uf phd computer science

We're sorry but you will need to enable Javascript to access all of the features of this site.

Stanford Online

Computer science ms degree.

Stanford School of Engineering

Get Started

In the Stanford Computer Science Master's degree , you will complete coursework covering the fundamental aspects of computer science and deepen your expertise in at least one specialized area of study.

If you want to pursue the degree on a part-time basis, so as not to interrupt your career, you can enroll in as few as one course per quarter.

For added flexibility, you can take courses online or in-person on Stanford’s campus. Each quarter, numerous computer science and other engineering courses are available online. While most specializations within the computer science degree require attending some in-person classes, you can complete the Artificial Intelligence, Information Management and Analytics, and Systems Specializations entirely through online coursework. (Note that students interested in earning the master's degree part-time or online must reside in the United States.) 

If you want more flexibility than the part-time master's degree, you can apply to take individual courses or pursue a graduate certificate without being formally admitted to Stanford master’s degree program. Choose from many options, including Foundations in Computer Science , Artificial Intelligence , Cybersecurity , Visual Computing , Software Systems , and Advanced Software Systems . Upon successful completion of each course, you will receive academic credit and a Stanford University transcript.

If you later choose to apply and are admitted into a master's degree program at Stanford, you may apply up to 18 units towards the master's degree (pending department approval).

Not sure which of these credentials is right for you? Compare our graduate certificate vs. master’s degree .

How Much It Will Cost

How long it will take.

To earn the Master of Science in Computer Science Degree, you must complete 45 units.

• As a part-time student, you can expect to finish the degree in 3 to 5 years.
• As a full-time student, you can expect to finish the degree in 1 to 2 years.

What You Need to Get Started

For admissions information , please visit the department's site or contact [email protected] .

For degree requirements , please review either the department's Guide to the MSCS Program Sheet or Stanford Bulletin . See the department's FAQs page .

For more about the policies, procedures, and logistics, please review our website .

While this degree can be completed online, it depends on your program plan and area of focus. Most courses in the Computer Science department are offered only on campus. Specific online course offerings depend heavily on your program plan, area of focus, and the online course offerings for any given academic quarter. Students who are outside the US cannot pursue the master's degree online.

What Our Learners Are Saying

Meet roslyn.

Roz discusses the connection between design and computer science as well as the goals accomplished by the courses she took through Stanford Online.

Watch Video

You May Also Like

Foundations in Computer Science Graduate Certificate

Artificial Intelligence Graduate Certificate

Advanced Software Systems Graduate Certificate

Introductory Programming Graduate Certificate

• Engineering
• Computer Science & Security
• Business & Management
• Energy & Sustainability
• Data Science
• Medicine & Health
• Explore All
• Technical Support
• Master’s Application FAQs
• Master’s Student FAQs
• Master's Tuition & Fees
• Grades & Policies
• Graduate Application FAQs
• Graduate Student FAQs
• Graduate Tuition & Fees
• Community Standards Review Process
• Academic Calendar
• Exams & Homework FAQs
• Enrollment FAQs
• Tuition, Fees, & Payments
• Custom & Executive Programs
• Free Online Courses
• Free Content Library
• School of Engineering
• Graduate School of Education
• Stanford Doerr School of Sustainability
• School of Humanities & Sciences
• Stanford Human Centered Artificial Intelligence (HAI)
• Graduate School of Business
• Stanford Law School
• School of Medicine
• Learning Collaborations
• Stanford Credentials
• What is a digital credential?
• Grades and Units Information
• Our Community
• Get Course Updates

• Costs, Scholarships & Aid
• Campus Life
• Faculty & Staff
• Family & Visitors
• DFW Community
• Galaxy Login
• Academic Calendar
• Human Resources
• Accessibility

Doctor of Philosophy in Computer Science

Program description.

The graduate programs in computer science offer intensive preparation in design, programming, theory and applications. Training is provided for both academically oriented students and students with professional goals in the many business, industrial and governmental occupations requiring advanced knowledge of computing theory and technology.

Courses and research opportunities are offered in a variety of subfields of computer science, including operating systems, computer architecture, computer graphics, pattern recognition, automata theory, combinatorics, artificial intelligence, machine learning, database design, computer networks, programming languages, software systems, analysis of algorithms, computational complexity, parallel processing, VLSI, virtual reality, internet of things, embedded and real-time systems, computational geometry, computer vision, design automation, cyber security, information assurance and data science.

The University maintains a large network of computer facilities including specialized computers for research within the program. In addition to computer science faculty, many other individuals at the University are involved in computer-related work in the physical and social sciences and in various areas of business and management. Computer science students with an interest in these important application areas may have opportunities to consult and work with talented faculty from a wide range of disciplines.

Career Opportunities

Graduates of the program seek academic positions at universities, as well as positions as researchers, senior software engineers, data scientists. Graduates often become industry experts in fields like cyber security, artificial intelligence, machine learning or natural language processing.

Marketable Skills

Review the marketable skills for this academic program.

Application Requirements

Test score required:  Yes

Deadlines:  University  deadlines  apply.

OTHER APPLICATION REQUIREMENTS

Admission Option One

• Master’s degree in computer science or its equivalent
• GPA of at least 3.5
• GRE revised scores of at least 308, 153, 155 and 4 for the combined, verbal, quantitative, and analytical writing components, respectively, are advisable.

Admission Option Two

• A BS degree in related area that includes two semesters of calculus and linear algebra
• GPA of at least 3.5 in the last 60 semester credit hours
• GRE revised scores of at least 315, 156, 159 and 4 for the combined, verbal, quantitative, and analytical writing components, respectively, are advisable.

Applicants are admitted on a competitive basis.

Contact Information

Shyam Karrah  Email: [email protected]

Dr. Ovidiu Daescu Interim Head Department of Computer Science Email: [email protected] Office: ECSS 3.904

Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas, ECW41 800 W. Campbell Road Richardson, TX 75080-3021 [email protected]

engineering.utdallas.edu

cs.utdallas.edu

Request More Information

Contact Email

We have received your request for more information, and thank you for your interest! We are excited to get to know you and for you to explore UT Dallas. You’ll begin receiving emails and information about our beautiful campus, excellent academic programs and admission processes. If you have any questions, email  [email protected].

The University of Texas at Dallas respects your right to privacy . By submitting this form, you consent to receive emails and calls from a representative of the University.

* Required Field

Skip to Content

• News & Events

PhD candidate receives scholarship for academia research

This unrestricted $7,500 award will allow Spoon to focus on her research for the final year of her PhD. 

What does it mean to have received this scholarship?

I had been seeking additional funding for the final year of PhD to allow me to focus on my research full-time, and this scholarship will help me to do that, which I am very grateful for. I have been funded by the National Science Foundation Graduate Research Fellowship the past 3 years but my funding is up in August, so I had been searching for funding opportunities for my final year of my PhD in order to free up as much time as possible to finish my dissertation research. 

