServe/DistServe • 18 Jan 2024

Our evaluations show that on various popular LLMs, applications, and latency requirements, DistServe can serve 7. 4x more requests or 12. 6x tighter SLO, compared to state-of-the-art systems, while staying within latency constraints for > 90% of requests.

Distributed, Parallel, and Cluster Computing

A Survey on Large Language Models for Software Engineering

We then present a detailed summarization of the recent SE studies for which LLMs are commonly utilized, including 947 studies for 112 specific code-related tasks across five crucial phases within the SE workflow.

Software Engineering

Empowering Robot Path Planning with Large Language Models: osmAG Map Topology & Hierarchy Comprehension with LLMs

Large Language Models (LLMs) have demonstrated great potential in robotic applications by providing essential general knowledge.

CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking

Time delay neural network (TDNN) has been proven to be efficient for speaker verification.

Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization

ntnu-arl/drpm • 14 Oct 2024

Degeneracies arising from uninformative geometry are known to deteriorate LiDAR-based localization and mapping.

MediaPipe: A Framework for Building Perception Pipelines

google-ai-edge/mediapipe • 14 Jun 2019

A developer can use MediaPipe to build prototypes by combining existing perception components, to advance them to polished cross-platform applications and measure system performance and resource consumption on target platforms.

Research Areas

Carnegie Mellon University School of Computer Science

Research at scs.

Through many research and educational partnerships, SCS faculty exercise daily leadership in the fields of information technology, networking, cybersecurity, machine learning, natural language processing, speech recognition, robotics and more. They work closely with nonprofit agencies and industry clients to develop and mature technologies from concept through delivery to end-users. Our diverse interdisciplinary research also extends into areas not traditionally considered part of computer science. We invite you to explore our department websites to learn more about our research.

Departments

Ray and Stephanie Lane Computational Biology Department

Computer Science Department

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Language Technologies Institute

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Robotics Institute

Software and Societal Systems Department

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Center for IDeaS

Center for Computational Analysis of Social and Organizational Systems

CyLab Security and Privacy Institute

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​Research Areas

Browse areas of research our faculty and students take part in.

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Computer Science & Engineering

The CSE Department is currently comprised of 50 distinguished faculty members and supports excellent research facilities. Classes are taught by a large, diverse faculty who are leading researchers in the course areas they teach. Faculty commitment to excellence in teaching has been recognized and honored with numerous engineering and university teaching awards. A large faculty enables the department to offer a wide range of classes in both traditional and cutting-edge topics in computer science. In addition to classes, students also have the opportunity to interact with faculty as course teaching assistants, tutors and readers, as graduate and undergraduate research assistants and through faculty mentoring.

Research Themes

The CSE Department has significant strengths in most major fields of computer science and engineering, including:

• Algorithms Complexity and cryptography
• Algorithms, Complexity and Cryptography (Theory group)
• Artificial Intelligence
• Bioinformatics
• Computer Architecture and Compilers
• Computing Education Research
• Databases and Information Management
• Embedded Systems & Software
• High-Performance Computing
• Human-Computer Interaction / The Design Lab
• Programming Systems
• Security and Cryptography
• Software Engineering
• Systems and Networking
• Ubiquitous Computing and eXtended Intelligence
• Visual Computing (Computer Graphics and Computer Vision)
• VLSI/CAD (Computer-Aided Design)
• Extended Reality (XR) Lab

Current Research Projects

The following is a listing of some of the major research projects currently underway by CSE faculty and researchers:

• Bioinformatics Consulting Group

For additional resources, see:

• Faculty Research Profiles
• Faculty Awards & Accomplishments
• Associated Centers, Institutes & Research Units
• Technical Reports

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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Artificial Intelligence, Machine Learning, Privacy/FATE

Theory and Computation

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

Computer Vision, Robotics, Graphics, HCI

Artificial intelligence (AI) researchers seek to understand and develop machines with human-level intelligence by exploring academic and real-world challenges. 

At USC, we are pioneering breakthroughs in a full spectrum of topics related to AI, including machine learning, human-robot interaction, information extraction, and privacy protection.

Our researchers are working in areas where AI has been studied for decades—like language—and where the tools are just starting to make inroads—such as efforts to combat human trafficking, conserve endangered wildlife, and diagnose and treat diseases, including Alzheimer's. 

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 healthcare, 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 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 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 algorithmic design and optimization, cryptography and security, and automated verification and formal methods.

