Combinational techniques: minimization, multiple output networks, state identification and fault detection, hazards, testability and design for test are examined. This course presents background in power and oppression to help predict how new technological and societal systems might interact and when they might confront or reinforce existing power systems. You signed in with another tab or window. Professor of Computer Science PhD, Harvard University Network security, blockchains, medical systems security, industrial systems security, wireless networks, unmanned aircraft systems, internet of things, telecommunications networks, traffic management, Tao Ju PhD, Rice University Computer graphics, visualization, mesh processing, medical imaging and modeling, Chenyang Lu Fullgraf Professor in the Department of Computer Science & Engineering PhD, University of Virginia Internet of things, real-time, embedded, and cyber-physical systems, cloud and edge computing, wireless sensor networks, Neal Patwari PhD, University of Michigan Application of statistical signal processing to wireless networks, and radio frequency signals, Weixiong Zhang PhD, University of California, Los Angeles Computational biology, genomics, machine learning and data mining, and combinatorial optimization, Kunal Agrawal PhD, Massachusetts Institute of Technology Parallel computing, cyber-physical systems and sensing, theoretical computer science, Roman Garnett PhD, University of Oxford Active learning (especially with atypical objectives), Bayesian optimization, and Bayesian nonparametric analysis, Brendan Juba PhD, Massachusetts Institute of Technology Theoretical approaches to artificial intelligence founded on computational complexity theory and theoretical computer science more broadly construed, Caitlin Kelleher Hugo F. & Ina Champ Urbauer Career Development Associate Professor PhD, Carnegie Mellon University Human-computer interaction, programming environments, and learning environments, I-Ting Angelina Lee PhD, Massachusetts Institute of Technology Designing linguistics for parallel programming, developing runtime system support for multi-threaded software, and building novel mechanisms in operating systems and hardware to efficiently support parallel abstractions, William D. Richard PhD, University of Missouri-Rolla Ultrasonic imaging, medical instrumentation, computer engineering, Yevgeniy Vorobeychik PhD, University of Michigan Artificial intelligence, machine learning, computational economics, security and privacy, multi-agent systems, William Yeoh PhD, University of Southern California Artificial intelligence, multi-agent systems, distributed constraint optimization, planning and scheduling, Ayan Chakrabarti PhD, Harvard University Computer vision computational photography, machine learning, Chien-Ju Ho PhD, University of California, Los Angeles Design and analysis of human-in-the-loop systems, with techniques from machine learning, algorithmic economics, and online behavioral social science, Ulugbek Kamilov PhD, cole Polytechnique Fdrale de Lausanne, Switzerland Computational imaging, image and signal processing, machine learning and optimization, Alvitta Ottley PhD, Tufts University Designing personalized and adaptive visualization systems, including information visualization, human-computer interaction, visual analytics, individual differences, personality, user modeling and adaptive interfaces, Netanel Raviv PhD, Technion, Haifa, Israel Mathematical tools for computation, privacy and machine learning, Ning Zhang PhD, Virginia Polytechnic Institute and State University System security, software security, BillSiever PhD, Missouri University of Science and Technology Computer architecture, organization, and embedded systems, Todd Sproull PhD, Washington University Computer networking and mobile application development, Dennis Cosgrove BS, University of Virginia Programming environments and parallel programming, Steve Cole PhD, Washington University in St. Louis Parallel computing, accelerating streaming applications on GPUs, Marion Neumann PhD, University of Bonn, Germany Machine learning with graphs; solving problems in agriculture and robotics, Jonathan Shidal PhD, Washington University Computer architecture and memory management, Douglas Shook MS, Washington University Imaging sensor design, compiler design and optimization, Hila Ben Abraham PhD, Washington University in St. Louis Parallel computing, accelerating streaming applications on GPUs, computer and network security, and malware analysis, Brian Garnett PhD, Rutgers University Discrete mathematics and probability, generally motivated by theoretical computer science, James Orr PhD, Washington University Real-time systems theory and implementation, cyber-physical systems, and operating systems, Jonathan S. Turner PhD, Northwestern University Design and analysis of internet routers and switching systems, networking and communications, algorithms, Jerome R. Cox Jr. ScD, Massachusetts Institute of Technology Computer system design, computer networking, biomedical computing, Takayuki D. Kimura PhD, University of Pennsylvania Communication and computation, visual programming, Seymour V. Pollack MS, Brooklyn Polytechnic Institute Intellectual property, information systems. We will look at questions including, "Why are acquaintances rather than friends more likely to get us job opportunities?" Students will be required to program in Python or MATLAB. A study of data models and the database management systems that support these data models. Throughout this course, there is an emphasis on correctness proofs and the ability to apply the techniques taught to design efficient algorithms for