What will this funding allow you to pursue?

In the final year of my PhD, I will complete two projects relying on restricted-use government data. 

The first project encompasses in part the thesis for my master’s degree in education policy, which I have been working on concurrently during my PhD in computer science. I am leading a data linkage project with restricted-use data from the U.S. Census Bureau and college and careers data from the National Center for Science and Engineering Statistics to measure how access to STEM careers differs for students from different geographic and demographic backgrounds, and for those who took different educational pathways to their jobs.

The second project is a collaboration with the U.S. Census Bureau Center for Economic Studies to link faculty employment records with detailed restricted-use demographic and earnings information over time to study earnings gaps in academia across gender, race, and institution.

I will also be helping with several collaborative projects in my lab group given my expertise in running large surveys of faculty through prior work. 

​What is the process like to receive this scholarship? 

The scholarship had a nomination process, but we could self-nominate, which is what I did. We wrote short answers about our research experiences, career plans, and motivations for applying for the scholarship.

• Graduate Student Stories

Apply   Visit   Give

Departments

• Ann and H.J. Smead Aerospace Engineering Sciences
• Chemical & Biological Engineering
• Civil, Environmental & Architectural Engineering
• Computer Science
• Electrical, Computer & Energy Engineering
• Paul M. Rady Mechanical Engineering
• Applied Mathematics
• Biomedical Engineering
• Creative Technology & Design
• Engineering Education
• Engineering Management
• Engineering Physics
• Integrated Design Engineering
• Environmental Engineering
• Materials Science & Engineering

Affiliates & Partners

• ATLAS Institute
• BOLD Center
• Colorado Mesa University
• Colorado Space Grant Consortium
• Discovery Learning
• Engineering Honors
• Engineering Leadership
• Entrepreneurship
• Herbst Program for Engineering, Ethics & Society
• Integrated Teaching and Learning
• Global Engineering
• Mortenson Center for Global Engineering
• National Center for Women & Information Technology
• Western Colorado University

Electrical and Computer Engineering

Graduate commencement celebrates the "profound impact" of students' work.

Master of Engineering degree recipient Isabella Racioppi shakes hands with department chair James Sturm in a ceremony celebrating those who earned graduate degrees in electrical and computer engineering during the 2023–2024 academic year. Photo by Tori Repp/Fotobuddy

The Department of Electrical and Computer Engineering held its graduate commencement ceremony on Monday, May 27, 2024, celebrating the 28 doctoral students and 16 master’s students who earned graduate degrees over the past year. The ceremony featured an invited speaker, 2023 Turing Award winner Avi Wigderson, a graduate alumnus, and the presentation of the department’s annual graduate student awards.

Department chair James Sturm, left, honoring the graduate commencement speaker and graduate alumnus Avi Wigderson, right. Photo by Tori Repp/Fotobuddy 

Surround yourself with good people, and commit yourself to the community through service, said Wigderson, who has mentored more than a hundred mathematicians over the past four decades. Wigderson, the Herbert H. Maass Professor in the Institute for Advanced Study’s School of Mathematics, earned his Ph.D. from Princeton in 1983 in what was then the Department of Electrical Engineering and Computer Science.

Lila Rodgers won the Bede Liu Best Dissertation Award in Electrical and Computer Engineering. Rodgers’s work has “had a profound impact on quantum information science,” according to Andrew Houck, the Anthony H.P. Lee '79 P11 P14 Professor of Electrical and Computer Engineering. Rodgers focused mainly on understanding diamond surfaces to improve quantum sensors, according to her adviser Nathalie de Leon, associate professor of electrical and computer engineering. She also led a team that developed a new way to design superconducting quantum circuits for use in quantum computing, work that has "changed the shape of our field," according to Houck. Rodgers previously won the Porter Ogden Jacobus Fellowship, Princeton's highest honor for graduate students.

Ruiyi Shen won the Pramod Subramanyan *17 Early Career Graduate Award, recognizing his outstanding academic and research performance during the first two years of the graduate program. With his adviser Yasaman Ghasempour, assistant professor of electrical and computer engineering, Shen has already published three papers, including one at a top wireless communications conference, with three more papers under review. His work focuses on how high-frequency signals scatter when they interact with complex surfaces.

Anjali Premkumar, left, who earned her Ph.D. in the 2023–2024 academic year, celebrated the day by hugging her fellow graduates. Photo by Tori Repp/Fotobuddy

Graduate Degree Recipients for Academic Year 2023-2024

Eric Blow Adviser: Paul Prucnal Thesis: Microwave Photonic Interference Cancellation: RF Analysis, III-V and Silicon Integration, Development of Balanced and Hybrid Architectures

Jacob Bryon Adviser: Andrew Houck  Thesis: Exploring Fundamentals of Circuit Quantumelectrodynamics Using Fluxonium

Pengning Chao Adviser: Alejandro Rodriguez Thesis: Probing Fundamental Performance Limits in Photonics Design

Grigory Chirkov Adviser: David Wentzlaff  Thesis: Challenges and Opportunities in the Future Multi-Chiplet Architectures

Andres Correa Hernandez Adviser: Claire Gmachl  Thesis: Machine Learning for Quantum Cascade Laser Design and Optimization

Youssef Elasser Adviser: Minjie Chen Thesis: Hybrid Switched-Capacitor Circuits and magnetics Co-Design for Vertical Power Delivery

Christopher Lee Grimm Adviser: Naveen Verma Thesis: Training Deep Neural networks with In-Memory Computing

William Brady Gunnarsson Adviser: Barry Rand Thesis: Toward Injection Lasing in Halide Perovskite Semiconductors

Kai-Chieh Hsu Adviser: Jaime Fernández Fisac  Thesis: Scaling Systematic Safety for Learning-Enabled Robot Autonomy

Junnan Hu Adviser: Barry Rand Thesis: Understanding the Instability Associated with Perovskite Photovoltaics: Intrinsic, Processing and Operational

Paul Jackson Adviser: David Wentzlaff Thesis: Utilizing Subword Serialization and Parallelism to Design Efficient High-Performance Processors

Aditi Jha Adviser: Jonathan Pillow Thesis: Inferring Latent Factors and States Underlying Behavior and Neural Dynamics

Sara Kacmoli Adviser: Claire Gmachl Thesis: Quantum Cascade Ring Laser Systems

Stephan Dohyun Kim Adviser: Nai Phuan Ong Thesis: A Study on Interacting Condensates in Superconducting Topological Materials

Jinseok Lee Adviser: Naveen Verma Thesis: Design and Analysis of Energy-efficient and High-precision Capacitor-based SRAM Analog In-memory Computing Macros

Sulin Liu Adviser: Peter Ramadge and Ryan Adams Thesis: Scalable and Interpretable Learning with Probabilistic Models for Knowledge Discovery

