Skip to content. | Skip to navigation

Personal tools

Home > Research > CSE Research Opportunities

CSE Research Opportunities

Research Opportunities in Computer Science and Engineering 

for Undergraduates

[Back to full list]

Visual Data Analytics

Research description:
Design and implement visualization and analytics programs for analyzing and understanding a wide variety of data (e.g., simulation data, biological data, social media data) and models (e.g., deep learning models), and for teaching and learning essential visualization concepts and techniques.

Student Involvement:
REU student will participate in the research projects and help to develop the prototype implementation
that eventually leads to publications.

Preferred discipline(s), expertise, lab skills, etc.: Basic graphics and/or visualization knowledge, familiar
with programming in C/C++, OpenGL/GLSL/WebGL, or D3.js.

Contact: Associate Professor Chaoli Wang, 383 Fitzpatrick, 574 631-9212, (
Department of Computer Science and Engineering

Profiling of Homomorphic Encryption in Secure Data Mining

Research description:
This project investigates the applications of homomorphic encryption schemes in current secure data analysis, and its goal is to understand the impact of the application on performance and accuracy of data mining. The objectives of the research are to:
(1) survey and learn existing homomorphic encryption schemes;
(2) implement the schemes in existing data mining algorithms;
(3) evaluate the impact of the homomorphic encryption on the accuracy and performance of data mining algorithms.

Student Involvement:
(1) learn and implement selected homomorphic encryption scheme(s);
(2) apply them in selected data mining algorithm(s);
(3) perform simulation and experiments to acquire data for accuracy and performance.

Preferred discipline(s), expertise, lab skills, etc.: 
(1) programming skills in C/C++/Java
(2) knowledge in number theory / group theory

Contact: Assistant Professor Taeho Jung, 351 Fitzpatrick Hall, 574 631-8322 (
Department of Computer Science and Engineering

Making iris recognition more reliable biometrics

Research description:

Iris Recognition
Only one picture presents an authentic, living eye. Three other images present either an iris printed on a paper, or an iris covered by a textured contact lens, or a cadaver eye. Can you recognize which one is authentic? In this research, you will design methods that answer this question automatically in a fraction of a second.

Biometric systems are exposed to “presentation attacks”, that is, presentation of artificial or nonliving objects (prosthetic eyes, gummy fingers, face masks, cadavers) to a biometric sensor, or non-conformant use of a biometric system (e.g., covering the face with a scarf, wearing textured contact lenses with an intention to subvert a system). The goal of such attacks is either impersonating of someone else or evading the recognition. This research will focus on designing of presentation attack detection methods for iris recognition. Students will use various computer vision techniques, including deep learning, and dynamic models of the eye, to make the iris recognition sensors more spoof-resistant.

Student Involvement:
A student will be involved in development of presentation attack detection methods for iris biometrics, tested on images/videos of authentic eyes and artificial objects.

Preferred discipline(s), expertise, lab skills, etc.: 
Any science and engineering discipline is acceptable, although those with a background in computer vision and biometrics are preferred. Student must have at least basic skills in Matlab and/or Python programming, and must be willing to work in a detailed and systematic manner.

Contact: Dr. Adam Czajka, 321B Stinson-Remick Hall, 574 631-7072 ( Department of Computer Science and Engineering