problems from a wide variety of application areas. GitHub. Suggested prerequisite: Having CSE 332 helps, but it's not required. Computational geometry is the algorithmic study of problems that involve geometric shapes such as points, lines, and polygons. Analyzing a large amount of data through data mining has become an effective means of extracting knowledge from data. Real world examples will be used to illustrate the rationales behind various security designs. Highly recommended for majors and for any student seeking a broader view of computer science or computer engineering. We will also investigate algorithms that extract basic properties of networks in order to find communities and infer node properties. Accepting a new assignment. Prerequisites: CSE 361S and 362M from Washington University in St. Louis or permission of the instructor. Prerequisites: ESE 260.Same as E35 ESE 465. If a student's interests are concentrated in the first two areas, a computer engineering degree might be best. Evaluation is based on written and programming assignments, a midterm exam and a final exam. The course examines hardware, software, and system-level design. We will also touch on concepts such as similarity-based learning, feature engineering, data manipulation, and visualization. Modern computing platforms exploit parallelism and architectural diversity (e.g., co-processors such as graphics engines and/or reconfigurable logic) to achieve the desired performance goals. Course Description. Exceptional spaces for discovery and creation McKelvey Hall, home to CSE, was designed with collaboration and innovation in mind. Prerequisite: CSE 131.Same as E81 CSE 260M, E81CSE513T Theory of Artificial Intelligence and Machine Learning. CSE 260 or something that makes you think a little bit about hardware may also help. Some prior exposure to artificial intelligence, machine learning, game theory, and microeconomics may be helpful, but is not required. E81CSE412A Introduction to Artificial Intelligence. Topics include cloud-based security and storage, Linux, Docker and Kubernetes, data modeling through JSON and SQL, database concepts and storage architectures, distributed systems, and finally real-world applications. Reload to refresh your session. E81CSE330S Rapid Prototype Development and Creative Programming. The course will end with a multi-week, open-ended final project. Introduces students to the different areas of research conducted in the department. . The process for requesting a fee waiver from the UW Graduate School is available on their application page. Provides an introduction to research skills, including literature review, problem formulation, presentation, and research ethics. Study of fundamental algorithms, data structures, and their effective use in a variety of applications. Open up Visual Studio 2019, connect to GitHub, and clone your newly created repository to create a local working copy on your h: drive. These opportunities will help students become global citizens who are better able to address current issues. We also learn how to critique existing work and how to formulate and explore sound research questions. Sequential techniques: synchronous circuits, machine minimization, optimal state assignment, asynchronous circuits, and built-in self-test techniques. This is a project-oriented course on digital VLSI design. Prerequisites: CSE 247, ESE 326, MATH 309, and programming experience. The course implements an interactive studio format: after the formal presentation of a topic, students develop a related project under the supervision of the instructor. CSE 332 OOP Principles. E81CSE534A Large-Scale Optimization for Data Science, Large-scale optimization is an essential component of modern data science, artificial intelligence, and machine learning. Prerequisite: CSE 247. Each project will then provide an opportunity to explore how to apply that model in the design of a new user interface. Online textbook purchase required. In addition to learning about IoT, students gain hands-on experience developing multi-platform solutions that control and communicate with Things using via mobile device friendly interfaces. Prerequisites: CSE 347 (may be taken concurrently), ESE 326 (or Math 3200), and Math 233 or equivalents. There will be an emphasis on hands-on experience through using each of the tools taught in this course in a small project. GitHub cse332s-sp23-wustl Overview Repositories Projects Packages People This organization has no public repositories. GitLab cse332-20au p2 An error occurred while fetching folder content. Homework problems, exams, and programming assignments will be administrated throughout the course to enhance students' learning. The topics include common mistakes, selection of techniques and metrics, summarizing measured data, comparing systems using random data, simple linear regression models, other regression models, experimental designs, 2**k experimental designs, factorial designs with replication, fractional factorial designs, one factor experiments, two factor full factorial design w/o replications, two factor full factorial designs with replications, general full factorial designs, introduction to queueing theory, analysis of single queues, queueing networks, operational laws, mean-value analysis, time series analysis, heavy tailed distributions, self-similar processes, long-range dependence, random number generation, analysis of simulation results, and art of data presentation. The students design combinational and sequential circuits at various levels of abstraction using a state-of-the-art CAD environment provided by Cadence Design Systems. System-level topics include real-time operating