Salim Ourari Adviser: Jeffrey Thompson Thesis: Telecom Band Quantum Memory in the Solid-State Towards Quantum Repeater Networks

Charles Link Patrick Adviser: Gerard Wysocki Thesis: Magneto-Optical Enhancement for Spectroscopic Methods: High-Finesse Cavity Spectroscopy

Anjali Premkumar Adviser: Andrew Houck Thesis: Hamiltonian and Materials Engineering for Superconducting Qubit Lifetime Enhancement

Lila Rodgers Adviser: Nathalie de Leon Thesis: Engineering Highly Coherent Qubits by Correlating Surface Spectroscopy with Quantum Measurement

Michael Soskind Adviser: Gerard Wysocki Thesis: Remote Spectroscopic Detection and 3D Localization of Trace-Gases

Prerit Terway Adviser: Niraj Jha Thesis: AI Framework for Improved System Design and Explainable Decisions

Pranav Thekke Madathil Adviser: Mansour Shayegan Thesis: Localization and Interaction in Ultra-High-Quality Two-Dimensional Electron Systems

Shikhar Tuli Adviser: Niraj Jha Thesis: Enhancing Deep Neural Networks in Diverse Resource-Constrained Hardware Settings

Yue Xing Adviser: Sharad Malik Thesis: Enabling SoC Verification through Instruction-Level Hardware Models

Zhixing Xu Adviser: Sharad Malik Thesis: Hardware-Supported Computer Security - Detection, Diagnosis and Defense

Yu Zeng Adviser: Sharad Malik Thesis: Automatic Generation of Hardware Abstractions from Register-Transfer Level (RTL) Designs

Weipeng Zhang Adviser: Paul Prucnal Thesis: Photonic Blind Interference Cancellation

Master of Science in Engineering

Haoyuan Cai Adviser: Jaime Fernández Fisac

Angela Mehta Adviser: Gerard Wysocki

Ronak Parikh Adviser: Maria Apostolaki

Hao Tang Adviser: Kaushik Sengupta

Andy Zhang Adviser: Tian-Ming Fu

Master of Engineering

Jacob Beyer Ariel Blumenberg Thomas Bosancic Edoardo Miguel Curto Correia Contente Benjamin Finch Alkin Kaz Vikash Modi Meet Patel Isabella Racioppi Dylan Tran Jae Yoon

• B.S. Students
• M.S. Students
• Ph.D. Students
• D-Clearance
• Directed Research
• Information for Graders and Course Producers
• Microsoft Imagine
• CS Student Organizations
• CS Library Guide
• CS Job Announcements

[UG/MS/PhD] ACE Lab Research Study Recruitment

The following announcement is from Xinyi Zhou ([email protected]). Please contact them directly if you have any questions.

Hi everyone!

We at the Adaptive Computing Experiences (ACE) Lab at the University of Southern California are conducting a research study to investigate biases in Human-AI Software Development Teams. We would love to hear your thoughts and experiences!

I am recruiting individuals who meet the following criteria for the study:

• 18 years old or above
• Reside in the United States
• English Speaker
• Programming experience of at least three years for students
• Regularly uses Large Language Model (LLM) tools for software development

If you decide to participate in this study, you will be asked to do the following activities:

• Complete a quick online survey.
• Participate in a 1:1 online session for 60 minutes.
• Participate in a 15-20-minute 1:1  interview after the online session.

During these activities, you will be asked questions about:

• Demographic details include age, occupation, role, years of experience, etc.
• Exploration of Cognitive Biases Related Behaviors and Their Impacts

Participants in this study will receive a $40 reward after the final interview.

If you are interested in participating in this study, please click this link to fill out our survey. If you have questions, please contact me at [email protected].

Published on June 4th, 2024

Last updated on June 4th, 2024

• CS Announcements
• Job/Research Opportunities
• Undergraduate
• Chair’s Welcome
• Awards and Honors
• CS@SC Institutes
• Media Coverage
• Newsletters and Fact Sheets
• CS Industry Affiliate Program
• Bekey Lecture
• Driving Directions
• Open Staff Positions
• Open Faculty Positions
• Centers and Institutes
• Research Areas and Labs
• Technical Reports
• Annual Research Review
• Undergraduate Research Experiences
• Faculty Directory
• Staff Directory
• Getting Started with CS@USC
• B.S. Program
• M.S. Program
• Ph.D. Program
• Data Science Program
• Graduate Certificate
• Distance Education
• K-12 Outreach
• Academic Advisement
• B.S. Application Information
• M.S. Application Information
• Ph.D. Application Information

University of Florida

Computer and Information Science and Engineering

CAI 6108 Machine Learning Engineering 3 Credits

Grading Scheme: Letter Grade

Covers foundational machine learning concepts with an emphasis on applying these concepts on real-world data through programming exercises and assignments using the relevant industry-standard Python tools, libraries, and frameworks.

Prerequisite: Knowledge of programming fundamentals. Experience with Python is a plus but not required.

Catalog Program Pages Referencing CAI 6108

CAI 6307 Natural Language Processing 3 Credits

Covers concepts in natural language processing ranging from shallow bag-of words to richer representations and formalisms, for applications such as translation, generation, extraction, summarization, and dialogue. Classic and state-of-the-art techniques and remaining challenges are discussed, as well as recent proposals for meeting those challenges (both symbolic and machine learning approaches). Intended for graduate students doing research related to natural language processing.

Prerequisite: Proficiency in programming (Python recommended) & familiarity with introductory machine learning or artificial intelligence is a plus.

Catalog Program Pages Referencing CAI 6307

CAI 6886 Project in Artificial Intelligence Systems 3 Credits

Using concepts learned in prerequisite courses, including AI ethics, machine learning, and the Artificial Intelligence Systems course, students will individually or as a team identify AI systems problems, formulate solutions, and apply AI Systems knowledge in the context of a real-world project. Project requirements include preparing a plan, technical final report, delivering an oral presentation, and creating a software repository.

Prerequisite: ( LAW 6930 & EGN 5216 ) AND ( CAP 6615 or EGN 6615) AND ( CAP 5416 or EEE 6512 or EEL 5406 ) AND (CAP 6XXX or EEE 6561 or EEL 5793).

Catalog Program Pages Referencing CAI 6886

CAP 5100 Human-Computer Interaction 3 Credits

Topics related to interaction with technology, including interface design, software tools, 3-D interaction, virtual environments, interaction devices, collaboration, and visualization.

Prerequisite: COP 3530 , and any one programming course ( COP 2800 , COP 3275 , or COP 3229 ).

Catalog Program Pages Referencing CAP 5100

CAP 5108 Research Methods for Human-Centered Computing 3 Credits

Introduces the fundamental methods and techniques to evaluate technologies and collect data from humans, including experimental design, types of variables, types of errors, hypothesis testing, survey design, behavioral and psychophysical methods.

