BS in Computer Science (CAS) http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised Sat, 20 Jan 2018 11:12:33 -0500 Joomla! - Open Source Content Management en-gb CSE Office Moved http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/376-cse-office-moved http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/376-cse-office-moved The CSE Department has moved to Mountaintop Building "C" effective Friday, January 19, 2018.

Office Hours for Jeanne and Heidi held in Packard Lab Room 354


Jeanne (3:00-4:30pm): January 19, January 22, January 23, and January 24
Heidi (2:00-3:30pm): January 19, January 25, January 26, February 1 and February 2

Faculty and Staff Offices Located in Building "C" (113 Research Drive)

Eric Baumer

235

Yinzhi Cao

328

Brian Chen

330

Liang Cheng

313

Mooi Choo Chuah

317

Brian Davison

233

Jeff Heflin

232

Xiaolei Huang

332

Daniel Lopresti

215

Hector Munoz-Avila

227

Roberto Palmieri

338

Michael Spear

339

John Spletzer

106

Jeanne Steinberg

214

Ting Wang

327

Heidi Wegrzyn

212

Sihong Xie

326

Miaomiao Zhang

337

 Faculty and Staff Offices Remaining in Packard Lab

James Femister 200b
Eric Fouh Mbindi 204a
Bryan Hodgson 115
Sharon Kalafut 200a
Hank Korth 414
Jason Loew 325

Computer Enginnering Program

Kerry Livermore 354

]]>
hew207@lehigh.edu (Heidi Wegrzyn) Tue, 16 Jan 2018 14:22:21 -0500
tmi http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/375-tmi http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/375-tmi
          Data X TMI     
Keynote

Heng Xu
Associate Professor of Information Sciences and Technology
Pennsylvania State University

"Your Privacy Is Your Friend's Privacy: Examining Interdependent Privacy Issues on Online Social Networks"

Monday, November 6, 4:00 PM
STEPS 101

Abstract:   The highly interactive nature of interpersonal communication on online social networks (OSNs) impels us to think about privacy as a communal matter, with users’ private information being revealed by not only their own voluntary disclosures, but also the activities of their social ties. The current privacy literature has identified two types of information disclosures in OSNs: self-disclosure, i.e., the disclosure of an OSN user’s private information by him/herself; and co-disclosure, i.e., the disclosure of the user’s private information by other users. Although co-disclosure has been increasingly identified as a new source of privacy threat inherent to the OSN context, few systematic attempts have been made to provide an empirical understanding on the commonalities and distinctions between self- vs. co-disclosure. To address this gap, we conducted two empirical studies (one theory-driven and the other data-driven) to measure OSN users’ concerns over co-disclosure and potential privacy harms caused by co-disclosure. This research serves as a starting point for theorizing privacy from the non-individualistic perspective and for understanding interdependent privacy issues as a result of interpersonal interaction and social influence.

Bio:  Dr. Heng Xu is Associate Professor of Information Sciences and Technology at the Pennsylvania State University, and leads the Privacy Assurance Lab (PAL). Her research focuses on understanding and assuring information privacy in different contexts, including location based services, social networks, medical practices, and children and adolescent online safety. Her work has been published in premier outlets across various fields such as Business, Law, Computer Science, and Human-Computer Interaction. During 2013-2016, Dr. Xu served as a program director at the U.S. National Science Foundation, and her effort was put into bringing the social, behavioral and economic sciences to studies of major challenges in Big Data, Cybersecurity & Privacy, and Smart Cities.

Panelists

Eric P. S. Baumer is assistant professor of Computer Science and Engineering at Lehigh University. His research examines interactions with algorithms in social computing systems. Recent work includes using computational tools to identify the language of political framing, and studying technology refusal in the context of Facebook. He holds an MS and PhD in Information and Computer Sciences from the University of California, Irvine, completed post-doctoral work at Cornell University, and holds a BS in Computer Science with a minor in Music from the University of Central Florida.

Haiyan Jia is an assistant professor in the Department of Journalism and Communication and the Data X Initiative. Her research explores how communication technology influences individuals and the society. Her work combines theories from information science, computer-mediated communication, social cognition, and developmental psychology to theorize and empirically examine people's privacy management strategies and behaviors on social media.

Daniel Lopresti is Professor and Chair of Lehigh's Department of Computer Science and Engineering, as well as Director of the university's Data X Initiative. He conducts research examining fundamental algorithmic and systems-related questions in pattern recognition, document analysis, and computer security, and has been frequently quoted as an expert on electronic voting security. He has held leadership roles in most of the major international conferences on document analysis over the past 10 years and is co-editor-in-chief of the International Journal on Document Analysis and Recognition. He chairs the Conferences & Meetings Committee of the International Association of Pattern Recognition. He also serves on the Computing Community Consortium Council of the Computing Research Association (CRA), whose mission is to catalyze the computing research community and enable the pursuit of innovative, high-impact research. He received his bachelor's degree in math from Dartmouth, and his Ph.D. in computer science from Princeton.