systems, scheduling, power management, and wireless sensor networks. This includes questions ranging from how the computing platform is designed to how are applications and algorithms expressed to exploit the platform's properties. Students will have the opportunity to work on topics in graphics, artificial intelligence, networking, physics, user interface design, and other topics. Tools covered include version control, the command line, debuggers, compilers, unit testing, IDEs, bug trackers, and more. University of Washington - Paul G. Allen School of Computer Science & Engineering, Box 352350 Seattle, WA 98195-2350 (206) 543-1695 voice, (206 . The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, dynamic programming, and reinforcement learning. Welcome to Virtual Lists. This course assumes no prior experience with programming.Same as E81 CSE 131, E81CSE502N Data Structures and Algorithms, Study of fundamental algorithms, data structures, and their effective use in a variety of applications. E81CSE518A Human-in-the-Loop Computation. Learn More Techniques for solving problems by programming. Outside of lectures and sections, there are several ways to ask questions or discuss course issues: Visit office hours ! Prerequisite: E81 CSE 330S or E81 CSE 332S and at least junior standing, E81CSE457A Introduction to Visualization. Prerequisites: CSE 131 and CSE 247Same as E81 CSE 332S, E81CSE505N Introduction to Digital Logic and Computer Design, Introduction to design methods for digital logic and fundamentals of computer architecture. Students electing the thesis option for their master's degree perform their thesis research under this course. Coding/information theory emerged in mid 20th century as a mathematical theory of communication with noise. Important design aspects of digital integrated circuits such as propagation delay, noise margins and power dissipation are covered in the class, and design challenges in sub-micron technology are addressed. Fundamentals of secure computing such as trust models and cryptography will lay the groundwork for studying key topics in the security of systems, networking, web design, machine learning algorithms, mobile applications, and physical devices. Students should apply to this joint program by February 1 of their junior year. Prerequisites: CSE 131, CSE 217A; Corequisite: CSE 247. Research: Participating in undergraduate research is a great way to learn more about a specific area. Students in the bachelor's/master's program can take advantage of the program's flexibility by taking graduate courses toward the graduate degree while still completing the undergraduate degree requirements. 1/21/2021 Syllabus for SP2021.E81.CSE.332S.01 - Object-Oriented Software Development Laboratory Course Syllabus CSE. 35001 /35690. In either case, the project serves as a focal point for crystallizing the concepts, techniques, and methodologies encountered throughout the curriculum. Prerequisite: permission of advisor and submission of a research proposal form. Study Resources. Unconstrained optimization techniques including Gradient methods, Newton's methods, Quasi-Newton methods, and conjugate methods will be introduced. E81CSE231S Introduction to Parallel and Concurrent Programming. This course covers the latest advances in networking. Prerequisite: CSE 473S (Introduction to Computer Networks) or permission of instructor. The Department of Computer Science & Engineering actively promotes a culture of strong undergraduate participation in research. School of Electrical Engineering & Computer . Sensor networks, high-speed routers, specialized FPGA hardware, wireless devices, RF tags, digital cameras, robots, large displays and multiprocessors are just a few of the hardware devices undergraduates often use in their projects. Prerequisites: CSE 247, CSE 417T, ESE 326, Math 233 and Math 309. This course uses web development as a vehicle for developing skills in rapid prototyping. Examples of embedded systems include PDAs, cellular phones, appliances, game consoles, automobiles, and iPods. Students will develop a quantum-computer simulator and make use of open simulators as well as actual devices that can realize quantum circuits on the internet. For each major type of course work you will need to generate a repository on GitHub. AI has made increasing inroads in a broad array of applications, many that have socially significant implications. Throughout the course, we will discuss the efficacy of these methods in concrete data science problems, under appropriate statistical models. Particular attention is given to the role of application development tools. Important design aspects of digital integrated circuits such as propagation delay, noise margins and power dissipation are covered in the class, and design challenges in sub-micron technology are addressed. The PDF will include content on the Minors tab only. Corequisite: CSE 247. Additional reference material is available. CSE 332 Lab 1 Cards, Hands, and Scores; CSE 332 Lab 2 Card Decks and Hands; CSE 332 Lab 3 Five Card Draw; CSE332 2014-2015 Studio Exercises 1; CSE332 2014-2015 Studio Exercises 2; CSE332 2014 . James Orr. This course offers an introduction to the tools and techniques that allow programmers to write code effectively. Topics include recent trends in wireless and mobile networking, wireless coding and modulation, wireless signal propagation, IEEE 802.11a/b/g/n/ac wireless local area networks, 60 GHz millimeter wave gigabit wireless networks, vehicular wireless networks, white spaces, Bluetooth and Bluetooth Smart, wireless personal area networks, wireless protocols for the Internet of Things, cellular networks: 1G/2G/3G, LTE, LTE-Advanced, and 