The topics covered include the review of greedy algorithms, dynamic programming, NP-completeness, approximation algorithms, the use of linear and convex programming for approximation, and online algorithms. Data science plays an increasingly important role in research, industry, and government. Prerequisites: CSE 247, ESE 326, Math 233, and Math 309. In this context, performance is frequently multidimensional, including resource efficiency, power, execution speed (which can be quantified via elapsed run time, data throughput, or latency), and so on. Also covered are algorithms for polygon triangulation, path planning, and the art gallery problem. The goal of the course is to design a microprocessor in 0.5 micron technology that will be fabricated by a semiconductor foundry. Any student can take the CSE 131 proficiency exam, and a suitable score will waive CSE 131 as a requirement. E81CSE347R Analysis of Algorithms Recitation. Undergraduates are encouraged to consider 500-level courses. This course introduces the fundamental techniques and concepts needed to study multi-agent systems, in which multiple autonomous entities with different information sets and goals interact. 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 . Emphasizes importance of data structure choice and implementation for obtaining the most efficient algorithm for solving a given problem. Student teams use Xilinx Vivado for HDL-based FPGA design and simulation; they also perform schematic capture, PCB layout, fabrication, and testing of the hardware portion of a selected computation system. We will examine the implications of the multicore hardware design, discuss challenges in writing high performance software, and study emerging technologies relevant to developing software for multicore systems. E81CSE433R Seminar: Capture The Flag (CTF) Studio. We begin by studying graph theory (allowing us to study the structure) and game theory (allowing us to study the interactions) of social networks and market behavior at the introductory level. Create a new C++ Console Application within your repository, make sure to name it something descriptive such as Lab3 . Bayesian probability allows us to model and reason about all types of uncertainty. 24. 3. We will use the representative power of graphs to model networks of social, technological, or biological interactions. E81CSE554A Geometric Computing for Biomedicine. Prerequisites: CSE 452A, CSE 554A, or CSE 559A. Prerequisite: CSE 247. The goal of the course is to design a microprocessor in 0.5 micron technology that will be fabricated by a semiconductor foundry. Topics include the application of blockchains, quantum computing, and AI to networking along with networking trends, data center network topologies, data center ethernet, carrier IP, multi-protocol label switching (MPLS), carrier ethernet, virtual bridging, LAN extension and virtualization using layer 3 protocols, virtual routing protocols, Internet of Things (IoT), data link layer and management protocols for IoT, networking layer protocols for IoT, 6LoWPAN, RPL, messaging protocols for IoT, MQTT, OpenFlow, software-defined networking (SDN), network function virtualization (NFV), big data, networking issues for big data, network configuration, data modeling, NETCONF, YIN, YANG, BEEP, and UML. 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. Please visit the following pages for information about computer science and engineering majors: Please visit the following pages for information about computer science and engineering minors: Visit online course listings to view semester offerings for E81 CSE. Topics typically include propositional and predicate logic; sets, relations, functions and graphs; proof by contradiction, induction and recursion; finite state machines and regular languages; and introduction to discrete probability, expected value and variance. 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. 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. Student at Washington University in St. Louis, Film and Media Studies + Marketing . S. Use Git or checkout with SVN using the web URL. Study of fundamental algorithms, data structures, and their effective use in a variety of applications. Provides an introduction to research skills, including literature review, problem formulation, presentation, and research ethics. Most applications courses provide background not only in the applications themselves but also in how the applications are designed and implemented. Courses in this area help students gain a solid understanding of how software systems are designed and implemented. This course will be taught using Zoom and will be recorded. Research: Participating in undergraduate research is a great way to learn more about a specific area. E81CSE132 Introduction to Computer Engineering. Object-Oriented Software Development Laboratory (E81 332S) Academic year. Corequisite: CSE 247. CSE 332 - Data Structures and Algorithm Analysis (156 Documents) CSE 351 - The Hardware/Software . E81CSE534A Large-Scale Optimization for Data Science, Large-scale optimization is an essential component of modern data science, artificial intelligence, and machine learning. The course material focuses on bottom-up design of digital integrated