Rebecca Wang is an assistant professor of Marketing and the Data X Initiative at Lehigh University. Her research focuses on customer engagement in the contexts of digital channels. By collaborating with industry partners and analyzing large datasets, she uses causal inference and statistical methods to answer questions related to direct marketing with new and mobile media. Prior to her academic career, she worked in industry as a consultant and a data engineer for six years. She holds an A.B. in Engineering Sciences from Dartmouth College, and a Ph.D. in Marketing from Northwestern University.

Gaia Bernstein is the Michael J. Zimmer Professor of Law, Director of the Institute of Privacy Protection and Co-Director of the Gibbons Institute for Law, Science and Technology at Seton Hall University School of Law. Professor Bernstein specializes in law and technology, information privacy, health privacy, intellectual property, law and genetics and reproductive technologies. Her scholarship examines the role of users in the adoption of new technologies. She is currently working on a book titled "The Over-Users: Technology Addiction and the Power of Awareness".

Najarian (Jari) Peters is an attorney and privacy compliance professional with over ten years of experience in academic, healthcare, and private organizations. Prior to joining Seton Hall faculty, she was the Senior Healthcare Compliance Manager for the Health and Wellness Business Unit of Panasonic North America. She has also served as Chief Compliance/Privacy Officer and Risk Manager Counsel for National Healthcare Associates and as Senior Compliance, Ethics Liaison, and HIPAA Privacy Officer for the Rutgers Office of Ethics Compliance and Corporate Integrity. After graduating law school, Ms. Peters joined Weill Cornell Medical School's Office of Research Compliance and Sponsored Programs. She earned her undergraduate degree in Political Science from Xavier University of Louisiana and her Juris Doctorate from Notre Dame Law School. Her research interests include voter privacy, algorithmic bias and accountability, and local broadband movements.

]]>
jgs2@lehigh.edu (Jeanne Steinberg) Tue, 31 Oct 2017 18:34:36 -0400
Spring 2018: Course for skill area http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/374-spring-2018-course-for-skill-area http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/374-spring-2018-course-for-skill-area
  • Theory Skills
    1. CSE 409 Theory of Computation
    2. CSE 426 Pattern Recognition
    3. CSE 498 Advanced Algorithms
  • Applied Theory Skills
    1. CSE 426 Pattern Recognition
    2. CSE 498 Advanced Algorithms
    3. CSE 498 Principles and Implementation of Information Privacy
  • Advanced application skills
    1. CSE 498 Big Data Analytics
    1. Knowledge-Based Systems Skills
      1. CSE 498 Big Data Analytics
    2. Computer Hardware, Systems & Networks
      1. CSE 401 Advanced Computer Architecture
      2. CSE 403 Advanced Operating Systems
      3. CSE 443 Network Security
    3. Security in Computational Environments
      1. CSE 443 Network Security
      2. CSE 498 Principles and Implementation of Information Privacy
    1. Software & Programming Skills
    ]]>
    hew207@lehigh.edu (Heidi Wegrzyn) Wed, 25 Oct 2017 18:18:32 -0400
    Spring 2018 Course Offering http://www.cse.lehigh.edu/academics/course-schedule-by-semester http://www.cse.lehigh.edu/academics/course-schedule-by-semester Spring 2018 Courses

    CSE 002 FUNDAMENTALS OF PROGRAMMING

    Problem-solving and object-oriented programming using Java. Includes laboratory. No prior programming experience needed. Click here for official description. All sections will offer Guided Study Groups.

    CSE 002-110, MW 3:10-4:00 F (lab) 3:10-4:00, Professor James Femister

    CSE 002-210, MW 11:10-12:00 F (lab) 11:10-12:00, Professor Brian Chen

    CSE 002-211, MW 11:10-12:00 F (lab), 12:10-1:00 Professor Brian Chen


    CSE 012-010 SURVEY OF COMPUTER SCIENCE, MWF 1:10-2:00, Professor Eric Fouh Mbindi

    This course provides a project-based exploration of fundamental concepts in computing and "computational thinking." Topics include but are not limited to networks, data visualization, information storage and retrieval, and the popular Python programming language. Each project presents applications of computing in solving real life problems. In this course you will learn to write Python code to visualize data from different sources. You will learn how information is transferred across networks and how to store and retrieve data from relational database management systems. Optional Structured Study Groups will be provided for students who express interest. Click here for official description. Guided Study Groups will be offered in this course.


    CSE 017 DATA STRUCTURES & PROGRAMMING

    This course is a programming-intensive exploration of software design concepts and implementation techniques. It builds on the student's existing knowledge of fundamental programming. Topics include object-oriented software design, problem-solving strategies, algorithm development, and classic data structures. Click here for official description.