5G. Topics to be covered are the theory of generalization (including VC-dimension, the bias-variance tradeoff, validation, and regularization) and linear and non-linear learning models (including linear and logistic regression, decision trees, ensemble methods, neural networks, nearest-neighbor methods, and support vector machines). Pre-Medical Option within Computer Science: Students may pursue a pre-medicine curriculum in conjunction with either the BS degree or the second major in computer science programs. If followed by a star, the player will . Please make sure to have a school email added to your github account before signing in! Required Text Topics covered include concurrency and synchronization features and software architecture patterns. This five-year program that leads to both the bachelor's and master's degrees offers the student an excellent opportunity to combine undergraduate and graduate studies in an integrated curriculum. Prerequisites: CSE 511A, CSE 517A, and CSE 571A. sauravhathi folder created and org all files. In any case for the debugging, I'd like to think I'd be fine with respect to that since I have a pretty good amount of experience debugging open source projects that are millions of lines of code. E81CSE260M Introduction to Digital Logic and Computer Design. Prerequisite: CSE 247. Over the course of the semester, students will be expected to present their interface evaluation results through written reports and in class presentations. This course introduces students to quantum computing, which leverages the effects of quantum-mechanical phenomena to solve problems. Prerequisites: CSE 240 and CSE 247. The PDF will include content on the Majors tab only. Projects will include identifying security vulnerabilities, exploiting vulnerabilities, and detecting and defending against exploits. Gitlab is basically identical to Github, except that it's a CSE-only version. Bachelor's/master's applications will be accepted until the last day of classes the semester prior to the student beginning the graduate program. E81CSE438S Mobile Application Development. Topics include syntactic and semantic analysis, symbol table management, code generation, and runtime libraries. Secure computing requires the secure design, implementation, and use of systems and algorithms across many areas of computer science. We will use the representative power of graphs to model networks of social, technological, or biological interactions. Alles zum Thema Abnehmen und Dit. Skip to content Toggle navigation. Prerequisite: CSE 131. Introduction to computer graphics. Website: heming-zhang.github.io Email: hemingzhang@wustl.edu EDUCATION Washington University in St.Louis, St.Louis, MO August 2019 - Present McKelvey School of Engineering Master of Science, Computer Science Major GPA: 4.0/4.0 Central China Normal University, Wuhan, China September 2015 - June 2019 School of Information Management Bachelor . Prerequisites: CSE 332S. Students are encouraged to apply to this program by October 1 of the first semester of their senior year, and a minimum GPA of 3.0 is required of all applicants. Prerequisites: Junior or senior standing and CSE 330S. Not open for credit to students who have completed CSE 332. Introduction to design methods for digital logic and fundamentals of computer architecture. Students intending to take CSE 497-498 must submit a project proposal form (PDF) for approval by the department during the spring semester of the junior year. Credit earned for CSE 400E can be counted toward a student's major or minor program, with the consent of the student's advisor. Time is provided at the end of the course for students to work on a project of their own interest. Integrity and security requirements are studied in the context of concurrent operations on a database, where the database may be distributed over one or more locations. However, students must also cultivate curiosity about data, including the data's provenance, ethical considerations such as bias, and skepticism concerning correlation and causality. E81CSE332S Object-Oriented Software Development Laboratory, Intensive focus on practical aspects of designing, implementing and debugging software, using object-oriented, procedural, and generic programming techniques. This course will study a large number of research papers that deal with various aspects of wireless sensor networks. Special topics may include large-scale systems, parallel optimization, and convex optimization. Recursion, iteration and simple data structures are covered. Prerequisite: ESE 326. A co-op experience can give students another perspective on their education and may lead to full-time employment. There are three main components in the course, preliminary cryptography, network protocol security and network application security. . E81CSE247 Data Structures and Algorithms. Research projects are available either for pay or for credit through CSE400E Independent Study. Find and fix vulnerabilities . 6. Lecture and discussion are supplemented by exercises in the different research areas and in critical reading, idea generation, and proposal writing. CS+Math:Thisapplied science major efficiently captures the intersection of the complementary studies of computer science and math. E81CSE543S Advanced Secure Software Engineering. You must be a member to see who's a part of this organization. This course offers an in-depth hands-on exploration of core OS abstractions, mechanisms and policies, with an increasing focus on understanding and evaluating their behaviors and interactions. Students apply their knowledge and skill to develop a project of their choosing using topics from the course.