Prerequisite: STA 3032 , COT 3100 , COP 3530 , or equivalent.

Catalog Program Pages Referencing CAP 5108

CAP 5404 Deep Learning for Computer Graphics 3 Credits, Max 6 Credits

Covers fundamental theory and application of popular artificial intelligence (AI) algorithms in computer graphics. Introduces several neural network architectures and the mathematical principles behind them. A semester-long project motivated by research publications teaches technical writing and graphics processing unit (GPU) programming on a GPU cluster. Convolutional neural networks for denoising movies and generative adversarial networks for animation are project examples.

Prerequisite: Proficiency in a programming Language (Python and/or C++ recommended), Data Structures and Algorithms, Linear Algebra, and Calculus.CAP5404

Catalog Program Pages Referencing CAP 5404

CAP 5416 Computer Vision 3 Credits

Introduction to image formation and analysis. Monocular imaging system projections, camera model calibration, and binocular imaging. Low-level vision techniques, segmentation and representation techniques, and high-level vision.

Prerequisite: PrerequisitesMAC 2312 or equivalent, COT 4501 or equivalent and Proficiency in MATLAB or C++ or Java. Course instructor will determine equivalency.

Catalog Program Pages Referencing CAP 5416

CAP 5510 Bioinformatics 3 Credits

Basic concepts of molecular biology and computer science. Sequence comparison and assembly, physical mapping of DNA, phylogenetic trees, genome rearrangements, gene identification, biomolecular cryptology, and molecular structure prediction.

Prerequisite: CIS 3020 or equivalent.

Catalog Program Pages Referencing CAP 5510

CAP 5705 Computer Graphics 3 Credits

Display device characteristics; system considerations, display algorithms. Curve and surface generation. Lighting models and image rendering.

Prerequisite: COP 3530 .

Catalog Program Pages Referencing CAP 5705

CAP 5771 Introduction to Data Science 3 Credits

Introducing the basics of data science including programming for data analytics, file management, relational databases, classification, clustering and regression. The foundation is laid for big data applications ranging from social networks to medical and business informatics.

Prerequisite: COP 3530 Data Structures and Algorithms or equivalent.

Catalog Program Pages Referencing CAP 5771

CAP 5841 Modeling and Computing with Geometry 3 Credits

Introduction to modeling and shaping curved smooth geometry and computing on the geometry. Topics include curves, surfaces and volumetric representations. The course leverages numerical computing techniques and 3D computer graphics programming. The course combines lecture and seminar elements: towards the end of the course, students give presentations of classic (or by mutual consent recent) literature on Modeling and Computing with Geometry.

Prerequisite: Calculus in several variables, Programming in Matlab or OpenGL.

Catalog Program Pages Referencing CAP 5841

CAP 6137 Malware Reverse Engineering 3 Credits

Introducing the theory and practice of software reverse engineering applied to analysis of malicious software (malware). Students learn techniques of static and dynamic analysis to help identify the behavior of programs presented without documentation or source code and to identify possible remediation and avoidance techniques.

Prerequisite: CDA 3101 ;

Corequisite: COP 5615  or consent of instructor.

Catalog Program Pages Referencing CAP 6137

CAP 6516 Medical Image Analysis 3 Credits

Image formation, reconstruction mathematics (Fourier slice theorem, Abel, Hankel and Radon transforms), PDE-based denoising and segmentation, multidimensional clustering algorithms, iso-surface extraction, basic differential geometry of curves and surfaces, multidimensional splines, active 2D/3D models, image matching/registration with application to multimodal co-registration.

Prerequisite: expertise in image proc./comp. vision, proficiency in C language or MATLAB.

Catalog Program Pages Referencing CAP 6516

CAP 6610 Machine Learning 3 Credits

Concepts in developing computer programs that learn and improve with experience. Emphasis on methods based on probability, statistics, and optimization.

Prerequisite: Mathematics for Intelligent Systems.

Catalog Program Pages Referencing CAP 6610

CAP 6615 Neural Networks for Computing 3 Credits

Neural network models and algorithms. Adaptive behavior, associative learning, competitive dynamics and biological mechanisms. Applications include computer vision, cognitive information processing, control, and signal analysis.

Prerequisite: CAP 5635.

Catalog Program Pages Referencing CAP 6615

CAP 6617 Advanced Machine Learning 3 Credits

Advanced concepts in developing computer programs that learn and improve with experience. Emphasis on methods based on probability, statistics, and optimization.

Prerequisite: CAP 6610 .

Catalog Program Pages Referencing CAP 6617

CAP 6701 Advanced Computer Graphics 3 Credits

Curved surface representations, representation and visualization of higher-dimensional fields, advanced rendering, collision detection and collision response, and scene navigation in context of high-level graphics environments.

Prerequisite: CAP 4730 or CAP 5705 or consent of instructor.

Catalog Program Pages Referencing CAP 6701

CAP 6769 Advanced Topics in Data Science 3 Credits

Advanced topics in data science such as relational databases and parallel and distributed processing in the cloud, tree-based classifiers and support vector machines, dimensionality reduction and theories of visualization.

Prerequisite: Graduate standing, CAP 5771

Catalog Program Pages Referencing CAP 6769

CAP 6779 Projects in Data Science 3 Credits

Advanced topics in data science, individual projects in application areas such as vision, natural language processing, computational fluid dynamics, social networks, bioinformatics, etc.

Prerequisite: Graduate standing and CAP 5571.

Catalog Program Pages Referencing CAP 6779

CDA 5155 Computer Architecture Principles 3 Credits

Fundamental design issues of processor and computer architecture, a variety of design approaches for CPU, memory, and system structure.

Prerequisite: CDA 3101 , COP 3530 , and COP 4600 .

Catalog Program Pages Referencing CDA 5155

CDA 5636 Embedded Systems 3 Credits

Design of efficient and trustworthy embedded and cyber-physical systems consisting of hardware, software, firmware, sensors and actuators. It covers fundamental issues related to modeling and specification, design space exploration, hardware-software partitioning, synthesis and compilation, real-time operating systems, and application-specific optimizations targeting area, power, performance, temperature, energy, and security.

Prerequisite: Computer Organization.

Catalog Program Pages Referencing CDA 5636

CDA 6325C Cyber-physical System Security 3 Credits

Covers foundational concepts of cyber-physical system security. In particular, hardware and software threats and mitigation strategies of integrating sensing and actuation, AI computation, infrastructure control, and networking. Students will analyze research papers, write technical essays, present security research problems, conduct hands-on testing, and learn the challenges of building secure systems.

Prerequisite: Basic proficiency with programming is required (e.g., Python, C++).

CEN 5035 Software Engineering 3 Credits

Topics in projects organization, specification techniques, reliability measurement, documentation.

Prerequisite: COP 3504 and COT 3100 .