    CSE 017-012, MWF 11:10-12:00, Professor James Femister

    CSE 017-010, MWF 10:10-11:00, Professor Eric Fouh Mbindi

     There will be weekly mandatory online quizzes and/or homework. One programming is assigned each week. Programming assignments are presented and discussed in-class during lecture. Each assignment covers one of the major topics in the course. There are two 50-minute exams during the semester, and a comprehensive 2-hour final exam at the end of the course.

    CSE 017-011, MWF 9:10-10:00, Professor Eric Fouh Mbindi

     There will be weekly mandatory online quizzes and/or homework. One programming is assigned each week. Programming assignments are presented and discussed in-class during lecture. Each assignment covers one of the major topics in the course. There are two 50-minute exams during the semester, and a comprehensive 2-hour final exam at the end of the course.


    ** NEW COURSE FOR 2017-2018** CSE 098 WOMEN IN TECHNOLOGY, F 2:10-4:00, Professor Daniel Lopresti (Course runs only first half of semester)


    The technology industry has been the engine of growth for the US economy for the past four decades. Emergent tech companies have shaped all of our lives, and created significant professional and financial opportunities for the leaders of these high growth ventures. Despite the many clear opportunities, women hold a minority of the leadership positions in the tech industry. Why? What can be done to change this? How can the next generation of female tech industry leaders succeed? Prerequisite: permission of instructor.


    ** NEW COURSE FOR 2017-2018** CSE 098/CSB 098 SOFTWARE PRODUCT MANAGMENT, F 2:10-4:00, Professor Daniel Lopresti (Course runs only second half of semester)

    Managing the product life cycle Writing great software is only half the challenge. Successful companies are built on top of product/market fit - having the right capabilities at the right time in the market. Product management is key to establishing product/market fit. This class will cover the various elements of product management including: Product definition - writing PRDs and MRDs, Competitive analysis, Pricing, Go-to-market channel strategies, Promotion and demand generation. Prerequisite: permission of instructor


    CSE 109 SYSTEMS SOFTWARE

    Advanced programming and data structures, including dynamic structures, memory allocation, data organization, symbol tables, hash tables, B-trees, data files. Object-oriented design and implementation of simple assemblers, loaders, interpreters, compilers and translators. Practical methods for implementing medium-scale programs. Click here for official description.

    CSE 109-010, MWF 1:10-2:00 F (lab) 12:10-1:00, Professor Jason Loew

    CSE 109-011, MWF 10:10-11:00 F (lab) 11:10-12:00, Professor Jason Loew


    CSE 160-010 INTRO TO DATA SCIENCE, MWF 10:10-11:00, Professor Brian Davison

     

    Interested in understanding the hype about data science, big data, or data analytics? This course introduces you to data science, a fast-growing and interdisciplinary field, focusing on the computational analysis of data to extract knowledge and insight. You will be introduced to the collection, preparation, analysis, modeling, and visualization of data, covering both conceptual and practical issues. Applications of data science across multiple fields are presented, and hands-on use of statistical and data manipulation software is included. The course is open to students from all areas of study; the only prerequisite is some programming experience (automatic if you've taken CSE 2, 12, or BIS 335, or permission of the instructor is available if you can show that you've successfully completed a programming course online, in high school, or elsewhere). 


    ** THIS COURSE REPLACES CSE 261**

    CSE 198 FOUNDATIONS OF DISCRETE STRUCTURES AND ALGORITHMS

    Basic representations used in algorithms: propositional and predicate logic, set operations and functions, relations and their representations, matrices and their representations, graphs and their representations, trees and their representations. Basic formalizations for proving algorithm correctness: logical consequences, induction, structural induction. Basic formalizations for algorithm analysis: counting, pigeonhole principle, permutations. Prerequisite: (Math 021 or Math 031 or Math 51 or Math 76) and (CSE 001 or CSE 002 or CSE 012)

    CSE 198-012, TR 10:45-12:00, Professor Ting Wang

    CSE 198-013, TR 1:10-2:25, Professor Xiaolei Huang


     CSE 202 COMPUTER ORGANIZATION AND ARCHITECTURE

    Interaction between low-level computer architectural properties and high-level program behaviors: instruction set design; digital logics and assembly language; processor organization; the memory hierarchy; multicore and GPU architectures; and processor interrupt/exception models. Click here for official description.

    CSE 202-010, TR 9:20-10:35, Professor Jason Loew

    CSE 202-011, MW 12:45-2:00, Professor Mark Erle


    CSE 216-010 SOFTWARE ENGINEERING, MW 11:10-12:25, Professor Michael Spear

    The software life-cycle; life-cycle models; software planning; testing; specification methods; maintenance. Emphasis on team work and large-scale software systems, including oral presentations and written reports. Click here for official description.


    CSE 241-010 DATABASE SYSTEMS

    Design of large databases: Integration of databases and applications using SQL and JDBC; transaction processing; performance tuning; data mining and data warehouses. Click here for official description.