circuits, starting from CMOS transistors, CMOS inverters, combinational circuits and sequential logic designs. Modern computing systems consist of multiple interconnected components that all influence performance. It also serves as a foundation for other system courses (e.g., those involving compilers, networks, and operating systems), where a deeper understanding of systems-level issues is required. From the 11th to the 18th centuries, part of the territory of the commune belonged to the Abbeys of Saint Melaine and Saint Georges in Rennes. Alles zum Thema Abnehmen und Dit. In this course, we learn about the state of the art in visualization research and gain hands-on experience with the research pipeline. Students will learn several algorithms suitable for both smooth and nonsmooth optimization, including gradient methods, proximal methods, mirror descent, Nesterov's acceleration, ADMM, quasi-Newton methods, stochastic optimization, variance reduction, and distributed optimization. E81CSE260M Introduction to Digital Logic and Computer Design. E81CSE584A Algorithms for Biosequence Comparison. The discipline of artificial intelligence (AI) is concerned with building systems that think and act like humans or rationally on some absolute scale. One of the main objectives of the course is to become familiar with the data science workflow, from posing a problem to understanding and preparing the data, training and evaluating a model, and then presenting and interpreting the results. Follow their code on GitHub. The course targets graduate students and advanced undergraduates. Sequence analysis topics include introduction to probability, probabilistic inference in missing data problems, hidden Markov models (HMMs), profile HMMs, sequence alignment, and identification of transcription-factor binding sites. A well-rounded study of computing includes training in each of these areas. Working closely with a faculty member, the student investigates an original idea (algorithm, model technique, etc. 6. CSE 332 Lab 1: Basic C++ Program Structure and Data Movement Due by: Monday September 26th, at 11:59 pm CT Final grade percentage: 8 percent Objective: This lab is intended to familiarize you with basic C++ program structure, data movement and execution control concepts, including: C++ header files and C++ source files; C++ STL string, input, Students will learn about hardcore imaging techniques and gain the mathematical fundamentals needed to build their own models for effective problem solving. The study of computer science and engineering is especially well suited and popular for study abroad. Prerequisites: 3xxS or 4xxS. Prerequisites: CSE 131, CSE 247, and CSE 330. This course is the recitation component of CSE 347. Roch Gurin Harold B. and Adelaide G. Welge Professor of Computer Science PhD, California Institute of Technology Computer networks and communication systems, Sanjoy Baruah PhD, University of Texas at Austin Real-time and safety-critical system design, cyber-physical systems, scheduling theory, resource allocation and sharing in distributed computing environments, Aaron Bobick James M. McKelvey Professor and Dean PhD, Massachusetts Institute of Technology Computer vision, graphics, human-robot collaboration, Michael R. Brent Henry Edwin Sever Professor of Engineering PhD, Massachusetts Institute of Technology Systems biology, computational and experimental genomics, mathematical modeling, algorithms for computational biology, bioinformatics, Jeremy Buhler PhD, Washington University Computational biology, genomics, algorithms for comparing and annotating large biosequences, Roger D. Chamberlain DSc, Washington University Computer engineering, parallel computation, computer architecture, multiprocessor systems, Yixin Chen PhD, University of Illinois at Urbana-Champaign Mathematical optimization, artificial intelligence, planning and scheduling, data mining, learning data warehousing, operations research, data security, Patrick Crowley PhD, University of Washington Computer and network systems, network security, Ron K. Cytron PhD, University of Illinois at Urbana-Champaign Programming languages, middleware, real-time systems, Christopher D. Gill DSc, Washington University Parallel and distributed real-time embedded systems, cyber-physicalsystems, concurrency platforms and middleware, formal models andanalysis of concurrency and timing, Raj Jain Barbara J. Create a new C++ Console Application within your repository, make sure to name it something descriptive such as Lab3. The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, dynamic programming, and reinforcement learning. Students also viewed. Prerequisites: CSE 361S and 362M from Washington University in St. Louis or permission of the instructor. The design theory for databases is developed and various tools are utilized to apply the theory. This Ille-et-Vilaine geographical article is a stub. sauravhathi folder created and org all files. This is a lecture-less class, please do the prep work and attend studio to keep up. During the process, students develop their own software systems. Throughout the semester, students will operate in different roles on a team, serving as lead developer, tester, and project manager. 