Catalog Program Pages Referencing CEN 5035

CEN 5726 Natural User Interaction 3 Credits

Introducing design, development, and evaluation of Natural User Interaction (NUI) technologies (e.g., non-keyboard and mouse technologies, such as touchscreen interaction, gesture interaction, speech interaction, etc.). Key concepts include hardware-to-software NUI pipeline and considerations in NUI software development (including existing platforms, toolkits, and APIs used to create NUI software).

Prerequisite: COP 3530 (C)

Catalog Program Pages Referencing CEN 5726

CEN 5728 User Experience Design 3 Credits

Introduces methods and tools used in User Experience Design (UXD): the early stages of software design focused on meeting user needs. Key concepts include user research, contextual design, design thinking, ideation, iterative design, prototyping, and design documentation. Software tools used in industry are used in class projects.

Prerequisite: COP 3530 or equivalent.

Catalog Program Pages Referencing CEN 5728

CEN 5735 Human-Centered Input Recognition Algorithms 3 Credits

Human-centered methods for the design and evaluation of intelligent algorithms for recognizing user input in advanced modalities such as gesture, handwriting, speech, and more. Algorithms and modalities vary; students will implement and extend an existing algorithm, evaluating it on user input data students will collect from real people.

Catalog Program Pages Referencing CEN 5735

CEN 6070 Software Testing and Verification 3 Credits

Concepts, principles, and methods for software testing and verification. Topics include human and machine-based testing strategies, formal proofs of correctness, and software reliability.

Prerequisite: CEN 5035 .

Catalog Program Pages Referencing CEN 6070

CEN 6075 Software Specification 3 Credits

Concepts, principles, and methods for practical specification. System modeling, requirements exploration, validation and prototyping, and documentation techniques.

Catalog Program Pages Referencing CEN 6075

CIS 5209 Penetration Testing -- Ethical Hacking 3 Credits

Introduction to the principles and techniques associated with the cybersecurity practice known as penetration testing or ethical hacking. The course covers planning, reconnaissance, scanning, exploitation, post-exploitation, and result reporting. The student discovers how system vulnerabilities can be exploited and learns to avoid such problems.

Catalog Program Pages Referencing CIS 5209

CIS 5370 Computer and Information Security 3 Credits

Covers systematic threat and risk assessment; programmed threats and controls in hardware, software, and human procedures; security policies, models, and mechanisms; theoretical limitations and practical implementations; certification and accreditation standards; and case study reviews. Coursework includes a significant term project.

Prerequisite: COP 4600 Operating Systems or equivalent

Catalog Program Pages Referencing CIS 5370

CIS 5371 Introduction to Cryptology 3 Credits

Introducing classical and modern cryptography and cryptanalysis, including symmetric and asymmetric (public key) ciphers. It covers cryptographic hash functions, block and stream ciphers, as well as differential and linear cryptanalysis. It reviews BAN logic, applications of cryptography, cryptographic standards and protocols, and analyzes case studies of failed implementations.

Prerequisite: COT 3100 Applications of Discrete Structures or equivalent ;

Corequisite:   COT 5405  Analysis of Algorithms or equivalent

Catalog Program Pages Referencing CIS 5371

CIS 6261 Trustworthy Machine Learning 3 Credits

Explores research at the intersection of machine learning and security and privacy. Topics include: adversarial machine learning; differential privacy; membership inference; fairness transparency; explainable/interpretable machine learning; deepfakes and disinformation.

Prerequisite: Knowledge of programming fundamentals, familiarity with machine learning and Python is a plus.

Catalog Program Pages Referencing CIS 6261

CIS 6307 Internet Data Streaming 3 Credits

Fundamental concepts, data structures and algorithms about extracting information from packet streams on the Internet in real time, with applications in network security, traffic engineering, e-commerce, and big data analytics

Prerequisite: Data Structures and Algorithms.

Catalog Program Pages Referencing CIS 6307

CIS 6905 Individual Study 1-3 Credits, Max 6 Credits

Individual Study

Prerequisite: consent of faculty member supervising the study.

Catalog Program Pages Referencing CIS 6905

CIS 6910 Supervised Research 1-5 Credits, Max 5 Credits

Grading Scheme: S/U

Supervised Research

Prerequisite: graduate status in CIS.

Catalog Program Pages Referencing CIS 6910

CIS 6930 Special Topics in CIS 3 Credits, Max 9 Credits

Special Topics in CIS

Prerequisite: vary depending on topics.

Catalog Program Pages Referencing CIS 6930

CIS 6935 Graduate Seminar 1-12 Credits, Max 12 Credits

Presentations by visiting researchers, faculty members, and graduate students.

Catalog Program Pages Referencing CIS 6935

CIS 6940 Supervised Teaching 3 Credits, Max 5 Credits

A supervised teaching experience.

Catalog Program Pages Referencing CIS 6940

CIS 6971 Research for Master's Thesis 1-15 Credits

Research for Master's Thesis

Catalog Program Pages Referencing CIS 6971

CIS 7979 Advanced Research 1-12 Credits

Research for doctoral students before admission to candidacy. Designed for students with a master's degree in the field of study or for students who have been accepted for a doctoral program. Not appropriate for students who have been admitted to candidacy.

Catalog Program Pages Referencing CIS 7979

CIS 7980 Research for Doctoral Dissertation 1-15 Credits

Research for Doctoral Dissertation

Catalog Program Pages Referencing CIS 7980

CNT 5106C Computer Networks 3 Credits

Design, implementation, and internals of networks. Routing, congestion control, internetworking, TCP/IP, optimization, and proxy services.

CNT 5410 Computer and Network Security 3 Credits

Issues, analysis, and solutions. Viruses, worms, logic bombs, network attacks, covert channels, steganography, cryptology, authentication, digital signatures, electronic commerce.

Prerequisite: COP 3530 , COT 5405 . ;

Corequisite: COP 4600 .

Catalog Program Pages Referencing CNT 5410

CNT 5517 Mobile Computing 3 Credits

Emerging topics of wireless and mobile computing and networking including mobile computing models, mobile-IP, adhoc networks, Bluetooth, and 802.11b. Mobile database access and mobile transactions in context of emerging field of M-commerce.

Prerequisite: CNT 4007C .

Catalog Program Pages Referencing CNT 5517

CNT 6107 Advanced Computer Networks 3 Credits

Computer network architecture, including topologies, media, switching, routing, congestion control, protocols, and case studies.

Prerequisite: COP 5615  , COP 5536  , and CNT 5106C

Catalog Program Pages Referencing CNT 6107

CNT 6530 Mobile Networking 3 Credits

Concepts of emerging mobile networks architecture, systematic analysis of effects of mobility on network performance, synthetic and data-driven mobility modeling and simulation, behavior analysis in mobile networks, mobile service and application structure, development, implementation, and evaluation. Topics include architecture, geographic routing and query resolution in ad hoc networks, sensor networks, Internet of Things, and vehicular networks.