    CSE 241-010, TR 10:45-12:00, Professor Hank Korth

    CSE 241-011, TR 2:35-3:50, Professor Sihong Xie


    CSE 252-010 COMPUTERS, INTERNET AND SOCIETY, TR 2:35-3:50, Professor Eric Baumer

    An interactive exploration of the current and future role of computers, the Internet, and related technologies in changing the standard of living, work environments, society and its ethical values. Privacy, security, depersonalization, responsibility, and professional ethics; the role of computer and Internet technologies in changing education, business modalities, collaboration mechanisms, and everyday life. (SS) Click here for official description.


    CSE 264-010 WEB APPLICATIONS, TR 2:35-3:50, Professor James Femister

    Practical experience in designing and implementing modern Web applications. Concepts, tools, and techniques, including: HTTP, HTML, CSS, DOM, JavaScript, Ajax, PHP, graphic design principles, mobile web development. Not available to students who have credit for IE 275. Click here for official description.


    CSE 280-010 CAPSTONE PROJECT I, MW 8:45-10:00, Professor John Spletzer

     First of a two semester capstone course sequence that involves the design, implementation, and evaluation of a computer science software project. Conducted by small student teams working from project definition to final documentation. Each student team has a CSE faculty member serving as its advisor. The first semester emphasis is on project definition, planning and implementation. Communication skills such as technical writing, oral presentations, and use of visual aids are also emphasized. Project work is supplemented by weekly seminars. Prerequisite: junior standing and CSE 216.


    ** NEW COURSE FOR 2017-2018** CSE 298/FIN 298 BLOCKCHAIN, MW 12:45-2:00, Professor Hank Korth

    Blockchain is the technology underlying Bitcoin, along with other digital currencies, and a technology applicable broadly in finance, accounting, and "smart" contracts. It offers the ability to decentralize financial transactions, automate record keeping, and increase privacy, but it remains controversial. Some describe it as "the most important invention since the Internet", yet others, including the CEO of a leading financial firm, have described Bitcoin as a "fraud" and that CEO has threatened to fire anyone in the firm caught trading it.

    This course will provide an introduction to the technology underlying blockchain, the current and potential applications of blockchain in business, and the resulting policy issues. The course is designed for students with either some business-course background, some computer-science background, or both. Prerequisite: permission of instructor.


    CSE 318-010 INTRODUCTION TO THE THEORY OF COMPUTATION, TR 10:45-12:00, Professor Hector Munoz-Avila

    Formal study of theoretical computational models: finite automata, pushdown automata, and Turing machines. Study of formal languages: regular, context-free, and decidable languages. Click here for official description.


    CSE 326/426 FOUNDATIONS OF MACHINE LEARNING, MW 2:35-3:50, Professor Miaomiao Zhang

    ** NEW FACULTY MEMBER FOR 2017-2018**

    An introductory course offers a broad overview of the main techniques in machine learning.  Students will study the basic concepts of advanced machine learning methods as well as their theoretical background. Topics of learning theory (bias/variance tradeoffs; VC theory); supervised learning parametric/nonparametric methods, Bayesian models, support vector machines, neural networks); unsupervised learning (dimensionality reduction, kernel tricks, clustering) and reinforcement learning will be covered.  Also note that this course is a prerequisite for CSE 347 Data Mining.   Click here for official description.


    CSE 327-011 ARTIFICIAL INTELLIGENCE OF THEORY AND PRACTICE, TR 1:10-2:25, Professor Jeff Heflin

    Introduction to the field of artificial intelligence: Problem solving, knowledge representation, reasoning, planning and machine learning. Use of AI systems or languages. Advanced topics such as natural language processing, vision, robotics, and uncertainty. Click here for official description.


    CSE 343-010/443-010 NETWORK SECURITY, MW 12:45-2:00, Professor Mooi Choo Chuah

    Overview of network security threats and vulnerabilities. Techniques and tools for detecting, responding to and recovering from security incidents. Fundamentals of cryptography. Hands-on experience with programming techniques for security protocols. Click here for official description.


    **NEW COURSE for 2017-2018** CSE 398/498-014 PRINCIPLES AND IMPLEMENTATION OF INFORMATION PRIVACY, TR 9:20-10:35, Professor Ting Wang

    With the tremendous success of data-driven services and applications (e.g., personalized recommendation, customized news, targeted ads) follows their immense threat to the privacy of people's sensitive information. This course discusses how to design and implement information systems that respect individuals' data privacy while still enabling high-quality services. Main topics covered in the course include: privacy-aware data publishing, privacy-aware data mining, privacy-aware mobile services, privacy-aware web services, and secure multiparty computation. The course will be a combination of lectures and paper presentations by the students. Students will also pursue a course research project. The final outputs of the project include a presentation and a short report. CSE 398 prerequisite: CSE 347/447, for CSE 498: permission of instructor.