6. Intended for non-majors. Each academic program can be tailored to a student's individual needs. Credits: 3.0. The course covers a variety of HCI techniques for use at different stages in the software development cycle, including techniques that can be used with and without users. 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. To help students balance their elective courses, most upper-level departmental courses are classified into one of the following categories: S for software systems, M for machines (hardware), T for theory, or A for applications. During the French Revolution, the village sided with its clergy and was punished by being sacked by a troupe of national guard in 1792.[3]. E81CSE447T Introduction to Formal Languages and Automata, An introduction to the theory of computation, with emphasis on the relationship between formal models of computation and the computational problems solvable by those models. Highly recommended for majors and for any student seeking a broader view of computer science or computer engineering. Software systems are collections of interacting software components that work together to support the needs of computer applications. These techniques are also of interest for more general string processing and for building and mining textual databases. Recursion, iteration and simple data structures are covered. Sign up cse332s-fl22-wustl. Topics include memory hierarchy, cache coherence protocol, memory models, scheduling, high-level parallel language models, concurrent programming (synchronization and concurrent data structures), algorithms for debugging parallel software, and performance analysis. Not open for credit to students who have completed CSE 332. Prerequisite: CSE 361S. 2022 Washington University in St.Louis, Barbara J. E81CSE587A Algorithms for Computational Biology. Secure computing requires the secure design, implementation, and use of systems and algorithms across many areas of computer science. In this class, part of the grade for each programming assignment will be based on the CSE 332 Programming Guidelines, which are intended to build good programming habits that will help avoid common mistakes and help make your programs more readable and better organized and documented. More About Virtual Base Classes Still Polymorphic Can convert between uses as Derived vs. Base Members of virtual Base class normally can be uniquely identified base class is instantiated only once if the variable is in both base and derived class, then derived class has higher precedence If the member is in 2 derived classes, then it is still . The course emphasizes familiarity and proficiency with a wide range of C++ language features through hands-on practice completing studio exercises and lab assignments, supplemented with readings and summary presentations for each session. By logging into this site you agree you are an authorized user and agree to use cookies on this site. Computational geometry is the algorithmic study of problems that involve geometric shapes such as points, lines, and polygons. View Sections. Board game; Washington University in St. Louis CSE 332. lab2-2.pdf. Prerequisite: CSE 473S. We will also touch on concepts such as similarity-based learning, feature engineering, data manipulation, and visualization. Prerequisites: CSE 247, ESE 326, and Math 233. Undergraduate Programs | Combined Undergraduate and Graduate Study | Undergraduate Courses | BroadeningExperiences | Research Opportunities | Advanced Placement/Proficiency. Provides a broad coverage of fundamental algorithm design techniques, with a focus on developing efficient algorithms for solving combinatorial and optimization problems. This is the best place to get detailed, hands-on debugging help. Disciplines such as medicine, business, science, and government are producing enormous amounts of data with increasing volume and complexity. We would like to show you a description here but the site won't allow us. Hardware/software co-design; processor interfacing; procedures for reliable digital design, both combinational and sequential; understanding manufacturers' specifications; use of test equipment. Prerequisites: CSE 247, ESE 326, MATH 309, and programming experience. Prerequisites: CSE 247 and CSE 361S. Features guest lectures and highly interactive discussions of diverse computer science topics. E81CSE427S Cloud Computing with Big Data Applications. Please use Piazza over email for asking questions. Topics to be covered include kernel methods (support vector machines, Gaussian processes), neural networks (deep learning), and unsupervised learning. This course does not teach programming in Python. In this course, students will work in groups to design, develop, test, publish, and market an iOS mobile application. Readings, lecture material, studio exercises, and lab assignments are closely integrated in an active-learning environment in which students gain experience and proficiency writing OS code, as well as tracing and evaluating OS operations via user-level programs and kernel-level monitoring tools. Provided that the 144-unit requirement is satisfied, up to 6 units of course work acceptable for the master's degree can be counted toward both the bachelor's and master's requirements.

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