Prerequisite: COP 3502c or COP 3503c.

Catalog Program Pages Referencing CNT 6530

CNT 6885 Distributed Multimedia Systems 3 Credits

Design issues; survey of recent advances, including compression, networking, and operating system issues.

Catalog Program Pages Referencing CNT 6885

COP 5536 Advanced Data Structures 3 Credits

Development of efficient data structures used to obtain more efficient solutions to classical problems, such as those based on graph theoretical models, as well as problems that arise in application areas of contemporary interest.

Catalog Program Pages Referencing COP 5536

COP 5556 Programming Language Principles 3 Credits

History of programming languages, formal models for specifying languages, design goals, run-time structures, and implementation techniques, along with survey of principal programming language paradigms.

Catalog Program Pages Referencing COP 5556

COP 5615 Distributed Operating System Principles 3 Credits

Concepts and techniques for efficient management of computer system resources.

Prerequisite: COP 4600 .

Catalog Program Pages Referencing COP 5615

COP 5618 Concurrent Programming 3 Credits

Overview of principles and programming techniques. Reasoning about concurrency, synchronization, program structuring, multi-threaded server applications.

Prerequisite: COP 3100, 3530 .

Catalog Program Pages Referencing COP 5618

COP 5725 Database Management Systems 3 Credits

Introduction to systems and procedures for managing large computerized databases.

Prerequisite: COP 3530 , 4600 , or equivalent.

Catalog Program Pages Referencing COP 5725

COP 6726 Database System Implementation 3 Credits

DBMS architecture, query processing and optimization, transaction processing, index structures, parallel query processing, object-oriented and object-relational databases, and related topics.

Prerequisite: COP 4600 and 4720 or COP 5725 .

Catalog Program Pages Referencing COP 6726

COT 5405 Analysis of Algorithms 3 Credits

Introduction and illustration of basic techniques for designing efficient algorithms and analyzing algorithm complexity.

Catalog Program Pages Referencing COT 5405

COT 5442 Approximation Algorithms 3 Credits

Fundamentals of algorithmic paradigms, analysis, techniques, and software. Topics include greedy methods, randomized algorithms, lP-rounding, approximability, covering, packing, clustering, and network problems.

Prerequisite: COP 3530 or COT 5405

Catalog Program Pages Referencing COT 5442

COT 5520 Computational Geometry 3 Credits

Design, analysis, and implementation of algorithms and date structures to solve geometric problems. Applications in graphics, robotics, computational biology, data mining, and scientific computing. Convex hulls, Voronoi diagrams, triangulations, arrangements, and range searching.

Catalog Program Pages Referencing COT 5520

COT 5615 Mathematics for Intelligent Systems 3 Credits

Mathematical methods commonly used to develop algorithms for computer systems that exhibit intelligent behavior.

Prerequisite: MAC 2313 , Multivariate Calculus; MAS 3114 or MAS 4105 , Linear Algebra; STA 4321 , Mathematical Statistics.

Catalog Program Pages Referencing COT 5615

COT 6315 Formal Languages and Computation Theory 3 Credits

Introduction to theoretical computer science including formal languages, automata theory, Turing machines, and computability.

Prerequisite: COP 3530 and familiarity with discrete mathematics and data structures.

Catalog Program Pages Referencing COT 6315

EGN 5949 Practicum/Internship/Cooperative Work Experience 1-6 Credits, Max 6 Credits

Practical cooperative engineering work under approved industrial and faculty supervision.

Prerequisite: graduate student.

Catalog Program Pages Referencing EGN 5949

EGN 6913 Engineering Graduate Research 0-3 Credits, Max 12 Credits

Course will provide the student with supervised research in a laboratory setting.

Catalog Program Pages Referencing EGN 6913

IDC 5715 Virtual Reality for the Social Good 3 Credits

A multidisciplinary approach to solving pressing social problems by blending social science practices with innovative technology. Students will explore effective messaging perspectives, virtual social spaces, and virtual reality technologies to create a compelling story for a social good issue. This class is for all students, regardless of major/prior experience.

Catalog Program Pages Referencing IDC 5715

Print options.

Print this page.

The PDF will include all information unique to this page.

This PDF includes all graduate catalog information.

Best Online Master’s in Data Science Programs for 2024

Exponential growth in data has translated into a demand for data scientists that outpaces how fast universities can train them. But what are the best options if you’re looking to break into data science and don’t have time for in-person classes? To answer that question, Fortune built our second ranking of online data science graduate programs. This ranking was last updated January 2023.

Berkeley’s Data Science Master’s

Johns Hopkins Engineering for Professionals Online MSE in Data Science

1. University of Southern California

• ACCEPTANCE RATE
• AVERAGE NUMBER OF YEARS OF WORK EXPERIENCE
• COST PER CREDIT
• GRE REQUIRED

2. University of California—Berkeley

3. Bay Path University

Learn Applied Data Science from UNC-Chapel Hill

4. New Jersey Institute of Technology

5. Clemson University

6. Illinois Institute of Technology

7. Oklahoma State University

8. Texas Tech University

Maryville University Master of Science in Data Science | Online

9. University of Missouri—Columbia

10. University of California–Los Angeles

11. DePaul University

12. University of California–Riverside

13. Worcester Polytechnic Institute

Syracuse University MS in Applied Data Science Online

14. Lewis University

15. University of Illinois at Urbana—Champaign

16. CUNY School of Professional Studies

17. University of Michigan–Dearborn

18. Regis University

19. Rice University

20. Eastern University

21. Syracuse University

22. Stevens Institute of Technology

23. pace university.

What is a master’s in data science and what will you learn in an online program?

Pursuing a master’s degree in the fast-growing field of data science can help you to advance your career in a wide variety of tech-related roles. Expect to learn a broad set of skills, including how to use computer programming languages and about applied statistics, database systems, and machine learning. The skills and concepts you learn in a master’s degree program will prepare you for a career in data science to help organizations make strategic decisions based on the data they collect. There’s no significant difference between online and on-campus data science programs—schools typically offer the same courses that are taught by the same professors, regardless of the format. 

General curriculum and skills taught

You can expect a comprehensive curriculum in an online master’s degree program in data science that draws on both statistical and computational methods. Programs will emphasize the real-world application of these knowledge and skills, while offering a multidisciplinary approach to the field that also draws on statistics, computer science, and law. Data science is about more than numbers, however; you will also learn “soft skills” about how to effectively communicate the lessons learned and collaborate with others to learn how to best utilize information in an ethical way . Core coursework at many data science programs covers the following topics:

• Machine learning
• Data mining
• Data visualization
• Cloud computing
• Research design
• Information ethics
• Statistical analysis
• Data engineering

Project-based learning

Beyond the core and advanced-level coursework that are common among all data science programs, some schools also offer mandatory or optional project-based learning opportunities. These projects focus on the real-world application of the skills learned in the program, and can be an opportunity for students to display the skills learned during a program to potential employers. The master’s degree programs at both the University of California-Berkeley and Bay Path University , for example, both include a culminating capstone project that draws upon the skills learned throughout the course of the program. Such projects may extend the length of a master’s degree program, however. 