    **NEW COURSE for 2017-2018** CSE 398/498-010 BIG DATA ANALYTICS, R 1:10-2:25, Professor Daniel Lopresti

     In this 3-credit project course, we will gain a practical working knowledge of large- scale data analysis using the popular open source Apache Spark framework. Spark provides a powerful model for distributing programs across clusters of machines and elegantly supports patterns that are commonly employed in big data analytics, including classification, collaborative filtering, and anomaly detection, among others.

    Working from the course textbook, we will study and program solutions for problems including: music recommender systems; predicting forest cover with decision trees; anomaly detection in network traffic with K-means clustering; understanding Wikipedia with Latent Semantic Analysis; analyzing co-occurrence networks with GraphX; geospatial and temporal data analysis on the New York City Taxi Trips data; estimating financial risk through Monte Carlo simulation; analyzing genomics data and the BDG project; and analyzing neuroimaging data with PySpark and Thunder.

    Supplemental readings will provide additional background for each application area, but most of the work in the course will involve implementing, studying, and enhancing the programming examples from the textbook. During class, students will take turns presenting their own solutions and helping to lead the discussion. A final project will be required.

    The Tuesday meeting time is tentative and if needed students will meet with the instructor separately each week at a mutually convenient time, either individually or in small groups.

    Enrollment in this course is limited and requires permission of the instructor. Please note that this is not a basic course on data mining, cluster computing, or programming in Scala; it assumes you already know something about these topics and/or you can learn them quickly on your own. Contact the instructor, Prof. Dan Lopresti, for details. This course will be taught using one of the new classrooms in Building C.


    CSE 401-010 ADVANCED COMPUTER ARCHITECTURE, TR 10:45-12:00, Professor Xiaochen Guo

    Design, analysis and performance of computer architectures; high-speed memory systems; cache design and analysis; modeling cache performance; principle of pipeline processing, performance of pipelined computers; scheduling and control of a pipeline; classification of parallel architectures; systolic and data flow architectures; multiprocessor performance; multiprocessor interconnections and cache coherence.


    CSE 403-010 ADVANCED OPERATING SYSTEMS, MW 11:10-12:25, Professor Roberto Palmieri

     ** NEW FACULTY MEMBER FOR 2017-2018**

    Principles of operating systems with emphasis on hardware and software requirements and design methodologies for multi-programming systems. Global topics include the related areas of process management, resource management, and file systems.


    CSE 409-010 THEORY OF COMPUTATION, TR 10:45-12:00, Professor Hector Munoz-Avila

    Finite automata. Pushdown automata, Relationship to definition and parsing of formal grammars. Credit will not be given for both CSE 318 and CSE 409.


    **THIS COURSE REPLACES CSE 441**

    CSE 498-010 ADVANCED ALGORITHMS, TR 2:35-3:50, Professor Hector Munoz-Avila

    Average-case runtime analysis of algorithms. Randomized algorithms and probabilistic analysis of their performance. Analysis of data structures including hash tables, augmented data structures with order statistics. Amortized analysis. Elementary computational geometry. Limits on algorithm space efficiency using PSPACE-completeness theory. Prerequisite: CSE 340 or MATH 340 or permission of instructor.


    This listing represents our current plan for the semester in question. Course offerings and class times are occasionally subject to change for reasons beyond our control.


    ]]>
    hew207@lehigh.edu (Heidi Wegrzyn) Wed, 11 Oct 2017 18:22:14 -0400
    Hittinger http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/372-hittinger http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/372-hittinger
              Hittinger      Data X Seminar Series

    Jeffrey Hittinger


    Computational Scientist
    Center for Applied Scientific Computing
    Lawrence Livermore National Laboratory

    "Making Every Bit Count: Variable Precision?"

    Thursday, October 19, 4:00 PM
    Packard Lab 466

    Abstract:   Decades ago, when memory was a scarce resource, computational scientists routinely worked in single precision and were more sophisticated in dealing with the pitfalls finite-precision arithmetic. Today, however, we typically compute and store results in 64-bit double precision by default even when very few significant digits are required. Many of these bits are representing errors – truncation, iteration, roundoff – instead of useful information about the solution. This over-allocation of resources is wasteful of power, bandwidth, storage, and FLOPs; we communicate and compute on many meaningless bits and do not take full advantage of the computer hardware we purchase.

    Because of the growing disparity of FLOPs to memory bandwidth in modern computer systems and the rise of General-Purpose GPU computing – which has better peak performance in single precision – there has been renewed interest in mixed precision computing, where tasks are identified that can be accomplished in single precision in conjunction with double precision. Such static optimizations reduce data movement and FLOPs, but their implementations are time consuming and difficult to maintain, particularly across computing platforms. Task-based mixed-precision would be more common if there were tools to simplify development, maintenance, and debugging. But why stop there? We often adapt mesh size, order, and models when simulating to focus the greatest effort only where needed. Why not do the same with precision?

