Computer Science and Engineering

The CSE Department Office has moved to Mountaintop Building C, effective January 19, 2018. Click here for information. Building C Location and Parking Map


Miaomiao ZhangRoberto Palmieri

As a result of the ongoing Data X Initiative, the CSE Department is pleased to welcome two new assistant professors in 2017-18, Miaomiao Zhang and Roberto Palmieri.

Dr. Zhang's research work focuses on developing novel models at the intersection of statistics, mathematics, and computer engineering in the field of medical and biological imaging. Before joining Lehigh University, Miaomiao completed her Ph.D. degree in the department of Computer Science at University of Utah under the supervision of Dr. Tom Fletcher. After that, she worked with Dr. Polina Golland as a postdoctoral associate at Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. She received the Young Scientist Award 2014 and was a runnerup for Young Scientist Award 2016.

Roberto Palmieri earned his Ph.D. in Computer Engineering from Sapienza University of Rome (Italy). After that, he was a post-doc at Sapienza University and subsequently at Virginia Tech. In 2014 he became a Research Assistant Professor in the Electrical and Computer Engineering department at Virginia Tech. He is interested in system research and distributed computation, spanning from theory to practice. Building fault-tolerant and high performance systems is one of his primary research goals, along with designing protocols for innovative synchronization patterns. Palmieri and his students have published more than 50 papers in the most recognized international conferences and journals of the area.



Computer Science and Engineering is at the core of the information age. To prepare our students for the tremendous opportunities in the field, the CSE Department is strongly committed to excellence in both education and research. We conduct ground-breaking work in artificial intelligence, bioinformatics, data mining, robotics, software security, computer networking, software systems, biomedical image processing, computer vision, mobile healthcare, and the WWW. Our faculty includes five NSF CAREER award winners, one of the most prestigious awards available to young researchers in CSE.

CSE is also deeply involved in Lehigh's Data X Strategic Initiative, including the development of new courses, new research directions, and an amazing new facility now under development on Lehigh's Mountaintop Campus. For more information on Data X, click here.

Lehigh undergraduates benefit from the personal attention typical of a small college, yet have exposure to state-of-the-art technologies available only at a research university. To provide flexibility, we offer a variety of different undergraduate degree programs, including B.S. degrees in the College of Engineering, and a B.S. and a B.A. degree in the College of Arts and Sciences. All of our B.S. programs are fully accredited. In addition, we offer a unique B.S. in Computer Science and Business which is accredited both in computer science and in business. Beyond their courses, students often work one-on-one with faculty, and can even become involved in their research projects. Internships provide real-world experience.

Our majors are designed to provide a strong foundation in the core areas of Computer Science and Engineering, from the hardware/software interface up through systems software, programming languages, software engineering, and the mathematical foundations of computing. Electives include topics in artificial intelligence, computer networking, parallel and distributed computing, security, robotics, bioinformatics, data mining, web and mobile application development, and databases. As a result, our graduates are in high demand, and are regularly recruited by many leading high tech companies.

Our vibrant graduate programs prepare students for positions in industry and academia. Our faculty have research funded by competitive sources including NSF, DARPA, NIH, and other federal and state agencies, as well as leading companies in the field.

For a list of major employers who have hired our graduates in the recent past, click here.

For a listing of planned CSE courses for Spring 2018, click here

New and special topic courses for Spring 2018:
  • CSE 098 Women in Technology, F 2:10-4:00, Prof. Daniel Lopresti -- 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. Course runs first half of semester (1/22/18-3/9/18). This course will be taught using the new telepresence classroom in Building C.  We have scheduled this course in a way that will allow students time to travel to and from mountaintop.  Flexibility on arrival and departure times will be granted.
  • CSE 098/CSB 098 Software Product Management, F 2:10-4:00, Prof. Daniel Lopresti--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. Course runs second half of semester (3/19/18-5/4/18). This course will be taught using the new telepresence classroom in Building C.  We have scheduled this course in a way that will allow students time to travel to and from mountaintop.  Flexibility on arrival and departure times will be granted.
  • CSE 298/FIN 098, MW 12:45-2:00, Prof. 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 398/498 Principles and Implementation of Information Privacy, TR 9:20-10:35, Prof. 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.
  • CSE 398/498 Big Data Analytics, TR 1:10-2:25, Prof. 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.

 Courses taught by new faculty 2017-2018:

  • CSE 326/426 Foundations of Machine Learning, MW 2:35-3:50, Prof. Miaomiao Zhang -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. Click here for official course description
  • CSE 403 Advanced Operating Systems, MW 11:10-12:25, Prof. Roberto Palmieri - 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. This course will be taught using one of the new classrooms in Building C.


Prospective students and their parents: if you're planning to visit Lehigh and have interests in Computer Science, Computer Science and Business, or Computer Engineering, please contact us at This email address is being protected from spambots. You need JavaScript enabled to view it. and we'll make sure you have a chance to meet with a faculty member to hear the details of our programs.

Prospective employers: demand for CSE graduates is extremely strong. Our students are aggressively recruited by many of the top companies in the US. If you wish to connect with CSE students at Lehigh, please contact us. We can help disseminate your recruiting materials, and we can also arrange for a room for you to present an overview session and meet with students during a campus visit. Send email to: This email address is being protected from spambots. You need JavaScript enabled to view it. .

New students often ask whether it is possible to take one of majors if they have had no programming experience in high school. Yes! Many of our majors first started their study of CSE at Lehigh with no previous background. We provide the appropriate introductory courses for students to succeed in CSE with or without past experience.

 Tapia Group 2016

Lehigh CSB student Bruke Mammo (left) and Professor of Practice Eric Fouh Mbindi (right) with Professor Richard Tapia of Rice University (center) at the 2016 ACM Richard Tapia Celebration of Diversity in Computing, Austin, TX.

© 2014-2016 Computer Science and Engineering, P.C. Rossin College of Engineering & Applied Science, Lehigh University, Bethlehem PA 18015.