In addition, we have strong connections to other fields, including economics and game theory, pure mathematics, applied mathematics and scientific computing, network science, and sociology, as well as the 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

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, graphics and human-computer interaction (HCI) represent the interface between computers and 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 robotics and novel haptic technology to complex human-robot interaction and multi-robot systems.

In computer vision and graphics, our researchers bridge the physical and digital worlds through advanced recognition and analysis algorithms, as well as immersive technologies like augmented and virtual reality.

Our strengths in computer vision 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. Their work includes image-based modeling and reconstruction, shape analysis, 3D face processing, human digitization, efficient physics simulation, and image and video-based rendering techniques.

Computer Graphics Group Geometry and Graphics Group  Computer Graphics, Animation and Simulation Laboratory Computer Graphics and Immersive Technologies (CGIT) Robotics and Autonomous Systems Center (RASC) Haptics Robotics and Virtual Interaction (HaRVI) Lab Autonomous Robotic Systems (ICAROS) Lab Interaction Lab Robotic Embedded Systems Lab Safe Autonomy and Intelligent Distributed Systems (SAIDS) Lab 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 Ulrich Neumann Wein-Min Shen Yolanda Gil

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Departmental Research Areas

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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

• Daniel Aliaga
• Aniket Bera
• Simina Branzei
• Brian Bullins*
• Joseph Campbell
• Berkay Celik
• Chris Clifton
• David Gleich
• Dan Goldwasser*
• Steve Hanneke*
• Sooyeon Jeong
• Rajiv Khanna*
• Zachary Kingston
• Anuran Makur
• Xupeng Miao
• Jennifer Neville*
• Alex Psomas
• Ahmed Qureshi
• Bruno Ribeiro*
• Tiark Rompf
• Abulhair Saparov*
• Muhammad Shahbaz
• Paul Valiant
• Jianguo Wang
• Yexiang Xue*
• Raymond Yeh
• Ruqi Zhang*
• Tianyi Zhang

(* indicates primary area of research)

Related Links

• Cognitive Robot Autonomy and Learning (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
• Abulhair Saparov
• Wojtek Szpankowski
• Kihara Bioinformatics Lab
• Kazemian Lab

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*
• 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*
• Elisha Sacks
• Xavier Tricoche
• Yexiang Xue

CSE Research Group

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*
• Susanne Hambrusch
• Jennifer Neville
• Sunil Prabhakar*
• Jianguo Wang*

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
• Saurabh Bagchi (by courtesy)
• Charlie Hu (by courtesy)
• Sanjay Rao (by courtesy)

  (* indicates primary area of research)

• Dependable Computing Systems Lab
• FRIENDS Lab

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

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*
• Aarushi Goel *
• Changhee Jung
• Aniket Kate*
• Ninghui Li*
• Hemanta Maji*
• Chunyi Peng
• Sunil Prabhakar
• Vernon Rego
• Eugene Spafford*
• Kazem Taram
• Dongyan Xu*
• Freedom Research Lab

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.

• Antonio Bianchi
• Doug Comer*
• Sonia Fahmy*
• Pedro Fonseca*
• Xupeng Miao*
• Kihong Park*
• Chunyi Peng*
• Bruno Ribeiro
• 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*
• Zhiyuan Li*
• Ryan Newton*
• Tiark Rompf*
• Xiangyu Zhang*
• Yung-Hsiang Lu (by courtesy)
• Milind Kulkarni (by courtesy)

PurPL - Center for Programming Principles and Software Systems

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

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*
• Joseph Campbell*
• Zachary Kingston*
• Ahmed Qureshi*

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
• 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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Faculty & Research

Our faculty work within and beyond the disciplines of engineering and foundational science. Our approach to teaching and research is, by design, highly interdisciplinary. We collaborate across academic areas, within the larger university, and with colleagues in academia, industry, government and public service organizations beyond Harvard.

Faculty members listed on this page as “Affiliates” have primary appointments in other departments at Harvard. These affiliated faculty members can serve on advisory committees, supervise undergraduate theses, and co-advise Ph.D. students. Harvard faculty interested in becoming CS affiliates may contact the assistant director for faculty and academic operations and area chair.

Prospective Ph.D. students looking for a primary research advisor should consider the Assistant Professors, Associate Professors, and Professors listed on this page. Affiliates cannot serve as primary research advisors for Ph.D. students.

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