    At LLNL, we are developing the methods and tools that will enable the routine use of dynamically adjustable precision at a per-bit level depending on the needs of the task at hand. Just as adaptive mesh resolution frameworks adapt spatial grid resolution to the needs of the underlying solution, our goal is to provide more or less precision as needed locally. Acceptance from the community will require that we address three concerns: that we can ensure accuracy, ensure efficiency, and ensure ease of use in development, debugging, and application. In this talk, I will discuss the benefits and the challenges of variable precision computing, highlighting aspects of our ongoing research in data representations, numerical algorithms, and testing and development tools.

    Bio:   Dr. Jeffrey Hittinger is a computational scientist in the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory, where he currently serves as Acting Deputy Director of CASC and leader of the Scientific Computing Group. At Livermore, he also leads a large interdisciplinary Strategic Initiative project on Variable Precision Computing. Dr. Hittinger has been actively involved in the Department of Energy (DOE) planning for exascale computing and co-chaired the working group that produced the Applied Mathematics Research for Exascale Computing community report for the DOE Office of Science Advanced Scientific Computing Research program. His current research interests include high-order numerical methods for hyperbolic systems, computational plasma physics, high-performance parallel computing, a posteriori error estimation, and code and solution verification. Dr. Hittinger earned his Ph.D. in Aerospace Engineering and Scientific Computing from the University of Michigan, where he also earned master's degrees in Applied Mathematics and in Aerospace Engineering. He is a graduate of Lehigh University, with a bachelor's degree in Mechanical Engineering.

    This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

    ]]>
    jgs2@lehigh.edu (Jeanne Steinberg) Mon, 09 Oct 2017 19:11:10 -0400
    Computer Science Electives http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/371-computer-science-electives http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/371-computer-science-electives COMPUTER SCIENCE ELECTIVE CHOICES

    Students are required to take 12 credits of Computer Science electives from the following list:

    CSE 202 Computer Organization and Architecture

    CSE 241 Data Base Systems and Applications

    CSE 264 Web Systems Programming (3)

    CSE 265 System and Network Administration (3)

    CSE 271 Programming in the C and Unix Environment (3)

    CSE 303 Operating Sytem Design (3)

    CSE 313  Computer Graphics (3)

    CSE 318  Automata and Formal Grammars (3)

    CSE 319 Image Analysis and Graphics (3)

    CSE 326 Foundations of Machine Learning (3)

    CSE 327 Artificial Intelligence Theory and Practice (3)

    CSE 331  User Interface Systems and Techniques (3)

    CSE 334 Software System Security (3)

    CSE 335  Topics on Intelligent Decision Support Systems (3)

    CSE 336  Embedded Systems (3)

    CSE 337 Reinforcement Learning (3)

    CSE 341 Database Systems, Algorithms, and Applications (3)

    CSE 342  Fundamentals of Internetworking (4)

    CSE 343  Network Security (3)

    CSE 345  WWW Search Engines (3)

    CSE 347 Data Mining (3)

    CSE 348  AI Game Programming (3)

    CSE 360  Introduction to Mobile Robotics (3)

    CSE 363 Network System Design (3)

    CSE 375  Hardware & Software Topics in Parallel Computing (3)

    Or other courses as approved by the CSE Department Chair.

    ]]>
    hew207@lehigh.edu (Heidi Wegrzyn) Mon, 09 Oct 2017 17:12:22 -0400
    Summer 2017 Courses http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/370-summer-2017-courses http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/370-summer-2017-courses Summer 2017 Courses

    Full Summer Session (May 23rd-August 10th)

    CSE 202 Computer Organization and Architecture

    Interaction between low-level computer architectural properties and high-level program behaviors: instruction set design; digital logics and assembly language; processor organization; the memory hierarchy; multicore and GPU architectures; and processor interrupt/exception models. Click here for official description.

    CSE 202-010, MW 4:00-5:35, Professor Mark Erle


    Summer Session 1 (May 23rd-June 29th)

    CSE 002 FUNDAMENTALS OF PROGRAMMING

    Problem-solving and object-oriented programming using Java. Includes laboratory. No prior programming experience needed. Click here for official description.

    CSE 002-010, TR 1:00-3:50, Professor James Femister

    CSE 017 Programming & Data Structures

    This course is a programming-intensive exploration of software design concepts and implementation techniques. It builds on the student's existing knowledge of fundamental programming. Topics include object-oriented software design, problem-solving strategies, algorithm development, and classic data structures. Click here for official description.

    CSE 017-010, MTWR 2:00-3:35, Professor Eric Fouh Mbindi

    CSE 109 Systems Software

    Advanced programming and data structures, including dynamic structures, memory allocation, data organization, symbol tables, hash tables, B-trees, data files. Object-oriented design and implementation of simple assemblers, loaders, interpreters, compilers and translators. Practical methods for implementing medium-scale programs. Click here for official description.

