Computer Science and Engineering

The CSE Department Office has moved to Mountaintop Building C, effective January 19, 2018. For information on our location and parking, click here.


Roberto PalmieriArielle Carr

As a result of the ongoing Data X Initiative, the CSE Department is pleased to welcome a new assistant professor in 2017-18, Roberto Palmieri. The CSE Department is also happy to welcome new professor of practice, Arielle Carr, in Fall 2018.

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.

Arielle Carr is a Ph.D. candidate studying Applied Mathematics at Virginia Tech in Blacksburg, Virginia. She earned her M.S. in Math from VT in 2015, and her B.S. in Math along with a minor in Computer Science from VT in 2012. Arielle also has a B.S. in Sociology with a minor in Education from Roanoke College in Salem, Virginia. She had taught at VT since 2012, serving as instructor-of-record for Integral Calculus, Numerical Analysis, and Linear Algebra.

A Note on Department Rankings

In choosing a school to attend to study computer science, you should weigh a number of factors including the quality of its curriculum, the accessibility of the department faculty and their research interests, and the career successes of its graduates. Attempting to distill these complex factors into a single numeric ranking can be very misleading. In fact, it is such a bad idea that the Computing Research Association, of which Lehigh CSE is an active member, issued a formal Statement regarding the misuse of department rankings in Computer Science. Click here for more details.



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 2019:
  • CSB 298 BLOCKCHAIN CONCEPTS & APPS, TR 9:20-10:35, Professor Hank Korth-- Blockchain is the technology underlying Bitcoin, along with other digital currencies, and a data-management technology applicable broadly in finance, accounting, supply-chain, and "smart" contracts.  It offers the ability to decentralize financial transactions, automate record keeping, and increase privacy. This course aims to give business students the basis for understanding the foundations of blockchain, to give computer-science students the basis for understanding the impact of blockchain, and to give all students the basis for a deeper understanding of this emerging technology. Prerequisite: ECO 001 AND (BIS 111 or CSE 001 or CSE 002 or CSE 012) AND (CSE 017 or MKT 111 or FIN 125 or SCM 186) . This course is open to all students with these prerequisites regardless of their college. Students should check with their advisors on how the course would count towards their degree programs. In CSB this counts as a professional elective but not as an approved CSE elective. It does not count towards the CS minor.
  • CSE 098 WOMEN IN TECHNOLOGY, F 2:10-4:00, Professor 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? What role can mentoring play in fostering a successful and fulfilling career? These are some of the questions we will examine in this one-credit, seminar-style course. Many of our guest instructors will be drawn from Silicon Valley firms, from startups to tech powerhouses. Prerequisite: permission of instructor.
  • CSE 350/450 SPECIAL TOPICS: AI FOR SOCIAL GOOD, WF 12:45-2:00, Professor Daniel Lopresti--Recent advances in artificial intelligence and other related areas of computer science are having an enormous impact on our society. Often they change our lives for the better, but sometimes there are unintended consequences resulting in charges that the high tech industry focuses on profits and ignores societal impact. To counter this, a recent movement toward “AI for Social Good” has taken hold within the research community. In this project-based seminar course, we will begin by surveying recent work in this area. Readings will be drawn from the current literature. Students will then form into teams and tackle a real-world problem, outlining the task, gathering the data, and implementing and testing solutions using methods from machine learning and data/text mining. Domains to be studied will include, among others, smart cities and open government data, and AI applied to the fight against human trafficking. This class will meet once a week on Wednesdays in Mountaintop Building C, with individual team meetings scheduled at mutual times during the week. Prerequisites: CSE 109 Systems Software and (CSE 326 Foundations of Machine Learning or CSE 347 Data Mining or CSE 398 Natural Language Processing or CSE 398 Text Mining) and permission of the instructor.
  • CSE 360/460-010 INTRODUCTION MOBILE ROBOTICS, MW 8:45-10:00, Professor John Spletzer--Algorithms employed in mobile robotics for navigation, sensing, and estimation. Common sensor systems, motion planning, robust estimation, bayesian estimation techniques, Kalman and Particle filters, localization and mapping. Prerequisites: MATH 023 or MATH 205 or MATH 231.
  • CSE 371/CSE 498 PRINCIPLES OF MOBILE COMPUTING, MW 11:10-12:25, Professor Mooi Choo Chuah--Lecture/seminar course covering the fundamental concepts and technology underlying mobile computing and its application as well as current research in these areas. Examples drawn from a variety of application domains such as health monitoring, energy management, commerce, and travel. Issues of system efficiency will be studied, including efficient handling of large data such as images and effective use of cloud storage. Research coverage will be drawn from the best publications in the recent research conferences. Deep learning methods will be covered. Students will do Android programming and possibly develop Alexa/Google home skills for homework assignments and final class projects. Prerequisites: CSE 109 and (CSE 202 or ECE 201).
  • CSE 398/498-014 ADVERSARIAL MACHINE LEARNING, MW 11:10-12:25, Professor Ting Wang--Machine learning has become one mainstream technique underlying numerous data-driven systems in a wide range of domains. This class will focus on understanding the challenges of applying machine learning in adversarial environments, wherein potential adversaries may purposely manipulate and sabotage the learning processes and outcomes. We will study the state-of-the-art attack and defense techniques, and understand their strengths and limitations. In this class, we will read a number of technical papers, and work on a research project in teams of 2-3 students. The goal of the project is to develop new attacks or defenses (or improve existing ones) for machine learning-powered systems and applications, with the ultimate goal of producing real and publishable results by the end of the semester. Prerequisites: CSE 326/426: Fundamentals of machine learning or CSE 347/447: Data Mining.

 Any issues or questions regarding registration, please contact This email address is being protected from spambots. You need JavaScript enabled to view it. .


Prospective students and their parents: if you're planning to visit Lehigh and have interests in Computer Science or Computer Science and Business, 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.

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