Specializations and concentrations

While the core coursework required for completing a master’s degree in data science is intentionally comprehensive, many programs offer specializations or concentrations so students can carve out a niche within this field. The University of Illinois at Urbana-Champaign offers advanced coursework in cloud computing and scientific visualization, while Texas Tech University has advanced coursework in multivariate analysis and project management. Concentration options may include:

• Business analytics
• Artificial intelligence

Admissions information

While admissions requirements can vary by school, graduate degree programs require the following of aspiring data scientists :

• Successful completion of a bachelor’s degree, as demonstrated by an official transcript from a college or university
• If you don’t have an undergraduate degree in a data-related field (like computer science), you may need to demonstrate that you have sufficient work and educational experience in fundamental concepts on your résumé
• You may also use your personal statement or essay to highlight your unique characteristics and goal for the program
• Letters of recommendation from supervisors, professors or alumni of the program
• Many top-ranked data science programs no longer require you to submit GMAT or GRE scores, though you may need to if you don’t meet minimum undergraduate GPA requirements
• Some master’s degree programs in data science, like the University of Illinois Urbana-Champaign, may require applicants to complete a data proficiency exam

GMAT, GRE & GPA

A majority of online master’s degree programs in data science have waived GRE or GMAT score requirements and, in fact, only two schools on Fortune’s ranking still require applicants to submit scores as part of that application process. That said, you may submit this information particularly if you want to provide additional supporting information that’s helpful in the admissions process. Moreover, GPA requirements also vary by school and may be waived with sufficient work experience.

Which factors drive acceptance?

While admissions officers strive to take a holistic approach when evaluating candidates, they will be particularly interested in your educational background and work experience in a data-related field. Applicants to some data science programs, like the University of Wisconsin-Madison and the University of Connecticut , must show they’ve completed particular quantitative college-level coursework, while other programs like Syracuse University place a greater emphasis on the personal essay and what applicants emphasize they’re looking for in the program, why they chose it, and what their goals are.

The online master’s in data science experience: What is it like to study online?

Online learning has been growing in popularity in recent years, and students considering a master’s degree program in data science can often choose between an in-person or online option within the same school. Data science programs may offer a mix of both synchronous and asynchronous learning, meaning courses that either need to be attended live at a particular time or at the student’s convenience, and could include some limited in-person elements.

For the most part, students can expect to participate in class discussions via video conferencing or using other technology. And because of the online format, many students who pursue a master’s degree in data science are working while attending school with a goal of either switching careers or advancing their current career in data science.

How to choose the best online master’s degree program in data science for you: Factors to consider beyond rankings

Fortune’s ranking of online master’s degree programs in data science is a good starting place when comparing various programs. We emphasize selectivity (schools with top-notch faculty that attract some of the brightest students) and demand (based on the size of the student body), since the people you meet in graduate school could be transformative to your future career.

That said, prospective students should also consider how a particular program will help you achieve your goals and advance in the field of data science. Other factors that may be important include cost, a school’s prestige, its curriculum, and the years of work experience schools may require of applicants.

Start times, schedule, and program length

As data science programs have grown in popularity, schools have beefed up the number of start dates they offer. The University of Illinois and UC Berkeley, the No. 1 and No. 2 ranked programs, both offer three start dates throughout the year. Students may have some flexibility in choosing their schedule and how long it takes to complete the program of their choice, though two years is common.

As indicated, some data science programs include project-based learning opportunities that focus on the real-world application of skills taught in the program. Because these projects can be useful to show potential employers, career switchers may want to consider prioritizing schools with project-based learning opportunities—even if they could extend the program’s length.

Concentrations

As you think about your career goals post-graduation, you should also consider the concentrations offered by various data science programs. By carving out a specialty within data science, that may make you a more attractive job candidate for some employers—and it could increase your earning potential. People with the title of “data scientist” can earn up to $170,000, while manager-level professionals in the field could fetch salaries of as much as $250,000. 

The cost of a data science program is undoubtedly a factor to consider when applying to school—and tuition varies widely. Students may be able to pay one-year tuition of about $20,000 (or less) at schools like the University of Illinois Urbana-Champaign, Loyola University Maryland, the University of Missouri-Columbia, and CUNY School of Professional Studies. That said, the cost of tuition exceeds $50,000 at UC Berkeley, Syracuse University, and the University of Denver.

Network and access to alumni

The more students a data science program has, the larger its alumni network. This is important to consider during your selection process, not only because your cohort can be a defining characteristic of your grad school experience even if you’re attending classes online. What’s more, the network and a school’s ability to connect you with alumni may help you when looking for jobs—and particularly if you’re not already working in the field.

Years of work experience

Because many data science programs are seeking out applicants who already have relevant work experience, it may be useful to see how your experience compares. What’s more, the amount of work experience will inherently influence how advanced your fellow students are in their careers. Worcester Polytechnic Institute reports that students have an average of 8 years of work experience, while roughly half of the master’s degree students in New York University’s program enroll straight out of undergrad.

Careers for master’s in data science graduates

There’s a hot job market for data scientists thanks to robust demand—and that means many graduates of master’s degree programs are fielding multiple, six-digit salary offers. Big tech companies are a likely career path for many data scientists. A survey of more than 11,000 data scientists found that the companies with the largest teams of data scientists are Microsoft, Facebook, and IBM. And Apple, for example, pays as much as $182,000 for data scientists.

Financing and scholarships

If your goal of obtaining a master’s degree in data science is to advance within your current company, then your employer may help pay for the cost of the program. New York University grants tuition scholarships to some master’s degree students, while UC Berkeley offers several fellowships of varying amounts. 

You may also want to seek out a growing number of scholarship or fellowship opportunities from private organizations. Some examples that are available to master’s degree students include:

• The Association of Computing Machinery (ACM) awards computational and data science fellowships to diverse candidates with a $15,000 annual stipend. 
• Acxiom awards $5,000 scholarships to U.S.-based students from diverse backgrounds who are enrolled full-time in various programs that include data science.
• Although it doesn’t specify the amount, the American Statistical Association (ASA) offers a pride scholarship to students enrolled in a data science graduate program and identify as LGBTQ+ or an ally.

Finally, current members of the military or veterans may want to consider covering the cost of your data science program with Post-9/11 GI Bill benefits or the Yellow Ribbon Program , which can cover any tuition and fees not covered by those benefits.