    CSE 109-010, MTWR 10:00-12:30, Professor Jason Loew

    CSE 264 Web Systems Programming

    Practical experience in designing and implementing modern Web applications. Concepts, tools, and techniques, including: HTTP, HTML, CSS, DOM, JavaScript, Ajax, PHP, graphic design principles, mobile web development. Not available to students who have credit for IE 275. Click here for official description. Click here for official description.

    CSE 264-010, TR 4:00-6:50, Professor James Femister

    ** NEW COURSE** CSE 298 Mobile Apps (Android)

    This is a project-oriented course that explores the concepts and technologies pertaining to application development for mobile devices. This course uses Android as the platform. Topics covered include mobile software architecture, user interface design, graphics, multimedia, Location-aware software development, network-centric software development, software development for mobile device sensors (such as cameras, recorders, accelerometer, gyroscope).

    CSE 298-010, MTWR 11:00-12:35, Professor Eric Fouh Mbindi


    Summer Session 2 (July 5th-August 10th)

    CSE 017 Programming & Data Structures

    This course is a programming-intensive exploration of software design concepts and implementation techniques. It builds on the student's existing knowledge of fundamental programming. Topics include object-oriented software design, problem-solving strategies, algorithm development, and classic data structures. Click here for official description.

    CSE 017-011, TR 9:00-11:50,  Professor James Femister

    CSE 109 Systems Software

    Advanced programming and data structures, including dynamic structures, memory allocation, data organization, symbol tables, hash tables, B-trees, data files. Object-oriented design and implementation of simple assemblers, loaders, interpreters, compilers and translators. Practical methods for implementing medium-scale programs. Click here for official description.

    CSE 109-011, TR 1:00-3:50, Professor James Femister

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    hew207@lehigh.edu (Heidi Wegrzyn) Mon, 09 Oct 2017 15:35:16 -0400
    Wonpil Im http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/369-wonpil-im http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/369-wonpil-im
              wonpil     
    Wonpil Im
    Professor
    Department of Bioengineering
    Lehigh University

    Toward Computational Glycobiology

    Tuesday, October 10, 4:00 PM
    Packard Lab 416

    Abstract:   In this talk, I would like to share our ongoing efforts toward computational (structural) glycobiology in terms of (1) glycan structure and dynamics in glycoproteins, (2) roles of glycans as ligands in protein-glycan and protein-protein interactions, (3) glycolipid structure and dynamics, and (4) bacterial outer membranes containing lipopolysaccharides and their interactions with membrane proteins. We have developed various tools available at GlycanStructure.ORG (http://www.glycanstructure.org): Glycan Reader for automatic detection and annotation of carbohydrates and glycosidic linkages in PDB files, Glycan Fragment DB for carbohydrate fragment structures in the PDB and torsion angle distributions of specific glycosidic linkages of searched structures, Glycan Modeler for modeling glycan structures from its sequence, and GS-align for glycan structure alignment and similarity measurement. A PDB survey study of N-glycan structures and protein-glycan interactions in the PDB is also presented for modeling glycan structures and protein-glycan interactions. In addition, I will also briefly talk about our CHARMM-GUI (http://www.charmm-gui.org) project.

    Bio:  Wonpil Im received in bachelor’s and master’s degrees from Hanyang University in Seoul. He then earned his Ph.D. in Biochemistry from Cornell University. He did his post-doctoral research at the Scripps Research Institute in La Jolla, California. In 2005, he was hired as an assistant professor in the Center for Computational Biology and Department of Molecular Biosciences at the University of Kansas, Lawrence. In 2011, he was promoted to associate professor and then professor in 2015. In 2016, he joined the Faculty in Departments of Biological Sciences and Bioengineering at Lehigh University, and he has been named the Presidential Endowed Chair in Health - Science and Engineering. Wonpil was recently awarded the Friedrich Wilhelm Bessel Research Award from the Humboldt Foundation and was named a KIAS Scholar from the Korea Institute for Advanced Study. Prior to Lehigh, he was awarded the Alfred P. Sloan Research Fellowship (2007), ACS HP Outstanding Junior Faculty Award (2011), J. Michael Young Undergrad Advisor Award (2011), Meredith Docking Scholar (2013), and University Scholarly Achievement Award (2015).

    Research in his lab is focused on the applications of theoretical/computational methods to chemical and physical problems in biology and material sciences. In particular, he is interested in modeling and simulations of biological membranes and associated proteins, glycoconjugates, and protein-ligand (drug) interactions. In addition, his lab has been developing CHARMM-GUI for the biomolecular modeling and simulation community.

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    jgs2@lehigh.edu (Jeanne Steinberg) Fri, 29 Sep 2017 11:45:13 -0400
    Data Science Minor http://www.cse.lehigh.edu/academics/undergraduate-computer-science/data-science-minor http://www.cse.lehigh.edu/academics/undergraduate-computer-science/data-science-minor Data Science Minor

    The computational analysis of data, both large and small, has become essential to academic and industrial institutions. The minor in Data Science provides an overview of data science as well as familiarity with its interdisciplinary statistical and computational foundations to understand, build and use computational tools to analyze data. The minor is open to undergraduates from all colleges.

    The minor is comprised of three required courses, one applied data mining / analytics course at the 200 or 300 level, and one or more approved electives relating to data science. See course requirements in catalog description below.

    A grade of C or better is required of all minor courses.

    Virtually every discipline collects data to gain a deeper understanding of their discipline and to make better decisions. The technical challenges associated with collecting, storing, processing, communicating, visualizing, analyzing, and interpreting the huge quantities of data that have become available today are far from trivial. The courses of the minor in Data Science help prepare students to develop computational solutions to analyze data and provide insights of value.

    To declare the minor, fill out the form that you may obtain from the CSE Department office.

    The minor is open to undergraduates from all colleges, and requires a minimum of 16 credit hours, consisting of the following:

    Three required courses (10-11 credits):

    CSE 160 Introduction to Data Science (3)

    CSE 017 Programming and Data Structures (3) OR CSE 109 Systems Software (4)

    MATH 312 Statistical Computing and Applications 4

    Total Credits 10-11

    One approved applied data mining / analytics course at the 200/300 level (3 credits):

    CSE 326 Fundamentals of Machine Learning (3)

    CSE 347 Data Mining (3)

    ISE 364 Introduction to Machine Learning (3)

    ISE 367 Mining of Large Datasets (3)

    MKT 325 Consumer Insights through Data Analysis (3)

    MKT 326 Marketing Analytics in a Digital Space (3)

    BIS 348 Predictive Analytics in Business (3)

    ECO 247 Sabermetrics (3)

    ECO 325 Consumer Insights through Data Analysis (3)

    ECO 360 Time Series Analysis (3)

    The director may approve additional applied data mining / analytics courses.

    One or more approved electives related to data science including, but not limited to an additional applied data mining/analytics course from above, or the following (3-4 credits)

    CSE 241 Database Systems and Applications (3)

    CSE 341 Database Systems, Algorithms, and Applications (3)

    CSE 327 Artificial Intelligence Theory and Practice (3)

    CSE 337 Reinforcement Learning (3)

    CSE 345 WWW Search Engines (3)

    CSE 375 Principles of Practice of Parallel Computing (3)

    ISE 111 Engineering Probability (3)

    ISE 121 Applied Engineering Statistics (3)

    ISE 224 Information Systems Analysis and Design (3)

    MATH 043 Survey of Linear Algebra (3)

    MATH 205 Linear Methods (3)

    MATH 242 Linear Algebra (3-4)

    STAT 342 Linear Algebra (3)

    MATH 309 Theory of Probability (3)

    MATH 334 Mathematical Statistics (3,4)

    PSYC 110 Statistical Analysis of Behavioral Data (4)

    PSYC 210 Experimental Research Methods and Laboratory (4)

    BIS 324 Business Data Management (3)

    ECO 245 Statistical Methods II (3)

    ECO 357 Econometrics (3)

    ECO 367 Applied Microeconometrics (3)

    The program director may approve additional data science-related electives.

    Many of the courses that apply to the minor have prerequisites. These prerequisites do not count toward the minor, and students attempting to complete the minor are not recused from these prerequisites.

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    hew207@lehigh.edu (Heidi Wegrzyn) Thu, 20 Jul 2017 15:10:52 -0400
    First Year Student Welcome http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/367-first-year-student-welcom http://www.cse.lehigh.edu/academics/undergraduate-computer-science/in-the-college-of-arts-and-sciences/2-uncategorised/367-first-year-student-welcom WELCOME FIRST YEAR STUDENTS!

    We are excited to have you joining us at Lehigh! We have prepared the following notes to answer many of the questions you might have, but if you cannot find the answer you are looking for, please let us know by contacting the CSE Department Coordinator, Heidi Wegrzyn.

    Fall 2017 Course Offering click here

    • Students interested in entering the P.C. Rossin College of Engineering and Applied Science, should be registering for CSE 002 Fundamentals of Programming.

    • Students interested in entering the Arts & Sciences College should be registering for CSE 001 Breadth of Computing and CSE 002 Fundamentals of Programming.

    •  Students interested in Computer Science and Business please refer to http://fysenroll.lehigh.edu/csb

    • When registering for CSE 002, take note to the section enrollment for each section. Due to lab constraints, if there are no seats available in a section you should register for another section of CSE 002.

    AP Credit Information click here

    Course Requirements for Major and Sample Course Sequences

    Student group information, click here.

    Please also look for emails or check our website for CSE Department events.

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    hew207@lehigh.edu (Heidi Wegrzyn) Mon, 12 Jun 2017 12:46:40 -0400