Frequently Asked Questions

While still relatively new, data science is a field that incorporates preparing and analyzing data to draw conclusions. Data scientists design and build new processes for data modeling by using algorithms, prototypes, predictive models, and custom analysis. People should pursue data science if they’re interested in asking questions and creating algorithms and statistical models to estimate the unknown. 

All of the data in the world is projected to grow to a staggering 181 zettabytes by 2025. And this growth has translated into high demand for data scientists—even outpacing the speed with which colleges and universities can train them. Data scientist ranks No. 3 among the 50 best occupations in the U.S., according to Glassdoor’s list of the best jobs for 2022 , and was beat out only by the roles of enterprise architect and full stack engineer.

Some people may choose to follow a step-by-step guide to become a data scientist. First, you may want to pursue an undergraduate degree that focuses on technical skills like programming or statistics. Then, you should identify an area of specialization and hone this specialization by enrolling in a master’s degree program in data science. Finally, you should showcase your data science experience when applying for jobs.

In addition to high demand, people with a master’s degree in data science can expect to enter a rapidly-growing field with solid salary prospects. Through 2026, the U.S. Bureau of Labor Statistics (BLS) projects data science jobs will grow by 28% per year . Even before graduation, some data science students in master’s degree programs are fielding offers of $125,000 and up .

As with any career, pay prospects can vary by company and role. Data scientists made a median salary of $164,500 in 2020, according to a 2021 survey of engineering professionals by the Institute of Electrical and Electronics Engineers (IEEE).

The median base salary for data scientists is $120,000, according to figures from Glassdoor, though the likely range for positions goes as high as $294,000. Some tech companies are even paying in excess of $300,000 for senior-level data scientist roles.

The sky’s the limit for job opportunities for data scientists, including careers in tech, entertainment, pharmaceuticals, telecom, sports, consulting, or even as a company executive who understands data. What’s more, new job titles are likely to be created, particularly related to ethical concerns with sensitive data and as companies look for new ways to utilize their massive data sets and emerging technologies such as cloud computing, A.I., and machine learning.

In 2012, Harvard Business Review called the role of a data scientist “ the sexiest job of the 21st century .” Ten years later, data science remains a good career field for many people thanks to the wide range of jobs available now and in the future, along with robust demand and six-figure salary prospects.

The class of 2022 from master’s degree programs in data science were fielding job offers, with competitive salaries, months ahead of graduation. Demand for data scientists is growing faster than colleges and universities can train them. Even so, job applicants should still expect a rigorous interview process that often entails showcasing examples of work or a commitment to staying up-to-date in a rapidly changing industry.

Computer Science & Engineering

UC SAN DIEGO CSE FELLOWS PROGRAM

Program overview.

The CSE Fellows Program is intended to support exceptional postdoctoral researchers in computer science. The program seeks to recruit 1-3 fellows a year for a two year postdoctoral appointment working alongside a UCSD CSE faculty mentor.

Fellows will be joining a vibrant CS research community and will have access to the broader UCSD research enterprise, including the Halicioglu Data Science Institute, San Diego SuperComputer, the Design Lab, and UCSD Health. The goal of this program is to enhance the diversity of research at UCSD CSE, while also preparing the Fellows for careers in academia or industry, and to offer opportunities for student mentorship, grant writing, and collaboration across disciplines.

CSE FELLOW PROGRAM

We are not accepting applications at this time. Please check back for program updates. Thank you!

2021 CSE FELLOWS

Eleonore Ferrier

Faculty Mentor: Nadir Weibel PhD:   Sorbonne Université, Paris, France

Ferrier’s research areas are surgical education, human learning and pedagogy. During this fellowship, she hopes to create a plan for 3D-surgical learning videos that provides trainers with pedagogical guidelines and specific technologies to capture surgeries in 3D while providing learners with a 3D interactive and immersive surgical experience to learn from past surgeries using Virtual Reality (VR). 

Noah Fleming

Faculty Mentors:   Russell Impagliazzo and Samuel Buss PhD: University of Toronto

Fleming hopes to develop deeper connections between the size required of proofs in certain fragments of logic and the complexity of solving certain total search problems -- computational problems guaranteed to always have a solution. These connections have underpinned recent advances in the areas of circuit and proof complexity, and a deeper understanding of such connections will hopefully lead to further advancements.

Following his appointment as a CSE Fellow, Noah accepted an Assistant Professor position at Memorial University.

Francesco Restuccia

Faculty Mentors: Sicun Gao, Nadia Polikarpova, Ryan Kastner PhD: Sant'Anna School of Advanced Studies Pisa (Italy)

Restuccia will work on advancing the security and safety of modern system-on-chip (SoC) platforms for the requirements of modern critical applications, such as autonomous vehicles, avionics, and space applications. A specific focus will be on Deep Neural Networks (DNN) hardware acceleration on FPGA SoCs.

2020 CSE Fellows

Samira Mirbagher Ajorpaz

Faculty Mentor: Dean Tullsen Ph.D., Texas A&M

Mirbagher Ajorpaz studies computer architecture, machine learning and computer system security. She works on designing prediction units into processors to increase efficiency; exploring machine learning technologies to fit into microprocessors’ small scale and tight timing margins; and developing more secure processor designs. In winter 2021, Ajorpaz is teaching CSE 240C, Advanced Microarchitecture.

Following her appointment as a CSE Fellow, Samira accepted an Assistant Professor position at North Carolina State University.

Faculty Mentor: Scott Klemmer Ph.D., Stanford University

Jane E studies the intersection between human-computer interaction, computer graphics and photography. Creative endeavors can be intimidating and sometimes it’s hard to get expert advice. The solution may be building coaching directly into the creative tool. These embedded insights could help photographers understand lighting, composition, etc. During her thesis work, she focused on designing camera guidance tools that could help novice photographers assess their artistic choices. Now, she wants to extend this coaching to other creative areas.

Faculty Mentors: Geoff Voelker and Stefan Savage Ph.D. candidate, UC Berkeley

Grant Ho studies computer security with a special focus on the intersection between data and security. He develops algorithms, systems and empirical insights to help organizations thwart sophisticated attacks. At CSE, he will study new defenses against targeted enterprise attacks, which have generated billions in losses, developing approaches that identify and mitigate attacks to make organizations more resilient and secure.

Following his appointment as a CSE Fellow, Grant accepted an Assistant Professor position at the University of Chicago.

Daniel Moghimi

Faculty Mentors: Deian Stefan and Nadia Heninger Ph.D.: Worcester Polytechnic Institute

Daniel Moghimi studies computer security, focusing on side-channel cryptanalysis, microarchitectural security and hardware-based trusted computing. At CSE, he will leverage algorithmic approaches and compiler-based techniques to build automated analysis tools and architectural security primitives, automatically testing the security of trusted applications and defining new execution models to support data privacy.

Following his appointment as a CSE Fellow, Daniel accepted an Assistant Professor position at UT Austin.